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# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
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const DOCUMENTATION_OPTIONS = {
|
||||
VERSION: '0.0.1',
|
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VERSION: '0.2.2',
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LANGUAGE: 'en',
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COLLAPSE_INDEX: false,
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BUILDER: 'html',
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<meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
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<meta name="viewport" content="width=device-width, initial-scale=1.0" />
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<title>text module — AMMICO 0.0.1 documentation</title>
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<title>text module — AMMICO 0.2.2 documentation</title>
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||||
@ -228,7 +228,7 @@
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||||
<li class="breadcrumb-item"><a href="modules.html">AMMICO package modules</a></li>
|
||||
<li class="breadcrumb-item active">text module</li>
|
||||
<li class="wy-breadcrumbs-aside">
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||||
<a href="https://github.com/ssciwr/AMMICO/blob/main/source/ammico.rst" class="fa fa-github"> Edit on GitHub</a>
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<a href="https://github.com/ssciwr/AMMICO/blob/main/docs/source/ammico.rst" class="fa fa-github"> Edit on GitHub</a>
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@ -6,14 +6,14 @@
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<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>
|
||||
<title>Instructions how to generate and enable a google Cloud Vision API key — AMMICO 0.2.2 documentation</title>
|
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<link rel="stylesheet" type="text/css" href="_static/pygments.css?v=80d5e7a1" />
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@ -73,7 +73,7 @@
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||||
<li><a href="index.html" class="icon icon-home" aria-label="Home"></a></li>
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<li class="breadcrumb-item active">Instructions how to generate and enable a google Cloud Vision API key</li>
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<a href="https://github.com/ssciwr/AMMICO/blob/main/source/create_API_key_link.md" class="fa fa-github"> Edit on GitHub</a>
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<meta name="viewport" content="width=device-width, initial-scale=1.0" />
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<title>FAQ — AMMICO 0.0.1 documentation</title>
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<title>FAQ — AMMICO 0.2.2 documentation</title>
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@ -87,7 +87,7 @@
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<li><a href="index.html" class="icon icon-home" aria-label="Home"></a></li>
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<li class="breadcrumb-item active">FAQ</li>
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||||
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||||
<hr/>
|
||||
@ -200,9 +200,9 @@ Google also temporarily logs some metadata about your Vision API requests (such
|
||||
<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>“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.</p>
|
||||
<p>ETHICAL DISCLOSURE STATEMENT:</p>
|
||||
<p>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>
|
||||
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||||
@ -5,14 +5,14 @@
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||||
<head>
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||||
<meta charset="utf-8" />
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||||
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
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<title>Index — AMMICO 0.0.1 documentation</title>
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<title>Index — AMMICO 0.2.2 documentation</title>
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@ -70,7 +70,7 @@
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<meta name="viewport" content="width=device-width, initial-scale=1.0" />
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<title>Welcome to AMMICO’s documentation! — AMMICO 0.0.1 documentation</title>
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<title>Welcome to AMMICO’s documentation! — AMMICO 0.2.2 documentation</title>
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@ -74,7 +74,7 @@
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||||
<li><a href="#" class="icon icon-home" aria-label="Home"></a></li>
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||||
<li class="breadcrumb-item active">Welcome to AMMICO’s documentation!</li>
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<a href="https://github.com/ssciwr/AMMICO/blob/main/source/index.rst" class="fa fa-github"> Edit on GitHub</a>
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@ -72,7 +72,7 @@
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<title>AMMICO package modules — AMMICO 0.0.1 documentation</title>
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<title>AMMICO package modules — AMMICO 0.2.2 documentation</title>
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@ -83,7 +83,7 @@
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<li><a href="index.html" class="icon icon-home" aria-label="Home"></a></li>
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<li class="breadcrumb-item active">AMMICO package modules</li>
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<title>AMMICO Demonstration Notebook — AMMICO 0.0.1 documentation</title>
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<title>AMMICO Demonstration Notebook — AMMICO 0.2.2 documentation</title>
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@ -106,7 +106,7 @@
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<li><a href="../index.html" class="icon icon-home" aria-label="Home"></a></li>
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<li class="breadcrumb-item active">AMMICO Demonstration Notebook</li>
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<hr/>
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@ -523,7 +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="4627534faec74c6cb30013b3b78abb84" class="no-scaled-link" src="../_images/text_detector.png" style="width: 800px;" /></p>
|
||||
<p><img alt="a481a91a1d8c4b57885989804c065f7e" 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>
|
||||
@ -615,7 +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="89ab67ce3b0e471e8741cfc2b23d97f1" class="no-scaled-link" src="../_images/summary_detector.png" style="width: 800px;" /></p>
|
||||
<p><img alt="faf82562ce8a4cd4babda34fde71e01e" 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>
|
||||
@ -878,7 +878,7 @@ directly on the right next to the image. This way, the user can directly inspect
|
||||
<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="08b5f56998844e20961d743a8d32ea91" class="no-scaled-link" src="../_images/emotion_detector.png" style="width: 800px;" /></p>
|
||||
<p><img alt="412f8c74483b4718849ff5047d5b90df" 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>
