зеркало из
https://github.com/ssciwr/AMMICO.git
synced 2025-10-29 21:16:06 +02:00
Deploying to gh-pages from @ ssciwr/AMMICO@174054f465 🚀
Этот коммит содержится в:
iulusoy
родитель
5004e46a84
Коммит
cb2e5213bf
Двоичные данные
build/doctrees/ammico.doctree
Двоичные данные
build/doctrees/ammico.doctree
Двоичный файл не отображается.
Двоичные данные
build/doctrees/create_API_key_link.doctree
Двоичные данные
build/doctrees/create_API_key_link.doctree
Двоичный файл не отображается.
Двоичные данные
build/doctrees/environment.pickle
Двоичные данные
build/doctrees/environment.pickle
Двоичный файл не отображается.
Двоичные данные
build/doctrees/faq_link.doctree
Обычный файл
Двоичные данные
build/doctrees/faq_link.doctree
Обычный файл
Двоичный файл не отображается.
Двоичные данные
build/doctrees/index.doctree
Двоичные данные
build/doctrees/index.doctree
Двоичный файл не отображается.
Двоичные данные
build/doctrees/license_link.doctree
Двоичные данные
build/doctrees/license_link.doctree
Двоичный файл не отображается.
Двоичные данные
build/doctrees/modules.doctree
Двоичные данные
build/doctrees/modules.doctree
Двоичный файл не отображается.
@ -18,7 +18,10 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# if running on google colab\n",
|
||||
"# if running on google colab\\\n",
|
||||
"# PLEASE RUN THIS ONLY AS CPU RUNTIME\n",
|
||||
"# for a GPU runtime, there are conflicts with pre-installed packages - \n",
|
||||
"# you first need to uninstall them (prepare a clean environment with no pre-installs) and then install ammico\n",
|
||||
"# flake8-noqa-cell\n",
|
||||
"\n",
|
||||
"if \"google.colab\" in str(get_ipython()):\n",
|
||||
@ -26,9 +29,10 @@
|
||||
" # install setuptools\n",
|
||||
" # %pip install setuptools==61 -qqq\n",
|
||||
" # uninstall some pre-installed packages due to incompatibility\n",
|
||||
" %pip uninstall --yes tensorflow-probability dopamine-rl lida pandas-gbq torchaudio torchdata torchtext orbax-checkpoint flex-y -qqq\n",
|
||||
" %pip uninstall --yes tensorflow-probability dopamine-rl lida pandas-gbq torchaudio torchdata torchtext orbax-checkpoint flex-y jax jaxlib -qqq\n",
|
||||
" # install ammico\n",
|
||||
" %pip install git+https://github.com/ssciwr/ammico.git -qqq\n",
|
||||
" # install older version of jax to support transformers use of diffusers\n",
|
||||
" # mount google drive for data and API key\n",
|
||||
" from google.colab import drive\n",
|
||||
"\n",
|
||||
@ -40,9 +44,15 @@
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Use a test dataset\n",
|
||||
"You can download a dataset for test purposes. Skip this step if you use your own data."
|
||||
"\n",
|
||||
"You can download this dataset for test purposes. Skip this step if you use your own data. If the data set on Hugging Face is gated or private, Hugging Face will ask you for a login token. However, for the default dataset in this notebook you do not need to provide one."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
@ -92,12 +102,12 @@
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import os\n",
|
||||
"# jax also sometimes leads to problems on google colab\n",
|
||||
"# if this is the case, try restarting the kernel and executing this \n",
|
||||
"# and the above two code cells again\n",
|
||||
"import ammico\n",
|
||||
"# for displaying a progress bar\n",
|
||||
"from tqdm import tqdm\n",
|
||||
"# to get the reference data for text_dict\n",
|
||||
"import importlib_resources\n",
|
||||
"pkg = importlib_resources.files(\"ammico\")"
|
||||
"from tqdm import tqdm"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -151,7 +161,7 @@
|
||||
"| `limit` | `int` | maximum number of files to read (defaults to `20`, for all images set to `None` or `-1`) |\n",
|
||||
"| `random_seed` | `str` | the random seed for shuffling the images; applies when only a few images are read and the selection should be preserved (defaults to `None`) |\n",
|
||||
"\n",
|
||||
"The `find_files` function returns a nested dict that contains the file ids and the paths to the files and is empty otherwise. This dict is filled step by step with more data as each detector class is run on the data (see below).\n",
|
||||
"The `find_files` function returns a nested dictionary that contains the file ids and the paths to the files and is empty otherwise. This dict is filled step by step with more data as each detector class is run on the data (see below).\n",
|
||||
"\n",
|
||||
"If you downloaded the test dataset above, you can directly provide the path you already set for the test directory, `data_path`. The below cell is already set up for the test dataset.\n",
|
||||
"\n",
|
||||
@ -183,9 +193,9 @@
|
||||
"\n",
|
||||
"If you want to run an analysis using the EmotionDetector detector type, you have first have to respond to an ethical disclosure statement. This disclosure statement ensures that you only use the full capabilities of the EmotionDetector after you have been made aware of its shortcomings.\n",
|
||||
"\n",
|
||||
"For this, answer \"yes\" or \"no\" to the below prompt. This will set an environment variable with the name given as in `accept_disclosure`. To re-run the disclosure prompt, unset the variable by uncommenting the line `os.environ.pop(accept_disclosure, None)`. To permanently set this envorinment variable, add it to your shell via your `.profile` or `.bashr` file.\n",
|
||||
"For this, answer \"yes\" or \"no\" to the below prompt. This will set an environment variable with the name given as in `accept_disclosure`. To re-run the disclosure prompt, unset the variable by uncommenting the line `os.environ.pop(accept_disclosure, None)`. To permanently set this environment variable, add it to your shell via your `.profile` or `.bashr` file.\n",
|
||||
"\n",
|
||||
"If the disclosure statement is accepted, the EmotionDetector will perform age, gender and race/ethnicity classification dependend on the provided thresholds. If the disclosure is rejected, only the presence of faces and emotion (if not wearing a mask) is detected."
|
||||
"If the disclosure statement is accepted, the EmotionDetector will perform age, gender and race/ethnicity classification depending on the provided thresholds. If the disclosure is rejected, only the presence of faces and emotion (if not wearing a mask) is detected."
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -203,6 +213,34 @@
|
||||
"_ = ammico.ethical_disclosure(accept_disclosure=accept_disclosure)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Privacy disclosure statement\n",
|
||||
"\n",
|
||||
"If you want to run an analysis using the TextDetector detector type, you have first have to respond to a privacy disclosure statement. This disclosure statement ensures that you are aware that your data will be sent to google cloud vision servers for analysis.\n",
|
||||
"\n",
|
||||
"For this, answer \"yes\" or \"no\" to the below prompt. This will set an environment variable with the name given as in `accept_privacy`. To re-run the disclosure prompt, unset the variable by uncommenting the line `os.environ.pop(accept_privacy, None)`. To permanently set this environment variable, add it to your shell via your `.profile` or `.bashr` file.\n",
|
||||
"\n",
|
||||
"If the privacy disclosure statement is accepted, the TextDetector will perform the text extraction, translation and if selected, analysis. If the privacy disclosure is rejected, no text processing will be carried out and you cannot use the TextDetector."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# respond to the privacy disclosure statement\n",
|
||||
"# this will set an environment variable for you\n",
|
||||
"# if you do not want to re-accept the privacy disclosure every time, you can set this environment variable in your shell\n",
|
||||
"# to re-set the environment variable, uncomment the below line\n",
|
||||
"accept_privacy = \"PRIVACY_AMMICO\"\n",
|
||||
"# os.environ.pop(accept_privacy, None)\n",
|
||||
"_ = ammico.privacy_disclosure(accept_privacy=accept_privacy)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
@ -253,9 +291,17 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# set the thresholds for the emotion detection\n",
|
||||
"emotion_threshold = 50 # this is the default value for the detection confidence\n",
|
||||
"# the lowest possible value is 0\n",
|
||||
"# the highest possible value is 100\n",
|
||||
"race_threshold = 50\n",
|
||||
"gender_threshold = 50\n",
|
||||
"for num, key in tqdm(enumerate(image_dict.keys()), total=len(image_dict)): # loop through all images\n",
|
||||
" image_dict[key] = ammico.EmotionDetector(image_dict[key]).analyse_image() # analyse image with EmotionDetector and update dict\n",
|
||||
" \n",
|
||||
" image_dict[key] = ammico.EmotionDetector(image_dict[key],\n",
|
||||
" emotion_threshold=emotion_threshold,\n",
|
||||
" race_threshold=race_threshold,\n",
|
||||
" gender_threshold=gender_threshold).analyse_image() # analyse image with EmotionDetector and update dict\n",
|
||||
" if num % dump_every == 0 or num == len(image_dict) - 1: # save results every dump_every to dump_file\n",
|
||||
" image_df = ammico.get_dataframe(image_dict)\n",
|
||||
" image_df.to_csv(dump_file)"
|
||||
@ -405,8 +451,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"csv_path = pkg / \"data\" / \"ref\" / \"test.csv\"\n",
|
||||
"ta = ammico.TextAnalyzer(csv_path=str(csv_path), column_key=\"text\")"
|
||||
"ta = ammico.TextAnalyzer(csv_path=\"../data/ref/test.csv\", column_key=\"text\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -530,6 +575,17 @@
|
||||
" image_df.to_csv(dump_file)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# write output to csv\n",
|
||||
"image_df = ammico.get_dataframe(image_dict)\n",
|
||||
"image_df.to_csv(\"/content/drive/MyDrive/misinformation-data/data_out.csv\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
@ -759,7 +815,7 @@
|
||||
"# analysis_type can be \n",
|
||||
"# \"summary\",\n",
|
||||
"# \"questions\",\n",
|
||||
"# \"summary_and_questions\".\n"
|
||||
"# \"summary_and_questions\"."
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -806,7 +862,7 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"You can also ask sequential questions if you pass the argument `cosequential_questions=True`. This means that the answers to previous questions will be passed as context to the next question. However, this method will work a bit slower, because for each image the answers to the questions will not be calculated simultaneously, but sequentially. "
|
||||
"You can also ask sequential questions if you pass the argument `consequential_questions=True`. This means that the answers to previous questions will be passed as context to the next question. However, this method will work a bit slower, because for each image the answers to the questions will not be calculated simultaneously, but sequentially. "
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -840,6 +896,17 @@
|
||||
"image_dict"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# write output to csv\n",
|
||||
"image_df = ammico.get_dataframe(image_dict)\n",
|
||||
"image_df.to_csv(\"/content/drive/MyDrive/misinformation-data/data_out.csv\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
@ -855,7 +922,7 @@
|
||||
"\n",
|
||||
"From the seven facial expressions, an overall dominating emotion category is identified: negative, positive, or neutral emotion. These are defined with the facial expressions angry, disgust, fear and sad for the negative category, happy for the positive category, and surprise and neutral for the neutral category.\n",
|
||||
"\n",
|
||||
"A similar threshold as for the emotion recognition is set for the race/ethnicity and gender detection, `race_threshold` and `gender_threshold`, with the default set to 50% so that a confidence for race / gender above 0.5 only will return a value in the analysis.\n",
|
||||
"A similar threshold as for the emotion recognition is set for the race/ethnicity and gender detection, `race_threshold` and `gender_threshold`, with the default set to 50% so that a confidence for race / gender above 0.5 only will return a value in the analysis. \n",
|
||||
"\n",
|
||||
"For age unfortunately no confidence value is accessible so that no threshold values can be set for this type of analysis. The [reported MAE of the model is ± 4.65](https://sefiks.com/2019/02/13/apparent-age-and-gender-prediction-in-keras/).\n",
|
||||
"\n",
|
||||
@ -876,6 +943,17 @@
|
||||
" accept_disclosure=\"DISCLOSURE_AMMICO\").analyse_image()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# write output to csv\n",
|
||||
"image_df = ammico.get_dataframe(image_dict)\n",
|
||||
"image_df.to_csv(\"/content/drive/MyDrive/misinformation-data/data_out.csv\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
@ -986,7 +1064,7 @@
|
||||
"source": [
|
||||
"The images are then processed and stored in a numerical representation, a tensor. These tensors do not change for the same image and same model - so if you run this analysis once, and save the tensors giving a path with the keyword `path_to_save_tensors`, a file with filename `.<Number_of_images>_<model_name>_saved_features_image.pt` will be placed there.\n",
|
||||
"\n",
|
||||
"This can save you time if you want to analyse same images with the same model but different questions. To run using the saved tensors, execute the below code giving the path and name of the tensor file. Any subsequent query of the model will run in a fraction of the time than it run in initially."
|
||||
"This can save you time if you want to analyse the same images with the same model but different questions. To run using the saved tensors, execute the below code giving the path and name of the tensor file. Any subsequent query of the model will run in a fraction of the time than it run in initially."
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -1050,7 +1128,7 @@
|
||||
"You can filter your results in 3 different ways:\n",
|
||||
"- `filter_number_of_images` limits the number of images found. That is, if the parameter `filter_number_of_images = 10`, then the first 10 images that best match the query will be shown. The other images ranks will be set to `None` and the similarity value to `0`.\n",
|
||||
"- `filter_val_limit` limits the output of images with a similarity value not bigger than `filter_val_limit`. That is, if the parameter `filter_val_limit = 0.2`, all images with similarity less than 0.2 will be discarded.\n",
|
||||
"- `filter_rel_error` (percentage) limits the output of images with a similarity value not bigger than `100 * abs(current_simularity_value - best_simularity_value_in_current_search)/best_simularity_value_in_current_search < filter_rel_error`. That is, if we set filter_rel_error = 30, it means that if the top1 image have 0.5 similarity value, we discard all image with similarity less than 0.35."
|
||||
"- `filter_rel_error` (percentage) limits the output of images with a similarity value not bigger than `100 * abs(current_similarity_value - best_similarity_value_in_current_search)/best_similarity_value_in_current_search < filter_rel_error`. That is, if we set filter_rel_error = 30, it means that if the top1 image have 0.5 similarity value, we discard all image with similarity less than 0.35."
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -1174,7 +1252,7 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Then using the same output function you can add the `itm=True` argument to output the new image order. Remember that for images querys, an error will be thrown with `itm=True` argument. You can also add the `image_gradcam_with_itm` along with `itm=True` argument to output the heat maps of the calculated images."
|
||||
"Then using the same output function you can add the `itm=True` argument to output the new image order. Remember that for images queries, an error will be thrown with `itm=True` argument. You can also add the `image_gradcam_with_itm` along with `itm=True` argument to output the heat maps of the calculated images."
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -1199,7 +1277,7 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Convert the dictionary of dictionarys into a dictionary with lists:"
|
||||
"Convert the dictionary of dictionaries into a dictionary with lists:"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -1367,7 +1445,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.5"
|
||||
"version": "3.11.9"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
||||
Двоичные данные
build/doctrees/notebooks/DemoNotebook_ammico.doctree
Двоичные данные
build/doctrees/notebooks/DemoNotebook_ammico.doctree
Двоичный файл не отображается.
Двоичные данные
build/doctrees/notebooks/Example cropposts.doctree
Двоичные данные
build/doctrees/notebooks/Example cropposts.doctree
Двоичный файл не отображается.
Двоичные данные
build/doctrees/readme_link.doctree
Двоичные данные
build/doctrees/readme_link.doctree
Двоичный файл не отображается.
Двоичные данные
build/doctrees/set_up_credentials.doctree
Двоичные данные
build/doctrees/set_up_credentials.doctree
Двоичный файл не отображается.
2
build/html/_sources/faq_link.md.txt
Обычный файл
2
build/html/_sources/faq_link.md.txt
Обычный файл
@ -0,0 +1,2 @@
|
||||
```{include} ../../FAQ.md
|
||||
```
|
||||
@ -11,6 +11,7 @@ Welcome to AMMICO's documentation!
|
||||
:caption: Contents:
|
||||
|
||||
readme_link
|
||||
faq_link
|
||||
create_API_key_link
|
||||
notebooks/DemoNotebook_ammico
|
||||
notebooks/Example cropposts
|
||||
|
||||
@ -18,7 +18,10 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# if running on google colab\n",
|
||||
"# if running on google colab\\\n",
|
||||
"# PLEASE RUN THIS ONLY AS CPU RUNTIME\n",
|
||||
"# for a GPU runtime, there are conflicts with pre-installed packages - \n",
|
||||
"# you first need to uninstall them (prepare a clean environment with no pre-installs) and then install ammico\n",
|
||||
"# flake8-noqa-cell\n",
|
||||
"\n",
|
||||
"if \"google.colab\" in str(get_ipython()):\n",
|
||||
@ -26,9 +29,10 @@
|
||||
" # install setuptools\n",
|
||||
" # %pip install setuptools==61 -qqq\n",
|
||||
" # uninstall some pre-installed packages due to incompatibility\n",
|
||||
" %pip uninstall --yes tensorflow-probability dopamine-rl lida pandas-gbq torchaudio torchdata torchtext orbax-checkpoint flex-y -qqq\n",
|
||||
" %pip uninstall --yes tensorflow-probability dopamine-rl lida pandas-gbq torchaudio torchdata torchtext orbax-checkpoint flex-y jax jaxlib -qqq\n",
|
||||
" # install ammico\n",
|
||||
" %pip install git+https://github.com/ssciwr/ammico.git -qqq\n",
|
||||
" # install older version of jax to support transformers use of diffusers\n",
|
||||
" # mount google drive for data and API key\n",
|
||||
" from google.colab import drive\n",
|
||||
"\n",
|
||||
@ -40,9 +44,15 @@
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Use a test dataset\n",
|
||||
"You can download a dataset for test purposes. Skip this step if you use your own data."
|
||||
"\n",
|
||||
"You can download this dataset for test purposes. Skip this step if you use your own data. If the data set on Hugging Face is gated or private, Hugging Face will ask you for a login token. However, for the default dataset in this notebook you do not need to provide one."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
@ -92,12 +102,12 @@
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import os\n",
|
||||
"# jax also sometimes leads to problems on google colab\n",
|
||||
"# if this is the case, try restarting the kernel and executing this \n",
|
||||
"# and the above two code cells again\n",
|
||||
"import ammico\n",
|
||||
"# for displaying a progress bar\n",
|
||||
"from tqdm import tqdm\n",
|
||||
"# to get the reference data for text_dict\n",
|
||||
"import importlib_resources\n",
|
||||
"pkg = importlib_resources.files(\"ammico\")"
|
||||
"from tqdm import tqdm"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -151,7 +161,7 @@
|
||||
"| `limit` | `int` | maximum number of files to read (defaults to `20`, for all images set to `None` or `-1`) |\n",
|
||||
"| `random_seed` | `str` | the random seed for shuffling the images; applies when only a few images are read and the selection should be preserved (defaults to `None`) |\n",
|
||||
"\n",
|
||||
"The `find_files` function returns a nested dict that contains the file ids and the paths to the files and is empty otherwise. This dict is filled step by step with more data as each detector class is run on the data (see below).\n",
|
||||
"The `find_files` function returns a nested dictionary that contains the file ids and the paths to the files and is empty otherwise. This dict is filled step by step with more data as each detector class is run on the data (see below).\n",
|
||||
"\n",
|
||||
"If you downloaded the test dataset above, you can directly provide the path you already set for the test directory, `data_path`. The below cell is already set up for the test dataset.\n",
|
||||
"\n",
|
||||
@ -183,9 +193,9 @@
|
||||
"\n",
|
||||
"If you want to run an analysis using the EmotionDetector detector type, you have first have to respond to an ethical disclosure statement. This disclosure statement ensures that you only use the full capabilities of the EmotionDetector after you have been made aware of its shortcomings.\n",
|
||||
"\n",
|
||||
"For this, answer \"yes\" or \"no\" to the below prompt. This will set an environment variable with the name given as in `accept_disclosure`. To re-run the disclosure prompt, unset the variable by uncommenting the line `os.environ.pop(accept_disclosure, None)`. To permanently set this envorinment variable, add it to your shell via your `.profile` or `.bashr` file.\n",
|
||||
"For this, answer \"yes\" or \"no\" to the below prompt. This will set an environment variable with the name given as in `accept_disclosure`. To re-run the disclosure prompt, unset the variable by uncommenting the line `os.environ.pop(accept_disclosure, None)`. To permanently set this environment variable, add it to your shell via your `.profile` or `.bashr` file.\n",
|
||||
"\n",
|
||||
"If the disclosure statement is accepted, the EmotionDetector will perform age, gender and race/ethnicity classification dependend on the provided thresholds. If the disclosure is rejected, only the presence of faces and emotion (if not wearing a mask) is detected."
|
||||
"If the disclosure statement is accepted, the EmotionDetector will perform age, gender and race/ethnicity classification depending on the provided thresholds. If the disclosure is rejected, only the presence of faces and emotion (if not wearing a mask) is detected."
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -203,6 +213,34 @@
|
||||
"_ = ammico.ethical_disclosure(accept_disclosure=accept_disclosure)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Privacy disclosure statement\n",
|
||||
"\n",
|
||||
"If you want to run an analysis using the TextDetector detector type, you have first have to respond to a privacy disclosure statement. This disclosure statement ensures that you are aware that your data will be sent to google cloud vision servers for analysis.\n",
|
||||
"\n",
|
||||
"For this, answer \"yes\" or \"no\" to the below prompt. This will set an environment variable with the name given as in `accept_privacy`. To re-run the disclosure prompt, unset the variable by uncommenting the line `os.environ.pop(accept_privacy, None)`. To permanently set this environment variable, add it to your shell via your `.profile` or `.bashr` file.\n",
|
||||
"\n",
|
||||
"If the privacy disclosure statement is accepted, the TextDetector will perform the text extraction, translation and if selected, analysis. If the privacy disclosure is rejected, no text processing will be carried out and you cannot use the TextDetector."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# respond to the privacy disclosure statement\n",
|
||||
"# this will set an environment variable for you\n",
|
||||
"# if you do not want to re-accept the privacy disclosure every time, you can set this environment variable in your shell\n",
|
||||
"# to re-set the environment variable, uncomment the below line\n",
|
||||
"accept_privacy = \"PRIVACY_AMMICO\"\n",
|
||||
"# os.environ.pop(accept_privacy, None)\n",
|
||||
"_ = ammico.privacy_disclosure(accept_privacy=accept_privacy)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
@ -253,9 +291,17 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# set the thresholds for the emotion detection\n",
|
||||
"emotion_threshold = 50 # this is the default value for the detection confidence\n",
|
||||
"# the lowest possible value is 0\n",
|
||||
"# the highest possible value is 100\n",
|
||||
"race_threshold = 50\n",
|
||||
"gender_threshold = 50\n",
|
||||
"for num, key in tqdm(enumerate(image_dict.keys()), total=len(image_dict)): # loop through all images\n",
|
||||
" image_dict[key] = ammico.EmotionDetector(image_dict[key]).analyse_image() # analyse image with EmotionDetector and update dict\n",
|
||||
" \n",
|
||||
" image_dict[key] = ammico.EmotionDetector(image_dict[key],\n",
|
||||
" emotion_threshold=emotion_threshold,\n",
|
||||
" race_threshold=race_threshold,\n",
|
||||
" gender_threshold=gender_threshold).analyse_image() # analyse image with EmotionDetector and update dict\n",
|
||||
" if num % dump_every == 0 or num == len(image_dict) - 1: # save results every dump_every to dump_file\n",
|
||||
" image_df = ammico.get_dataframe(image_dict)\n",
|
||||
" image_df.to_csv(dump_file)"
|
||||
@ -405,8 +451,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"csv_path = pkg / \"data\" / \"ref\" / \"test.csv\"\n",
|
||||
"ta = ammico.TextAnalyzer(csv_path=str(csv_path), column_key=\"text\")"
|
||||
"ta = ammico.TextAnalyzer(csv_path=\"../data/ref/test.csv\", column_key=\"text\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -530,6 +575,17 @@
|
||||
" image_df.to_csv(dump_file)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# write output to csv\n",
|
||||
"image_df = ammico.get_dataframe(image_dict)\n",
|
||||
"image_df.to_csv(\"/content/drive/MyDrive/misinformation-data/data_out.csv\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
@ -759,7 +815,7 @@
|
||||
"# analysis_type can be \n",
|
||||
"# \"summary\",\n",
|
||||
"# \"questions\",\n",
|
||||
"# \"summary_and_questions\".\n"
|
||||
"# \"summary_and_questions\"."
