diff --git a/build/html/index.html b/build/html/index.html
index f8e7fff..9fa606f 100644
--- a/build/html/index.html
+++ b/build/html/index.html
@@ -127,12 +127,12 @@
- AMMICO package modules
- License
diff --git a/build/html/modules.html b/build/html/modules.html
index 60879c5..e16e986 100644
--- a/build/html/modules.html
+++ b/build/html/modules.html
@@ -56,12 +56,12 @@
- AMMICO package modules
- License
@@ -95,32 +95,7 @@
AMMICO package modules
diff --git a/build/html/notebooks/DemoNotebook_ammico.html b/build/html/notebooks/DemoNotebook_ammico.html
index 81a0fff..5ae93d8 100644
--- a/build/html/notebooks/DemoNotebook_ammico.html
+++ b/build/html/notebooks/DemoNotebook_ammico.html
@@ -525,7 +525,7 @@ directly on the right next to the image. This way, the user can directly inspect
The detector modules
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 TextDetector class (text 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.
-
+
The user can set if the text should be further summarized, and analyzed for sentiment and named entity recognition, by setting the keyword analyse_text to True (the default is False). 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 model_names to a list of selected models, on for each task:
model_names=["sshleifer/distilbart-cnn-12-6", "distilbert-base-uncased-finetuned-sst-2-english", "dbmdz/bert-large-cased-finetuned-conll03-english"] for summary, sentiment, and ner. To be even more specific, revision numbers can also be selected by specifying the optional keyword revision_numbers to a list of revision numbers for each model, for example revision_numbers=["a4f8f3e", "af0f99b", "f2482bf"].
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
@@ -617,7 +617,7 @@ directly on the right next to the image. This way, the user can directly inspect
Image summary and query
The SummaryDetector can be used to generate image captions (summary) as well as visual question answering (VQA).
-
+
This module is based on the LAVIS 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 analysis_type keyword. Setting it to summary will generate a caption (summary), questions will prepare answers (VQA) to a list of questions as set by the user,
summary_and_questions 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.
The implemented models are listed below.
@@ -880,7 +880,7 @@ directly on the right next to the image. This way, the user can directly inspect
Detection of faces and facial expression analysis
Faces and facial expressions are detected and analyzed using the EmotionDetector class from the faces 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).
-
+
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 "face": "No", "multiple_faces": "No", "no_faces": 0, "wears_mask": ["No"], "age": [None], "gender": [None], "race": [None], "emotion": [None], "emotion (category)": [None] 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: "face": "Yes", "multiple_faces": "Yes", "no_faces": 2, "wears_mask": ["No", "No"], "age": [27, 28], "gender": ["Man", "Man"], "race": ["asian", None], "emotion": ["angry", "neutral"], "emotion (category)": ["Negative", "Neutral"], where for the two faces that are detected (given by no_faces), 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, "emotion" returns a list ["angry", "neutral"] signifying the first face expressing anger, and the second face having a neutral expression).
diff --git a/build/html/objects.inv b/build/html/objects.inv
index 6e7356e..5a26880 100644
Binary files a/build/html/objects.inv and b/build/html/objects.inv differ
diff --git a/build/html/py-modindex.html b/build/html/py-modindex.html
index 5b888bf..83e0721 100644
--- a/build/html/py-modindex.html
+++ b/build/html/py-modindex.html
@@ -89,12 +89,8 @@
diff --git a/build/html/searchindex.js b/build/html/searchindex.js
index 373a151..bb9135c 100644
--- a/build/html/searchindex.js
+++ b/build/html/searchindex.js
@@ -1 +1 @@
-Search.setIndex({"alltitles": {"1. First, install tensorflow (https://www.tensorflow.org/install/pip)": [[2, "first-install-tensorflow-https-www-tensorflow-org-install-pip"]], "2. Second, install pytorch": [[2, "second-install-pytorch"]], "3. After we prepared right environment we can install the ammico package": [[2, "after-we-prepared-right-environment-we-can-install-the-ammico-package"]], "AMMICO - AI-based Media and Misinformation Content Analysis Tool": [[8, null]], "AMMICO Demonstration Notebook": [[6, null]], "AMMICO package modules": [[5, null]], "BLIP2 models": [[6, "BLIP2-models"]], "Color analysis of pictures": [[6, "Color-analysis-of-pictures"]], "Color/hue detection": [[8, "color-hue-detection"]], "Compatibility problems solving": [[2, "compatibility-problems-solving"]], "Content extraction": [[8, "content-extraction"]], "Contents:": [[3, null]], "Crop posts module": [[7, null]], "Cropping of posts": [[8, "cropping-of-posts"]], "Detection of faces and facial expression analysis": [[6, "Detection-of-faces-and-facial-expression-analysis"]], "Emotion recognition": [[8, "emotion-recognition"]], "Ethical disclosure statement": [[6, "Ethical-disclosure-statement"]], "FAQ": [[2, null]], "Features": [[8, "features"]], "Formulate your search queries": [[6, "Formulate-your-search-queries"]], "Further detector modules": [[6, "Further-detector-modules"]], "Image Multimodal Search": [[6, "Image-Multimodal-Search"]], "Image summary and query": [[6, "Image-summary-and-query"]], "Import the ammico package": [[6, "Import-the-ammico-package"]], "Improve the search results": [[6, "Improve-the-search-results"]], "Indexing and extracting features from images in selected folder": [[6, "Indexing-and-extracting-features-from-images-in-selected-folder"]], "Indices and tables": [[3, "indices-and-tables"]], "Installation": [[8, "installation"]], "Instructions how to generate and enable a google Cloud Vision API key": [[1, null], [9, null]], "License": [[4, null]], "Micromamba": [[2, "micromamba"]], "Privacy disclosure statement": [[6, "Privacy-disclosure-statement"]], "Read in a csv file containing text and translating/analysing the text": [[6, "Read-in-a-csv-file-containing-text-and-translating/analysing-the-text"]], "Save search results to csv": [[6, "Save-search-results-to-csv"]], "Step 0: Create and set a Google Cloud Vision Key": [[6, "Step-0:-Create-and-set-a-Google-Cloud-Vision-Key"]], "Step 1: Read your data into AMMICO": [[6, "Step-1:-Read-your-data-into-AMMICO"]], "Step 2: Inspect the input files using the graphical user interface": [[6, "Step-2:-Inspect-the-input-files-using-the-graphical-user-interface"]], "Step 3: Analyze all images": [[6, "Step-3:-Analyze-all-images"]], "Step 4: Convert analysis output to pandas dataframe and write csv": [[6, "Step-4:-Convert-analysis-output-to-pandas-dataframe-and-write-csv"]], "Text extraction": [[8, "text-extraction"]], "The detector modules": [[6, "The-detector-modules"]], "Usage": [[8, "usage"]], "Use a test dataset": [[6, "Use-a-test-dataset"]], "Welcome to AMMICO\u2019s documentation!": [[3, null]], "What happens if I don\u2019t have internet access - can I still use ammico?": [[2, 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