|
||||
|
||||
@ -6,7 +6,7 @@
|
||||
<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>Crop posts module — AMMICO 0.0.1 documentation</title>
|
||||
<title>Crop posts module — AMMICO 0.2.2 documentation</title>
|
||||
<link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=80d5e7a1" />
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@ -14,7 +14,7 @@
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@ -76,7 +76,7 @@
|
||||
<li><a href="../index.html" class="icon icon-home" aria-label="Home"></a></li>
|
||||
<li class="breadcrumb-item active">Crop posts module</li>
|
||||
<li class="wy-breadcrumbs-aside">
|
||||
<a href="https://github.com/ssciwr/AMMICO/blob/main/source/notebooks/Example cropposts.ipynb" class="fa fa-github"> Edit on GitHub</a>
|
||||
<a href="https://github.com/ssciwr/AMMICO/blob/main/docs/source/notebooks/Example cropposts.ipynb" class="fa fa-github"> Edit on GitHub</a>
|
||||
</li>
|
||||
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@ -5,14 +5,14 @@
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<title>Python Module Index — AMMICO 0.0.1 documentation</title>
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<title>Python Module Index — AMMICO 0.2.2 documentation</title>
|
||||
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@ -6,14 +6,14 @@
|
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<meta name="viewport" content="width=device-width, initial-scale=1.0" />
|
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<title>AMMICO - AI Media and Misinformation Content Analysis Tool — AMMICO 0.0.1 documentation</title>
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<title>AMMICO - AI Media and Misinformation Content Analysis Tool — AMMICO 0.2.2 documentation</title>
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@ -85,7 +85,7 @@
|
||||
<li><a href="index.html" class="icon icon-home" aria-label="Home"></a></li>
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||||
<li class="breadcrumb-item active">AMMICO - AI Media and Misinformation Content Analysis Tool</li>
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<li class="wy-breadcrumbs-aside">
|
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<a href="https://github.com/ssciwr/AMMICO/blob/main/source/readme_link.md" class="fa fa-github"> Edit on GitHub</a>
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<a href="https://github.com/ssciwr/AMMICO/blob/main/docs/source/readme_link.md" class="fa fa-github"> Edit on GitHub</a>
|
||||
</li>
|
||||
</ul>
|
||||
<hr/>
|
||||
@ -147,25 +147,25 @@
|
||||
<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 in the <a class="reference internal" href="#FAQ.md"><span class="xref myst">FAQ</span></a>.</p>
|
||||
<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 external" href="https://ssciwr.github.io/AMMICO/build/html/faq_link.html">FAQ</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 google colab: [<img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg" />].</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: <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>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: [<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 />
|
||||
<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"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg" /></a></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: [<img alt="Open In Colab" src="https://colab.research.google.com/github/ssciwr/ammico/blob/main/ammico/notebooks/cropposts.ipynb" /></strong></p></li>
|
||||
<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"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg" /></a></strong></p></li>
|
||||
</ol>
|
||||
</section>
|
||||
<section id="features">
|
||||
<h2>Features<a class="headerlink" href="#features" title="Link to this heading"></a></h2>
|
||||
<section id="text-extraction">
|
||||
<h3>Text extraction<a class="headerlink" href="#text-extraction" title="Link to this heading"></a></h3>
|
||||
<p>The text is extracted from the images using <a class="reference external" href="https://cloud.google.com/vision">google-cloud-vision</a>. For this, you need an API key. Set up your google account following the instructions on the google Vision AI website or as described <a class="reference internal" href="#docs/source/set_up_credentials.md"><span class="xref myst">here</span></a>.
|
||||
<p>The text is extracted from the images using <a class="reference external" href="https://cloud.google.com/vision">google-cloud-vision</a>. For this, you need an API key. Set up your google account following the instructions on the google Vision AI website or as described <a class="reference external" href="https://ssciwr.github.io/AMMICO/build/html/create_API_key_link.html">here</a>.
|
||||
You then need to export the location of the API key as an environment variable:</p>
|
||||
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">export</span> <span class="n">GOOGLE_APPLICATION_CREDENTIALS</span><span class="o">=</span><span class="s2">"location of your .json"</span>
|
||||
</pre></div>
|
||||
|
||||
@ -5,7 +5,7 @@
|
||||
<head>
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||||
<meta charset="utf-8" />
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||||
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
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<title>Search — AMMICO 0.0.1 documentation</title>
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||||
<title>Search — AMMICO 0.2.2 documentation</title>
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@ -13,7 +13,7 @@
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||||
@ -6,14 +6,14 @@
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|
||||
<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>
|
||||
<title>Instructions how to generate and enable a google Cloud Vision API key — AMMICO 0.2.2 documentation</title>
|
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|
||||
@ -71,7 +71,7 @@
|
||||
<li><a href="index.html" class="icon icon-home" aria-label="Home"></a></li>
|
||||
<li class="breadcrumb-item active">Instructions how to generate and enable a google Cloud Vision API key</li>
|
||||
<li class="wy-breadcrumbs-aside">
|
||||
<a href="https://github.com/ssciwr/AMMICO/blob/main/source/set_up_credentials.md" class="fa fa-github"> Edit on GitHub</a>
|
||||
<a href="https://github.com/ssciwr/AMMICO/blob/main/docs/source/set_up_credentials.md" class="fa fa-github"> Edit on GitHub</a>
|
||||
</li>
|
||||
</ul>
|
||||
<hr/>
|
||||
|
||||
@ -15,7 +15,7 @@ sys.path.insert(0, os.path.abspath("../../ammico/"))
|
||||
project = "AMMICO"
|
||||
copyright = "2022, Scientific Software Center, Heidelberg University"
|
||||
author = "Scientific Software Center, Heidelberg University"
|
||||
release = "0.0.1"
|
||||
release = "0.2.2"
|
||||
|
||||
# -- General configuration ---------------------------------------------------
|
||||
# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration
|
||||
@ -31,7 +31,7 @@ html_context = {
|
||||
"github_user": "ssciwr", # Username
|
||||
"github_repo": "AMMICO", # Repo name
|
||||
"github_version": "main", # Version
|
||||
"conf_py_path": "/source/", # Path in the checkout to the docs root
|
||||
"conf_py_path": "/docs/source/", # Path in the checkout to the docs root
|
||||
}
|
||||
|
||||
templates_path = ["_templates"]
|
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