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -806,7 +862,7 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"You can also ask sequential questions if you pass the argument `cosequential_questions=True`. This means that the answers to previous questions will be passed as context to the next question. However, this method will work a bit slower, because for each image the answers to the questions will not be calculated simultaneously, but sequentially. "
|
||||
"You can also ask sequential questions if you pass the argument `consequential_questions=True`. This means that the answers to previous questions will be passed as context to the next question. However, this method will work a bit slower, because for each image the answers to the questions will not be calculated simultaneously, but sequentially. "
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -840,6 +896,17 @@
|
||||
"image_dict"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# write output to csv\n",
|
||||
"image_df = ammico.get_dataframe(image_dict)\n",
|
||||
"image_df.to_csv(\"/content/drive/MyDrive/misinformation-data/data_out.csv\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
@ -855,7 +922,7 @@
|
||||
"\n",
|
||||
"From the seven facial expressions, an overall dominating emotion category is identified: negative, positive, or neutral emotion. These are defined with the facial expressions angry, disgust, fear and sad for the negative category, happy for the positive category, and surprise and neutral for the neutral category.\n",
|
||||
"\n",
|
||||
"A similar threshold as for the emotion recognition is set for the race/ethnicity and gender detection, `race_threshold` and `gender_threshold`, with the default set to 50% so that a confidence for race / gender above 0.5 only will return a value in the analysis.\n",
|
||||
"A similar threshold as for the emotion recognition is set for the race/ethnicity and gender detection, `race_threshold` and `gender_threshold`, with the default set to 50% so that a confidence for race / gender above 0.5 only will return a value in the analysis. \n",
|
||||
"\n",
|
||||
"For age unfortunately no confidence value is accessible so that no threshold values can be set for this type of analysis. The [reported MAE of the model is ± 4.65](https://sefiks.com/2019/02/13/apparent-age-and-gender-prediction-in-keras/).\n",
|
||||
"\n",
|
||||
@ -876,6 +943,17 @@
|
||||
" accept_disclosure=\"DISCLOSURE_AMMICO\").analyse_image()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# write output to csv\n",
|
||||
"image_df = ammico.get_dataframe(image_dict)\n",
|
||||
"image_df.to_csv(\"/content/drive/MyDrive/misinformation-data/data_out.csv\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
@ -986,7 +1064,7 @@
|
||||
"source": [
|
||||
"The images are then processed and stored in a numerical representation, a tensor. These tensors do not change for the same image and same model - so if you run this analysis once, and save the tensors giving a path with the keyword `path_to_save_tensors`, a file with filename `.<Number_of_images>_<model_name>_saved_features_image.pt` will be placed there.\n",
|
||||
"\n",
|
||||
"This can save you time if you want to analyse same images with the same model but different questions. To run using the saved tensors, execute the below code giving the path and name of the tensor file. Any subsequent query of the model will run in a fraction of the time than it run in initially."
|
||||
"This can save you time if you want to analyse the same images with the same model but different questions. To run using the saved tensors, execute the below code giving the path and name of the tensor file. Any subsequent query of the model will run in a fraction of the time than it run in initially."
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -1050,7 +1128,7 @@
|
||||
"You can filter your results in 3 different ways:\n",
|
||||
"- `filter_number_of_images` limits the number of images found. That is, if the parameter `filter_number_of_images = 10`, then the first 10 images that best match the query will be shown. The other images ranks will be set to `None` and the similarity value to `0`.\n",
|
||||
"- `filter_val_limit` limits the output of images with a similarity value not bigger than `filter_val_limit`. That is, if the parameter `filter_val_limit = 0.2`, all images with similarity less than 0.2 will be discarded.\n",
|
||||
"- `filter_rel_error` (percentage) limits the output of images with a similarity value not bigger than `100 * abs(current_simularity_value - best_simularity_value_in_current_search)/best_simularity_value_in_current_search < filter_rel_error`. That is, if we set filter_rel_error = 30, it means that if the top1 image have 0.5 similarity value, we discard all image with similarity less than 0.35."
|
||||
"- `filter_rel_error` (percentage) limits the output of images with a similarity value not bigger than `100 * abs(current_similarity_value - best_similarity_value_in_current_search)/best_similarity_value_in_current_search < filter_rel_error`. That is, if we set filter_rel_error = 30, it means that if the top1 image have 0.5 similarity value, we discard all image with similarity less than 0.35."
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -1174,7 +1252,7 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Then using the same output function you can add the `itm=True` argument to output the new image order. Remember that for images querys, an error will be thrown with `itm=True` argument. You can also add the `image_gradcam_with_itm` along with `itm=True` argument to output the heat maps of the calculated images."
|
||||
"Then using the same output function you can add the `itm=True` argument to output the new image order. Remember that for images queries, an error will be thrown with `itm=True` argument. You can also add the `image_gradcam_with_itm` along with `itm=True` argument to output the heat maps of the calculated images."
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -1199,7 +1277,7 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Convert the dictionary of dictionarys into a dictionary with lists:"
|
||||
"Convert the dictionary of dictionaries into a dictionary with lists:"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -1367,7 +1445,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.5"
|
||||
"version": "3.11.9"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
||||
@ -1 +1 @@
|
||||
.clearfix{*zoom:1}.clearfix:after,.clearfix:before{display:table;content:""}.clearfix:after{clear:both}@font-face{font-family:FontAwesome;font-style:normal;font-weight:400;src:url(fonts/fontawesome-webfont.eot?674f50d287a8c48dc19ba404d20fe713?#iefix) format("embedded-opentype"),url(fonts/fontawesome-webfont.woff2?af7ae505a9eed503f8b8e6982036873e) format("woff2"),url(fonts/fontawesome-webfont.woff?fee66e712a8a08eef5805a46892932ad) format("woff"),url(fonts/fontawesome-webfont.ttf?b06871f281fee6b241d60582ae9369b9) format("truetype"),url(fonts/fontawesome-webfont.svg?912ec66d7572ff821749319396470bde#FontAwesome) format("svg")}.fa:before{font-family:FontAwesome;font-style:normal;font-weight:400;line-height:1}.fa:before,a .fa{text-decoration:inherit}.fa:before,a .fa,li .fa{display:inline-block}li .fa-large:before{width:1.875em}ul.fas{list-style-type:none;margin-left:2em;text-indent:-.8em}ul.fas li .fa{width:.8em}ul.fas li .fa-large:before{vertical-align:baseline}.fa-book:before,.icon-book:before{content:"\f02d"}.fa-caret-down:before,.icon-caret-down:before{content:"\f0d7"}.fa-caret-up:before,.icon-caret-up:before{content:"\f0d8"}.fa-caret-left:before,.icon-caret-left:before{content:"\f0d9"}.fa-caret-right:before,.icon-caret-right:before{content:"\f0da"}.rst-versions{position:fixed;bottom:0;left:0;width:300px;color:#fcfcfc;background:#1f1d1d;font-family:Lato,proxima-nova,Helvetica Neue,Arial,sans-serif;z-index:400}.rst-versions a{color:#2980b9;text-decoration:none}.rst-versions .rst-badge-small{display:none}.rst-versions .rst-current-version{padding:12px;background-color:#272525;display:block;text-align:right;font-size:90%;cursor:pointer;color:#27ae60}.rst-versions .rst-current-version:after{clear:both;content:"";display:block}.rst-versions .rst-current-version .fa{color:#fcfcfc}.rst-versions .rst-current-version .fa-book,.rst-versions .rst-current-version .icon-book{float:left}.rst-versions .rst-current-version.rst-out-of-date{background-color:#e74c3c;color:#fff}.rst-versions .rst-current-version.rst-active-old-version{background-color:#f1c40f;color:#000}.rst-versions.shift-up{height:auto;max-height:100%;overflow-y:scroll}.rst-versions.shift-up .rst-other-versions{display:block}.rst-versions .rst-other-versions{font-size:90%;padding:12px;color:grey;display:none}.rst-versions .rst-other-versions hr{display:block;height:1px;border:0;margin:20px 0;padding:0;border-top:1px solid #413d3d}.rst-versions .rst-other-versions dd{display:inline-block;margin:0}.rst-versions .rst-other-versions dd a{display:inline-block;padding:6px;color:#fcfcfc}.rst-versions.rst-badge{width:auto;bottom:20px;right:20px;left:auto;border:none;max-width:300px;max-height:90%}.rst-versions.rst-badge .fa-book,.rst-versions.rst-badge .icon-book{float:none;line-height:30px}.rst-versions.rst-badge.shift-up .rst-current-version{text-align:right}.rst-versions.rst-badge.shift-up .rst-current-version .fa-book,.rst-versions.rst-badge.shift-up .rst-current-version .icon-book{float:left}.rst-versions.rst-badge>.rst-current-version{width:auto;height:30px;line-height:30px;padding:0 6px;display:block;text-align:center}@media screen and (max-width:768px){.rst-versions{width:85%;display:none}.rst-versions.shift{display:block}}
|
||||
.clearfix{*zoom:1}.clearfix:after,.clearfix:before{display:table;content:""}.clearfix:after{clear:both}@font-face{font-family:FontAwesome;font-style:normal;font-weight:400;src:url(fonts/fontawesome-webfont.eot?674f50d287a8c48dc19ba404d20fe713?#iefix) format("embedded-opentype"),url(fonts/fontawesome-webfont.woff2?af7ae505a9eed503f8b8e6982036873e) format("woff2"),url(fonts/fontawesome-webfont.woff?fee66e712a8a08eef5805a46892932ad) format("woff"),url(fonts/fontawesome-webfont.ttf?b06871f281fee6b241d60582ae9369b9) format("truetype"),url(fonts/fontawesome-webfont.svg?912ec66d7572ff821749319396470bde#FontAwesome) format("svg")}.fa:before{font-family:FontAwesome;font-style:normal;font-weight:400;line-height:1}.fa:before,a .fa{text-decoration:inherit}.fa:before,a .fa,li .fa{display:inline-block}li .fa-large:before{width:1.875em}ul.fas{list-style-type:none;margin-left:2em;text-indent:-.8em}ul.fas li .fa{width:.8em}ul.fas li .fa-large:before{vertical-align:baseline}.fa-book:before,.icon-book:before{content:"\f02d"}.fa-caret-down:before,.icon-caret-down:before{content:"\f0d7"}.fa-caret-up:before,.icon-caret-up:before{content:"\f0d8"}.fa-caret-left:before,.icon-caret-left:before{content:"\f0d9"}.fa-caret-right:before,.icon-caret-right:before{content:"\f0da"}.rst-versions{position:fixed;bottom:0;left:0;width:300px;color:#fcfcfc;background:#1f1d1d;font-family:Lato,proxima-nova,Helvetica Neue,Arial,sans-serif;z-index:400}.rst-versions a{color:#2980b9;text-decoration:none}.rst-versions .rst-badge-small{display:none}.rst-versions .rst-current-version{padding:12px;background-color:#272525;display:block;text-align:right;font-size:90%;cursor:pointer;color:#27ae60}.rst-versions .rst-current-version:after{clear:both;content:"";display:block}.rst-versions .rst-current-version .fa{color:#fcfcfc}.rst-versions .rst-current-version .fa-book,.rst-versions .rst-current-version .icon-book{float:left}.rst-versions .rst-current-version.rst-out-of-date{background-color:#e74c3c;color:#fff}.rst-versions .rst-current-version.rst-active-old-version{background-color:#f1c40f;color:#000}.rst-versions.shift-up{height:auto;max-height:100%;overflow-y:scroll}.rst-versions.shift-up .rst-other-versions{display:block}.rst-versions .rst-other-versions{font-size:90%;padding:12px;color:grey;display:none}.rst-versions .rst-other-versions hr{display:block;height:1px;border:0;margin:20px 0;padding:0;border-top:1px solid #413d3d}.rst-versions .rst-other-versions dd{display:inline-block;margin:0}.rst-versions .rst-other-versions dd a{display:inline-block;padding:6px;color:#fcfcfc}.rst-versions .rst-other-versions .rtd-current-item{font-weight:700}.rst-versions.rst-badge{width:auto;bottom:20px;right:20px;left:auto;border:none;max-width:300px;max-height:90%}.rst-versions.rst-badge .fa-book,.rst-versions.rst-badge .icon-book{float:none;line-height:30px}.rst-versions.rst-badge.shift-up .rst-current-version{text-align:right}.rst-versions.rst-badge.shift-up .rst-current-version .fa-book,.rst-versions.rst-badge.shift-up .rst-current-version .icon-book{float:left}.rst-versions.rst-badge>.rst-current-version{width:auto;height:30px;line-height:30px;padding:0 6px;display:block;text-align:center}@media screen and (max-width:768px){.rst-versions{width:85%;display:none}.rst-versions.shift{display:block}}#flyout-search-form{padding:6px}
|
||||
Различия файлов скрыты, потому что одна или несколько строк слишком длинны
Двоичные данные
build/html/_static/fonts/Lato/lato-bold.eot
Обычный файл
Двоичные данные
build/html/_static/fonts/Lato/lato-bold.eot
Обычный файл
Двоичный файл не отображается.
Двоичные данные
build/html/_static/fonts/Lato/lato-bold.ttf
Обычный файл
Двоичные данные
build/html/_static/fonts/Lato/lato-bold.ttf
Обычный файл
Двоичный файл не отображается.
Двоичные данные
build/html/_static/fonts/Lato/lato-bold.woff
Обычный файл
Двоичные данные
build/html/_static/fonts/Lato/lato-bold.woff
Обычный файл
Двоичный файл не отображается.
Двоичные данные
build/html/_static/fonts/Lato/lato-bold.woff2
Обычный файл
Двоичные данные
build/html/_static/fonts/Lato/lato-bold.woff2
Обычный файл
Двоичный файл не отображается.
Двоичные данные
build/html/_static/fonts/Lato/lato-bolditalic.eot
Обычный файл
Двоичные данные
build/html/_static/fonts/Lato/lato-bolditalic.eot
Обычный файл
Двоичный файл не отображается.
Двоичные данные
build/html/_static/fonts/Lato/lato-bolditalic.ttf
Обычный файл
Двоичные данные
build/html/_static/fonts/Lato/lato-bolditalic.ttf
Обычный файл
Двоичный файл не отображается.
Двоичные данные
build/html/_static/fonts/Lato/lato-bolditalic.woff
Обычный файл
Двоичные данные
build/html/_static/fonts/Lato/lato-bolditalic.woff
Обычный файл
Двоичный файл не отображается.
Двоичные данные
build/html/_static/fonts/Lato/lato-bolditalic.woff2
Обычный файл
Двоичные данные
build/html/_static/fonts/Lato/lato-bolditalic.woff2
Обычный файл
Двоичный файл не отображается.
Двоичные данные
build/html/_static/fonts/Lato/lato-italic.eot
Обычный файл
Двоичные данные
build/html/_static/fonts/Lato/lato-italic.eot
Обычный файл
Двоичный файл не отображается.
Двоичные данные
build/html/_static/fonts/Lato/lato-italic.ttf
Обычный файл
Двоичные данные
build/html/_static/fonts/Lato/lato-italic.ttf
Обычный файл
Двоичный файл не отображается.
Двоичные данные
build/html/_static/fonts/Lato/lato-italic.woff
Обычный файл
Двоичные данные
build/html/_static/fonts/Lato/lato-italic.woff
Обычный файл
Двоичный файл не отображается.
Двоичные данные
build/html/_static/fonts/Lato/lato-italic.woff2
Обычный файл
Двоичные данные
build/html/_static/fonts/Lato/lato-italic.woff2
Обычный файл
Двоичный файл не отображается.
Двоичные данные
build/html/_static/fonts/Lato/lato-regular.eot
Обычный файл
Двоичные данные
build/html/_static/fonts/Lato/lato-regular.eot
Обычный файл
Двоичный файл не отображается.
Двоичные данные
build/html/_static/fonts/Lato/lato-regular.ttf
Обычный файл
Двоичные данные
build/html/_static/fonts/Lato/lato-regular.ttf
Обычный файл
Двоичный файл не отображается.
Двоичные данные
build/html/_static/fonts/Lato/lato-regular.woff
Обычный файл
Двоичные данные
build/html/_static/fonts/Lato/lato-regular.woff
Обычный файл
Двоичный файл не отображается.
Двоичные данные
build/html/_static/fonts/Lato/lato-regular.woff2
Обычный файл
Двоичные данные
build/html/_static/fonts/Lato/lato-regular.woff2
Обычный файл
Двоичный файл не отображается.
Двоичные данные
build/html/_static/fonts/RobotoSlab/roboto-slab-v7-bold.eot
Обычный файл
Двоичные данные
build/html/_static/fonts/RobotoSlab/roboto-slab-v7-bold.eot
Обычный файл
Двоичный файл не отображается.
Двоичные данные
build/html/_static/fonts/RobotoSlab/roboto-slab-v7-bold.ttf
Обычный файл
Двоичные данные
build/html/_static/fonts/RobotoSlab/roboto-slab-v7-bold.ttf
Обычный файл
Двоичный файл не отображается.
Двоичные данные
build/html/_static/fonts/RobotoSlab/roboto-slab-v7-bold.woff
Обычный файл
Двоичные данные
build/html/_static/fonts/RobotoSlab/roboto-slab-v7-bold.woff
Обычный файл
Двоичный файл не отображается.
Двоичные данные
build/html/_static/fonts/RobotoSlab/roboto-slab-v7-bold.woff2
Обычный файл
Двоичные данные
build/html/_static/fonts/RobotoSlab/roboto-slab-v7-bold.woff2
Обычный файл
Двоичный файл не отображается.
Двоичные данные
build/html/_static/fonts/RobotoSlab/roboto-slab-v7-regular.eot
Обычный файл
Двоичные данные
build/html/_static/fonts/RobotoSlab/roboto-slab-v7-regular.eot
Обычный файл
Двоичный файл не отображается.
Двоичные данные
build/html/_static/fonts/RobotoSlab/roboto-slab-v7-regular.ttf
Обычный файл
Двоичные данные
build/html/_static/fonts/RobotoSlab/roboto-slab-v7-regular.ttf
Обычный файл
Двоичный файл не отображается.
Двоичные данные
build/html/_static/fonts/RobotoSlab/roboto-slab-v7-regular.woff
Обычный файл
Двоичные данные
build/html/_static/fonts/RobotoSlab/roboto-slab-v7-regular.woff
Обычный файл
Двоичный файл не отображается.
Двоичные данные
build/html/_static/fonts/RobotoSlab/roboto-slab-v7-regular.woff2
Обычный файл
Двоичные данные
build/html/_static/fonts/RobotoSlab/roboto-slab-v7-regular.woff2
Обычный файл
Двоичный файл не отображается.
