locsec/AMMICO
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Код Задачи Пакеты Проекты Релизы Вики Активность

update notebooks for documentation

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Этот коммит содержится в:
Inga Ulusoy 2023-05-25 11:48:06 +02:00
родитель dac45272b5
Коммит 8041a9c3bb
8 изменённых файлов: 712 добавлений и 215 удалений
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8
ammico/text.py
Просмотреть файл

@ -1,4 +1,5 @@
from google.cloud import vision
from google.auth.exceptions import DefaultCredentialsError
from googletrans import Translator
import spacy
from spacytextblob.spacytextblob import SpacyTextBlob
@ -60,7 +61,12 @@ class TextDetector(utils.AnalysisMethod):
def get_text_from_image(self):
"""Detects text on the image."""
path = self.subdict["filename"]
client = vision.ImageAnnotatorClient()
try:
client = vision.ImageAnnotatorClient()
except DefaultCredentialsError:
raise DefaultCredentialsError(
"Please provide credentials for google cloud vision API, see https://cloud.google.com/docs/authentication/application-default-credentials."
)
with io.open(path, "rb") as image_file:
content = image_file.read()
image = vision.Image(content=content)

2
ammico/utils.py
Просмотреть файл

@ -45,7 +45,7 @@ class AnalysisMethod:
def find_files(path=None, pattern="*.png", recursive=True, limit=20):
"""Find image files on the file system
"""Find image files on the file system.
:param path:
The base directory where we are looking for the images. Defaults

106
docs/source/notebooks/Example faces.ipynb
Просмотреть файл

@ -1,6 +1,7 @@
{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"id": "d2c4d40d-8aca-4024-8d19-a65c4efe825d",
"metadata": {},
@ -14,16 +15,41 @@
"id": "51f8888b-d1a3-4b85-a596-95c0993fa192",
"metadata": {},
"source": [
"This notebooks shows some preliminary work on detecting facial expressions with DeepFace. It is mainly meant to explore its capabilities and to decide on future research directions. We package our code into a `ammico` package that is imported here:"
"Facial expressions can be detected using [DeepFace](https://github.com/serengil/deepface) and [RetinaFace](https://github.com/serengil/retinaface).\n",
"\n",
"The first cell is only run on google colab and installs the [ammico](https://github.com/ssciwr/AMMICO) package.\n",
"\n",
"After that, we can import `ammico` and read in the files given a folder path."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "50c1c1c7",
"metadata": {},
"outputs": [],
"source": [
"# if running on google colab\n",
"# flake8-noqa-cell\n",
"import os\n",
"\n",
"if \"google.colab\" in str(get_ipython()):\n",
" # update python version\n",
" # install setuptools\n",
" # %pip install setuptools==61 -qqq\n",
" # install ammico\n",
" %pip install git+https://github.com/ssciwr/ammico.git -qqq\n",
" # mount google drive for data and API key\n",
" from google.colab import drive\n",
"\n",
" drive.mount(\"/content/drive\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b21e52a5-d379-42db-aae6-f2ab9ed9a369",
"metadata": {
"tags": []
},
"metadata": {},
"outputs": [],
"source": [
"import ammico\n",
@ -32,22 +58,23 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "a2bd2153",
"metadata": {},
"source": [
"We select a subset of image files to try facial expression detection on. The `find_files` function finds image files within a given directory:"
"We select a subset of image files to try facial expression detection on, see the `limit` keyword. The `find_files` function finds image files within a given directory:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "afe7e638-f09d-47e7-9295-1c374bd64c53",
"metadata": {
"tags": []
},
"metadata": {},
"outputs": [],
"source": [
"# Here you need to provide the path to your google drive folder\n",
"# or local folder containing the images\n",
"images = mutils.find_files(\n",
" path=\"data/\",\n",
" limit=10,\n",
@ -55,24 +82,7 @@
]
},
{
"cell_type": "markdown",
"id": "e149bfe5-90b0-49b2-af3d-688e41aab019",
"metadata": {},
"source": [
"If you want to fine tune the discovery of image files, you can provide more parameters:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f38bb8ed-1004-4e33-8ed6-793cb5869400",
"metadata": {},
"outputs": [],
"source": [
"?mutils.find_files"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "705e7328",
"metadata": {},
@ -84,50 +94,47 @@
"cell_type": "code",
"execution_count": null,
"id": "b37c0c91",
"metadata": {
"tags": []
},
"metadata": {},
"outputs": [],
"source": [
"mydict = mutils.initialize_dict(images)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "a9372561",