4
build/html/_static/js/html5shiv-printshiv.min.js
поставляемый
4
build/html/_static/js/html5shiv-printshiv.min.js
поставляемый
@ -1,4 +0,0 @@
|
||||
/**
|
||||
* @preserve HTML5 Shiv 3.7.3-pre | @afarkas @jdalton @jon_neal @rem | MIT/GPL2 Licensed
|
||||
*/
|
||||
!function(a,b){function c(a,b){var c=a.createElement("p"),d=a.getElementsByTagName("head")[0]||a.documentElement;return c.innerHTML="x<style>"+b+"</style>",d.insertBefore(c.lastChild,d.firstChild)}function d(){var a=y.elements;return"string"==typeof a?a.split(" "):a}function e(a,b){var c=y.elements;"string"!=typeof c&&(c=c.join(" ")),"string"!=typeof a&&(a=a.join(" ")),y.elements=c+" "+a,j(b)}function f(a){var b=x[a[v]];return b||(b={},w++,a[v]=w,x[w]=b),b}function g(a,c,d){if(c||(c=b),q)return c.createElement(a);d||(d=f(c));var e;return e=d.cache[a]?d.cache[a].cloneNode():u.test(a)?(d.cache[a]=d.createElem(a)).cloneNode():d.createElem(a),!e.canHaveChildren||t.test(a)||e.tagUrn?e:d.frag.appendChild(e)}function h(a,c){if(a||(a=b),q)return a.createDocumentFragment();c=c||f(a);for(var e=c.frag.cloneNode(),g=0,h=d(),i=h.length;i>g;g++)e.createElement(h[g]);return e}function i(a,b){b.cache||(b.cache={},b.createElem=a.createElement,b.createFrag=a.createDocumentFragment,b.frag=b.createFrag()),a.createElement=function(c){return y.shivMethods?g(c,a,b):b.createElem(c)},a.createDocumentFragment=Function("h,f","return function(){var n=f.cloneNode(),c=n.createElement;h.shivMethods&&("+d().join().replace(/[\w\-:]+/g,function(a){return b.createElem(a),b.frag.createElement(a),'c("'+a+'")'})+");return n}")(y,b.frag)}function j(a){a||(a=b);var d=f(a);return!y.shivCSS||p||d.hasCSS||(d.hasCSS=!!c(a,"article,aside,dialog,figcaption,figure,footer,header,hgroup,main,nav,section{display:block}mark{background:#FF0;color:#000}template{display:none}")),q||i(a,d),a}function k(a){for(var b,c=a.getElementsByTagName("*"),e=c.length,f=RegExp("^(?:"+d().join("|")+")$","i"),g=[];e--;)b=c[e],f.test(b.nodeName)&&g.push(b.applyElement(l(b)));return g}function l(a){for(var b,c=a.attributes,d=c.length,e=a.ownerDocument.createElement(A+":"+a.nodeName);d--;)b=c[d],b.specified&&e.setAttribute(b.nodeName,b.nodeValue);return e.style.cssText=a.style.cssText,e}function m(a){for(var b,c=a.split("{"),e=c.length,f=RegExp("(^|[\\s,>+~])("+d().join("|")+")(?=[[\\s,>+~#.:]|$)","gi"),g="$1"+A+"\\:$2";e--;)b=c[e]=c[e].split("}"),b[b.length-1]=b[b.length-1].replace(f,g),c[e]=b.join("}");return c.join("{")}function n(a){for(var b=a.length;b--;)a[b].removeNode()}function o(a){function b(){clearTimeout(g._removeSheetTimer),d&&d.removeNode(!0),d=null}var d,e,g=f(a),h=a.namespaces,i=a.parentWindow;return!B||a.printShived?a:("undefined"==typeof h[A]&&h.add(A),i.attachEvent("onbeforeprint",function(){b();for(var f,g,h,i=a.styleSheets,j=[],l=i.length,n=Array(l);l--;)n[l]=i[l];for(;h=n.pop();)if(!h.disabled&&z.test(h.media)){try{f=h.imports,g=f.length}catch(o){g=0}for(l=0;g>l;l++)n.push(f[l]);try{j.push(h.cssText)}catch(o){}}j=m(j.reverse().join("")),e=k(a),d=c(a,j)}),i.attachEvent("onafterprint",function(){n(e),clearTimeout(g._removeSheetTimer),g._removeSheetTimer=setTimeout(b,500)}),a.printShived=!0,a)}var p,q,r="3.7.3",s=a.html5||{},t=/^<|^(?:button|map|select|textarea|object|iframe|option|optgroup)$/i,u=/^(?:a|b|code|div|fieldset|h1|h2|h3|h4|h5|h6|i|label|li|ol|p|q|span|strong|style|table|tbody|td|th|tr|ul)$/i,v="_html5shiv",w=0,x={};!function(){try{var a=b.createElement("a");a.innerHTML="<xyz></xyz>",p="hidden"in a,q=1==a.childNodes.length||function(){b.createElement("a");var a=b.createDocumentFragment();return"undefined"==typeof a.cloneNode||"undefined"==typeof a.createDocumentFragment||"undefined"==typeof a.createElement}()}catch(c){p=!0,q=!0}}();var y={elements:s.elements||"abbr article aside audio bdi canvas data datalist details dialog figcaption figure footer header hgroup main mark meter nav output picture progress section summary template time video",version:r,shivCSS:s.shivCSS!==!1,supportsUnknownElements:q,shivMethods:s.shivMethods!==!1,type:"default",shivDocument:j,createElement:g,createDocumentFragment:h,addElements:e};a.html5=y,j(b);var z=/^$|\b(?:all|print)\b/,A="html5shiv",B=!q&&function(){var c=b.documentElement;return!("undefined"==typeof b.namespaces||"undefined"==typeof b.parentWindow||"undefined"==typeof c.applyElement||"undefined"==typeof c.removeNode||"undefined"==typeof a.attachEvent)}();y.type+=" print",y.shivPrint=o,o(b),"object"==typeof module&&module.exports&&(module.exports=y)}("undefined"!=typeof window?window:this,document);
|
||||
4
build/html/_static/js/html5shiv.min.js
поставляемый
4
build/html/_static/js/html5shiv.min.js
поставляемый
@ -1,4 +0,0 @@
|
||||
/**
|
||||
* @preserve HTML5 Shiv 3.7.3 | @afarkas @jdalton @jon_neal @rem | MIT/GPL2 Licensed
|
||||
*/
|
||||
!function(a,b){function c(a,b){var c=a.createElement("p"),d=a.getElementsByTagName("head")[0]||a.documentElement;return c.innerHTML="x<style>"+b+"</style>",d.insertBefore(c.lastChild,d.firstChild)}function d(){var a=t.elements;return"string"==typeof a?a.split(" "):a}function e(a,b){var c=t.elements;"string"!=typeof c&&(c=c.join(" ")),"string"!=typeof a&&(a=a.join(" ")),t.elements=c+" "+a,j(b)}function f(a){var b=s[a[q]];return b||(b={},r++,a[q]=r,s[r]=b),b}function g(a,c,d){if(c||(c=b),l)return c.createElement(a);d||(d=f(c));var e;return e=d.cache[a]?d.cache[a].cloneNode():p.test(a)?(d.cache[a]=d.createElem(a)).cloneNode():d.createElem(a),!e.canHaveChildren||o.test(a)||e.tagUrn?e:d.frag.appendChild(e)}function h(a,c){if(a||(a=b),l)return a.createDocumentFragment();c=c||f(a);for(var e=c.frag.cloneNode(),g=0,h=d(),i=h.length;i>g;g++)e.createElement(h[g]);return e}function i(a,b){b.cache||(b.cache={},b.createElem=a.createElement,b.createFrag=a.createDocumentFragment,b.frag=b.createFrag()),a.createElement=function(c){return t.shivMethods?g(c,a,b):b.createElem(c)},a.createDocumentFragment=Function("h,f","return function(){var n=f.cloneNode(),c=n.createElement;h.shivMethods&&("+d().join().replace(/[\w\-:]+/g,function(a){return b.createElem(a),b.frag.createElement(a),'c("'+a+'")'})+");return n}")(t,b.frag)}function j(a){a||(a=b);var d=f(a);return!t.shivCSS||k||d.hasCSS||(d.hasCSS=!!c(a,"article,aside,dialog,figcaption,figure,footer,header,hgroup,main,nav,section{display:block}mark{background:#FF0;color:#000}template{display:none}")),l||i(a,d),a}var k,l,m="3.7.3-pre",n=a.html5||{},o=/^<|^(?:button|map|select|textarea|object|iframe|option|optgroup)$/i,p=/^(?:a|b|code|div|fieldset|h1|h2|h3|h4|h5|h6|i|label|li|ol|p|q|span|strong|style|table|tbody|td|th|tr|ul)$/i,q="_html5shiv",r=0,s={};!function(){try{var a=b.createElement("a");a.innerHTML="<xyz></xyz>",k="hidden"in a,l=1==a.childNodes.length||function(){b.createElement("a");var a=b.createDocumentFragment();return"undefined"==typeof a.cloneNode||"undefined"==typeof a.createDocumentFragment||"undefined"==typeof a.createElement}()}catch(c){k=!0,l=!0}}();var t={elements:n.elements||"abbr article aside audio bdi canvas data datalist details dialog figcaption figure footer header hgroup main mark meter nav output picture progress section summary template time video",version:m,shivCSS:n.shivCSS!==!1,supportsUnknownElements:l,shivMethods:n.shivMethods!==!1,type:"default",shivDocument:j,createElement:g,createDocumentFragment:h,addElements:e};a.html5=t,j(b),"object"==typeof module&&module.exports&&(module.exports=t)}("undefined"!=typeof window?window:this,document);
|
||||
224
build/html/_static/js/versions.js
Обычный файл
224
build/html/_static/js/versions.js
Обычный файл
@ -0,0 +1,224 @@
|
||||
const themeFlyoutDisplay = "hidden";
|
||||
const themeVersionSelector = "True";
|
||||
const themeLanguageSelector = "True";
|
||||
|
||||
if (themeFlyoutDisplay === "attached") {
|
||||
function renderLanguages(config) {
|
||||
if (!config.projects.translations.length) {
|
||||
return "";
|
||||
}
|
||||
|
||||
const languagesHTML = `
|
||||
<dl>
|
||||
<dt>Languages</dt>
|
||||
${config.projects.translations
|
||||
.map(
|
||||
(translation) => `
|
||||
<dd ${translation.slug == config.projects.current.slug ? 'class="rtd-current-item"' : ""}>
|
||||
<a href="${translation.urls.documentation}">${translation.language.code}</a>
|
||||
</dd>
|
||||
`,
|
||||
)
|
||||
.join("\n")}
|
||||
</dl>
|
||||
`;
|
||||
return languagesHTML;
|
||||
}
|
||||
|
||||
function renderVersions(config) {
|
||||
if (!config.versions.active.length) {
|
||||
return "";
|
||||
}
|
||||
const versionsHTML = `
|
||||
<dl>
|
||||
<dt>Versions</dt>
|
||||
${config.versions.active
|
||||
.map(
|
||||
(version) => `
|
||||
<dd ${version.slug === config.versions.current.slug ? 'class="rtd-current-item"' : ""}>
|
||||
<a href="${version.urls.documentation}">${version.slug}</a>
|
||||
</dd>
|
||||
`,
|
||||
)
|
||||
.join("\n")}
|
||||
</dl>
|
||||
`;
|
||||
return versionsHTML;
|
||||
}
|
||||
|
||||
function renderDownloads(config) {
|
||||
if (!Object.keys(config.versions.current.downloads).length) {
|
||||
return "";
|
||||
}
|
||||
const downloadsNameDisplay = {
|
||||
pdf: "PDF",
|
||||
epub: "Epub",
|
||||
htmlzip: "HTML",
|
||||
};
|
||||
|
||||
const downloadsHTML = `
|
||||
<dl>
|
||||
<dt>Downloads</dt>
|
||||
${Object.entries(config.versions.current.downloads)
|
||||
.map(
|
||||
([name, url]) => `
|
||||
<dd>
|
||||
<a href="${url}">${downloadsNameDisplay[name]}</a>
|
||||
</dd>
|
||||
`,
|
||||
)
|
||||
.join("\n")}
|
||||
</dl>
|
||||
`;
|
||||
return downloadsHTML;
|
||||
}
|
||||
|
||||
document.addEventListener("readthedocs-addons-data-ready", function (event) {
|
||||
const config = event.detail.data();
|
||||
|
||||
const flyout = `
|
||||
<div class="rst-versions" data-toggle="rst-versions" role="note">
|
||||
<span class="rst-current-version" data-toggle="rst-current-version">
|
||||
<span class="fa fa-book"> Read the Docs</span>
|
||||
v: ${config.versions.current.slug}
|
||||
<span class="fa fa-caret-down"></span>
|
||||
</span>
|
||||
<div class="rst-other-versions">
|
||||
<div class="injected">
|
||||
${renderLanguages(config)}
|
||||
${renderVersions(config)}
|
||||
${renderDownloads(config)}
|
||||
<dl>
|
||||
<dt>On Read the Docs</dt>
|
||||
<dd>
|
||||
<a href="${config.projects.current.urls.home}">Project Home</a>
|
||||
</dd>
|
||||
<dd>
|
||||
<a href="${config.projects.current.urls.builds}">Builds</a>
|
||||
</dd>
|
||||
<dd>
|
||||
<a href="${config.projects.current.urls.downloads}">Downloads</a>
|
||||
</dd>
|
||||
</dl>
|
||||
<dl>
|
||||
<dt>Search</dt>
|
||||
<dd>
|
||||
<form id="flyout-search-form">
|
||||
<input
|
||||
class="wy-form"
|
||||
type="text"
|
||||
name="q"
|
||||
aria-label="Search docs"
|
||||
placeholder="Search docs"
|
||||
/>
|
||||
</form>
|
||||
</dd>
|
||||
</dl>
|
||||
<hr />
|
||||
<small>
|
||||
<span>Hosted by <a href="https://about.readthedocs.org/?utm_source=&utm_content=flyout">Read the Docs</a></span>
|
||||
</small>
|
||||
</div>
|
||||
</div>
|
||||
`;
|
||||
|
||||
// Inject the generated flyout into the body HTML element.
|
||||
document.body.insertAdjacentHTML("beforeend", flyout);
|
||||
|
||||
// Trigger the Read the Docs Addons Search modal when clicking on the "Search docs" input from inside the flyout.
|
||||
document
|
||||
.querySelector("#flyout-search-form")
|
||||
.addEventListener("focusin", () => {
|
||||
const event = new CustomEvent("readthedocs-search-show");
|
||||
document.dispatchEvent(event);
|
||||
});
|
||||
})
|
||||
}
|
||||
|
||||
if (themeLanguageSelector || themeVersionSelector) {
|
||||
function onSelectorSwitch(event) {
|
||||
const option = event.target.selectedIndex;
|
||||
const item = event.target.options[option];
|
||||
window.location.href = item.dataset.url;
|
||||
}
|
||||
|
||||
document.addEventListener("readthedocs-addons-data-ready", function (event) {
|
||||
const config = event.detail.data();
|
||||
|
||||
const versionSwitch = document.querySelector(
|
||||
"div.switch-menus > div.version-switch",
|
||||
);
|
||||
if (themeVersionSelector) {
|
||||
let versions = config.versions.active;
|
||||
if (config.versions.current.hidden || config.versions.current.type === "external") {
|
||||
versions.unshift(config.versions.current);
|
||||
}
|
||||
const versionSelect = `
|
||||
<select>
|
||||
${versions
|
||||
.map(
|
||||
(version) => `
|
||||
<option
|
||||
value="${version.slug}"
|
||||
${config.versions.current.slug === version.slug ? 'selected="selected"' : ""}
|
||||
data-url="${version.urls.documentation}">
|
||||
${version.slug}
|
||||
</option>`,
|
||||
)
|
||||
.join("\n")}
|
||||
</select>
|
||||
`;
|
||||
|
||||
versionSwitch.innerHTML = versionSelect;
|
||||
versionSwitch.firstElementChild.addEventListener("change", onSelectorSwitch);
|
||||
}
|
||||
|
||||
const languageSwitch = document.querySelector(
|
||||
"div.switch-menus > div.language-switch",
|
||||
);
|
||||
|
||||
if (themeLanguageSelector) {
|
||||
if (config.projects.translations.length) {
|
||||
// Add the current language to the options on the selector
|
||||
let languages = config.projects.translations.concat(
|
||||
config.projects.current,
|
||||
);
|
||||
languages = languages.sort((a, b) =>
|
||||
a.language.name.localeCompare(b.language.name),
|
||||
);
|
||||
|
||||
const languageSelect = `
|
||||
<select>
|
||||
${languages
|
||||
.map(
|
||||
(language) => `
|
||||
<option
|
||||
value="${language.language.code}"
|
||||
${config.projects.current.slug === language.slug ? 'selected="selected"' : ""}
|
||||
data-url="${language.urls.documentation}">
|
||||
${language.language.name}
|
||||
</option>`,
|
||||
)
|
||||
.join("\n")}
|
||||
</select>
|
||||
`;
|
||||
|
||||
languageSwitch.innerHTML = languageSelect;
|
||||
languageSwitch.firstElementChild.addEventListener("change", onSelectorSwitch);
|
||||
}
|
||||
else {
|
||||
languageSwitch.remove();
|
||||
}
|
||||
}
|
||||
});
|
||||
}
|
||||
|
||||
document.addEventListener("readthedocs-addons-data-ready", function (event) {
|
||||
// Trigger the Read the Docs Addons Search modal when clicking on "Search docs" input from the topnav.
|
||||
document
|
||||
.querySelector("[role='search'] input")
|
||||
.addEventListener("focusin", () => {
|
||||
const event = new CustomEvent("readthedocs-search-show");
|
||||
document.dispatchEvent(event);
|
||||
});
|
||||
});
|
||||
@ -1,3 +1,5 @@
|
||||
|
||||
|
||||
<!DOCTYPE html>
|
||||
<html class="writer-html5" lang="en" data-content_root="./">
|
||||
<head>
|
||||
@ -6,19 +8,15 @@
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
|
||||
<title>text module — AMMICO 0.0.1 documentation</title>
|
||||
<link rel="stylesheet" type="text/css" href="_static/pygments.css?v=80d5e7a1" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/css/theme.css?v=19f00094" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/css/theme.css?v=7ab3649f" />
|
||||
|
||||
|
||||
<!--[if lt IE 9]>
|
||||
<script src="_static/js/html5shiv.min.js"></script>
|
||||
<![endif]-->
|
||||
|
||||
<script src="_static/jquery.js?v=5d32c60e"></script>
|
||||
<script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
|
||||
<script src="_static/documentation_options.js?v=d45e8c67"></script>
|
||||
<script src="_static/doctools.js?v=9a2dae69"></script>
|
||||
<script src="_static/sphinx_highlight.js?v=dc90522c"></script>
|
||||
<script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
|
||||
<script src="_static/jquery.js?v=5d32c60e"></script>
|
||||
<script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
|
||||
<script src="_static/documentation_options.js?v=d45e8c67"></script>
|
||||
<script src="_static/doctools.js?v=9a2dae69"></script>
|
||||
<script src="_static/sphinx_highlight.js?v=dc90522c"></script>
|
||||
<script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
|
||||
<script src="_static/js/theme.js"></script>
|
||||
<link rel="index" title="Index" href="genindex.html" />
|
||||
<link rel="search" title="Search" href="search.html" />
|
||||
@ -48,7 +46,7 @@
|
||||
<p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
|
||||
<ul class="current">
|
||||
<li class="toctree-l1"><a class="reference internal" href="readme_link.html">AMMICO - AI Media and Misinformation Content Analysis Tool</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="readme_link.html#faq">FAQ</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="faq_link.html">FAQ</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="create_API_key_link.html">Instructions how to generate and enable a google Cloud Vision API key</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="notebooks/DemoNotebook_ammico.html">AMMICO Demonstration Notebook</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="notebooks/DemoNotebook_ammico.html#Step-0:-Create-and-set-a-Google-Cloud-Vision-Key">Step 0: Create and set a Google Cloud Vision Key</a></li>
|
||||
@ -79,6 +77,7 @@
|
||||
<li class="toctree-l4"><a class="reference internal" href="#text.TextDetector.translate_text"><code class="docutils literal notranslate"><span class="pre">TextDetector.translate_text()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="#text.privacy_disclosure"><code class="docutils literal notranslate"><span class="pre">privacy_disclosure()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="#module-summary">summary module</a><ul>
|
||||
@ -307,7 +306,7 @@
|
||||
|
||||
<dl class="py class">
|
||||
<dt class="sig sig-object py" id="text.TextDetector">
|
||||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">text.</span></span><span class="sig-name descname"><span class="pre">TextDetector</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">subdict</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">analyse_text</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip_extraction</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_names</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">list</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">revision_numbers</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">list</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#text.TextDetector" title="Link to this definition"></a></dt>
|
||||
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">text.</span></span><span class="sig-name descname"><span class="pre">TextDetector</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">subdict</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">analyse_text</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip_extraction</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_names</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">list</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">revision_numbers</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">list</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">accept_privacy</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'PRIVACY_AMMICO'</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#text.TextDetector" title="Link to this definition"></a></dt>
|
||||
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">AnalysisMethod</span></code></p>
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="text.TextDetector.analyse_image">
|
||||
@ -375,6 +374,17 @@
|
||||
|
||||
</dd></dl>
|
||||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="text.privacy_disclosure">
|
||||
<span class="sig-prename descclassname"><span class="pre">text.</span></span><span class="sig-name descname"><span class="pre">privacy_disclosure</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">accept_privacy</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'PRIVACY_AMMICO'</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#text.privacy_disclosure" title="Link to this definition"></a></dt>
|
||||
<dd><p>Asks the user to accept the privacy statement.</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
|
||||
<dd class="field-odd"><p><strong>accept_privacy</strong> (<em>str</em>) – The name of the disclosure variable (default: “PRIVACY_AMMICO”).</p>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd></dl>
|
||||
|
||||
</section>
|
||||
<section id="module-summary">
|
||||
<span id="summary-module"></span><h1>summary module<a class="headerlink" href="#module-summary" title="Link to this heading"></a></h1>
|
||||
|
||||
@ -1,3 +1,5 @@
|
||||
|
||||
|
||||
<!DOCTYPE html>
|
||||
<html class="writer-html5" lang="en" data-content_root="./">
|
||||
<head>
|
||||
@ -6,24 +8,20 @@
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
|
||||
<title>Instructions how to generate and enable a google Cloud Vision API key — AMMICO 0.0.1 documentation</title>
|
||||
<link rel="stylesheet" type="text/css" href="_static/pygments.css?v=80d5e7a1" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/css/theme.css?v=19f00094" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/css/theme.css?v=7ab3649f" />
|
||||
|
||||
|
||||
<!--[if lt IE 9]>
|
||||
<script src="_static/js/html5shiv.min.js"></script>
|
||||
<![endif]-->
|
||||
|
||||
<script src="_static/jquery.js?v=5d32c60e"></script>
|
||||
<script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
|
||||
<script src="_static/documentation_options.js?v=d45e8c67"></script>
|
||||
<script src="_static/doctools.js?v=9a2dae69"></script>
|
||||
<script src="_static/sphinx_highlight.js?v=dc90522c"></script>
|
||||
<script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
|
||||
<script src="_static/jquery.js?v=5d32c60e"></script>
|
||||
<script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
|
||||
<script src="_static/documentation_options.js?v=d45e8c67"></script>
|
||||
<script src="_static/doctools.js?v=9a2dae69"></script>
|
||||
<script src="_static/sphinx_highlight.js?v=dc90522c"></script>
|
||||
<script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
|
||||
<script src="_static/js/theme.js"></script>
|
||||
<link rel="index" title="Index" href="genindex.html" />
|
||||
<link rel="search" title="Search" href="search.html" />
|
||||
<link rel="next" title="AMMICO Demonstration Notebook" href="notebooks/DemoNotebook_ammico.html" />
|
||||
<link rel="prev" title="AMMICO - AI Media and Misinformation Content Analysis Tool" href="readme_link.html" />
|
||||
<link rel="prev" title="FAQ" href="faq_link.html" />
|
||||
</head>
|
||||
|
||||
<body class="wy-body-for-nav">
|
||||
@ -48,7 +46,7 @@
|
||||
<p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
|
||||
<ul class="current">
|
||||
<li class="toctree-l1"><a class="reference internal" href="readme_link.html">AMMICO - AI Media and Misinformation Content Analysis Tool</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="readme_link.html#faq">FAQ</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="faq_link.html">FAQ</a></li>
|
||||
<li class="toctree-l1 current"><a class="current reference internal" href="#">Instructions how to generate and enable a google Cloud Vision API key</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="notebooks/DemoNotebook_ammico.html">AMMICO Demonstration Notebook</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="notebooks/DemoNotebook_ammico.html#Step-0:-Create-and-set-a-Google-Cloud-Vision-Key">Step 0: Create and set a Google Cloud Vision Key</a></li>
|
||||
@ -135,7 +133,7 @@
|
||||
</div>
|
||||
</div>
|
||||
<footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
|
||||
<a href="readme_link.html" class="btn btn-neutral float-left" title="AMMICO - AI Media and Misinformation Content Analysis Tool" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
|
||||
<a href="faq_link.html" class="btn btn-neutral float-left" title="FAQ" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
|
||||
<a href="notebooks/DemoNotebook_ammico.html" class="btn btn-neutral float-right" title="AMMICO Demonstration Notebook" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
|
||||
</div>
|
||||
|
||||
|
||||
241
build/html/faq_link.html
Обычный файл
241
build/html/faq_link.html
Обычный файл
@ -0,0 +1,241 @@
|
||||
|
||||
|
||||
<!DOCTYPE html>
|
||||
<html class="writer-html5" lang="en" data-content_root="./">
|
||||
<head>
|
||||
<meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
|
||||
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
|
||||
<title>FAQ — AMMICO 0.0.1 documentation</title>
|
||||
<link rel="stylesheet" type="text/css" href="_static/pygments.css?v=80d5e7a1" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/css/theme.css?v=7ab3649f" />
|
||||
|
||||
|
||||
<script src="_static/jquery.js?v=5d32c60e"></script>
|
||||
<script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
|
||||
<script src="_static/documentation_options.js?v=d45e8c67"></script>
|
||||
<script src="_static/doctools.js?v=9a2dae69"></script>
|
||||
<script src="_static/sphinx_highlight.js?v=dc90522c"></script>
|
||||
<script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
|
||||
<script src="_static/js/theme.js"></script>
|
||||
<link rel="index" title="Index" href="genindex.html" />
|
||||
<link rel="search" title="Search" href="search.html" />
|
||||
<link rel="next" title="Instructions how to generate and enable a google Cloud Vision API key" href="create_API_key_link.html" />
|
||||
<link rel="prev" title="AMMICO - AI Media and Misinformation Content Analysis Tool" href="readme_link.html" />
|
||||
</head>
|
||||
|
||||
<body class="wy-body-for-nav">
|
||||
<div class="wy-grid-for-nav">
|
||||
<nav data-toggle="wy-nav-shift" class="wy-nav-side">
|
||||
<div class="wy-side-scroll">
|
||||
<div class="wy-side-nav-search" >
|
||||
|
||||
|
||||
|
||||
<a href="index.html" class="icon icon-home">
|
||||
AMMICO
|
||||
</a>
|
||||
<div role="search">
|
||||
<form id="rtd-search-form" class="wy-form" action="search.html" method="get">
|
||||
<input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
|
||||
<input type="hidden" name="check_keywords" value="yes" />
|
||||
<input type="hidden" name="area" value="default" />
|
||||
</form>
|
||||
</div>
|
||||
</div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
|
||||
<p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
|
||||
<ul class="current">
|
||||
<li class="toctree-l1"><a class="reference internal" href="readme_link.html">AMMICO - AI Media and Misinformation Content Analysis Tool</a></li>
|
||||
<li class="toctree-l1 current"><a class="current reference internal" href="#">FAQ</a><ul>
|
||||
<li class="toctree-l2"><a class="reference internal" href="#compatibility-problems-solving">Compatibility problems solving</a><ul>
|
||||
<li class="toctree-l3"><a class="reference internal" href="#first-install-tensorflow-https-www-tensorflow-org-install-pip">1. First, install tensorflow (https://www.tensorflow.org/install/pip)</a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="#second-install-pytorch">2. Second, install pytorch</a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="#after-we-prepared-right-environment-we-can-install-the-ammico-package">3. After we prepared right environment we can install the <code class="docutils literal notranslate"><span class="pre">ammico</span></code> package</a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="#micromamba">Micromamba</a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="#windows">Windows</a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="#what-happens-to-the-images-that-are-sent-to-google-cloud-vision">What happens to the images that are sent to google Cloud Vision?</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="#what-happens-to-the-text-that-is-sent-to-google-translate">What happens to the text that is sent to google Translate?</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="#what-happens-if-i-don-t-have-internet-access-can-i-still-use-ammico">What happens if I don’t have internet access - can I still use ammico?</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="#why-don-t-i-get-probabilistic-assessments-of-age-gender-and-race-when-running-the-emotion-detector">Why don’t I get probabilistic assessments of age, gender and race when running the Emotion Detector?</a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="create_API_key_link.html">Instructions how to generate and enable a google Cloud Vision API key</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="notebooks/DemoNotebook_ammico.html">AMMICO Demonstration Notebook</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="notebooks/DemoNotebook_ammico.html#Step-0:-Create-and-set-a-Google-Cloud-Vision-Key">Step 0: Create and set a Google Cloud Vision Key</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="notebooks/DemoNotebook_ammico.html#Step-1:-Read-your-data-into-AMMICO">Step 1: Read your data into AMMICO</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="notebooks/DemoNotebook_ammico.html#The-detector-modules">The detector modules</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="notebooks/Example%20cropposts.html">Crop posts module</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="modules.html">AMMICO package modules</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="license_link.html">License</a></li>
|
||||
</ul>
|
||||
|
||||
</div>
|
||||
</div>
|
||||
</nav>
|
||||
|
||||
<section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu" >
|
||||
<i data-toggle="wy-nav-top" class="fa fa-bars"></i>
|
||||
<a href="index.html">AMMICO</a>
|
||||
</nav>
|
||||
|
||||
<div class="wy-nav-content">
|
||||
<div class="rst-content">
|
||||
<div role="navigation" aria-label="Page navigation">
|
||||
<ul class="wy-breadcrumbs">
|
||||
<li><a href="index.html" class="icon icon-home" aria-label="Home"></a></li>
|
||||
<li class="breadcrumb-item active">FAQ</li>
|
||||
<li class="wy-breadcrumbs-aside">
|
||||
<a href="https://github.com/ssciwr/AMMICO/blob/main/source/faq_link.md" class="fa fa-github"> Edit on GitHub</a>
|
||||
</li>
|
||||
</ul>
|
||||
<hr/>
|
||||
</div>
|
||||
<div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
|
||||
<div itemprop="articleBody">
|
||||
|
||||
<section id="faq">
|
||||
<h1>FAQ<a class="headerlink" href="#faq" title="Link to this heading"></a></h1>
|
||||
<section id="compatibility-problems-solving">
|
||||
<h2>Compatibility problems solving<a class="headerlink" href="#compatibility-problems-solving" title="Link to this heading"></a></h2>
|
||||
<p>Some ammico components require <code class="docutils literal notranslate"><span class="pre">tensorflow</span></code> (e.g. Emotion detector), some <code class="docutils literal notranslate"><span class="pre">pytorch</span></code> (e.g. Summary detector). Sometimes there are compatibility problems between these two frameworks. To avoid these problems on your machines, you can prepare proper environment before installing the package (you need conda on your machine):</p>
|
||||
<section id="first-install-tensorflow-https-www-tensorflow-org-install-pip">
|
||||
<h3>1. First, install tensorflow (https://www.tensorflow.org/install/pip)<a class="headerlink" href="#first-install-tensorflow-https-www-tensorflow-org-install-pip" title="Link to this heading"></a></h3>
|
||||
<ul>
|
||||
<li><p>create a new environment with python and activate it</p>
|
||||
<p><code class="docutils literal notranslate"><span class="pre">conda</span> <span class="pre">create</span> <span class="pre">-n</span> <span class="pre">ammico_env</span> <span class="pre">python=3.10</span></code></p>
|
||||
<p><code class="docutils literal notranslate"><span class="pre">conda</span> <span class="pre">activate</span> <span class="pre">ammico_env</span></code></p>
|
||||
</li>
|
||||
<li><p>install cudatoolkit from conda-forge</p>
|
||||
<p><code class="docutils literal notranslate"> <span class="pre">conda</span> <span class="pre">install</span> <span class="pre">-c</span> <span class="pre">conda-forge</span> <span class="pre">cudatoolkit=11.8.0</span></code></p>
|
||||
</li>
|
||||
<li><p>install nvidia-cudnn-cu11 from pip</p>
|
||||
<p><code class="docutils literal notranslate"><span class="pre">python</span> <span class="pre">-m</span> <span class="pre">pip</span> <span class="pre">install</span> <span class="pre">nvidia-cudnn-cu11==8.6.0.163</span></code></p>
|
||||
</li>
|
||||
<li><p>add script that runs when conda environment <code class="docutils literal notranslate"><span class="pre">ammico_env</span></code> is activated to put the right libraries on your LD_LIBRARY_PATH</p>
|
||||
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>mkdir -p $CONDA_PREFIX/etc/conda/activate.d
|
||||
echo 'CUDNN_PATH=$(dirname $(python -c "import nvidia.cudnn;print(nvidia.cudnn.__file__)"))' >> $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh
|
||||
echo 'export LD_LIBRARY_PATH=$CUDNN_PATH/lib:$CONDA_PREFIX/lib/:$LD_LIBRARY_PATH' >> $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh
|
||||
source $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh
|
||||
</pre></div>
|
||||
</div>
|
||||
</li>
|
||||
<li><p>deactivate and re-activate conda environment to call script above</p>
|
||||
<p><code class="docutils literal notranslate"><span class="pre">conda</span> <span class="pre">deactivate</span></code></p>
|
||||
<p><code class="docutils literal notranslate"><span class="pre">conda</span> <span class="pre">activate</span> <span class="pre">ammico_env</span> </code></p>
|
||||
</li>
|
||||
<li><p>install tensorflow</p>
|
||||
<p><code class="docutils literal notranslate"><span class="pre">python</span> <span class="pre">-m</span> <span class="pre">pip</span> <span class="pre">install</span> <span class="pre">tensorflow==2.12.1</span></code></p>
|
||||
</li>
|
||||
</ul>
|
||||
</section>
|
||||
<section id="second-install-pytorch">
|
||||
<h3>2. Second, install pytorch<a class="headerlink" href="#second-install-pytorch" title="Link to this heading"></a></h3>
|
||||
<ul>
|
||||
<li><p>install pytorch for same cuda version as above</p>
|
||||
<p><code class="docutils literal notranslate"><span class="pre">python</span> <span class="pre">-m</span> <span class="pre">pip</span> <span class="pre">install</span> <span class="pre">torch</span> <span class="pre">torchvision</span> <span class="pre">torchaudio</span> <span class="pre">--index-url</span> <span class="pre">https://download.pytorch.org/whl/cu118</span></code></p>
|
||||
</li>
|
||||
</ul>
|
||||
</section>
|
||||
<section id="after-we-prepared-right-environment-we-can-install-the-ammico-package">
|
||||
<h3>3. After we prepared right environment we can install the <code class="docutils literal notranslate"><span class="pre">ammico</span></code> package<a class="headerlink" href="#after-we-prepared-right-environment-we-can-install-the-ammico-package" title="Link to this heading"></a></h3>
|
||||
<ul class="simple">
|
||||
<li><p><code class="docutils literal notranslate"><span class="pre">python</span> <span class="pre">-m</span> <span class="pre">pip</span> <span class="pre">install</span> <span class="pre">ammico</span></code></p></li>
|
||||
</ul>
|
||||
<p>It is done.</p>
|
||||
</section>
|
||||
<section id="micromamba">
|
||||
<h3>Micromamba<a class="headerlink" href="#micromamba" title="Link to this heading"></a></h3>
|
||||
<p>If you are using micromamba you can prepare environment with just one command:</p>
|
||||
<p><code class="docutils literal notranslate"><span class="pre">micromamba</span> <span class="pre">create</span> <span class="pre">--no-channel-priority</span> <span class="pre">-c</span> <span class="pre">nvidia</span> <span class="pre">-c</span> <span class="pre">pytorch</span> <span class="pre">-c</span> <span class="pre">conda-forge</span> <span class="pre">-n</span> <span class="pre">ammico_env</span> <span class="pre">"python=3.10"</span> <span class="pre">pytorch</span> <span class="pre">torchvision</span> <span class="pre">torchaudio</span> <span class="pre">pytorch-cuda</span> <span class="pre">"tensorflow-gpu<=2.12.3"</span> <span class="pre">"numpy<=1.23.4"</span></code></p>
|
||||
</section>
|
||||
<section id="windows">
|
||||
<h3>Windows<a class="headerlink" href="#windows" title="Link to this heading"></a></h3>
|
||||
<p>To make pycocotools work on Windows OS you may need to install <code class="docutils literal notranslate"><span class="pre">vs_BuildTools.exe</span></code> from https://visualstudio.microsoft.com/visual-cpp-build-tools/ and choose following elements:</p>
|
||||
<ul class="simple">
|
||||
<li><p><code class="docutils literal notranslate"><span class="pre">Visual</span> <span class="pre">Studio</span> <span class="pre">extension</span> <span class="pre">development</span></code></p></li>
|
||||
<li><p><code class="docutils literal notranslate"><span class="pre">MSVC</span> <span class="pre">v143</span> <span class="pre">-</span> <span class="pre">VS</span> <span class="pre">2022</span> <span class="pre">C++</span> <span class="pre">x64/x86</span> <span class="pre">build</span> <span class="pre">tools</span></code></p></li>
|
||||
<li><p><code class="docutils literal notranslate"><span class="pre">Windows</span> <span class="pre">11</span> <span class="pre">SDK</span></code> for Windows 11 (or <code class="docutils literal notranslate"><span class="pre">Windows</span> <span class="pre">10</span> <span class="pre">SDK</span></code> for Windows 10)</p></li>
|
||||
</ul>
|
||||
<p>Be careful, it requires around 7 GB of disk space.</p>
|
||||
<p><img alt="Screenshot 2023-06-01 165712" src="https://github.com/ssciwr/AMMICO/assets/8105097/3dfb302f-c390-46a7-a700-4e044f56c30f" /></p>
|
||||
</section>
|
||||
</section>
|
||||
<section id="what-happens-to-the-images-that-are-sent-to-google-cloud-vision">
|
||||
<h2>What happens to the images that are sent to google Cloud Vision?<a class="headerlink" href="#what-happens-to-the-images-that-are-sent-to-google-cloud-vision" title="Link to this heading"></a></h2>
|
||||
<p>You have to accept the privacy statement of ammico to run this type of analyis.</p>
|
||||
<p>According to the <a class="reference external" href="https://cloud.google.com/vision/docs/data-usage">google Vision API</a>, the images that are uploaded and analysed are not stored and not shared with third parties:</p>
|
||||
<blockquote>
|
||||
<div><p>We won’t make the content that you send available to the public. We won’t share the content with any third party. The content is only used by Google as necessary to provide the Vision API service. Vision API complies with the Cloud Data Processing Addendum.</p>
|
||||
</div></blockquote>
|
||||
<blockquote>
|
||||
<div><p>For online (immediate response) operations (<code class="docutils literal notranslate"><span class="pre">BatchAnnotateImages</span></code> and <code class="docutils literal notranslate"><span class="pre">BatchAnnotateFiles</span></code>), the image data is processed in memory and not persisted to disk.
|
||||
For asynchronous offline batch operations (<code class="docutils literal notranslate"><span class="pre">AsyncBatchAnnotateImages</span></code> and <code class="docutils literal notranslate"><span class="pre">AsyncBatchAnnotateFiles</span></code>), we must store that image for a short period of time in order to perform the analysis and return the results to you. The stored image is typically deleted right after the processing is done, with a failsafe Time to live (TTL) of a few hours.
|
||||
Google also temporarily logs some metadata about your Vision API requests (such as the time the request was received and the size of the request) to improve our service and combat abuse.</p>
|
||||
</div></blockquote>
|
||||
</section>
|
||||
<section id="what-happens-to-the-text-that-is-sent-to-google-translate">
|
||||
<h2>What happens to the text that is sent to google Translate?<a class="headerlink" href="#what-happens-to-the-text-that-is-sent-to-google-translate" title="Link to this heading"></a></h2>
|
||||
<p>You have to accept the privacy statement of ammico to run this type of analyis.</p>
|
||||
<p>According to <a class="reference external" href="https://cloud.google.com/translate/data-usage">google Translate</a>, the data is not stored after processing and not made available to third parties:</p>
|
||||
<blockquote>
|
||||
<div><p>We will not make the content of the text that you send available to the public. We will not share the content with any third party. The content of the text is only used by Google as necessary to provide the Cloud Translation API service. Cloud Translation API complies with the Cloud Data Processing Addendum.</p>
|
||||
</div></blockquote>
|
||||
<blockquote>
|
||||
<div><p>When you send text to Cloud Translation API, text is held briefly in-memory in order to perform the translation and return the results to you.</p>
|
||||
</div></blockquote>
|
||||
</section>
|
||||
<section id="what-happens-if-i-don-t-have-internet-access-can-i-still-use-ammico">
|
||||
<h2>What happens if I don’t have internet access - can I still use ammico?<a class="headerlink" href="#what-happens-if-i-don-t-have-internet-access-can-i-still-use-ammico" title="Link to this heading"></a></h2>
|
||||
<p>Some features of ammico require internet access; a general answer to this question is not possible, some services require an internet connection, others can be used offline:</p>
|
||||
<ul class="simple">
|
||||
<li><p>Text extraction: To extract text from images, and translate the text, the data needs to be processed by google Cloud Vision and google Translate, which run in the cloud. Without internet access, text extraction and translation is not possible.</p></li>
|
||||
<li><p>Image summary and query: After an initial download of the models, the <code class="docutils literal notranslate"><span class="pre">summary</span></code> module does not require an internet connection.</p></li>
|
||||
<li><p>Facial expressions: After an initial download of the models, the <code class="docutils literal notranslate"><span class="pre">faces</span></code> module does not require an internet connection.</p></li>
|
||||
<li><p>Multimodal search: After an initial download of the models, the <code class="docutils literal notranslate"><span class="pre">multimodal_search</span></code> module does not require an internet connection.</p></li>
|
||||
<li><p>Color analysis: The <code class="docutils literal notranslate"><span class="pre">color</span></code> module does not require an internet connection.</p></li>
|
||||
</ul>
|
||||
</section>
|
||||
<section id="why-don-t-i-get-probabilistic-assessments-of-age-gender-and-race-when-running-the-emotion-detector">
|
||||
<h2>Why don’t I get probabilistic assessments of age, gender and race when running the Emotion Detector?<a class="headerlink" href="#why-don-t-i-get-probabilistic-assessments-of-age-gender-and-race-when-running-the-emotion-detector" title="Link to this heading"></a></h2>
|
||||
<p>Due to well documented biases in the detection of minorities with computer vision tools, and to the ethical implications of such detection, these parts of the tool are not directly made available to users. To access these capabilities, users must first agree with a ethical disclosure statement that reads:</p>
|
||||
<p>“DeepFace and RetinaFace provide wrappers to trained models in face recognition and emotion detection. Age, gender and race/ethnicity models were trained on the backbone of VGG-Face with transfer learning.
|
||||
ETHICAL DISCLOSURE STATEMENT:
|
||||
The Emotion Detector uses DeepFace and RetinaFace to probabilistically assess the gender, age and race of the detected faces. Such assessments may not reflect how the individuals identify. Additionally, the classification is carried out in simplistic categories and contains only the most basic classes (for example, “male” and “female” for gender, and seven non-overlapping categories for ethnicity). To access these probabilistic assessments, you must therefore agree with the following statement: “I understand the ethical and privacy implications such assessments have for the interpretation of the results and that this analysis may result in personal and possibly sensitive data, and I wish to proceed.”</p>
|
||||
<p>This disclosure statement is included as a separate line of code early in the flow of the Emotion Detector. Once the user has agreed with the statement, further data analyses will also include these assessments.</p>
|
||||
</section>
|
||||
</section>
|
||||
|
||||
|
||||
</div>
|
||||
</div>
|
||||
<footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
|
||||
<a href="readme_link.html" class="btn btn-neutral float-left" title="AMMICO - AI Media and Misinformation Content Analysis Tool" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
|
||||
<a href="create_API_key_link.html" class="btn btn-neutral float-right" title="Instructions how to generate and enable a google Cloud Vision API key" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
|
||||
</div>
|
||||
|
||||
<hr/>
|
||||
|
||||
<div role="contentinfo">
|
||||
<p>© Copyright 2022, Scientific Software Center, Heidelberg University.</p>
|
||||
</div>
|
||||
|
||||
Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
|
||||
<a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
|
||||
provided by <a href="https://readthedocs.org">Read the Docs</a>.
|
||||
|
||||
|
||||
</footer>
|
||||
</div>
|
||||
</div>
|
||||
</section>
|
||||
</div>
|
||||
<script>
|
||||
jQuery(function () {
|
||||
SphinxRtdTheme.Navigation.enable(true);
|
||||
});
|
||||
</script>
|
||||
|
||||
</body>
|
||||
</html>
|
||||
@ -1,3 +1,5 @@
|
||||
|
||||
|
||||
<!DOCTYPE html>
|
||||
<html class="writer-html5" lang="en" data-content_root="./">
|
||||
<head>
|
||||
@ -5,19 +7,15 @@
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
|
||||
<title>Index — AMMICO 0.0.1 documentation</title>
|
||||
<link rel="stylesheet" type="text/css" href="_static/pygments.css?v=80d5e7a1" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/css/theme.css?v=19f00094" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/css/theme.css?v=7ab3649f" />
|
||||
|
||||
|
||||
<!--[if lt IE 9]>
|
||||
<script src="_static/js/html5shiv.min.js"></script>
|
||||
<![endif]-->
|
||||
|
||||
<script src="_static/jquery.js?v=5d32c60e"></script>
|
||||
<script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
|
||||
<script src="_static/documentation_options.js?v=d45e8c67"></script>
|
||||
<script src="_static/doctools.js?v=9a2dae69"></script>
|
||||
<script src="_static/sphinx_highlight.js?v=dc90522c"></script>
|
||||
<script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
|
||||
<script src="_static/jquery.js?v=5d32c60e"></script>
|
||||
<script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
|
||||
<script src="_static/documentation_options.js?v=d45e8c67"></script>
|
||||
<script src="_static/doctools.js?v=9a2dae69"></script>
|
||||
<script src="_static/sphinx_highlight.js?v=dc90522c"></script>
|
||||
<script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
|
||||
<script src="_static/js/theme.js"></script>
|
||||
<link rel="index" title="Index" href="#" />
|
||||
<link rel="search" title="Search" href="search.html" />
|
||||
@ -45,7 +43,7 @@
|
||||
<p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
|
||||
<ul>
|
||||
<li class="toctree-l1"><a class="reference internal" href="readme_link.html">AMMICO - AI Media and Misinformation Content Analysis Tool</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="readme_link.html#faq">FAQ</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="faq_link.html">FAQ</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="create_API_key_link.html">Instructions how to generate and enable a google Cloud Vision API key</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="notebooks/DemoNotebook_ammico.html">AMMICO Demonstration Notebook</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="notebooks/DemoNotebook_ammico.html#Step-0:-Create-and-set-a-Google-Cloud-Vision-Key">Step 0: Create and set a Google Cloud Vision Key</a></li>
|
||||
@ -396,11 +394,13 @@
|
||||
<td style="width: 33%; vertical-align: top;"><ul>
|
||||
<li><a href="ammico.html#multimodal_search.MultimodalSearch.parsing_images">parsing_images() (multimodal_search.MultimodalSearch method)</a>
|
||||
</li>
|
||||
</ul></td>
|
||||
<td style="width: 33%; vertical-align: top;"><ul>
|
||||
<li><a href="ammico.html#cropposts.paste_image_and_comment">paste_image_and_comment() (in module cropposts)</a>
|
||||
</li>
|
||||
</ul></td>
|
||||
<td style="width: 33%; vertical-align: top;"><ul>
|
||||
<li><a href="ammico.html#text.PostprocessText">PostprocessText (class in text)</a>
|
||||
</li>
|
||||
<li><a href="ammico.html#text.privacy_disclosure">privacy_disclosure() (in module text)</a>
|
||||
</li>
|
||||
</ul></td>
|
||||
</tr></table>
|
||||
|
||||
@ -1,3 +1,5 @@
|
||||
|
||||
|
||||
<!DOCTYPE html>
|
||||
<html class="writer-html5" lang="en" data-content_root="./">
|
||||
<head>
|
||||
@ -6,21 +8,17 @@
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
|
||||
<title>Welcome to AMMICO’s documentation! — AMMICO 0.0.1 documentation</title>
|
||||
<link rel="stylesheet" type="text/css" href="_static/pygments.css?v=80d5e7a1" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/css/theme.css?v=19f00094" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/css/theme.css?v=7ab3649f" />
|
||||
|
||||
|
||||
<!--[if lt IE 9]>
|
||||
<script src="_static/js/html5shiv.min.js"></script>
|
||||
<![endif]-->
|
||||
|
||||
<script src="_static/jquery.js?v=5d32c60e"></script>
|
||||
<script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
|
||||
<script src="_static/documentation_options.js?v=d45e8c67"></script>
|
||||
<script src="_static/doctools.js?v=9a2dae69"></script>
|
||||
<script src="_static/sphinx_highlight.js?v=dc90522c"></script>
|
||||
<script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
|
||||
<script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
|
||||
<script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
|
||||
<script src="_static/jquery.js?v=5d32c60e"></script>
|
||||
<script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
|
||||
<script src="_static/documentation_options.js?v=d45e8c67"></script>
|
||||
<script src="_static/doctools.js?v=9a2dae69"></script>
|
||||
<script src="_static/sphinx_highlight.js?v=dc90522c"></script>
|
||||
<script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
|
||||
<script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
|
||||
<script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
|
||||
<script src="_static/js/theme.js"></script>
|
||||
<link rel="index" title="Index" href="genindex.html" />
|
||||
<link rel="search" title="Search" href="search.html" />
|
||||
@ -49,7 +47,7 @@
|
||||
<p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
|
||||
<ul>
|
||||
<li class="toctree-l1"><a class="reference internal" href="readme_link.html">AMMICO - AI Media and Misinformation Content Analysis Tool</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="readme_link.html#faq">FAQ</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="faq_link.html">FAQ</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="create_API_key_link.html">Instructions how to generate and enable a google Cloud Vision API key</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="notebooks/DemoNotebook_ammico.html">AMMICO Demonstration Notebook</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="notebooks/DemoNotebook_ammico.html#Step-0:-Create-and-set-a-Google-Cloud-Vision-Key">Step 0: Create and set a Google Cloud Vision Key</a></li>
|
||||
@ -91,16 +89,16 @@
|
||||
<ul>
|
||||
<li class="toctree-l1"><a class="reference internal" href="readme_link.html">AMMICO - AI Media and Misinformation Content Analysis Tool</a><ul>
|
||||
<li class="toctree-l2"><a class="reference internal" href="readme_link.html#installation">Installation</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="readme_link.html#compatibility-problems-solving">Compatibility problems solving</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="readme_link.html#usage">Usage</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="readme_link.html#features">Features</a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="readme_link.html#faq">FAQ</a><ul>
|
||||
<li class="toctree-l2"><a class="reference internal" href="readme_link.html#what-happens-to-the-images-that-are-sent-to-google-cloud-vision">What happens to the images that are sent to google Cloud Vision?</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="readme_link.html#what-happens-to-the-text-that-is-sent-to-google-translate">What happens to the text that is sent to google Translate?</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="readme_link.html#what-happens-if-i-don-t-have-internet-access-can-i-still-use-ammico">What happens if I don’t have internet access - can I still use ammico?</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="readme_link.html#why-don-t-i-get-probabilistic-assessments-of-age-gender-and-race-when-running-the-emotion-detector">Why don’t I get probabilistic assessments of age, gender and race when running the Emotion Detector?</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="faq_link.html">FAQ</a><ul>
|
||||
<li class="toctree-l2"><a class="reference internal" href="faq_link.html#compatibility-problems-solving">Compatibility problems solving</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="faq_link.html#what-happens-to-the-images-that-are-sent-to-google-cloud-vision">What happens to the images that are sent to google Cloud Vision?</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="faq_link.html#what-happens-to-the-text-that-is-sent-to-google-translate">What happens to the text that is sent to google Translate?</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="faq_link.html#what-happens-if-i-don-t-have-internet-access-can-i-still-use-ammico">What happens if I don’t have internet access - can I still use ammico?</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="faq_link.html#why-don-t-i-get-probabilistic-assessments-of-age-gender-and-race-when-running-the-emotion-detector">Why don’t I get probabilistic assessments of age, gender and race when running the Emotion Detector?</a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="create_API_key_link.html">Instructions how to generate and enable a google Cloud Vision API key</a></li>
|
||||
|
||||
@ -1,3 +1,5 @@
|
||||
|
||||
|
||||
<!DOCTYPE html>
|
||||
<html class="writer-html5" lang="en" data-content_root="./">
|
||||
<head>
|
||||
@ -6,19 +8,15 @@
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
|
||||
<title>License — AMMICO 0.0.1 documentation</title>
|
||||
<link rel="stylesheet" type="text/css" href="_static/pygments.css?v=80d5e7a1" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/css/theme.css?v=19f00094" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/css/theme.css?v=7ab3649f" />
|
||||
|
||||
|
||||
<!--[if lt IE 9]>
|
||||
<script src="_static/js/html5shiv.min.js"></script>
|
||||
<![endif]-->
|
||||
|
||||
<script src="_static/jquery.js?v=5d32c60e"></script>
|
||||
<script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
|
||||
<script src="_static/documentation_options.js?v=d45e8c67"></script>
|
||||
<script src="_static/doctools.js?v=9a2dae69"></script>
|
||||
<script src="_static/sphinx_highlight.js?v=dc90522c"></script>
|
||||
<script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
|
||||
<script src="_static/jquery.js?v=5d32c60e"></script>
|
||||
<script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
|
||||
<script src="_static/documentation_options.js?v=d45e8c67"></script>
|
||||