"metadata": {},
"source": [
"To check the analysis, you can inspect the analyzed elements here. Loading the results takes a moment, so please be patient. If you are sure of what you are doing, you can skip this and directly export a csv file in the step below.\n",
"Here, we display the face recognition results provided by the DeepFace library. Click on the tabs to see the results in the right sidebar:"
"Here, we display the face recognition results provided by the DeepFace and RetinaFace libraries. Click on the tabs to see the results in the right sidebar. You may need to increment the `port` number if you are already running several notebook instances on the same server."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "992499ed-33f1-4425-ad5d-738cf565d175",
"metadata": {
"tags": []
},
"metadata": {},
"outputs": [],
"source": [
"mdisplay.explore_analysis(mydict, identify=\"faces\")"
"analysis_explorer = mdisplay.AnalysisExplorer(mydict, identify=\"faces\")\n",
"analysis_explorer.run_server(port = 8050)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "6f974341",
"metadata": {},
"source": [
"Directly carry out the analysis and export the result into a csv: Analysis - "
"Instead of inspecting each of the images, you can also directly carry out the analysis and export the result into a csv. This may take a while depending on how many images you have loaded."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6f97c7d0",
"metadata": {
"tags": []
},
"metadata": {},
"outputs": [],
"source": [
"for key in mydict.keys():\n",
@ -135,20 +142,19 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "174357b1",
"metadata": {},
"source": [
"Convert the dictionary of dictionarys into a dictionary with lists:"
"These steps are required to convert the dictionary of dictionarys into a dictionary with lists, that can be converted into a pandas dataframe and exported to a csv file."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "604bd257",
"metadata": {
"tags": []
},
"metadata": {},
"outputs": [],
"source": [
"outdict = mutils.append_data_to_dict(mydict)\n",
@ -156,6 +162,7 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "8373d9f8",
"metadata": {},
@ -167,32 +174,29 @@
"cell_type": "code",
"execution_count": null,
"id": "aa4b518a",
"metadata": {
"tags": []
},
"metadata": {},
"outputs": [],
"source": [
"df.head(10)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "579cd59f",
"metadata": {},
"source": [
"Write the csv file:"
"Write the csv file - here you should provide a file path and file name for the csv file to be written."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4618decb",
"metadata": {
"tags": []
},
"metadata": {},
"outputs": [],
"source": [
"df.to_csv(\"data/data_out.csv\")"
"df.to_csv(\"data_out.csv\")"
]
},
{
@ -206,7 +210,7 @@
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},

270
docs/source/notebooks/Example multimodal.ipynb
Просмотреть файл

@ -1,6 +1,7 @@
{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"id": "22df2297-0629-45aa-b88c-6c61f1544db6",
"metadata": {},
@ -14,7 +15,34 @@
"id": "9eeeb302-296e-48dc-86c7-254aa02f2b3a",
"metadata": {},
"source": [
"This notebooks shows some preliminary work on Image Multimodal Search with lavis library. It is mainly meant to explore its capabilities and to decide on future research directions. We package our code into a `ammico` package that is imported here:"
"This notebooks shows how to carry out an image multimodal search with the [LAVIS](https://github.com/salesforce/LAVIS) library. \n",
"\n",
"The first cell is only run on google colab and installs the [ammico](https://github.com/ssciwr/AMMICO) package.\n",
"\n",
"After that, we can import `ammico` and read in the files given a folder path."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0b0a6bdf",
"metadata": {},
"outputs": [],
"source": [
"# if running on google colab\n",
"# flake8-noqa-cell\n",
"import os\n",
"\n",
"if \"google.colab\" in str(get_ipython()):\n",
" # update python version\n",
" # install setuptools\n",
" # %pip install setuptools==61 -qqq\n",
" # install ammico\n",
" %pip install git+https://github.com/ssciwr/ammico.git -qqq\n",
" # mount google drive for data and API key\n",
" from google.colab import drive\n",
"\n",
" drive.mount(\"/content/drive\")"
]
},
{
@ -26,18 +54,10 @@
},
"outputs": [],
"source": [
"import ammico\n",
"import ammico.utils as mutils\n",
"import ammico.multimodal_search as ms"
]
},
{
"cell_type": "markdown",
"id": "acf08b44-3ea6-44cd-926d-15c0fd9f39e0",
"metadata": {},
"source": [
"Set an image path as input file path."
]
},
{
"cell_type": "code",
"execution_count": null,
@ -47,12 +67,22 @@
},
"outputs": [],
"source": [
"images = ammico.utils.find_files(\n",
"images = mutils.find_files(\n",
" path=\"data/\",\n",
" limit=10,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a08bd3a9-e954-4a0e-ad64-6817abd3a25a",
"metadata": {},
"outputs": [],
"source": [
"images"
]
},
{
"cell_type": "code",
"execution_count": null,
@ -62,10 +92,21 @@
},
"outputs": [],
"source": [
"mydict = ammico.utils.initialize_dict(images)"
"mydict = mutils.initialize_dict(images)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4c091f95-07cf-42c3-82c8-5f3a3c5929f8",
"metadata": {},
"outputs": [],
"source": [
"mydict"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "987540a8-d800-4c70-a76b-7bfabaf123fa",
"metadata": {},
@ -74,11 +115,18 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "66d6ede4-00bc-4aeb-9a36-e52d7de33fe5",
"metadata": {},
"source": [
"You can choose one of the following models: blip, blip2, albef, clip_base, clip_vitl14, clip_vitl14_336"
"First you need to select a model. You can choose one of the following models: \n",
"- [blip](https://github.com/salesforce/BLIP)\n",
"- [blip2](https://huggingface.co/docs/transformers/main/model_doc/blip-2) \n",
"- [albef](https://github.com/salesforce/ALBEF) \n",
"- [clip_base](https://github.com/openai/CLIP/blob/main/model-card.md)\n",
"- [clip_vitl14](https://github.com/mlfoundations/open_clip) \n",
"- [clip_vitl14_336](https://github.com/mlfoundations/open_clip)"
]
},
{
@ -98,6 +146,35 @@
"# model_type = \"clip_vitl14_336\""
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "357828c9",
"metadata": {},
"source": [
"To process the loaded images using the selected model, use the below code:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f6f2c9b1-4a91-47cb-86b5-2c9c67e4837b",
"metadata": {},
"outputs": [],
"source": [
"my_obj = ms.MultimodalSearch(mydict)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "16603ded-078e-4362-847b-57ad76829327",
"metadata": {},
"outputs": [],
"source": [
"my_obj.subdict"
]
},
{
"cell_type": "code",
"execution_count": null,
@ -114,15 +191,31 @@
" image_keys,\n",
" image_names,\n",
" features_image_stacked,\n",
") = ms.MultimodalSearch.parsing_images(mydict, model_type, path_to_saved_tensors=\".\")"
") = my_obj.parsing_images(\n",
" model_type, \n",
" path_to_save_tensors=\"data/\",\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f236c3b1-c3a6-471a-9fc5-ef831b675286",
"metadata": {},
"outputs": [],
"source": [
"features_image_stacked"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "9ff8a894-566b-4c4f-acca-21c50b5b1f52",
"metadata": {},
"source": [
"The tensors of all images `features_image_stacked` was saved in `<Number_of_images>_<model_name>_saved_features_image.pt`. If you run it once for current model and current set of images you do not need to repeat it again. Instead you can load this features with the command:"
"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 will save you a lot of 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."
]
},
{
@ -135,28 +228,35 @@
"outputs": [],
"source": [
"# (\n",
"# model,\n",
"# vis_processors,\n",
"# txt_processors,\n",
"# image_keys,\n",
"# image_names,\n",
"# features_image_stacked,\n",
"# ) = ms.MultimodalSearch.parsing_images(mydict, model_type,\"18_clip_base_saved_features_image.pt\")"
"# model,\n",
"# vis_processors,\n",
"# txt_processors,\n",
"# image_keys,\n",
"# image_names,\n",
"# features_image_stacked,\n",
"# ) = my_obj.parsing_images(\n",
"# model_type,\n",
"# path_to_load_tensors=\"/content/drive/MyDrive/misinformation-data/5_clip_base_saved_features_image.pt\",\n",
"# )"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "309923c1-d6f8-4424-8fca-bde5f3a98b38",
"metadata": {},
"source": [
"Here we already processed our image folder with 18 images with `clip_base` model. So you need just write the name `18_clip_base_saved_features_image.pt` of the saved file that consists of tensors of all images as a 3rd argument to the previous function. "
"Here we already processed our image folder with 5 images and the `clip_base` model. So you need just to write the name `5_clip_base_saved_features_image.pt` of the saved file that consists of tensors of all images as keyword argument for `path_to_load_tensors`. "
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "162a52e8-6652-4897-b92e-645cab07aaef",
"metadata": {},
"source": [
"## Formulate your search queries\n",
"\n",
"Next, you need to form search queries. You can search either by image or by text. You can search for a single query, or you can search for several queries at once, the computational time should not be much different. The format of the queries is as follows:"
]
},
@ -171,12 +271,13 @@
"source": [
"search_query3 = [\n",
" {\"text_input\": \"politician press conference\"},\n",
" {\"text_input\": \"a person wearing a mask\"},\n",
" {\"image\": \"data/106349S_por.png\"},\n",
" {\"text_input\": \"a world map\"},\n",
" {\"text_input\": \"a dog\"},\n",
"]"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "8bcf3127-3dfd-4ff4-b9e7-a043099b1418",
"metadata": {},
@ -196,8 +297,7 @@
},
"outputs": [],
"source": [
"similarity = ms.MultimodalSearch.multimodal_search(\n",
" mydict,\n",
"similarity, sorted_lists = my_obj.multimodal_search(\n",
" model,\n",
" vis_processors,\n",
" txt_processors,\n",
@ -205,10 +305,42 @@
" image_keys,\n",
" features_image_stacked,\n",
" search_query3,\n",
" filter_number_of_images=20,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "65210ca2-b674-44bd-807a-4165e14bad74",
"metadata": {},
"outputs": [],
"source": [
"similarity"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "557473df-e2b9-4ef0-9439-3daadf6741ac",
"metadata": {},
"outputs": [],
"source": [
"sorted_lists"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c93d7e88-594d-4095-b5f2-7bf01210dc61",
"metadata": {},
"outputs": [],
"source": [
"mydict"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "e1cf7e46-0c2c-4fb2-b89a-ef585ccb9339",