<script src="_static/doctools.js?v=9a2dae69"></script>
|
||||
<script src="_static/sphinx_highlight.js?v=dc90522c"></script>
|
||||
<script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
|
||||
<script src="_static/js/theme.js"></script>
|
||||
<link rel="index" title="Index" href="genindex.html" />
|
||||
<link rel="search" title="Search" href="search.html" />
|
||||
@ -47,7 +45,7 @@
|
||||
<p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
|
||||
<ul class="current">
|
||||
<li class="toctree-l1"><a class="reference internal" href="readme_link.html">AMMICO - AI Media and Misinformation Content Analysis Tool</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="readme_link.html#faq">FAQ</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="faq_link.html">FAQ</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="create_API_key_link.html">Instructions how to generate and enable a google Cloud Vision API key</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="notebooks/DemoNotebook_ammico.html">AMMICO Demonstration Notebook</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="notebooks/DemoNotebook_ammico.html#Step-0:-Create-and-set-a-Google-Cloud-Vision-Key">Step 0: Create and set a Google Cloud Vision Key</a></li>
|
||||
|
||||
@ -1,3 +1,5 @@
|
||||
|
||||
|
||||
<!DOCTYPE html>
|
||||
<html class="writer-html5" lang="en" data-content_root="./">
|
||||
<head>
|
||||
@ -6,19 +8,15 @@
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
|
||||
<title>AMMICO package modules — AMMICO 0.0.1 documentation</title>
|
||||
<link rel="stylesheet" type="text/css" href="_static/pygments.css?v=80d5e7a1" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/css/theme.css?v=19f00094" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/css/theme.css?v=7ab3649f" />
|
||||
|
||||
|
||||
<!--[if lt IE 9]>
|
||||
<script src="_static/js/html5shiv.min.js"></script>
|
||||
<![endif]-->
|
||||
|
||||
<script src="_static/jquery.js?v=5d32c60e"></script>
|
||||
<script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
|
||||
<script src="_static/documentation_options.js?v=d45e8c67"></script>
|
||||
<script src="_static/doctools.js?v=9a2dae69"></script>
|
||||
<script src="_static/sphinx_highlight.js?v=dc90522c"></script>
|
||||
<script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
|
||||
<script src="_static/jquery.js?v=5d32c60e"></script>
|
||||
<script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
|
||||
<script src="_static/documentation_options.js?v=d45e8c67"></script>
|
||||
<script src="_static/doctools.js?v=9a2dae69"></script>
|
||||
<script src="_static/sphinx_highlight.js?v=dc90522c"></script>
|
||||
<script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
|
||||
<script src="_static/js/theme.js"></script>
|
||||
<link rel="index" title="Index" href="genindex.html" />
|
||||
<link rel="search" title="Search" href="search.html" />
|
||||
@ -48,7 +46,7 @@
|
||||
<p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
|
||||
<ul class="current">
|
||||
<li class="toctree-l1"><a class="reference internal" href="readme_link.html">AMMICO - AI Media and Misinformation Content Analysis Tool</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="readme_link.html#faq">FAQ</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="faq_link.html">FAQ</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="create_API_key_link.html">Instructions how to generate and enable a google Cloud Vision API key</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="notebooks/DemoNotebook_ammico.html">AMMICO Demonstration Notebook</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="notebooks/DemoNotebook_ammico.html#Step-0:-Create-and-set-a-Google-Cloud-Vision-Key">Step 0: Create and set a Google Cloud Vision Key</a></li>
|
||||
@ -120,6 +118,7 @@
|
||||
<li class="toctree-l3"><a class="reference internal" href="ammico.html#text.TextDetector.translate_text"><code class="docutils literal notranslate"><span class="pre">TextDetector.translate_text()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="ammico.html#text.privacy_disclosure"><code class="docutils literal notranslate"><span class="pre">privacy_disclosure()</span></code></a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="ammico.html#module-summary">summary module</a><ul>
|
||||
|
||||
@ -1,3 +1,5 @@
|
||||
|
||||
|
||||
<!DOCTYPE html>
|
||||
<html class="writer-html5" lang="en" data-content_root="../">
|
||||
<head>
|
||||
@ -6,22 +8,18 @@
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
|
||||
<title>AMMICO Demonstration Notebook — AMMICO 0.0.1 documentation</title>
|
||||
<link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=80d5e7a1" />
|
||||
<link rel="stylesheet" type="text/css" href="../_static/css/theme.css?v=19f00094" />
|
||||
<link rel="stylesheet" type="text/css" href="../_static/css/theme.css?v=7ab3649f" />
|
||||
<link rel="stylesheet" type="text/css" href="../_static/nbsphinx-code-cells.css?v=2aa19091" />
|
||||
|
||||
|
||||
<!--[if lt IE 9]>
|
||||
<script src="../_static/js/html5shiv.min.js"></script>
|
||||
<![endif]-->
|
||||
|
||||
<script src="../_static/jquery.js?v=5d32c60e"></script>
|
||||
<script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
|
||||
<script src="../_static/documentation_options.js?v=d45e8c67"></script>
|
||||
<script src="../_static/doctools.js?v=9a2dae69"></script>
|
||||
<script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
|
||||
<script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
|
||||
<script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
|
||||
<script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
|
||||
<script src="../_static/jquery.js?v=5d32c60e"></script>
|
||||
<script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
|
||||
<script src="../_static/documentation_options.js?v=d45e8c67"></script>
|
||||
<script src="../_static/doctools.js?v=9a2dae69"></script>
|
||||
<script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
|
||||
<script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
|
||||
<script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
|
||||
<script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
|
||||
<script src="../_static/js/theme.js"></script>
|
||||
<link rel="index" title="Index" href="../genindex.html" />
|
||||
<link rel="search" title="Search" href="../search.html" />
|
||||
@ -51,7 +49,7 @@
|
||||
<p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
|
||||
<ul class="current">
|
||||
<li class="toctree-l1"><a class="reference internal" href="../readme_link.html">AMMICO - AI Media and Misinformation Content Analysis Tool</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../readme_link.html#faq">FAQ</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../faq_link.html">FAQ</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../create_API_key_link.html">Instructions how to generate and enable a google Cloud Vision API key</a></li>
|
||||
<li class="toctree-l1 current"><a class="current reference internal" href="#">AMMICO Demonstration Notebook</a><ul>
|
||||
<li class="toctree-l2"><a class="reference internal" href="#Use-a-test-dataset">Use a test dataset</a></li>
|
||||
@ -62,6 +60,7 @@
|
||||
<li class="toctree-l1"><a class="reference internal" href="#Step-1:-Read-your-data-into-AMMICO">Step 1: Read your data into AMMICO</a><ul>
|
||||
<li class="toctree-l2"><a class="reference internal" href="#Step-2:-Inspect-the-input-files-using-the-graphical-user-interface">Step 2: Inspect the input files using the graphical user interface</a><ul>
|
||||
<li class="toctree-l3"><a class="reference internal" href="#Ethical-disclosure-statement">Ethical disclosure statement</a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="#Privacy-disclosure-statement">Privacy disclosure statement</a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="#Step-3:-Analyze-all-images">Step 3: Analyze all images</a></li>
|
||||
@ -127,7 +126,10 @@
|
||||
<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># if running on google colab</span>
|
||||
<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># if running on google colab\</span>
|
||||
<span class="c1"># PLEASE RUN THIS ONLY AS CPU RUNTIME</span>
|
||||
<span class="c1"># for a GPU runtime, there are conflicts with pre-installed packages -</span>
|
||||
<span class="c1"># you first need to uninstall them (prepare a clean environment with no pre-installs) and then install ammico</span>
|
||||
<span class="c1"># flake8-noqa-cell</span>
|
||||
|
||||
<span class="k">if</span> <span class="s2">"google.colab"</span> <span class="ow">in</span> <span class="nb">str</span><span class="p">(</span><span class="n">get_ipython</span><span class="p">()):</span>
|
||||
@ -135,9 +137,10 @@
|
||||
<span class="c1"># install setuptools</span>
|
||||
<span class="c1"># %pip install setuptools==61 -qqq</span>
|
||||
<span class="c1"># uninstall some pre-installed packages due to incompatibility</span>
|
||||
<span class="o">%</span><span class="k">pip</span> uninstall --yes tensorflow-probability dopamine-rl lida pandas-gbq torchaudio torchdata torchtext orbax-checkpoint flex-y -qqq
|
||||
<span class="o">%</span><span class="k">pip</span> uninstall --yes tensorflow-probability dopamine-rl lida pandas-gbq torchaudio torchdata torchtext orbax-checkpoint flex-y jax jaxlib -qqq
|
||||
<span class="c1"># install ammico</span>
|
||||
<span class="o">%</span><span class="k">pip</span> install git+https://github.com/ssciwr/ammico.git -qqq
|
||||
<span class="c1"># install older version of jax to support transformers use of diffusers</span>
|
||||
<span class="c1"># mount google drive for data and API key</span>
|
||||
<span class="kn">from</span> <span class="nn">google.colab</span> <span class="kn">import</span> <span class="n">drive</span>
|
||||
|
||||
@ -147,7 +150,7 @@
|
||||
</div>
|
||||
<section id="Use-a-test-dataset">
|
||||
<h2>Use a test dataset<a class="headerlink" href="#Use-a-test-dataset" title="Link to this heading"></a></h2>
|
||||
<p>You can download a dataset for test purposes. Skip this step if you use your own data.</p>
|
||||
<p>You can download this dataset for test purposes. Skip this step if you use your own data. If the data set on Hugging Face is gated or private, Hugging Face will ask you for a login token. However, for the default dataset in this notebook you do not need to provide one.</p>
|
||||
<div class="nbinput nblast docutils container">
|
||||
<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
|
||||
</pre></div>
|
||||
@ -183,12 +186,12 @@
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
|
||||
<span class="c1"># jax also sometimes leads to problems on google colab</span>
|
||||
<span class="c1"># if this is the case, try restarting the kernel and executing this</span>
|
||||
<span class="c1"># and the above two code cells again</span>
|
||||
<span class="kn">import</span> <span class="nn">ammico</span>
|
||||
<span class="c1"># for displaying a progress bar</span>
|
||||
<span class="kn">from</span> <span class="nn">tqdm</span> <span class="kn">import</span> <span class="n">tqdm</span>
|
||||
<span class="c1"># to get the reference data for text_dict</span>
|
||||
<span class="kn">import</span> <span class="nn">importlib_resources</span>
|
||||
<span class="n">pkg</span> <span class="o">=</span> <span class="n">importlib_resources</span><span class="o">.</span><span class="n">files</span><span class="p">(</span><span class="s2">"ammico"</span><span class="p">)</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@ -251,7 +254,7 @@ tf.ones([2, 2])
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
<p>The <code class="docutils literal notranslate"><span class="pre">find_files</span></code> function returns a nested dict that contains the file ids and the paths to the files and is empty otherwise. This dict is filled step by step with more data as each detector class is run on the data (see below).</p>
|
||||
<p>The <code class="docutils literal notranslate"><span class="pre">find_files</span></code> function returns a nested dictionary that contains the file ids and the paths to the files and is empty otherwise. This dict is filled step by step with more data as each detector class is run on the data (see below).</p>
|
||||
<p>If you downloaded the test dataset above, you can directly provide the path you already set for the test directory, <code class="docutils literal notranslate"><span class="pre">data_path</span></code>. The below cell is already set up for the test dataset.</p>
|
||||
<p>If you use your own dataset, you need to toggle the path and provide the directory where you have saved your data.</p>
|
||||
<div class="nbinput nblast docutils container">
|
||||
@ -274,8 +277,8 @@ directly on the right next to the image. This way, the user can directly inspect
|
||||
<section id="Ethical-disclosure-statement">
|
||||
<h3>Ethical disclosure statement<a class="headerlink" href="#Ethical-disclosure-statement" title="Link to this heading"></a></h3>
|
||||
<p>If you want to run an analysis using the EmotionDetector detector type, you have first have to respond to an ethical disclosure statement. This disclosure statement ensures that you only use the full capabilities of the EmotionDetector after you have been made aware of its shortcomings.</p>
|
||||
<p>For this, answer “yes” or “no” to the below prompt. This will set an environment variable with the name given as in <code class="docutils literal notranslate"><span class="pre">accept_disclosure</span></code>. To re-run the disclosure prompt, unset the variable by uncommenting the line <code class="docutils literal notranslate"><span class="pre">os.environ.pop(accept_disclosure,</span> <span class="pre">None)</span></code>. To permanently set this envorinment variable, add it to your shell via your <code class="docutils literal notranslate"><span class="pre">.profile</span></code> or <code class="docutils literal notranslate"><span class="pre">.bashr</span></code> file.</p>
|
||||
<p>If the disclosure statement is accepted, the EmotionDetector will perform age, gender and race/ethnicity classification dependend on the provided thresholds. If the disclosure is rejected, only the presence of faces and emotion (if not wearing a mask) is detected.</p>
|
||||
<p>For this, answer “yes” or “no” to the below prompt. This will set an environment variable with the name given as in <code class="docutils literal notranslate"><span class="pre">accept_disclosure</span></code>. To re-run the disclosure prompt, unset the variable by uncommenting the line <code class="docutils literal notranslate"><span class="pre">os.environ.pop(accept_disclosure,</span> <span class="pre">None)</span></code>. To permanently set this environment variable, add it to your shell via your <code class="docutils literal notranslate"><span class="pre">.profile</span></code> or <code class="docutils literal notranslate"><span class="pre">.bashr</span></code> file.</p>
|
||||
<p>If the disclosure statement is accepted, the EmotionDetector will perform age, gender and race/ethnicity classification depending on the provided thresholds. If the disclosure is rejected, only the presence of faces and emotion (if not wearing a mask) is detected.</p>
|
||||
<div class="nbinput nblast docutils container">
|
||||
<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
|
||||
</pre></div>
|
||||
@ -290,6 +293,26 @@ directly on the right next to the image. This way, the user can directly inspect
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
</section>
|
||||
<section id="Privacy-disclosure-statement">
|
||||
<h3>Privacy disclosure statement<a class="headerlink" href="#Privacy-disclosure-statement" title="Link to this heading"></a></h3>
|
||||
<p>If you want to run an analysis using the TextDetector detector type, you have first have to respond to a privacy disclosure statement. This disclosure statement ensures that you are aware that your data will be sent to google cloud vision servers for analysis.</p>
|
||||
<p>For this, answer “yes” or “no” to the below prompt. This will set an environment variable with the name given as in <code class="docutils literal notranslate"><span class="pre">accept_privacy</span></code>. To re-run the disclosure prompt, unset the variable by uncommenting the line <code class="docutils literal notranslate"><span class="pre">os.environ.pop(accept_privacy,</span> <span class="pre">None)</span></code>. To permanently set this environment variable, add it to your shell via your <code class="docutils literal notranslate"><span class="pre">.profile</span></code> or <code class="docutils literal notranslate"><span class="pre">.bashr</span></code> file.</p>
|
||||
<p>If the privacy disclosure statement is accepted, the TextDetector will perform the text extraction, translation and if selected, analysis. If the privacy disclosure is rejected, no text processing will be carried out and you cannot use the TextDetector.</p>
|
||||
<div class="nbinput nblast docutils container">
|
||||
<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># respond to the privacy disclosure statement</span>
|
||||
<span class="c1"># this will set an environment variable for you</span>
|
||||
<span class="c1"># if you do not want to re-accept the privacy disclosure every time, you can set this environment variable in your shell</span>
|
||||
<span class="c1"># to re-set the environment variable, uncomment the below line</span>
|
||||
<span class="n">accept_privacy</span> <span class="o">=</span> <span class="s2">"PRIVACY_AMMICO"</span>
|
||||
<span class="c1"># os.environ.pop(accept_privacy, None)</span>
|
||||
<span class="n">_</span> <span class="o">=</span> <span class="n">ammico</span><span class="o">.</span><span class="n">privacy_disclosure</span><span class="p">(</span><span class="n">accept_privacy</span><span class="o">=</span><span class="n">accept_privacy</span><span class="p">)</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="nbinput nblast docutils container">
|
||||
<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
|
||||
</pre></div>
|
||||
@ -321,9 +344,17 @@ directly on the right next to the image. This way, the user can directly inspect
|
||||
<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">num</span><span class="p">,</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="nb">enumerate</span><span class="p">(</span><span class="n">image_dict</span><span class="o">.</span><span class="n">keys</span><span class="p">()),</span> <span class="n">total</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><span class="n">image_dict</span><span class="p">)):</span> <span class="c1"># loop through all images</span>
|
||||
<span class="n">image_dict</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">ammico</span><span class="o">.</span><span class="n">EmotionDetector</span><span class="p">(</span><span class="n">image_dict</span><span class="p">[</span><span class="n">key</span><span class="p">])</span><span class="o">.</span><span class="n">analyse_image</span><span class="p">()</span> <span class="c1"># analyse image with EmotionDetector and update dict</span>
|
||||
|
||||
<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># set the thresholds for the emotion detection</span>
|
||||
<span class="n">emotion_threshold</span> <span class="o">=</span> <span class="mi">50</span> <span class="c1"># this is the default value for the detection confidence</span>
|
||||
<span class="c1"># the lowest possible value is 0</span>
|
||||
<span class="c1"># the highest possible value is 100</span>
|
||||
<span class="n">race_threshold</span> <span class="o">=</span> <span class="mi">50</span>
|
||||
<span class="n">gender_threshold</span> <span class="o">=</span> <span class="mi">50</span>
|
||||
<span class="k">for</span> <span class="n">num</span><span class="p">,</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="nb">enumerate</span><span class="p">(</span><span class="n">image_dict</span><span class="o">.</span><span class="n">keys</span><span class="p">()),</span> <span class="n">total</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><span class="n">image_dict</span><span class="p">)):</span> <span class="c1"># loop through all images</span>
|
||||
<span class="n">image_dict</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">ammico</span><span class="o">.</span><span class="n">EmotionDetector</span><span class="p">(</span><span class="n">image_dict</span><span class="p">[</span><span class="n">key</span><span class="p">],</span>
|
||||
<span class="n">emotion_threshold</span><span class="o">=</span><span class="n">emotion_threshold</span><span class="p">,</span>
|
||||
<span class="n">race_threshold</span><span class="o">=</span><span class="n">race_threshold</span><span class="p">,</span>
|
||||
<span class="n">gender_threshold</span><span class="o">=</span><span class="n">gender_threshold</span><span class="p">)</span><span class="o">.</span><span class="n">analyse_image</span><span class="p">()</span> <span class="c1"># analyse image with EmotionDetector and update dict</span>
|
||||
<span class="k">if</span> <span class="n">num</span> <span class="o">%</span> <span class="n">dump_every</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">or</span> <span class="n">num</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">image_dict</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">:</span> <span class="c1"># save results every dump_every to dump_file</span>
|
||||
<span class="n">image_df</span> <span class="o">=</span> <span class="n">ammico</span><span class="o">.</span><span class="n">get_dataframe</span><span class="p">(</span><span class="n">image_dict</span><span class="p">)</span>
|
||||
<span class="n">image_df</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="n">dump_file</span><span class="p">)</span>
|
||||
@ -420,8 +451,7 @@ directly on the right next to the image. This way, the user can directly inspect
|
||||
<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">csv_path</span> <span class="o">=</span> <span class="n">pkg</span> <span class="o">/</span> <span class="s2">"data"</span> <span class="o">/</span> <span class="s2">"ref"</span> <span class="o">/</span> <span class="s2">"test.csv"</span>
|
||||
<span class="n">ta</span> <span class="o">=</span> <span class="n">ammico</span><span class="o">.</span><span class="n">TextAnalyzer</span><span class="p">(</span><span class="n">csv_path</span><span class="o">=</span><span class="nb">str</span><span class="p">(</span><span class="n">csv_path</span><span class="p">),</span> <span class="n">column_key</span><span class="o">=</span><span class="s2">"text"</span><span class="p">)</span>
|
||||
<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">ta</span> <span class="o">=</span> <span class="n">ammico</span><span class="o">.</span><span class="n">TextAnalyzer</span><span class="p">(</span><span class="n">csv_path</span><span class="o">=</span><span class="s2">"../data/ref/test.csv"</span><span class="p">,</span> <span class="n">column_key</span><span class="o">=</span><span class="s2">"text"</span><span class="p">)</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
@ -493,7 +523,7 @@ directly on the right next to the image. This way, the user can directly inspect
|
||||
<section id="The-detector-modules">
|
||||
<h1>The detector modules<a class="headerlink" href="#The-detector-modules" title="Link to this heading"></a></h1>
|
||||
<p>The different detector modules with their options are explained in more detail in this section. ## Text detector Text on the images can be extracted using the <code class="docutils literal notranslate"><span class="pre">TextDetector</span></code> class (<code class="docutils literal notranslate"><span class="pre">text</span></code> module). The text is initally extracted using the Google Cloud Vision API and then translated into English with googletrans. The translated text is cleaned of whitespace, linebreaks, and numbers using Python syntax and spaCy.</p>
|
||||
<p><img alt="10a9852f3be149b8b5518e25c50f6ca8" class="no-scaled-link" src="../_images/text_detector.png" style="width: 800px;" /></p>
|
||||
<p><img alt="4627534faec74c6cb30013b3b78abb84" class="no-scaled-link" src="../_images/text_detector.png" style="width: 800px;" /></p>
|
||||
<p>The user can set if the text should be further summarized, and analyzed for sentiment and named entity recognition, by setting the keyword <code class="docutils literal notranslate"><span class="pre">analyse_text</span></code> to <code class="docutils literal notranslate"><span class="pre">True</span></code> (the default is <code class="docutils literal notranslate"><span class="pre">False</span></code>). If set, the transformers pipeline is used for each of these tasks, with the default models as of 03/2023. Other models can be selected by setting the optional keyword <code class="docutils literal notranslate"><span class="pre">model_names</span></code> to a list of selected models, on for each task:
|
||||
<code class="docutils literal notranslate"><span class="pre">model_names=["sshleifer/distilbart-cnn-12-6",</span> <span class="pre">"distilbert-base-uncased-finetuned-sst-2-english",</span> <span class="pre">"dbmdz/bert-large-cased-finetuned-conll03-english"]</span></code> for summary, sentiment, and ner. To be even more specific, revision numbers can also be selected by specifying the optional keyword <code class="docutils literal notranslate"><span class="pre">revision_numbers</span></code> to a list of revision numbers for each model, for example <code class="docutils literal notranslate"><span class="pre">revision_numbers=["a4f8f3e",</span> <span class="pre">"af0f99b",</span> <span class="pre">"f2482bf"]</span></code>.</p>
|
||||
<p>Please note that for the Google Cloud Vision API (the TextDetector class) you need to set a key in order to process the images. This key is ideally set as an environment variable using for example</p>
|
||||
@ -524,6 +554,16 @@ directly on the right next to the image. This way, the user can directly inspect
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="nbinput nblast docutils container">
|
||||
<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># write output to csv</span>
|
||||
<span class="n">image_df</span> <span class="o">=</span> <span class="n">ammico</span><span class="o">.</span><span class="n">get_dataframe</span><span class="p">(</span><span class="n">image_dict</span><span class="p">)</span>
|
||||
<span class="n">image_df</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="s2">"/content/drive/MyDrive/misinformation-data/data_out.csv"</span><span class="p">)</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<p>The models can be adapted interactively in the notebook interface and the best models can then be used in a subsequent analysis of the whole data set.</p>
|
||||
<p>A detailed description of the output keys and data types is given in the following table.</p>
|
||||
<table class="docutils align-default">
|
||||
@ -575,7 +615,7 @@ directly on the right next to the image. This way, the user can directly inspect
|
||||
<section id="Image-summary-and-query">
|
||||
<h2>Image summary and query<a class="headerlink" href="#Image-summary-and-query" title="Link to this heading"></a></h2>
|
||||
<p>The <code class="docutils literal notranslate"><span class="pre">SummaryDetector</span></code> can be used to generate image captions (<code class="docutils literal notranslate"><span class="pre">summary</span></code>) as well as visual question answering (<code class="docutils literal notranslate"><span class="pre">VQA</span></code>).</p>
|
||||