"metadata": {},
@ -229,6 +361,7 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "cd3ee120-8561-482b-a76a-e8f996783325",
"metadata": {},
@ -245,10 +378,80 @@
},
"outputs": [],
"source": [
"ms.MultimodalSearch.show_results(mydict, search_query3[0])"
"my_obj.show_results(\n",
" search_query3[0],\n",
")"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "0b750e9f-fe64-4028-9caf-52d7187462f1",
"metadata": {},
"source": [
"## Improve the search results\n",
"\n",
"For even better results, a slightly different approach has been prepared that can improve search results. It is quite resource-intensive, so it is applied after the main algorithm has found the most relevant images. This approach works only with text queries. Among the parameters you can choose 3 models: `\"blip_base\"`, `\"blip_large\"`, `\"blip2_coco\"`. If you get an `Out of Memory` error, try reducing the batch_size value (minimum = 1), which is the number of images being processed simultaneously. With the parameter `need_grad_cam = True/False` you can enable the calculation of the heat map of each image to be processed. Thus the `image_text_match_reordering` function calculates new similarity values and new ranks for each image. The resulting values are added to the general dictionary."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b3af7b39-6d0d-4da3-9b8f-7dfd3f5779be",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"itm_model = \"blip_base\"\n",
"# itm_model = \"blip_large\"\n",
"# itm_model = \"blip2_coco\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "caf1f4ae-4b37-4954-800e-7120f0419de5",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"itm_scores, image_gradcam_with_itm = my_obj.image_text_match_reordering(\n",
" search_query3,\n",
" itm_model,\n",
" image_keys,\n",
" sorted_lists,\n",
" batch_size=1,\n",
" need_grad_cam=True,\n",
")"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "9e98c150-5fab-4251-bce7-0d8fc7b385b9",
"metadata": {},
"source": [
"Then using the same output function you can add the `ITM=True` arguments to output the new image order. You can also add the `image_gradcam_with_itm` argument to output the heat maps of the calculated images. "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6a829b99-5230-463a-8b11-30ffbb67fc3a",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"my_obj.show_results(\n",
" search_query3[0], itm=True, image_gradcam_with_itm=image_gradcam_with_itm\n",
")"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "d86ab96b-1907-4b7f-a78e-3983b516d781",
"metadata": {
@ -259,6 +462,7 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "4bdbc4d4-695d-4751-ab7c-d2d98e2917d7",
"metadata": {
@ -277,11 +481,12 @@
},
"outputs": [],
"source": [
"outdict = ammico.utils.append_data_to_dict(mydict)\n",
"df = ammico.utils.dump_df(outdict)"
"outdict = mutils.append_data_to_dict(mydict)\n",
"df = mutils.dump_df(outdict)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "ea2675d5-604c-45e7-86d2-080b1f4559a0",
"metadata": {
@ -304,6 +509,7 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "05546d99-afab-4565-8f30-f14e1426abcf",
"metadata": {},
@ -320,13 +526,13 @@
},
"outputs": [],
"source": [
"df.to_csv(\"./data_out.csv\")"
"df.to_csv(\"data/data_out.csv\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2ef1132f-eb2a-43d7-be1f-69e879490f33",
"id": "b6a79201-7c17-496c-a6a1-b8ecfd3dd1e8",
"metadata": {},
"outputs": [],
"source": []

112
docs/source/notebooks/Example objects.ipynb
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@ -1,10 +1,11 @@
{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# Objects Expression recognition"
"# Objects recognition"
]
},
{
@ -12,15 +13,39 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"This notebooks shows some preliminary work on detecting objects expressions with cvlib. It is mainly meant to explore its capabilities and to decide on future research directions. We package our code into a `ammico` package that is imported here:"
"This notebooks shows how to detect objects quickly using [cvlib](https://github.com/arunponnusamy/cvlib) and the [YOLOv4](https://github.com/AlexeyAB/darknet) model. This library detects faces, people, and several inanimate objects; we currently have restricted the output to person, bicycle, car, motorcycle, airplane, bus, train, truck, boat, traffic light, cell phone.\n",
"\n",
"The first cell is only run on google colab and installs the [ammico](https://github.com/ssciwr/AMMICO) package.\n",
"\n",
"After that, we can import `ammico` and read in the files given a folder path."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"metadata": {},
"outputs": [],
"source": [
"# if running on google colab\n",
"# flake8-noqa-cell\n",
"import os\n",
"\n",
"if \"google.colab\" in str(get_ipython()):\n",
" # update python version\n",
" # install setuptools\n",
" # %pip install setuptools==61 -qqq\n",
" # install ammico\n",
" %pip install git+https://github.com/ssciwr/ammico.git -qqq\n",
" # mount google drive for data and API key\n",
" from google.colab import drive\n",
"\n",
" drive.mount(\"/content/drive\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import ammico\n",
@ -30,6 +55,7 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@ -39,11 +65,11 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"metadata": {},
"outputs": [],
"source": [
"# Here you need to provide the path to your google drive folder\n",
"# or local folder containing the images\n",
"images = mutils.find_files(\n",
" path=\"data/\",\n",
" limit=10,\n",
@ -53,47 +79,25 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"metadata": {},
"outputs": [],
"source": [
"mydict = mutils.initialize_dict(images)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Manually inspect what was detected\n",
"\n",
"To check the analysis, you can inspect the analyzed elements here. Loading the results takes a moment, so please be patient. If you are sure of what you are doing."
"## Detect objects and directly write to csv\n",
"You can directly carry out the analysis and export the result into a csv. This may take a while depending on how many images you have loaded."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"mdisplay.explore_analysis(mydict, identify=\"objects\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Detect objects and directly write to csv"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"for key in mydict:\n",
@ -101,6 +105,7 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@ -110,9 +115,7 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"metadata": {},
"outputs": [],
"source": [
"outdict = mutils.append_data_to_dict(mydict)\n",
@ -120,6 +123,7 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@ -129,15 +133,14 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"metadata": {},
"outputs": [],
"source": [
"df.head(10)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@ -147,12 +150,31 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"metadata": {},
"outputs": [],
"source": [
"df.to_csv(\"./data_out.csv\")"
"df.to_csv(\"data_out.csv\")"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Manually inspect what was detected\n",
"\n",
"To check the analysis, you can inspect the analyzed elements here. Loading the results takes a moment, so please be patient. If you are sure of what you are doing, you can directly export a csv file in the step above.\n",
"Here, we display the object detection results provided by the above library. Click on the tabs to see the results in the right sidebar. You may need to increment the `port` number if you are already running several notebook instances on the same server."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"analysis_explorer = mdisplay.AnalysisExplorer(mydict, identify=\"objects\")\n",
"analysis_explorer.run_server(port=8056)"
]
},
{
@ -165,7 +187,7 @@
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},

114
docs/source/notebooks/Example summary.ipynb
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@ -1,6 +1,7 @@
{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@ -12,7 +13,33 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"This notebooks shows some preliminary work on Image Captioning and Visual question answering with lavis. It is mainly meant to explore its capabilities and to decide on future research directions. We package our code into a `ammico` package that is imported here:"
"This notebooks shows how to generate image captions and use the visual question answering with [LAVIS](https://github.com/salesforce/LAVIS). \n",
"\n",
"The first cell is only run on google colab and installs the [ammico](https://github.com/ssciwr/AMMICO) package.\n",
"\n",
"After that, we can import `ammico` and read in the files given a folder path."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# if running on google colab\n",
"# flake8-noqa-cell\n",
"import os\n",
"\n",
"if \"google.colab\" in str(get_ipython()):\n",
" # update python version\n",
" # install setuptools\n",
" # %pip install setuptools==61 -qqq\n",
" # install ammico\n",
" %pip install git+https://github.com/ssciwr/ammico.git -qqq\n",
" # mount google drive for data and API key\n",
" from google.colab import drive\n",
"\n",
" drive.mount(\"/content/drive\")"
]
},
{
@ -23,18 +50,12 @@
},
"outputs": [],
"source": [
"import ammico\n",
"from ammico import utils as mutils\n",
"from ammico import display as mdisplay\n",
"import ammico.summary as sm"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Set an image path as input file path."
]
},
{
"cell_type": "code",
"execution_count": null,
@ -43,6 +64,8 @@
},
"outputs": [],
"source": [
"# Here you need to provide the path to your google drive folder\n",
"# or local folder containing the images\n",
"images = mutils.find_files(\n",
" path=\"data/\",\n",
" limit=10,\n",
@ -61,6 +84,7 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@ -68,10 +92,13 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Here you can choose between two models: \"base\" or \"large\""
"Here you can choose between two models: \"base\" or \"large\". This will generate the caption for each image and directly put the results in a dataframe. This dataframe can be exported as a csv file.\n",
"\n",
"The results are written into the columns `const_image_summary` - this will always be the same result (as always the same seed will be used). The column `3_non-deterministic summary` displays three different answers generated with different seeds, these are most likely different when you run the analysis again."