<p><img alt="95bce83c68244063a385bb5376de0dac" class="no-scaled-link" src="../_images/summary_detector.png" style="width: 800px;" /></p>
|
||||
<p><img alt="89ab67ce3b0e471e8741cfc2b23d97f1" class="no-scaled-link" src="../_images/summary_detector.png" style="width: 800px;" /></p>
|
||||
<p>This module is based on the <a class="reference external" href="https://github.com/salesforce/LAVIS">LAVIS</a> library. Since the models can be quite large, an initial object is created which will load the necessary models into RAM/VRAM and then use them in the analysis. The user can specify the type of analysis to be performed using the <code class="docutils literal notranslate"><span class="pre">analysis_type</span></code> keyword. Setting it to <code class="docutils literal notranslate"><span class="pre">summary</span></code> will generate a caption (summary), <code class="docutils literal notranslate"><span class="pre">questions</span></code> will prepare answers (VQA) to a list of questions as set by the user,
|
||||
<code class="docutils literal notranslate"><span class="pre">summary_and_questions</span></code> will do both. Note that the desired analysis type needs to be set here in the initialization of the detector object, and not when running the analysis for each image; the same holds true for the selected model.</p>
|
||||
<p>The implemented models are listed below.</p>
|
||||
@ -761,7 +801,7 @@ directly on the right next to the image. This way, the user can directly inspect
|
||||
<span class="c1"># "summary",</span>
|
||||
<span class="c1"># "questions",</span>
|
||||
<span class="c1"># "summary_and_questions".</span>
|
||||
<br/></pre></div>
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<p>You can also pass a question with previous answers as context into this model and pass in questions like this one to get a more accurate answer:</p>
|
||||
@ -794,7 +834,7 @@ directly on the right next to the image. This way, the user can directly inspect
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<p>You can also ask sequential questions if you pass the argument <code class="docutils literal notranslate"><span class="pre">cosequential_questions=True</span></code>. This means that the answers to previous questions will be passed as context to the next question. However, this method will work a bit slower, because for each image the answers to the questions will not be calculated simultaneously, but sequentially.</p>
|
||||
<p>You can also ask sequential questions if you pass the argument <code class="docutils literal notranslate"><span class="pre">consequential_questions=True</span></code>. This means that the answers to previous questions will be passed as context to the next question. However, this method will work a bit slower, because for each image the answers to the questions will not be calculated simultaneously, but sequentially.</p>
|
||||
<div class="nbinput nblast docutils container">
|
||||
<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
|
||||
</pre></div>
|
||||
@ -823,12 +863,22 @@ directly on the right next to the image. This way, the user can directly inspect
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="nbinput nblast docutils container">
|
||||
<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># write output to csv</span>
|
||||
<span class="n">image_df</span> <span class="o">=</span> <span class="n">ammico</span><span class="o">.</span><span class="n">get_dataframe</span><span class="p">(</span><span class="n">image_dict</span><span class="p">)</span>
|
||||
<span class="n">image_df</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="s2">"/content/drive/MyDrive/misinformation-data/data_out.csv"</span><span class="p">)</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
</section>
|
||||
</section>
|
||||
<section id="Detection-of-faces-and-facial-expression-analysis">
|
||||
<h2>Detection of faces and facial expression analysis<a class="headerlink" href="#Detection-of-faces-and-facial-expression-analysis" title="Link to this heading"></a></h2>
|
||||
<p>Faces and facial expressions are detected and analyzed using the <code class="docutils literal notranslate"><span class="pre">EmotionDetector</span></code> class from the <code class="docutils literal notranslate"><span class="pre">faces</span></code> module. Initially, it is detected if faces are present on the image using RetinaFace, followed by analysis if face masks are worn (Face-Mask-Detection). The probabilistic detection of age, gender, race, and emotions is carried out with deepface, but only if the disclosure statement has been accepted (see above).</p>
|
||||
<p><img alt="c8ab1d7bfd1546ca904f1ff91de93fb7" class="no-scaled-link" src="../_images/emotion_detector.png" style="width: 800px;" /></p>
|
||||
<p><img alt="08b5f56998844e20961d743a8d32ea91" class="no-scaled-link" src="../_images/emotion_detector.png" style="width: 800px;" /></p>
|
||||
<p>Depending on the features found on the image, the face detection module returns a different analysis content: If no faces are found on the image, all further steps are skipped and the result <code class="docutils literal notranslate"><span class="pre">"face":</span> <span class="pre">"No",</span> <span class="pre">"multiple_faces":</span> <span class="pre">"No",</span> <span class="pre">"no_faces":</span> <span class="pre">0,</span> <span class="pre">"wears_mask":</span> <span class="pre">["No"],</span> <span class="pre">"age":</span> <span class="pre">[None],</span> <span class="pre">"gender":</span> <span class="pre">[None],</span> <span class="pre">"race":</span> <span class="pre">[None],</span> <span class="pre">"emotion":</span> <span class="pre">[None],</span> <span class="pre">"emotion</span> <span class="pre">(category)":</span> <span class="pre">[None]</span></code> is returned. If one or several faces are found, up to three faces are analyzed if they are partially concealed by a face mask. If
|
||||
yes, only age and gender are detected; if no, also race, emotion, and dominant emotion are detected. In case of the latter, the output could look like this: <code class="docutils literal notranslate"><span class="pre">"face":</span> <span class="pre">"Yes",</span> <span class="pre">"multiple_faces":</span> <span class="pre">"Yes",</span> <span class="pre">"no_faces":</span> <span class="pre">2,</span> <span class="pre">"wears_mask":</span> <span class="pre">["No",</span> <span class="pre">"No"],</span> <span class="pre">"age":</span> <span class="pre">[27,</span> <span class="pre">28],</span> <span class="pre">"gender":</span> <span class="pre">["Man",</span> <span class="pre">"Man"],</span> <span class="pre">"race":</span> <span class="pre">["asian",</span> <span class="pre">None],</span> <span class="pre">"emotion":</span> <span class="pre">["angry",</span> <span class="pre">"neutral"],</span> <span class="pre">"emotion</span> <span class="pre">(category)":</span> <span class="pre">["Negative",</span> <span class="pre">"Neutral"]</span></code>, where for the two faces that are detected (given by <code class="docutils literal notranslate"><span class="pre">no_faces</span></code>), some of the values are returned as a list
|
||||
with the first item for the first (largest) face and the second item for the second (smaller) face (for example, <code class="docutils literal notranslate"><span class="pre">"emotion"</span></code> returns a list <code class="docutils literal notranslate"><span class="pre">["angry",</span> <span class="pre">"neutral"]</span></code> signifying the first face expressing anger, and the second face having a neutral expression).</p>
|
||||
@ -850,6 +900,16 @@ default is set to 50%, so that a confidence above 0.5 results in an emotion bein
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="nbinput nblast docutils container">
|
||||
<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># write output to csv</span>
|
||||
<span class="n">image_df</span> <span class="o">=</span> <span class="n">ammico</span><span class="o">.</span><span class="n">get_dataframe</span><span class="p">(</span><span class="n">image_dict</span><span class="p">)</span>
|
||||
<span class="n">image_df</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="s2">"/content/drive/MyDrive/misinformation-data/data_out.csv"</span><span class="p">)</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<p>The thresholds can be adapted interactively in the notebook interface and the optimal value can then be used in a subsequent analysis of the whole data set.</p>
|
||||
<p>The output keys that are generated are</p>
|
||||
<table class="docutils align-default">
|
||||
@ -946,7 +1006,7 @@ default is set to 50%, so that a confidence above 0.5 results in an emotion bein
|
||||
</div>
|
||||
</div>
|
||||
<p>The images are then processed and stored in a numerical representation, a tensor. These tensors do not change for the same image and same model - so if you run this analysis once, and save the tensors giving a path with the keyword <code class="docutils literal notranslate"><span class="pre">path_to_save_tensors</span></code>, a file with filename <code class="docutils literal notranslate"><span class="pre">.<Number_of_images>_<model_name>_saved_features_image.pt</span></code> will be placed there.</p>
|
||||
<p>This can save you time if you want to analyse same images with the same model but different questions. To run using the saved tensors, execute the below code giving the path and name of the tensor file. Any subsequent query of the model will run in a fraction of the time than it run in initially.</p>
|
||||
<p>This can save you time if you want to analyse the same images with the same model but different questions. To run using the saved tensors, execute the below code giving the path and name of the tensor file. Any subsequent query of the model will run in a fraction of the time than it run in initially.</p>
|
||||
<div class="nbinput nblast docutils container">
|
||||
<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
|
||||
</pre></div>
|
||||
@ -989,7 +1049,7 @@ default is set to 50%, so that a confidence above 0.5 results in an emotion bein
|
||||
</div>
|
||||
</div>
|
||||
<p>You can filter your results in 3 different ways: - <code class="docutils literal notranslate"><span class="pre">filter_number_of_images</span></code> limits the number of images found. That is, if the parameter <code class="docutils literal notranslate"><span class="pre">filter_number_of_images</span> <span class="pre">=</span> <span class="pre">10</span></code>, then the first 10 images that best match the query will be shown. The other images ranks will be set to <code class="docutils literal notranslate"><span class="pre">None</span></code> and the similarity value to <code class="docutils literal notranslate"><span class="pre">0</span></code>. - <code class="docutils literal notranslate"><span class="pre">filter_val_limit</span></code> limits the output of images with a similarity value not bigger than <code class="docutils literal notranslate"><span class="pre">filter_val_limit</span></code>. That is, if the parameter <code class="docutils literal notranslate"><span class="pre">filter_val_limit</span> <span class="pre">=</span> <span class="pre">0.2</span></code>, all images
|
||||
with similarity less than 0.2 will be discarded. - <code class="docutils literal notranslate"><span class="pre">filter_rel_error</span></code> (percentage) limits the output of images with a similarity value not bigger than <code class="docutils literal notranslate"><span class="pre">100</span> <span class="pre">*</span> <span class="pre">abs(current_simularity_value</span> <span class="pre">-</span> <span class="pre">best_simularity_value_in_current_search)/best_simularity_value_in_current_search</span> <span class="pre"><</span> <span class="pre">filter_rel_error</span></code>. That is, if we set filter_rel_error = 30, it means that if the top1 image have 0.5 similarity value, we discard all image with similarity less than 0.35.</p>
|
||||
with similarity less than 0.2 will be discarded. - <code class="docutils literal notranslate"><span class="pre">filter_rel_error</span></code> (percentage) limits the output of images with a similarity value not bigger than <code class="docutils literal notranslate"><span class="pre">100</span> <span class="pre">*</span> <span class="pre">abs(current_similarity_value</span> <span class="pre">-</span> <span class="pre">best_similarity_value_in_current_search)/best_similarity_value_in_current_search</span> <span class="pre"><</span> <span class="pre">filter_rel_error</span></code>. That is, if we set filter_rel_error = 30, it means that if the top1 image have 0.5 similarity value, we discard all image with similarity less than 0.35.</p>
|
||||
<div class="nbinput nblast docutils container">
|
||||
<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
|
||||
</pre></div>
|
||||
@ -1083,7 +1143,7 @@ images being processed simultaneously. With the parameter <code class="docutils
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<p>Then using the same output function you can add the <code class="docutils literal notranslate"><span class="pre">itm=True</span></code> argument to output the new image order. Remember that for images querys, an error will be thrown with <code class="docutils literal notranslate"><span class="pre">itm=True</span></code> argument. You can also add the <code class="docutils literal notranslate"><span class="pre">image_gradcam_with_itm</span></code> along with <code class="docutils literal notranslate"><span class="pre">itm=True</span></code> argument to output the heat maps of the calculated images.</p>
|
||||
<p>Then using the same output function you can add the <code class="docutils literal notranslate"><span class="pre">itm=True</span></code> argument to output the new image order. Remember that for images queries, an error will be thrown with <code class="docutils literal notranslate"><span class="pre">itm=True</span></code> argument. You can also add the <code class="docutils literal notranslate"><span class="pre">image_gradcam_with_itm</span></code> along with <code class="docutils literal notranslate"><span class="pre">itm=True</span></code> argument to output the heat maps of the calculated images.</p>
|
||||
<div class="nbinput nblast docutils container">
|
||||
<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
|
||||
</pre></div>
|
||||
@ -1097,7 +1157,7 @@ images being processed simultaneously. With the parameter <code class="docutils
|
||||
</section>
|
||||
<section id="Save-search-results-to-csv">
|
||||
<h3>Save search results to csv<a class="headerlink" href="#Save-search-results-to-csv" title="Link to this heading"></a></h3>
|
||||
<p>Convert the dictionary of dictionarys into a dictionary with lists:</p>
|
||||
<p>Convert the dictionary of dictionaries into a dictionary with lists:</p>
|
||||
<div class="nbinput nblast docutils container">
|
||||
<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
|
||||
</pre></div>
|
||||
|
||||
@ -18,7 +18,10 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# if running on google colab\n",
|
||||
"# if running on google colab\\\n",
|
||||
"# PLEASE RUN THIS ONLY AS CPU RUNTIME\n",
|
||||
"# for a GPU runtime, there are conflicts with pre-installed packages - \n",
|
||||
"# you first need to uninstall them (prepare a clean environment with no pre-installs) and then install ammico\n",
|
||||
"# flake8-noqa-cell\n",
|
||||
"\n",
|
||||
"if \"google.colab\" in str(get_ipython()):\n",
|
||||
@ -26,9 +29,10 @@
|
||||
" # install setuptools\n",
|
||||
" # %pip install setuptools==61 -qqq\n",
|
||||
" # uninstall some pre-installed packages due to incompatibility\n",
|
||||
" %pip uninstall --yes tensorflow-probability dopamine-rl lida pandas-gbq torchaudio torchdata torchtext orbax-checkpoint flex-y -qqq\n",
|
||||
" %pip uninstall --yes tensorflow-probability dopamine-rl lida pandas-gbq torchaudio torchdata torchtext orbax-checkpoint flex-y jax jaxlib -qqq\n",
|
||||
" # install ammico\n",
|
||||
" %pip install git+https://github.com/ssciwr/ammico.git -qqq\n",
|
||||
" # install older version of jax to support transformers use of diffusers\n",
|
||||
" # mount google drive for data and API key\n",
|
||||
" from google.colab import drive\n",
|
||||
"\n",
|
||||
@ -40,9 +44,15 @@
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Use a test dataset\n",
|
||||
"You can download a dataset for test purposes. Skip this step if you use your own data."
|
||||
"\n",
|
||||
"You can download this dataset for test purposes. Skip this step if you use your own data. If the data set on Hugging Face is gated or private, Hugging Face will ask you for a login token. However, for the default dataset in this notebook you do not need to provide one."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
@ -92,12 +102,12 @@
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import os\n",
|
||||
"# jax also sometimes leads to problems on google colab\n",
|
||||
"# if this is the case, try restarting the kernel and executing this \n",
|
||||
"# and the above two code cells again\n",
|
||||
"import ammico\n",
|
||||
"# for displaying a progress bar\n",
|
||||
"from tqdm import tqdm\n",
|
||||
"# to get the reference data for text_dict\n",
|
||||
"import importlib_resources\n",
|
||||
"pkg = importlib_resources.files(\"ammico\")"
|
||||
"from tqdm import tqdm"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -151,7 +161,7 @@
|
||||
"| `limit` | `int` | maximum number of files to read (defaults to `20`, for all images set to `None` or `-1`) |\n",
|
||||
"| `random_seed` | `str` | the random seed for shuffling the images; applies when only a few images are read and the selection should be preserved (defaults to `None`) |\n",
|
||||
"\n",
|
||||
"The `find_files` function returns a nested dict that contains the file ids and the paths to the files and is empty otherwise. This dict is filled step by step with more data as each detector class is run on the data (see below).\n",
|
||||
"The `find_files` function returns a nested dictionary that contains the file ids and the paths to the files and is empty otherwise. This dict is filled step by step with more data as each detector class is run on the data (see below).\n",
|
||||
"\n",
|
||||
"If you downloaded the test dataset above, you can directly provide the path you already set for the test directory, `data_path`. The below cell is already set up for the test dataset.\n",
|
||||
"\n",
|
||||
@ -183,9 +193,9 @@
|
||||
"\n",
|
||||
"If you want to run an analysis using the EmotionDetector detector type, you have first have to respond to an ethical disclosure statement. This disclosure statement ensures that you only use the full capabilities of the EmotionDetector after you have been made aware of its shortcomings.\n",
|
||||
"\n",
|
||||
"For this, answer \"yes\" or \"no\" to the below prompt. This will set an environment variable with the name given as in `accept_disclosure`. To re-run the disclosure prompt, unset the variable by uncommenting the line `os.environ.pop(accept_disclosure, None)`. To permanently set this envorinment variable, add it to your shell via your `.profile` or `.bashr` file.\n",
|
||||
"For this, answer \"yes\" or \"no\" to the below prompt. This will set an environment variable with the name given as in `accept_disclosure`. To re-run the disclosure prompt, unset the variable by uncommenting the line `os.environ.pop(accept_disclosure, None)`. To permanently set this environment variable, add it to your shell via your `.profile` or `.bashr` file.\n",
|
||||
"\n",
|
||||
"If the disclosure statement is accepted, the EmotionDetector will perform age, gender and race/ethnicity classification dependend on the provided thresholds. If the disclosure is rejected, only the presence of faces and emotion (if not wearing a mask) is detected."
|
||||
"If the disclosure statement is accepted, the EmotionDetector will perform age, gender and race/ethnicity classification depending on the provided thresholds. If the disclosure is rejected, only the presence of faces and emotion (if not wearing a mask) is detected."
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -203,6 +213,34 @@
|
||||
"_ = ammico.ethical_disclosure(accept_disclosure=accept_disclosure)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Privacy disclosure statement\n",
|
||||
"\n",
|
||||
"If you want to run an analysis using the TextDetector detector type, you have first have to respond to a privacy disclosure statement. This disclosure statement ensures that you are aware that your data will be sent to google cloud vision servers for analysis.\n",
|
||||
"\n",
|
||||
"For this, answer \"yes\" or \"no\" to the below prompt. This will set an environment variable with the name given as in `accept_privacy`. To re-run the disclosure prompt, unset the variable by uncommenting the line `os.environ.pop(accept_privacy, None)`. To permanently set this environment variable, add it to your shell via your `.profile` or `.bashr` file.\n",
|
||||
"\n",
|
||||
"If the privacy disclosure statement is accepted, the TextDetector will perform the text extraction, translation and if selected, analysis. If the privacy disclosure is rejected, no text processing will be carried out and you cannot use the TextDetector."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# respond to the privacy disclosure statement\n",
|
||||
"# this will set an environment variable for you\n",
|
||||
"# if you do not want to re-accept the privacy disclosure every time, you can set this environment variable in your shell\n",
|
||||
"# to re-set the environment variable, uncomment the below line\n",
|
||||
"accept_privacy = \"PRIVACY_AMMICO\"\n",
|
||||
"# os.environ.pop(accept_privacy, None)\n",
|
||||
"_ = ammico.privacy_disclosure(accept_privacy=accept_privacy)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
@ -253,9 +291,17 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# set the thresholds for the emotion detection\n",
|
||||
"emotion_threshold = 50 # this is the default value for the detection confidence\n",
|
||||
"# the lowest possible value is 0\n",
|
||||
"# the highest possible value is 100\n",
|
||||
"race_threshold = 50\n",
|
||||
"gender_threshold = 50\n",
|
||||
"for num, key in tqdm(enumerate(image_dict.keys()), total=len(image_dict)): # loop through all images\n",
|
||||
" image_dict[key] = ammico.EmotionDetector(image_dict[key]).analyse_image() # analyse image with EmotionDetector and update dict\n",
|
||||
" \n",
|
||||
" image_dict[key] = ammico.EmotionDetector(image_dict[key],\n",
|
||||
" emotion_threshold=emotion_threshold,\n",
|
||||
" race_threshold=race_threshold,\n",
|
||||
" gender_threshold=gender_threshold).analyse_image() # analyse image with EmotionDetector and update dict\n",
|
||||
" if num % dump_every == 0 or num == len(image_dict) - 1: # save results every dump_every to dump_file\n",
|
||||
" image_df = ammico.get_dataframe(image_dict)\n",
|
||||
" image_df.to_csv(dump_file)"
|
||||
@ -405,8 +451,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"csv_path = pkg / \"data\" / \"ref\" / \"test.csv\"\n",
|
||||
"ta = ammico.TextAnalyzer(csv_path=str(csv_path), column_key=\"text\")"
|
||||
"ta = ammico.TextAnalyzer(csv_path=\"../data/ref/test.csv\", column_key=\"text\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -530,6 +575,17 @@
|
||||
" image_df.to_csv(dump_file)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# write output to csv\n",
|
||||
"image_df = ammico.get_dataframe(image_dict)\n",
|
||||
"image_df.to_csv(\"/content/drive/MyDrive/misinformation-data/data_out.csv\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
@ -759,7 +815,7 @@
|
||||
"# analysis_type can be \n",
|
||||
"# \"summary\",\n",
|
||||
"# \"questions\",\n",
|
||||
"# \"summary_and_questions\".\n"
|
||||
"# \"summary_and_questions\"."
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -806,7 +862,7 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"You can also ask sequential questions if you pass the argument `cosequential_questions=True`. This means that the answers to previous questions will be passed as context to the next question. However, this method will work a bit slower, because for each image the answers to the questions will not be calculated simultaneously, but sequentially. "
|
||||
"You can also ask sequential questions if you pass the argument `consequential_questions=True`. This means that the answers to previous questions will be passed as context to the next question. However, this method will work a bit slower, because for each image the answers to the questions will not be calculated simultaneously, but sequentially. "
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -840,6 +896,17 @@
|
||||
"image_dict"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# write output to csv\n",
|
||||
"image_df = ammico.get_dataframe(image_dict)\n",
|
||||
"image_df.to_csv(\"/content/drive/MyDrive/misinformation-data/data_out.csv\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
@ -855,7 +922,7 @@
|
||||
"\n",
|
||||
"From the seven facial expressions, an overall dominating emotion category is identified: negative, positive, or neutral emotion. These are defined with the facial expressions angry, disgust, fear and sad for the negative category, happy for the positive category, and surprise and neutral for the neutral category.\n",
|
||||
"\n",
|
||||
"A similar threshold as for the emotion recognition is set for the race/ethnicity and gender detection, `race_threshold` and `gender_threshold`, with the default set to 50% so that a confidence for race / gender above 0.5 only will return a value in the analysis.\n",
|
||||
"A similar threshold as for the emotion recognition is set for the race/ethnicity and gender detection, `race_threshold` and `gender_threshold`, with the default set to 50% so that a confidence for race / gender above 0.5 only will return a value in the analysis. \n",
|
||||
"\n",
|
||||
"For age unfortunately no confidence value is accessible so that no threshold values can be set for this type of analysis. The [reported MAE of the model is ± 4.65](https://sefiks.com/2019/02/13/apparent-age-and-gender-prediction-in-keras/).\n",
|
||||
"\n",
|
||||
@ -876,6 +943,17 @@
|
||||
" accept_disclosure=\"DISCLOSURE_AMMICO\").analyse_image()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# write output to csv\n",
|
||||
"image_df = ammico.get_dataframe(image_dict)\n",
|
||||
"image_df.to_csv(\"/content/drive/MyDrive/misinformation-data/data_out.csv\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
@ -986,7 +1064,7 @@
|
||||
"source": [
|
||||
"The images are then processed and stored in a numerical representation, a tensor. These tensors do not change for the same image and same model - so if you run this analysis once, and save the tensors giving a path with the keyword `path_to_save_tensors`, a file with filename `.<Number_of_images>_<model_name>_saved_features_image.pt` will be placed there.\n",
|
||||
"\n",
|
||||
"This can save you time if you want to analyse same images with the same model but different questions. To run using the saved tensors, execute the below code giving the path and name of the tensor file. Any subsequent query of the model will run in a fraction of the time than it run in initially."
|
||||
"This can save you time if you want to analyse the same images with the same model but different questions. To run using the saved tensors, execute the below code giving the path and name of the tensor file. Any subsequent query of the model will run in a fraction of the time than it run in initially."