]
},
{
@ -83,8 +110,8 @@
"outputs": [],
"source": [
"obj = sm.SummaryDetector(mydict)\n",
"summary_model, summary_vis_processors = obj.load_model(\"base\")\n",
"# summary_model, summary_vis_processors = obj.load_model(\"large\")"
"summary_model, summary_vis_processors = obj.load_model(model_type=\"base\")\n",
"# summary_model, summary_vis_processors = mutils.load_model(\"large\")"
]
},
{
@ -97,17 +124,18 @@
"source": [
"for key in mydict:\n",
" mydict[key] = sm.SummaryDetector(mydict[key]).analyse_image(\n",
" summary_model, summary_vis_processors\n",
" summary_model=summary_model, summary_vis_processors=summary_vis_processors\n",
" )"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"tags": []
},
"source": [
"Convert the dictionary of dictionaries into a dictionary with lists:"
"Convert the dictionary of dictionarys into a dictionary with lists:"
]
},
{
@ -123,6 +151,7 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@ -141,6 +170,7 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@ -150,15 +180,14 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"metadata": {},
"outputs": [],
"source": [
"df.to_csv(\"./data_out.csv\")"
"df.to_csv(\"data_out.csv\")"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@ -179,10 +208,12 @@
},
"outputs": [],
"source": [
"mdisplay.explore_analysis(mydict, identify=\"summary\")"
"analysis_explorer = mdisplay.AnalysisExplorer(mydict, identify=\"summary\")\n",
"analysis_explorer.run_server(port=8055)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@ -190,10 +221,11 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Set the list of questions"
"Set the list of questions as a list of strings:"
]
},
{
@ -209,6 +241,33 @@
"]"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Explore the analysis using the interface:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"analysis_explorer = mdisplay.AnalysisExplorer(mydict, identify=\"summary\")\n",
"analysis_explorer.run_server(port=8055)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Or directly analyze for further processing\n",
"Instead of inspecting each of the images, you can also directly carry out the analysis and export the result into a csv. This may take a while depending on how many images you have loaded."
]
},
{
"cell_type": "code",
"execution_count": null,
@ -220,19 +279,12 @@
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"mdisplay.explore_analysis(mydict, identify=\"summary\")"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Convert the dictionary of dictionarys into a dictionary with lists:"
"## Convert to dataframe and write csv\n",
"These steps are required to convert the dictionary of dictionarys into a dictionary with lists, that can be converted into a pandas dataframe and exported to a csv file."
]
},
{
@ -260,7 +312,7 @@
"metadata": {},
"outputs": [],
"source": [
"df2.to_csv(\"./data_out2.csv\")"
"df2.to_csv(\"data_out2.csv\")"
]
},
{
@ -273,7 +325,7 @@
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},

294
docs/source/notebooks/Example text.ipynb
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@ -1,21 +1,36 @@
{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"id": "dcaa3da1",
"metadata": {},
"source": [
"# Text extraction on image\n",
"Inga Ulusoy, SSC, July 2022"
"# Notebook for text extraction on image\n",
"\n",
"The text extraction and analysis is carried out using a variety of tools: \n",
"\n",
"1. Text extraction from the image using [google-cloud-vision](https://cloud.google.com/vision) \n",
"1. Language detection of the extracted text using [Googletrans](https://py-googletrans.readthedocs.io/en/latest/) \n",
"1. Translation into English or other languages using [Googletrans](https://py-googletrans.readthedocs.io/en/latest/) \n",
"1. Cleaning of the text using [spacy](https://spacy.io/) \n",
"1. Spell-check using [TextBlob](https://textblob.readthedocs.io/en/dev/index.html) \n",
"1. Subjectivity analysis using [TextBlob](https://textblob.readthedocs.io/en/dev/index.html) \n",
"1. Text summarization using [transformers](https://huggingface.co/docs/transformers/index) pipelines\n",
"1. Sentiment analysis using [transformers](https://huggingface.co/docs/transformers/index) pipelines \n",
"1. Named entity recognition using [transformers](https://huggingface.co/docs/transformers/index) pipelines \n",
"1. Topic analysis using [BERTopic](https://github.com/MaartenGr/BERTopic) \n",
"\n",
"The first cell is only run on google colab and installs the [ammico](https://github.com/ssciwr/AMMICO) package.\n",
"\n",
"After that, we can import `ammico` and read in the files given a folder path."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f43f327c",
"metadata": {
"tags": []
},
"metadata": {},
"outputs": [],
"source": [
"# if running on google colab\n",
@ -25,9 +40,9 @@
"if \"google.colab\" in str(get_ipython()):\n",
" # update python version\n",
" # install setuptools\n",
" !pip install setuptools==61 -qqq\n",
" # %pip install setuptools==61 -qqq\n",
" # install ammico\n",
" !pip install git+https://github.com/ssciwr/ammico.git -qqq\n",
" %pip install git+https://github.com/ssciwr/ammico.git -qqq\n",
" # mount google drive for data and API key\n",
" from google.colab import drive\n",
"\n",
@ -38,49 +53,53 @@
"cell_type": "code",
"execution_count": null,
"id": "cf362e60",
"metadata": {
"tags": []
},
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import ammico\n",
"from ammico import utils as mutils\n",
"from ammico import display as mdisplay"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "27675810",
"metadata": {
"tags": []
},
"outputs": [],
"attachments": {},
"cell_type": "markdown",
"id": "fddba721",
"metadata": {},
"source": [
"# download the models if they are not there yet\n",
"!python -m spacy download en_core_web_md\n",
"!python -m textblob.download_corpora"
"We select a subset of image files to try the text extraction on, see the `limit` keyword. The `find_files` function finds image files within a given directory: "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6da3a7aa",
"metadata": {
"tags": []
},
"id": "27675810",
"metadata": {},
"outputs": [],
"source": [
"images = mutils.find_files(path=\"data\", limit=10)"
"# Here you need to provide the path to your google drive folder\n",
"# or local folder containing the images\n",
"images = mutils.find_files(\n",
" path=\"data/\",\n",
" limit=10,\n",
")"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "3a7dfe11",
"metadata": {},
"source": [
"We need to initialize the main dictionary that contains all information for the images and is updated through each subsequent analysis:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8b32409f",
"metadata": {
"tags": []
},
"metadata": {},
"outputs": [],
"source": [
"mydict = mutils.initialize_dict(images)"
@ -91,45 +110,61 @@
"id": "7b8b929f",
"metadata": {},
"source": [
"## google cloud vision API\n",
"First 1000 images per month are free."