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -1050,7 +1128,7 @@
|
||||
"You can filter your results in 3 different ways:\n",
|
||||
"- `filter_number_of_images` limits the number of images found. That is, if the parameter `filter_number_of_images = 10`, then the first 10 images that best match the query will be shown. The other images ranks will be set to `None` and the similarity value to `0`.\n",
|
||||
"- `filter_val_limit` limits the output of images with a similarity value not bigger than `filter_val_limit`. That is, if the parameter `filter_val_limit = 0.2`, all images with similarity less than 0.2 will be discarded.\n",
|
||||
"- `filter_rel_error` (percentage) limits the output of images with a similarity value not bigger than `100 * abs(current_simularity_value - best_simularity_value_in_current_search)/best_simularity_value_in_current_search < filter_rel_error`. That is, if we set filter_rel_error = 30, it means that if the top1 image have 0.5 similarity value, we discard all image with similarity less than 0.35."
|
||||
"- `filter_rel_error` (percentage) limits the output of images with a similarity value not bigger than `100 * abs(current_similarity_value - best_similarity_value_in_current_search)/best_similarity_value_in_current_search < filter_rel_error`. That is, if we set filter_rel_error = 30, it means that if the top1 image have 0.5 similarity value, we discard all image with similarity less than 0.35."
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -1174,7 +1252,7 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Then using the same output function you can add the `itm=True` argument to output the new image order. Remember that for images querys, an error will be thrown with `itm=True` argument. You can also add the `image_gradcam_with_itm` along with `itm=True` argument to output the heat maps of the calculated images."
|
||||
"Then using the same output function you can add the `itm=True` argument to output the new image order. Remember that for images queries, an error will be thrown with `itm=True` argument. You can also add the `image_gradcam_with_itm` along with `itm=True` argument to output the heat maps of the calculated images."
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -1199,7 +1277,7 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Convert the dictionary of dictionarys into a dictionary with lists:"
|
||||
"Convert the dictionary of dictionaries into a dictionary with lists:"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -1367,7 +1445,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.5"
|
||||
"version": "3.11.9"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
||||
@ -1,3 +1,5 @@
|
||||
|
||||
|
||||
<!DOCTYPE html>
|
||||
<html class="writer-html5" lang="en" data-content_root="../">
|
||||
<head>
|
||||
@ -6,22 +8,18 @@
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
|
||||
<title>Crop posts module — AMMICO 0.0.1 documentation</title>
|
||||
<link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=80d5e7a1" />
|
||||
<link rel="stylesheet" type="text/css" href="../_static/css/theme.css?v=19f00094" />
|
||||
<link rel="stylesheet" type="text/css" href="../_static/css/theme.css?v=7ab3649f" />
|
||||
<link rel="stylesheet" type="text/css" href="../_static/nbsphinx-code-cells.css?v=2aa19091" />
|
||||
|
||||
|
||||
<!--[if lt IE 9]>
|
||||
<script src="../_static/js/html5shiv.min.js"></script>
|
||||
<![endif]-->
|
||||
|
||||
<script src="../_static/jquery.js?v=5d32c60e"></script>
|
||||
<script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
|
||||
<script src="../_static/documentation_options.js?v=d45e8c67"></script>
|
||||
<script src="../_static/doctools.js?v=9a2dae69"></script>
|
||||
<script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
|
||||
<script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
|
||||
<script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
|
||||
<script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
|
||||
<script src="../_static/jquery.js?v=5d32c60e"></script>
|
||||
<script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
|
||||
<script src="../_static/documentation_options.js?v=d45e8c67"></script>
|
||||
<script src="../_static/doctools.js?v=9a2dae69"></script>
|
||||
<script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
|
||||
<script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
|
||||
<script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
|
||||
<script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
|
||||
<script src="../_static/js/theme.js"></script>
|
||||
<link rel="index" title="Index" href="../genindex.html" />
|
||||
<link rel="search" title="Search" href="../search.html" />
|
||||
@ -51,7 +49,7 @@
|
||||
<p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
|
||||
<ul class="current">
|
||||
<li class="toctree-l1"><a class="reference internal" href="../readme_link.html">AMMICO - AI Media and Misinformation Content Analysis Tool</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../readme_link.html#faq">FAQ</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../faq_link.html">FAQ</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../create_API_key_link.html">Instructions how to generate and enable a google Cloud Vision API key</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="DemoNotebook_ammico.html">AMMICO Demonstration Notebook</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="DemoNotebook_ammico.html#Step-0:-Create-and-set-a-Google-Cloud-Vision-Key">Step 0: Create and set a Google Cloud Vision Key</a></li>
|
||||
|
||||
Двоичные данные
build/html/objects.inv
Двоичные данные
build/html/objects.inv
Двоичный файл не отображается.
@ -1,3 +1,5 @@
|
||||
|
||||
|
||||
<!DOCTYPE html>
|
||||
<html class="writer-html5" lang="en" data-content_root="./">
|
||||
<head>
|
||||
@ -5,19 +7,15 @@
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
|
||||
<title>Python Module Index — AMMICO 0.0.1 documentation</title>
|
||||
<link rel="stylesheet" type="text/css" href="_static/pygments.css?v=80d5e7a1" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/css/theme.css?v=19f00094" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/css/theme.css?v=7ab3649f" />
|
||||
|
||||
|
||||
<!--[if lt IE 9]>
|
||||
<script src="_static/js/html5shiv.min.js"></script>
|
||||
<![endif]-->
|
||||
|
||||
<script src="_static/jquery.js?v=5d32c60e"></script>
|
||||
<script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
|
||||
<script src="_static/documentation_options.js?v=d45e8c67"></script>
|
||||
<script src="_static/doctools.js?v=9a2dae69"></script>
|
||||
<script src="_static/sphinx_highlight.js?v=dc90522c"></script>
|
||||
<script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
|
||||
<script src="_static/jquery.js?v=5d32c60e"></script>
|
||||
<script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
|
||||
<script src="_static/documentation_options.js?v=d45e8c67"></script>
|
||||
<script src="_static/doctools.js?v=9a2dae69"></script>
|
||||
<script src="_static/sphinx_highlight.js?v=dc90522c"></script>
|
||||
<script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
|
||||
<script src="_static/js/theme.js"></script>
|
||||
<link rel="index" title="Index" href="genindex.html" />
|
||||
<link rel="search" title="Search" href="search.html" />
|
||||
@ -52,7 +50,7 @@
|
||||
<p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
|
||||
<ul>
|
||||
<li class="toctree-l1"><a class="reference internal" href="readme_link.html">AMMICO - AI Media and Misinformation Content Analysis Tool</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="readme_link.html#faq">FAQ</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="faq_link.html">FAQ</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="create_API_key_link.html">Instructions how to generate and enable a google Cloud Vision API key</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="notebooks/DemoNotebook_ammico.html">AMMICO Demonstration Notebook</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="notebooks/DemoNotebook_ammico.html#Step-0:-Create-and-set-a-Google-Cloud-Vision-Key">Step 0: Create and set a Google Cloud Vision Key</a></li>
|
||||
|
||||
@ -1,3 +1,5 @@
|
||||
|
||||
|
||||
<!DOCTYPE html>
|
||||
<html class="writer-html5" lang="en" data-content_root="./">
|
||||
<head>
|
||||
@ -6,23 +8,19 @@
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
|
||||
<title>AMMICO - AI Media and Misinformation Content Analysis Tool — AMMICO 0.0.1 documentation</title>
|
||||
<link rel="stylesheet" type="text/css" href="_static/pygments.css?v=80d5e7a1" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/css/theme.css?v=19f00094" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/css/theme.css?v=7ab3649f" />
|
||||
|
||||
|
||||
<!--[if lt IE 9]>
|
||||
<script src="_static/js/html5shiv.min.js"></script>
|
||||
<![endif]-->
|
||||
|
||||
<script src="_static/jquery.js?v=5d32c60e"></script>
|
||||
<script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
|
||||
<script src="_static/documentation_options.js?v=d45e8c67"></script>
|
||||
<script src="_static/doctools.js?v=9a2dae69"></script>
|
||||
<script src="_static/sphinx_highlight.js?v=dc90522c"></script>
|
||||
<script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
|
||||
<script src="_static/jquery.js?v=5d32c60e"></script>
|
||||
<script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
|
||||
<script src="_static/documentation_options.js?v=d45e8c67"></script>
|
||||
<script src="_static/doctools.js?v=9a2dae69"></script>
|
||||
<script src="_static/sphinx_highlight.js?v=dc90522c"></script>
|
||||
<script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
|
||||
<script src="_static/js/theme.js"></script>
|
||||
<link rel="index" title="Index" href="genindex.html" />
|
||||
<link rel="search" title="Search" href="search.html" />
|
||||
<link rel="next" title="Instructions how to generate and enable a google Cloud Vision API key" href="create_API_key_link.html" />
|
||||
<link rel="next" title="FAQ" href="faq_link.html" />
|
||||
<link rel="prev" title="Welcome to AMMICO’s documentation!" href="index.html" />
|
||||
</head>
|
||||
|
||||
@ -49,14 +47,6 @@
|
||||
<ul class="current">
|
||||
<li class="toctree-l1 current"><a class="current reference internal" href="#">AMMICO - AI Media and Misinformation Content Analysis Tool</a><ul>
|
||||
<li class="toctree-l2"><a class="reference internal" href="#installation">Installation</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="#compatibility-problems-solving">Compatibility problems solving</a><ul>
|
||||
<li class="toctree-l3"><a class="reference internal" href="#first-install-tensorflow-https-www-tensorflow-org-install-pip">1. First, install tensorflow (https://www.tensorflow.org/install/pip)</a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="#second-install-pytorch">2. Second, install pytorch</a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="#after-we-prepared-right-environment-we-can-install-the-ammico-package">3. After we prepared right environment we can install the <code class="docutils literal notranslate"><span class="pre">ammico</span></code> package</a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="#micromamba">Micromamba</a></li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="#windows">Windows</a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="#usage">Usage</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="#features">Features</a><ul>
|
||||
<li class="toctree-l3"><a class="reference internal" href="#text-extraction">Text extraction</a></li>
|
||||
@ -68,13 +58,7 @@
|
||||
</li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="#faq">FAQ</a><ul>
|
||||
<li class="toctree-l2"><a class="reference internal" href="#what-happens-to-the-images-that-are-sent-to-google-cloud-vision">What happens to the images that are sent to google Cloud Vision?</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="#what-happens-to-the-text-that-is-sent-to-google-translate">What happens to the text that is sent to google Translate?</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="#what-happens-if-i-don-t-have-internet-access-can-i-still-use-ammico">What happens if I don’t have internet access - can I still use ammico?</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="#why-don-t-i-get-probabilistic-assessments-of-age-gender-and-race-when-running-the-emotion-detector">Why don’t I get probabilistic assessments of age, gender and race when running the Emotion Detector?</a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="faq_link.html">FAQ</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="create_API_key_link.html">Instructions how to generate and enable a google Cloud Vision API key</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="notebooks/DemoNotebook_ammico.html">AMMICO Demonstration Notebook</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="notebooks/DemoNotebook_ammico.html#Step-0:-Create-and-set-a-Google-Cloud-Vision-Key">Step 0: Create and set a Google Cloud Vision Key</a></li>
|
||||
@ -115,7 +99,8 @@
|
||||
<img alt="GitHub Workflow Status" src="https://img.shields.io/github/actions/workflow/status/ssciwr/AMMICO/ci.yml?branch=main" />
|
||||
<img alt="codecov" src="https://img.shields.io/codecov/c/github/ssciwr/AMMICO" />
|
||||
<img alt="Quality Gate Status" src="https://sonarcloud.io/api/project_badges/measure?project=ssciwr_ammico&metric=alert_status" />
|
||||
<img alt="Language" src="https://img.shields.io/github/languages/top/ssciwr/AMMICO" /></p>
|
||||
<img alt="Language" src="https://img.shields.io/github/languages/top/ssciwr/AMMICO" />
|
||||
<a class="reference external" href="https://colab.research.google.com/github/ssciwr/ammico/blob/main/ammico/notebooks/DemoNotebook_ammico.ipynb"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg" /></a></p>
|
||||
<p>This package extracts data from images such as social media posts that contain an image part and a text part. The analysis can generate a very large number of features, depending on the user input. See <a class="reference external" href="https://dx.doi.org/10.31235/osf.io/v8txj">our paper</a> for a more in-depth description.</p>
|
||||
<p><strong><em>This project is currently under development!</em></strong></p>
|
||||
<p>Use pre-processed image files such as social media posts with comments and process to collect information:</p>
|
||||
@ -162,83 +147,18 @@
|
||||
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="n">ammico</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>This will install the package and its dependencies locally. If after installation you get some errors when running some modules, please follow the instructions below.</p>
|
||||
</section>
|
||||
<section id="compatibility-problems-solving">
|
||||
<h2>Compatibility problems solving<a class="headerlink" href="#compatibility-problems-solving" title="Link to this heading"></a></h2>
|
||||
<p>Some ammico components require <code class="docutils literal notranslate"><span class="pre">tensorflow</span></code> (e.g. Emotion detector), some <code class="docutils literal notranslate"><span class="pre">pytorch</span></code> (e.g. Summary detector). Sometimes there are compatibility problems between these two frameworks. To avoid these problems on your machines, you can prepare proper environment before installing the package (you need conda on your machine):</p>
|
||||
<section id="first-install-tensorflow-https-www-tensorflow-org-install-pip">
|
||||
<h3>1. First, install tensorflow (https://www.tensorflow.org/install/pip)<a class="headerlink" href="#first-install-tensorflow-https-www-tensorflow-org-install-pip" title="Link to this heading"></a></h3>
|
||||
<ul>
|
||||
<li><p>create a new environment with python and activate it</p>
|
||||
<p><code class="docutils literal notranslate"><span class="pre">conda</span> <span class="pre">create</span> <span class="pre">-n</span> <span class="pre">ammico_env</span> <span class="pre">python=3.10</span></code></p>
|
||||
<p><code class="docutils literal notranslate"><span class="pre">conda</span> <span class="pre">activate</span> <span class="pre">ammico_env</span></code></p>
|
||||
</li>
|
||||
<li><p>install cudatoolkit from conda-forge</p>
|
||||
<p><code class="docutils literal notranslate"> <span class="pre">conda</span> <span class="pre">install</span> <span class="pre">-c</span> <span class="pre">conda-forge</span> <span class="pre">cudatoolkit=11.8.0</span></code></p>
|
||||
</li>
|
||||
<li><p>install nvidia-cudnn-cu11 from pip</p>
|
||||
<p><code class="docutils literal notranslate"><span class="pre">python</span> <span class="pre">-m</span> <span class="pre">pip</span> <span class="pre">install</span> <span class="pre">nvidia-cudnn-cu11==8.6.0.163</span></code></p>
|
||||
</li>
|
||||
<li><p>add script that runs when conda environment <code class="docutils literal notranslate"><span class="pre">ammico_env</span></code> is activated to put the right libraries on your LD_LIBRARY_PATH</p>
|
||||
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>mkdir -p $CONDA_PREFIX/etc/conda/activate.d
|
||||
echo 'CUDNN_PATH=$(dirname $(python -c "import nvidia.cudnn;print(nvidia.cudnn.__file__)"))' >> $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh
|
||||
echo 'export LD_LIBRARY_PATH=$CUDNN_PATH/lib:$CONDA_PREFIX/lib/:$LD_LIBRARY_PATH' >> $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh
|
||||
source $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh
|
||||
</pre></div>
|
||||
</div>
|
||||
</li>
|
||||
<li><p>deactivate and re-activate conda environment to call script above</p>
|
||||
<p><code class="docutils literal notranslate"><span class="pre">conda</span> <span class="pre">deactivate</span></code></p>
|
||||
<p><code class="docutils literal notranslate"><span class="pre">conda</span> <span class="pre">activate</span> <span class="pre">ammico_env</span> </code></p>
|
||||
</li>
|
||||
<li><p>install tensorflow</p>
|
||||
<p><code class="docutils literal notranslate"><span class="pre">python</span> <span class="pre">-m</span> <span class="pre">pip</span> <span class="pre">install</span> <span class="pre">tensorflow==2.12.1</span></code></p>
|
||||
</li>
|
||||
</ul>
|
||||
</section>
|
||||
<section id="second-install-pytorch">
|
||||
<h3>2. Second, install pytorch<a class="headerlink" href="#second-install-pytorch" title="Link to this heading"></a></h3>
|
||||
<ul>
|
||||
<li><p>install pytorch for same cuda version as above</p>
|
||||
<p><code class="docutils literal notranslate"><span class="pre">python</span> <span class="pre">-m</span> <span class="pre">pip</span> <span class="pre">install</span> <span class="pre">torch</span> <span class="pre">torchvision</span> <span class="pre">torchaudio</span> <span class="pre">--index-url</span> <span class="pre">https://download.pytorch.org/whl/cu118</span></code></p>
|
||||
</li>
|
||||
</ul>
|
||||
</section>
|
||||
<section id="after-we-prepared-right-environment-we-can-install-the-ammico-package">
|
||||
<h3>3. After we prepared right environment we can install the <code class="docutils literal notranslate"><span class="pre">ammico</span></code> package<a class="headerlink" href="#after-we-prepared-right-environment-we-can-install-the-ammico-package" title="Link to this heading"></a></h3>
|
||||
<ul class="simple">
|
||||
<li><p><code class="docutils literal notranslate"><span class="pre">python</span> <span class="pre">-m</span> <span class="pre">pip</span> <span class="pre">install</span> <span class="pre">ammico</span></code></p></li>
|
||||
</ul>
|
||||
<p>It is done.</p>
|
||||
</section>
|
||||
<section id="micromamba">
|
||||
<h3>Micromamba<a class="headerlink" href="#micromamba" title="Link to this heading"></a></h3>
|
||||
<p>If you are using micromamba you can prepare environment with just one command:</p>
|
||||
<p><code class="docutils literal notranslate"><span class="pre">micromamba</span> <span class="pre">create</span> <span class="pre">--no-channel-priority</span> <span class="pre">-c</span> <span class="pre">nvidia</span> <span class="pre">-c</span> <span class="pre">pytorch</span> <span class="pre">-c</span> <span class="pre">conda-forge</span> <span class="pre">-n</span> <span class="pre">ammico_env</span> <span class="pre">"python=3.10"</span> <span class="pre">pytorch</span> <span class="pre">torchvision</span> <span class="pre">torchaudio</span> <span class="pre">pytorch-cuda</span> <span class="pre">"tensorflow-gpu<=2.12.3"</span> <span class="pre">"numpy<=1.23.4"</span></code></p>
|
||||
</section>
|
||||
<section id="windows">
|
||||
<h3>Windows<a class="headerlink" href="#windows" title="Link to this heading"></a></h3>
|
||||
<p>To make pycocotools work on Windows OS you may need to install <code class="docutils literal notranslate"><span class="pre">vs_BuildTools.exe</span></code> from https://visualstudio.microsoft.com/visual-cpp-build-tools/ and choose following elements:</p>
|
||||
<ul class="simple">
|
||||
<li><p><code class="docutils literal notranslate"><span class="pre">Visual</span> <span class="pre">Studio</span> <span class="pre">extension</span> <span class="pre">development</span></code></p></li>
|
||||
<li><p><code class="docutils literal notranslate"><span class="pre">MSVC</span> <span class="pre">v143</span> <span class="pre">-</span> <span class="pre">VS</span> <span class="pre">2022</span> <span class="pre">C++</span> <span class="pre">x64/x86</span> <span class="pre">build</span> <span class="pre">tools</span></code></p></li>
|
||||
<li><p><code class="docutils literal notranslate"><span class="pre">Windows</span> <span class="pre">11</span> <span class="pre">SDK</span></code> for Windows 11 (or <code class="docutils literal notranslate"><span class="pre">Windows</span> <span class="pre">10</span> <span class="pre">SDK</span></code> for Windows 10)</p></li>
|
||||
</ul>
|
||||
<p>Be careful, it requires around 7 GB of disk space.</p>
|
||||
<p><img alt="Screenshot 2023-06-01 165712" src="https://github.com/ssciwr/AMMICO/assets/8105097/3dfb302f-c390-46a7-a700-4e044f56c30f" /></p>
|
||||
</section>
|
||||
<p>This will install the package and its dependencies locally. If after installation you get some errors when running some modules, please follow the instructions in the <a class="reference internal" href="#FAQ.md"><span class="xref myst">FAQ</span></a>.</p>
|
||||
</section>
|
||||
<section id="usage">
|
||||
<h2>Usage<a class="headerlink" href="#usage" title="Link to this heading"></a></h2>
|
||||
<p>The main demonstration notebook can be found in the <code class="docutils literal notranslate"><span class="pre">notebooks</span></code> folder and also on <a class="reference external" href="https://colab.research.google.com/github/ssciwr/ammico/blob/main/ammico/notebooks/DemoNotebook_ammico.ipynb">google colab</a>.</p>
|
||||
<p>The main demonstration notebook can be found in the <code class="docutils literal notranslate"><span class="pre">notebooks</span></code> folder and also on google colab: [<img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg" />].</p>
|
||||
<p>There are further sample notebooks in the <code class="docutils literal notranslate"><span class="pre">notebooks</span></code> folder for the more experimental features:</p>
|
||||
<ol class="arabic simple">
|
||||
<li><p>Topic analysis: Use the notebook <code class="docutils literal notranslate"><span class="pre">get-text-from-image.ipynb</span></code> to analyse the topics of the extraced text.<br />
|
||||
<strong>You can run this notebook on google colab: <a class="reference external" href="https://colab.research.google.com/github/ssciwr/ammico/blob/main/ammico/notebooks/get-text-from-image.ipynb">Here</a></strong><br />
|
||||
<strong>You can run this notebook on google colab: [<img alt="Open In Colab" src="https://colab.research.google.com/github/ssciwr/ammico/blob/main/ammico/notebooks/get-text-from-image.ipynb" /></strong><br />
|
||||
Place the data files and google cloud vision API key in your google drive to access the data.</p></li>
|
||||
<li><p>To crop social media posts use the <code class="docutils literal notranslate"><span class="pre">cropposts.ipynb</span></code> notebook.
|
||||
<strong>You can run this notebook on google colab: <a class="reference external" href="https://colab.research.google.com/github/ssciwr/ammico/blob/main/ammico/notebooks/cropposts.ipynb">Here</a></strong></p></li>
|
||||
<strong>You can run this notebook on google colab: [<img alt="Open In Colab" src="https://colab.research.google.com/github/ssciwr/ammico/blob/main/ammico/notebooks/cropposts.ipynb" /></strong></p></li>
|
||||
</ol>
|
||||
</section>
|
||||
<section id="features">
|
||||
@ -257,7 +177,7 @@ You then need to export the location of the API key as an environment variable:<
|
||||
</section>
|
||||
<section id="content-extraction">
|
||||
<h3>Content extraction<a class="headerlink" href="#content-extraction" title="Link to this heading"></a></h3>
|
||||
<p>The image content (“caption”) is extracted using the <a class="reference external" href="https://github.com/salesforce/LAVIS">LAVIS</a> library. This library enables vision intelligence extraction using several state-of-the-art models, depending on the task. Further, it allows feature extraction from the images, where users can input textual and image queries, and the images in the database are matched to that query (multimodal search). Another option is question answering, where the user inputs a text question and the library finds the images that match the query.</p>
|
||||
<p>The image content (“caption”) is extracted using the <a class="reference external" href="https://github.com/salesforce/LAVIS">LAVIS</a> library. This library enables vision intelligence extraction using several state-of-the-art models such as BLIP and BLIP2, depending on the task and user selection. Further, it allows feature extraction from the images, where users can input textual and image queries, and the images in the database are matched to that query (multimodal search). Another option is question answering, where the user inputs a text question and the library finds the images that match the query.</p>
|
||||
</section>
|
||||
<section id="emotion-recognition">
|
||||
<h3>Emotion recognition<a class="headerlink" href="#emotion-recognition" title="Link to this heading"></a></h3>
|
||||
@ -272,47 +192,6 @@ You then need to export the location of the API key as an environment variable:<
|
||||
<p>Social media posts can automatically be cropped to remove further comments on the page and restrict the textual content to the first comment only.</p>
|
||||
</section>
|
||||
</section>
|
||||
</section>
|
||||
<section id="faq">
|
||||
<h1>FAQ<a class="headerlink" href="#faq" title="Link to this heading"></a></h1>
|
||||
<section id="what-happens-to-the-images-that-are-sent-to-google-cloud-vision">
|
||||
<h2>What happens to the images that are sent to google Cloud Vision?<a class="headerlink" href="#what-happens-to-the-images-that-are-sent-to-google-cloud-vision" title="Link to this heading"></a></h2>
|
||||
<p>According to the <a class="reference external" href="https://cloud.google.com/vision/docs/data-usage">google Vision API</a>, the images that are uploaded and analysed are not stored and not shared with third parties:</p>
|
||||
<blockquote>
|
||||
<div><p>We won’t make the content that you send available to the public. We won’t share the content with any third party. The content is only used by Google as necessary to provide the Vision API service. Vision API complies with the Cloud Data Processing Addendum.</p>
|
||||
</div></blockquote>
|
||||
<blockquote>
|
||||
<div><p>For online (immediate response) operations (<code class="docutils literal notranslate"><span class="pre">BatchAnnotateImages</span></code> and <code class="docutils literal notranslate"><span class="pre">BatchAnnotateFiles</span></code>), the image data is processed in memory and not persisted to disk.