"## Google cloud vision API\n",
"\n",
"For this you need an API key and have the app activated in your google console. The first 1000 images per month are free (July 2022)."
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "cbf74c0b-52fe-4fb8-b617-f18611e8f986",
"metadata": {},
"source": [
"```\n",
"os.environ[\n",
" \"GOOGLE_APPLICATION_CREDENTIALS\"\n",
"] = \"your-credentials.json\"\n",
"```"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "0891b795-c7fe-454c-a45d-45fadf788142",
"metadata": {},
"source": [
"## Inspect the elements per image"
"## Inspect the elements per image\n",
"To check the analysis, you can inspect the analyzed elements here. Loading the results takes a moment, so please be patient. If you are sure of what you are doing, you can skip this and directly export a csv file in the step below.\n",
"Here, we display the text extraction and translation results provided by the above libraries. Click on the tabs to see the results in the right sidebar. You may need to increment the `port` number if you are already running several notebook instances on the same server."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7c6ecc88",
"metadata": {
"tags": []
},
"metadata": {},
"outputs": [],
"source": [
"mdisplay.explore_analysis(mydict, identify=\"text-on-image\")"
"analysis_explorer = mdisplay.AnalysisExplorer(mydict, identify=\"text-on-image\")\n",
"analysis_explorer.run_server(port=8054)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "9c3e72b5-0e57-4019-b45e-3e36a74e7f52",
"metadata": {},
"source": [
"## Or directly analyze for further processing"
"## Or directly analyze for further processing\n",
"Instead of inspecting each of the images, you can also directly carry out the analysis and export the result into a csv. This may take a while depending on how many images you have loaded. Set the keyword `analyse_text` to `True` if you want the text to be analyzed (spell check, subjectivity, text summary, sentiment, NER)."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "365c78b1-7ff4-4213-86fa-6a0a2d05198f",
"metadata": {
"tags": []
},
"metadata": {},
"outputs": [],
"source": [
"for key in mydict:\n",
@ -139,56 +174,211 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "3c063eda",
"metadata": {},
"source": [
"## Convert to dataframe and write csv"
"## Convert to dataframe and write csv\n",
"These steps are required to convert the dictionary of dictionarys into a dictionary with lists, that can be converted into a pandas dataframe and exported to a csv file."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5709c2cd",
"metadata": {
"tags": []
},
"metadata": {},
"outputs": [],
"source": [
"outdict = mutils.append_data_to_dict(mydict)\n",
"df = mutils.dump_df(outdict)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "ae182eb7",
"metadata": {},
"source": [
"Check the dataframe:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c4f05637",
"metadata": {
"tags": []
},
"metadata": {},
"outputs": [],
"source": [
"# check the dataframe\n",
"df.head(10)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "eedf1e47",
"metadata": {},
"source": [
"Write the csv file - here you should provide a file path and file name for the csv file to be written."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bf6c9ddb",
"metadata": {
"tags": []
},
"metadata": {},
"outputs": [],
"source": [
"# Write the csv\n",
"df.to_csv(\"./data_out.csv\")"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "4bc8ac0a",
"metadata": {},
"source": [
"## Topic analysis\n",
"The topic analysis is carried out using [BERTopic](https://maartengr.github.io/BERTopic/index.html) using an embedded model through a [spaCy](https://spacy.io/) pipeline."
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "4931941b",
"metadata": {},
"source": [
"BERTopic takes a list of strings as input. The more items in the list, the better for the topic modeling. If the below returns an error for `analyse_topic()`, the reason can be that your dataset is too small.\n",
"\n",
"You can pass which dataframe entry you would like to have analyzed. The default is `text_english`, but you could for example also select `text_summary` or `text_english_correct` setting the keyword `analyze_text` as so:\n",
"\n",
"`ammico.text.PostprocessText(mydict=mydict, analyze_text=\"text_summary\").analyse_topic()`\n",
"\n",
"### Option 1: Use the dictionary as obtained from the above analysis."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9012544e-f818-46ea-b087-3e150850a5d5",
"id": "a3450a61",
"metadata": {},
"outputs": [],
"source": [
"# make a list of all the text_english entries per analysed image from the mydict variable as above\n",
"topic_model, topic_df, most_frequent_topics = ammico.text.PostprocessText(\n",
" mydict=mydict\n",
").analyse_topic()"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "95667342",
"metadata": {},
"source": [
"### Option 2: Read in a csv\n",
"Not to analyse too many images on google Cloud Vision, use the csv output to obtain the text (when rerunning already analysed images)."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5530e436",
"metadata": {},
"outputs": [],
"source": [
"input_file_path = \"data_out.csv\"\n",
"topic_model, topic_df, most_frequent_topics = ammico.text.PostprocessText(\n",
" use_csv=True, csv_path=input_file_path\n",
").analyse_topic(return_topics=10)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "0b6ef6d7",
"metadata": {},
"source": [
"### Access frequent topics\n",
"A topic of `-1` stands for an outlier and should be ignored. Topic count is the number of occurence of that topic. The output is structured from most frequent to least frequent topic."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "43288cda-61bb-4ff1-a209-dcfcc4916b1f",
"metadata": {},
"outputs": [],
"source": [
"print(topic_df)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "b3316770",
"metadata": {},
"source": [
"### Get information for specific topic\n",
"The most frequent topics can be accessed through `most_frequent_topics` with the most occuring topics first in the list."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "db14fe03",
"metadata": {},
"outputs": [],
"source": [
"for topic in most_frequent_topics:\n",
" print(\"Topic:\", topic)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "d10f701e",
"metadata": {},
"source": [
"### Topic visualization\n",
"The topics can also be visualized. Careful: This only works if there is sufficient data (quantity and quality)."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2331afe6",
"metadata": {},
"outputs": [],
"source": [
"topic_model.visualize_topics()"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "f4eaf353",
"metadata": {},
"source": [
"### Save the model\n",
"The model can be saved for future use."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e5e8377c",
"metadata": {},
"outputs": [],
"source": [
"topic_model.save(\"misinfo_posts\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7c94edb9",
"metadata": {},
"outputs": [],
"source": []
@ -196,7 +386,7 @@
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},

21
notebooks/multimodal_search.ipynb сгенерированный
Просмотреть файл

@ -1,6 +1,7 @@
{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"id": "22df2297-0629-45aa-b88c-6c61f1544db6",
"metadata": {},
@ -9,6 +10,7 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "9eeeb302-296e-48dc-86c7-254aa02f2b3a",
"metadata": {},
@ -104,6 +106,7 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "987540a8-d800-4c70-a76b-7bfabaf123fa",
"metadata": {},
@ -112,6 +115,7 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "66d6ede4-00bc-4aeb-9a36-e52d7de33fe5",
"metadata": {},
@ -143,6 +147,7 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "357828c9",
"metadata": {},
@ -203,6 +208,7 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "9ff8a894-566b-4c4f-acca-21c50b5b1f52",
"metadata": {},
@ -235,6 +241,7 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "309923c1-d6f8-4424-8fca-bde5f3a98b38",
"metadata": {},
@ -243,6 +250,7 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "162a52e8-6652-4897-b92e-645cab07aaef",
"metadata": {},
@ -269,6 +277,7 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "8bcf3127-3dfd-4ff4-b9e7-a043099b1418",
"metadata": {},
@ -331,6 +340,7 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "e1cf7e46-0c2c-4fb2-b89a-ef585ccb9339",
"metadata": {},
@ -351,6 +361,7 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "cd3ee120-8561-482b-a76a-e8f996783325",
"metadata": {},
@ -373,6 +384,7 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "0b750e9f-fe64-4028-9caf-52d7187462f1",
"metadata": {},
@ -416,6 +428,7 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "9e98c150-5fab-4251-bce7-0d8fc7b385b9",
"metadata": {},
@ -438,6 +451,7 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "d86ab96b-1907-4b7f-a78e-3983b516d781",
"metadata": {
@ -448,6 +462,7 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "4bdbc4d4-695d-4751-ab7c-d2d98e2917d7",
"metadata": {
@ -466,11 +481,12 @@
},
"outputs": [],
"source": [
"outdict = ammico.utils.append_data_to_dict(mydict)\n",
"df = ammico.utils.dump_df(outdict)"
"outdict = mutils.append_data_to_dict(mydict)\n",
"df = mutils.dump_df(outdict)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "ea2675d5-604c-45e7-86d2-080b1f4559a0",
"metadata": {
@ -493,6 +509,7 @@
]
},
{
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"metadata": {},