|
||||
For asynchronous offline batch operations (<code class="docutils literal notranslate"><span class="pre">AsyncBatchAnnotateImages</span></code> and <code class="docutils literal notranslate"><span class="pre">AsyncBatchAnnotateFiles</span></code>), we must store that image for a short period of time in order to perform the analysis and return the results to you. The stored image is typically deleted right after the processing is done, with a failsafe Time to live (TTL) of a few hours.
|
||||
Google also temporarily logs some metadata about your Vision API requests (such as the time the request was received and the size of the request) to improve our service and combat abuse.</p>
|
||||
</div></blockquote>
|
||||
</section>
|
||||
<section id="what-happens-to-the-text-that-is-sent-to-google-translate">
|
||||
<h2>What happens to the text that is sent to google Translate?<a class="headerlink" href="#what-happens-to-the-text-that-is-sent-to-google-translate" title="Link to this heading"></a></h2>
|
||||
<p>According to <a class="reference external" href="https://cloud.google.com/translate/data-usage">google Translate</a>, the data is not stored after processing and not made available to third parties:</p>
|
||||
<blockquote>
|
||||
<div><p>We will not make the content of the text that you send available to the public. We will not share the content with any third party. The content of the text is only used by Google as necessary to provide the Cloud Translation API service. Cloud Translation API complies with the Cloud Data Processing Addendum.</p>
|
||||
</div></blockquote>
|
||||
<blockquote>
|
||||
<div><p>When you send text to Cloud Translation API, text is held briefly in-memory in order to perform the translation and return the results to you.</p>
|
||||
</div></blockquote>
|
||||
</section>
|
||||
<section id="what-happens-if-i-don-t-have-internet-access-can-i-still-use-ammico">
|
||||
<h2>What happens if I don’t have internet access - can I still use ammico?<a class="headerlink" href="#what-happens-if-i-don-t-have-internet-access-can-i-still-use-ammico" title="Link to this heading"></a></h2>
|
||||
<p>Some features of ammico require internet access; a general answer to this question is not possible, some services require an internet connection, others can be used offline:</p>
|
||||
<ul class="simple">
|
||||
<li><p>Text extraction: To extract text from images, and translate the text, the data needs to be processed by google Cloud Vision and google Translate, which run in the cloud. Without internet access, text extraction and translation is not possible.</p></li>
|
||||
<li><p>Image summary and query: After an initial download of the models, the <code class="docutils literal notranslate"><span class="pre">summary</span></code> module does not require an internet connection.</p></li>
|
||||
<li><p>Facial expressions: After an initial download of the models, the <code class="docutils literal notranslate"><span class="pre">faces</span></code> module does not require an internet connection.</p></li>
|
||||
<li><p>Multimodal search: After an initial download of the models, the <code class="docutils literal notranslate"><span class="pre">multimodal_search</span></code> module does not require an internet connection.</p></li>
|
||||
<li><p>Color analysis: The <code class="docutils literal notranslate"><span class="pre">color</span></code> module does not require an internet connection.</p></li>
|
||||
</ul>
|
||||
</section>
|
||||
<section id="why-don-t-i-get-probabilistic-assessments-of-age-gender-and-race-when-running-the-emotion-detector">
|
||||
<h2>Why don’t I get probabilistic assessments of age, gender and race when running the Emotion Detector?<a class="headerlink" href="#why-don-t-i-get-probabilistic-assessments-of-age-gender-and-race-when-running-the-emotion-detector" title="Link to this heading"></a></h2>
|
||||
<p>Due to well documented biases in the detection of minorities with computer vision tools, and to the ethical implications of such detection, these parts of the tool are not directly made available to users. To access these capabilities, users must first agree with a ethical disclosure statement that reads: “The Emotion Detector uses RetinaFace to probabilistically assess the gender, age and race of the detected faces. Such assessments may not reflect how the individuals identified by the tool view themselves. Additionally, the classification is carried out in simplistic categories and contains only the most basic classes, for example “male” and “female” for gender. By continuing to use the tool, you certify that you understand the ethical implications such assessments have for the interpretation of the results.”
|
||||
This disclosure statement is included as a separate line of code early in the flow of the Emotion Detector. Once the user has agreed with the statement, further data analyses will also include these assessments.</p>
|
||||
</section>
|
||||
</section>
|
||||
|
||||
|
||||
@ -320,7 +199,7 @@ This disclosure statement is included as a separate line of code early in the fl
|
||||
</div>
|
||||
<footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
|
||||
<a href="index.html" class="btn btn-neutral float-left" title="Welcome to AMMICO’s documentation!" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
|
||||
<a href="create_API_key_link.html" class="btn btn-neutral float-right" title="Instructions how to generate and enable a google Cloud Vision API key" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
|
||||
<a href="faq_link.html" class="btn btn-neutral float-right" title="FAQ" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
|
||||
</div>
|
||||
|
||||
<hr/>
|
||||
|
||||
@ -1,3 +1,5 @@
|
||||
|
||||
|
||||
<!DOCTYPE html>
|
||||
<html class="writer-html5" lang="en" data-content_root="./">
|
||||
<head>
|
||||
@ -5,20 +7,16 @@
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
|
||||
<title>Search — AMMICO 0.0.1 documentation</title>
|
||||
<link rel="stylesheet" type="text/css" href="_static/pygments.css?v=80d5e7a1" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/css/theme.css?v=19f00094" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/css/theme.css?v=7ab3649f" />
|
||||
|
||||
|
||||
|
||||
<!--[if lt IE 9]>
|
||||
<script src="_static/js/html5shiv.min.js"></script>
|
||||
<![endif]-->
|
||||
|
||||
<script src="_static/jquery.js?v=5d32c60e"></script>
|
||||
<script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
|
||||
<script src="_static/documentation_options.js?v=d45e8c67"></script>
|
||||
<script src="_static/doctools.js?v=9a2dae69"></script>
|
||||
<script src="_static/sphinx_highlight.js?v=dc90522c"></script>
|
||||
<script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
|
||||
<script src="_static/jquery.js?v=5d32c60e"></script>
|
||||
<script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
|
||||
<script src="_static/documentation_options.js?v=d45e8c67"></script>
|
||||
<script src="_static/doctools.js?v=9a2dae69"></script>
|
||||
<script src="_static/sphinx_highlight.js?v=dc90522c"></script>
|
||||
<script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
|
||||
<script src="_static/js/theme.js"></script>
|
||||
<script src="_static/searchtools.js"></script>
|
||||
<script src="_static/language_data.js"></script>
|
||||
@ -48,7 +46,7 @@
|
||||
<p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
|
||||
<ul>
|
||||
<li class="toctree-l1"><a class="reference internal" href="readme_link.html">AMMICO - AI Media and Misinformation Content Analysis Tool</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="readme_link.html#faq">FAQ</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="faq_link.html">FAQ</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="create_API_key_link.html">Instructions how to generate and enable a google Cloud Vision API key</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="notebooks/DemoNotebook_ammico.html">AMMICO Demonstration Notebook</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="notebooks/DemoNotebook_ammico.html#Step-0:-Create-and-set-a-Google-Cloud-Vision-Key">Step 0: Create and set a Google Cloud Vision Key</a></li>
|
||||
|
||||
Различия файлов скрыты, потому что одна или несколько строк слишком длинны
@ -1,3 +1,5 @@
|
||||
|
||||
|
||||
<!DOCTYPE html>
|
||||
<html class="writer-html5" lang="en" data-content_root="./">
|
||||
<head>
|
||||
@ -6,19 +8,15 @@
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
|
||||
<title>Instructions how to generate and enable a google Cloud Vision API key — AMMICO 0.0.1 documentation</title>
|
||||
<link rel="stylesheet" type="text/css" href="_static/pygments.css?v=80d5e7a1" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/css/theme.css?v=19f00094" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/css/theme.css?v=7ab3649f" />
|
||||
|
||||
|
||||
<!--[if lt IE 9]>
|
||||
<script src="_static/js/html5shiv.min.js"></script>
|
||||
<![endif]-->
|
||||
|
||||
<script src="_static/jquery.js?v=5d32c60e"></script>
|
||||
<script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
|
||||
<script src="_static/documentation_options.js?v=d45e8c67"></script>
|
||||
<script src="_static/doctools.js?v=9a2dae69"></script>
|
||||
<script src="_static/sphinx_highlight.js?v=dc90522c"></script>
|
||||
<script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
|
||||
<script src="_static/jquery.js?v=5d32c60e"></script>
|
||||
<script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
|
||||
<script src="_static/documentation_options.js?v=d45e8c67"></script>
|
||||
<script src="_static/doctools.js?v=9a2dae69"></script>
|
||||
<script src="_static/sphinx_highlight.js?v=dc90522c"></script>
|
||||
<script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
|
||||
<script src="_static/js/theme.js"></script>
|
||||
<link rel="index" title="Index" href="genindex.html" />
|
||||
<link rel="search" title="Search" href="search.html" />
|
||||
@ -46,7 +44,7 @@
|
||||
<p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
|
||||
<ul>
|
||||
<li class="toctree-l1"><a class="reference internal" href="readme_link.html">AMMICO - AI Media and Misinformation Content Analysis Tool</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="readme_link.html#faq">FAQ</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="faq_link.html">FAQ</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="create_API_key_link.html">Instructions how to generate and enable a google Cloud Vision API key</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="notebooks/DemoNotebook_ammico.html">AMMICO Demonstration Notebook</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="notebooks/DemoNotebook_ammico.html#Step-0:-Create-and-set-a-Google-Cloud-Vision-Key">Step 0: Create and set a Google Cloud Vision Key</a></li>
|
||||
|
||||
2
source/faq_link.md
Обычный файл
2
source/faq_link.md
Обычный файл
@ -0,0 +1,2 @@
|
||||
```{include} ../../FAQ.md
|
||||
```
|
||||
@ -11,6 +11,7 @@ Welcome to AMMICO's documentation!
|
||||
:caption: Contents:
|
||||
|
||||
readme_link
|
||||
faq_link
|
||||
create_API_key_link
|
||||
notebooks/DemoNotebook_ammico
|
||||
notebooks/Example cropposts
|
||||
|
||||
@ -18,7 +18,10 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# if running on google colab\n",
|
||||
"# if running on google colab\\\n",
|
||||
"# PLEASE RUN THIS ONLY AS CPU RUNTIME\n",
|
||||
"# for a GPU runtime, there are conflicts with pre-installed packages - \n",
|
||||
"# you first need to uninstall them (prepare a clean environment with no pre-installs) and then install ammico\n",
|
||||
"# flake8-noqa-cell\n",
|
||||
"\n",
|
||||
"if \"google.colab\" in str(get_ipython()):\n",
|
||||
@ -26,9 +29,10 @@
|
||||
" # install setuptools\n",
|
||||
" # %pip install setuptools==61 -qqq\n",
|
||||
" # uninstall some pre-installed packages due to incompatibility\n",
|
||||
" %pip uninstall --yes tensorflow-probability dopamine-rl lida pandas-gbq torchaudio torchdata torchtext orbax-checkpoint flex-y -qqq\n",
|
||||
" %pip uninstall --yes tensorflow-probability dopamine-rl lida pandas-gbq torchaudio torchdata torchtext orbax-checkpoint flex-y jax jaxlib -qqq\n",
|
||||
" # install ammico\n",
|
||||
" %pip install git+https://github.com/ssciwr/ammico.git -qqq\n",
|
||||
" # install older version of jax to support transformers use of diffusers\n",
|
||||
" # mount google drive for data and API key\n",
|
||||
" from google.colab import drive\n",
|
||||
"\n",
|
||||
@ -40,9 +44,15 @@
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Use a test dataset\n",
|
||||
"You can download a dataset for test purposes. Skip this step if you use your own data."
|
||||
"\n",
|
||||
"You can download this dataset for test purposes. Skip this step if you use your own data. If the data set on Hugging Face is gated or private, Hugging Face will ask you for a login token. However, for the default dataset in this notebook you do not need to provide one."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
@ -92,12 +102,12 @@
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import os\n",
|
||||
"# jax also sometimes leads to problems on google colab\n",
|
||||
"# if this is the case, try restarting the kernel and executing this \n",
|
||||
"# and the above two code cells again\n",
|
||||
"import ammico\n",
|
||||
"# for displaying a progress bar\n",
|
||||
"from tqdm import tqdm\n",
|
||||
"# to get the reference data for text_dict\n",
|
||||
"import importlib_resources\n",
|
||||
"pkg = importlib_resources.files(\"ammico\")"
|
||||
"from tqdm import tqdm"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -151,7 +161,7 @@
|
||||
"| `limit` | `int` | maximum number of files to read (defaults to `20`, for all images set to `None` or `-1`) |\n",
|
||||
"| `random_seed` | `str` | the random seed for shuffling the images; applies when only a few images are read and the selection should be preserved (defaults to `None`) |\n",
|
||||
"\n",
|
||||
"The `find_files` function returns a nested dict that contains the file ids and the paths to the files and is empty otherwise. This dict is filled step by step with more data as each detector class is run on the data (see below).\n",
|
||||
"The `find_files` function returns a nested dictionary that contains the file ids and the paths to the files and is empty otherwise. This dict is filled step by step with more data as each detector class is run on the data (see below).\n",
|
||||
"\n",
|
||||
"If you downloaded the test dataset above, you can directly provide the path you already set for the test directory, `data_path`. The below cell is already set up for the test dataset.\n",
|
||||
"\n",
|
||||
@ -183,9 +193,9 @@
|
||||
"\n",
|
||||
"If you want to run an analysis using the EmotionDetector detector type, you have first have to respond to an ethical disclosure statement. This disclosure statement ensures that you only use the full capabilities of the EmotionDetector after you have been made aware of its shortcomings.\n",
|
||||
"\n",
|
||||
"For this, answer \"yes\" or \"no\" to the below prompt. This will set an environment variable with the name given as in `accept_disclosure`. To re-run the disclosure prompt, unset the variable by uncommenting the line `os.environ.pop(accept_disclosure, None)`. To permanently set this envorinment variable, add it to your shell via your `.profile` or `.bashr` file.\n",
|
||||
"For this, answer \"yes\" or \"no\" to the below prompt. This will set an environment variable with the name given as in `accept_disclosure`. To re-run the disclosure prompt, unset the variable by uncommenting the line `os.environ.pop(accept_disclosure, None)`. To permanently set this environment variable, add it to your shell via your `.profile` or `.bashr` file.\n",
|
||||
"\n",
|
||||
"If the disclosure statement is accepted, the EmotionDetector will perform age, gender and race/ethnicity classification dependend on the provided thresholds. If the disclosure is rejected, only the presence of faces and emotion (if not wearing a mask) is detected."
|
||||
"If the disclosure statement is accepted, the EmotionDetector will perform age, gender and race/ethnicity classification depending on the provided thresholds. If the disclosure is rejected, only the presence of faces and emotion (if not wearing a mask) is detected."
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -203,6 +213,34 @@
|
||||
"_ = ammico.ethical_disclosure(accept_disclosure=accept_disclosure)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Privacy disclosure statement\n",
|
||||
"\n",
|
||||
"If you want to run an analysis using the TextDetector detector type, you have first have to respond to a privacy disclosure statement. This disclosure statement ensures that you are aware that your data will be sent to google cloud vision servers for analysis.\n",
|
||||
"\n",
|
||||
"For this, answer \"yes\" or \"no\" to the below prompt. This will set an environment variable with the name given as in `accept_privacy`. To re-run the disclosure prompt, unset the variable by uncommenting the line `os.environ.pop(accept_privacy, None)`. To permanently set this environment variable, add it to your shell via your `.profile` or `.bashr` file.\n",
|
||||
"\n",
|
||||
"If the privacy disclosure statement is accepted, the TextDetector will perform the text extraction, translation and if selected, analysis. If the privacy disclosure is rejected, no text processing will be carried out and you cannot use the TextDetector."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# respond to the privacy disclosure statement\n",
|
||||
"# this will set an environment variable for you\n",
|
||||
"# if you do not want to re-accept the privacy disclosure every time, you can set this environment variable in your shell\n",
|
||||
"# to re-set the environment variable, uncomment the below line\n",
|
||||
"accept_privacy = \"PRIVACY_AMMICO\"\n",
|
||||
"# os.environ.pop(accept_privacy, None)\n",
|
||||
"_ = ammico.privacy_disclosure(accept_privacy=accept_privacy)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
@ -253,9 +291,17 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# set the thresholds for the emotion detection\n",
|
||||
"emotion_threshold = 50 # this is the default value for the detection confidence\n",
|
||||
"# the lowest possible value is 0\n",
|
||||
"# the highest possible value is 100\n",
|
||||
"race_threshold = 50\n",
|
||||
"gender_threshold = 50\n",
|
||||
"for num, key in tqdm(enumerate(image_dict.keys()), total=len(image_dict)): # loop through all images\n",
|
||||
" image_dict[key] = ammico.EmotionDetector(image_dict[key]).analyse_image() # analyse image with EmotionDetector and update dict\n",
|
||||
" \n",
|
||||
" image_dict[key] = ammico.EmotionDetector(image_dict[key],\n",
|
||||
" emotion_threshold=emotion_threshold,\n",
|
||||
" race_threshold=race_threshold,\n",
|
||||
" gender_threshold=gender_threshold).analyse_image() # analyse image with EmotionDetector and update dict\n",
|
||||
" if num % dump_every == 0 or num == len(image_dict) - 1: # save results every dump_every to dump_file\n",
|
||||
" image_df = ammico.get_dataframe(image_dict)\n",
|
||||
" image_df.to_csv(dump_file)"
|
||||
@ -405,8 +451,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"csv_path = pkg / \"data\" / \"ref\" / \"test.csv\"\n",
|
||||
"ta = ammico.TextAnalyzer(csv_path=str(csv_path), column_key=\"text\")"
|
||||
"ta = ammico.TextAnalyzer(csv_path=\"../data/ref/test.csv\", column_key=\"text\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -530,6 +575,17 @@
|
||||
" image_df.to_csv(dump_file)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# write output to csv\n",
|
||||
"image_df = ammico.get_dataframe(image_dict)\n",
|
||||
"image_df.to_csv(\"/content/drive/MyDrive/misinformation-data/data_out.csv\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
@ -759,7 +815,7 @@
|
||||
"# analysis_type can be \n",
|
||||
"# \"summary\",\n",
|
||||
"# \"questions\",\n",
|
||||
"# \"summary_and_questions\".\n"
|
||||
"# \"summary_and_questions\"."
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -806,7 +862,7 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"You can also ask sequential questions if you pass the argument `cosequential_questions=True`. This means that the answers to previous questions will be passed as context to the next question. However, this method will work a bit slower, because for each image the answers to the questions will not be calculated simultaneously, but sequentially. "
|
||||
"You can also ask sequential questions if you pass the argument `consequential_questions=True`. This means that the answers to previous questions will be passed as context to the next question. However, this method will work a bit slower, because for each image the answers to the questions will not be calculated simultaneously, but sequentially. "
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -840,6 +896,17 @@
|
||||
"image_dict"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# write output to csv\n",
|
||||
"image_df = ammico.get_dataframe(image_dict)\n",
|
||||
"image_df.to_csv(\"/content/drive/MyDrive/misinformation-data/data_out.csv\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
@ -855,7 +922,7 @@
|
||||
"\n",
|
||||
"From the seven facial expressions, an overall dominating emotion category is identified: negative, positive, or neutral emotion. These are defined with the facial expressions angry, disgust, fear and sad for the negative category, happy for the positive category, and surprise and neutral for the neutral category.\n",
|
||||
"\n",
|
||||
"A similar threshold as for the emotion recognition is set for the race/ethnicity and gender detection, `race_threshold` and `gender_threshold`, with the default set to 50% so that a confidence for race / gender above 0.5 only will return a value in the analysis.\n",
|
||||
"A similar threshold as for the emotion recognition is set for the race/ethnicity and gender detection, `race_threshold` and `gender_threshold`, with the default set to 50% so that a confidence for race / gender above 0.5 only will return a value in the analysis. \n",
|
||||
"\n",
|
||||
"For age unfortunately no confidence value is accessible so that no threshold values can be set for this type of analysis. The [reported MAE of the model is ± 4.65](https://sefiks.com/2019/02/13/apparent-age-and-gender-prediction-in-keras/).\n",
|
||||
"\n",
|
||||
@ -876,6 +943,17 @@
|
||||
" accept_disclosure=\"DISCLOSURE_AMMICO\").analyse_image()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# write output to csv\n",
|
||||
"image_df = ammico.get_dataframe(image_dict)\n",
|
||||
"image_df.to_csv(\"/content/drive/MyDrive/misinformation-data/data_out.csv\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
@ -986,7 +1064,7 @@
|
||||
"source": [
|
||||
"The images are then processed and stored in a numerical representation, a tensor. These tensors do not change for the same image and same model - so if you run this analysis once, and save the tensors giving a path with the keyword `path_to_save_tensors`, a file with filename `.<Number_of_images>_<model_name>_saved_features_image.pt` will be placed there.\n",
|
||||
"\n",
|
||||
"This can save you time if you want to analyse same images with the same model but different questions. To run using the saved tensors, execute the below code giving the path and name of the tensor file. Any subsequent query of the model will run in a fraction of the time than it run in initially."
|
||||
"This can save you time if you want to analyse the same images with the same model but different questions. To run using the saved tensors, execute the below code giving the path and name of the tensor file. Any subsequent query of the model will run in a fraction of the time than it run in initially."
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -1050,7 +1128,7 @@
|
||||
"You can filter your results in 3 different ways:\n",
|
||||
"- `filter_number_of_images` limits the number of images found. That is, if the parameter `filter_number_of_images = 10`, then the first 10 images that best match the query will be shown. The other images ranks will be set to `None` and the similarity value to `0`.\n",
|
||||
"- `filter_val_limit` limits the output of images with a similarity value not bigger than `filter_val_limit`. That is, if the parameter `filter_val_limit = 0.2`, all images with similarity less than 0.2 will be discarded.\n",
|
||||
"- `filter_rel_error` (percentage) limits the output of images with a similarity value not bigger than `100 * abs(current_simularity_value - best_simularity_value_in_current_search)/best_simularity_value_in_current_search < filter_rel_error`. That is, if we set filter_rel_error = 30, it means that if the top1 image have 0.5 similarity value, we discard all image with similarity less than 0.35."
|
||||
"- `filter_rel_error` (percentage) limits the output of images with a similarity value not bigger than `100 * abs(current_similarity_value - best_similarity_value_in_current_search)/best_similarity_value_in_current_search < filter_rel_error`. That is, if we set filter_rel_error = 30, it means that if the top1 image have 0.5 similarity value, we discard all image with similarity less than 0.35."
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -1174,7 +1252,7 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Then using the same output function you can add the `itm=True` argument to output the new image order. Remember that for images querys, an error will be thrown with `itm=True` argument. You can also add the `image_gradcam_with_itm` along with `itm=True` argument to output the heat maps of the calculated images."
|
||||
"Then using the same output function you can add the `itm=True` argument to output the new image order. Remember that for images queries, an error will be thrown with `itm=True` argument. You can also add the `image_gradcam_with_itm` along with `itm=True` argument to output the heat maps of the calculated images."
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -1199,7 +1277,7 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Convert the dictionary of dictionarys into a dictionary with lists:"
|
||||
"Convert the dictionary of dictionaries into a dictionary with lists:"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -1367,7 +1445,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.5"
|
||||
"version": "3.11.9"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
||||
Загрузка…
x
Ссылка в новой задаче
Block a user