├── .gitignore ├── Instructions ├── Exercises │ ├── 01-analyze-images.md │ ├── 02-image-classification.md │ ├── 03-custom-vision-object-detection.md │ ├── 04-face-service.md │ ├── 05-ocr.md │ ├── 06-video-indexer.md │ ├── 07-custom-vision-image-classification.md │ ├── 08-gen-ai-vision.md │ └── 09-dall-e.md ├── Labs │ ├── 01-analyze-images.md │ ├── 02-ocr.md │ ├── 03-face-service.md │ ├── 04-image-classification.md │ ├── 05-custom-vision-object-detection.md │ ├── 06-video-indexer.md │ ├── 08-gen-ai-vision.md │ ├── 09-dall-e.md │ └── media │ │ ├── ai-foundry-chat-playground.png │ │ ├── ai-foundry-home.png │ │ ├── ai-foundry-model-deployment.png │ │ ├── ai-foundry-openai-get-endpoint.png │ │ ├── fruit.jpg │ │ ├── object-region.jpg │ │ ├── object-tag.jpg │ │ ├── object-tags.jpg │ │ ├── tagged-images.jpg │ │ ├── test-apple.jpg │ │ ├── upload_apples.jpg │ │ ├── video-indexer-custom-model.png │ │ ├── video-indexer-edit-name.png │ │ ├── video-indexer-insights.png │ │ ├── video-indexer-known-person.png │ │ ├── video-indexer-search.png │ │ ├── video-indexer-transcript.png │ │ ├── video-indexer-unknown-person.png │ │ ├── video-indexer-view-menu.png │ │ └── video-indexer-widgets.png └── media │ ├── Business-card.jpg │ ├── IMG_TEST_1.jpg │ ├── IMG_TEST_2.jpg │ ├── IMG_TEST_3.jpg │ ├── Lincoln.jpg │ ├── Note.jpg │ ├── ai-foundry-home.png │ ├── ai-foundry-project.png │ ├── building.jpg │ ├── chat-playground-image.png │ ├── custom-vision-metrics.png │ ├── detected_faces1.jpg │ ├── detected_faces2.jpg │ ├── detected_faces3.jpg │ ├── face1.jpg │ ├── face2.jpg │ ├── faces.jpg │ ├── fruit.jpg │ ├── images-playground.png │ ├── mystery-fruit.jpeg │ ├── object-detection-output.jpg │ ├── object-region.jpg │ ├── object-tag.jpg │ ├── object-tags.jpg │ ├── objects.jpg │ ├── orange.jpeg │ ├── people.jpg │ ├── person.jpg │ ├── produce.jpg │ ├── street.jpg │ ├── tagged-images.jpg │ ├── test-apple.jpg │ ├── test-object-detection.png │ ├── text.jpg │ ├── upload_apples.jpg │ ├── video-indexer-custom-model.png │ ├── video-indexer-edit-name.png │ ├── video-indexer-insights.png │ ├── video-indexer-known-person.png │ ├── video-indexer-search.png │ ├── video-indexer-transcript.png │ ├── video-indexer-unknown-person.png │ ├── video-indexer-view-menu.png │ └── video-indexer-widgets.png ├── LICENSE ├── Labfiles ├── 01-analyze-images │ ├── C-Sharp │ │ ├── image-analysis │ │ │ ├── Program.cs │ │ │ ├── appsettings.json │ │ │ ├── image-analysis.csproj │ │ │ └── images │ │ │ │ ├── building.jpg │ │ │ │ ├── person.jpg │ │ │ │ └── street.jpg │ │ └── readme.txt │ └── Python │ │ ├── image-analysis │ │ ├── .env │ │ ├── image-analysis.py │ │ └── images │ │ │ ├── building.jpg │ │ │ ├── person.jpg │ │ │ └── street.jpg │ │ └── readme.txt ├── 02-image-classification │ ├── replace.ps1 │ ├── test-images │ │ ├── IMG_TEST_1.jpg │ │ ├── IMG_TEST_2.jpg │ │ └── IMG_TEST_3.jpg │ └── training-images │ │ ├── IMG_20200229_164759.jpg │ │ ├── IMG_20200229_164760.jpg │ │ ├── IMG_20200229_164804.jpg │ │ ├── IMG_20200229_164811.jpg │ │ ├── IMG_20200229_164819.jpg │ │ ├── IMG_20200229_164823.jpg │ │ ├── IMG_20200229_164830.jpg │ │ ├── IMG_20200229_164851.jpg │ │ ├── IMG_20200229_164901.jpg │ │ ├── IMG_20200229_164918.jpg │ │ ├── IMG_20200229_164919.jpg │ │ ├── IMG_20200229_164925.jpg │ │ ├── IMG_20200229_164926jpg.jpg │ │ ├── IMG_20200229_164932.jpg │ │ ├── IMG_20200229_164936.jpg │ │ ├── IMG_20200229_164947.jpg │ │ ├── IMG_20200229_164952.jpg │ │ ├── IMG_20200229_164957.jpg │ │ ├── IMG_20200229_164958.jpg │ │ ├── IMG_20200229_165001.jpg │ │ ├── IMG_20200229_165002.jpg │ │ ├── IMG_20200229_165008.jpg │ │ ├── IMG_20200229_165016.jpg │ │ ├── IMG_20200229_165020.jpg │ │ ├── IMG_20200229_165021.jpg │ │ ├── IMG_20200229_165026.jpg │ │ ├── IMG_20200229_165027.jpg │ │ ├── IMG_20200229_165033.jpg │ │ ├── IMG_20200229_165046.jpg │ │ ├── IMG_20200229_165047.jpg │ │ ├── IMG_20200229_165108.jpg │ │ ├── IMG_20200229_165112.jpg │ │ ├── IMG_20200229_165115.jpg │ │ ├── IMG_20200229_165126.jpg │ │ ├── IMG_20200229_165132.jpg │ │ ├── IMG_20200229_165147.jpg │ │ ├── IMG_20200229_165152.jpg │ │ ├── IMG_20200229_165157.jpg │ │ ├── IMG_20200229_165202.jpg │ │ ├── IMG_20200229_165219.jpg │ │ ├── IMG_20200229_165220.jpg │ │ ├── IMG_20200229_165223.jpg │ │ ├── IMG_20200229_165232.jpg │ │ ├── IMG_20200229_165234.jpg │ │ ├── IMG_20200229_165236.jpg │ │ └── training_labels.json ├── 03-object-detection │ ├── C-Sharp │ │ ├── readme.txt │ │ ├── test-detector │ │ │ ├── Program.cs │ │ │ ├── appsettings.json │ │ │ ├── produce.jpg │ │ │ └── test-detector.csproj │ │ └── train-detector │ │ │ ├── Program.cs │ │ │ ├── appsettings.json │ │ │ ├── images │ │ │ ├── image11.jpg │ │ │ ├── image12.jpg │ │ │ ├── image13.jpg │ │ │ ├── image14.jpg │ │ │ ├── image15.jpg │ │ │ ├── image16.jpg │ │ │ ├── image17.jpg │ │ │ ├── image18.jpg │ │ │ ├── image19.jpg │ │ │ ├── image20.jpg │ │ │ ├── image21.jpg │ │ │ ├── image22.jpg │ │ │ ├── image23.jpg │ │ │ ├── image24.jpg │ │ │ ├── image25.jpg │ │ │ ├── image26.jpg │ │ │ ├── image27.jpg │ │ │ ├── image28.jpg │ │ │ ├── image29.jpg │ │ │ ├── image30.jpg │ │ │ ├── image31.jpg │ │ │ ├── image32.jpg │ │ │ └── image33.jpg │ │ │ ├── tagged-images.json │ │ │ └── train-detector.csproj │ ├── Python │ │ ├── readme.txt │ │ ├── test-detector │ │ │ ├── .env │ │ │ ├── produce.jpg │ │ │ └── test-detector.py │ │ └── train-detector │ │ │ ├── .env │ │ │ ├── images │ │ │ ├── image11.jpg │ │ │ ├── image12.jpg │ │ │ ├── image13.jpg │ │ │ ├── image14.jpg │ │ │ ├── image15.jpg │ │ │ ├── image16.jpg │ │ │ ├── image17.jpg │ │ │ ├── image18.jpg │ │ │ ├── image19.jpg │ │ │ ├── image20.jpg │ │ │ ├── image21.jpg │ │ │ ├── image22.jpg │ │ │ ├── image23.jpg │ │ │ ├── image24.jpg │ │ │ ├── image25.jpg │ │ │ ├── image26.jpg │ │ │ ├── image27.jpg │ │ │ ├── image28.jpg │ │ │ ├── image29.jpg │ │ │ ├── image30.jpg │ │ │ ├── image31.jpg │ │ │ ├── image32.jpg │ │ │ └── image33.jpg │ │ │ ├── tagged-images.json │ │ │ └── train-detector.py │ └── training-images │ │ ├── image1.jpg │ │ ├── image10.jpg │ │ ├── image2.jpg │ │ ├── image3.jpg │ │ ├── image4.jpg │ │ ├── image5.jpg │ │ ├── image6.jpg │ │ ├── image7.jpg │ │ ├── image8.jpg │ │ └── image9.jpg ├── 04-face │ ├── C-Sharp │ │ ├── computer-vision │ │ │ ├── Program.cs │ │ │ ├── appsettings.json │ │ │ ├── detect-people.csproj │ │ │ └── images │ │ │ │ └── people.jpg │ │ ├── face-api │ │ │ ├── Program.cs │ │ │ ├── analyze-faces.csproj │ │ │ ├── appsettings.json │ │ │ └── images │ │ │ │ ├── Aisha │ │ │ │ ├── face1.jpg │ │ │ │ ├── face3.jpg │ │ │ │ ├── face5.jpg │ │ │ │ ├── face6.jpg │ │ │ │ └── face8.jpg │ │ │ │ ├── Sama │ │ │ │ ├── face10.jpg │ │ │ │ ├── face11.jpg │ │ │ │ ├── face2.jpg │ │ │ │ ├── face7.jpg │ │ │ │ └── face9.jpg │ │ │ │ ├── people.jpg │ │ │ │ ├── people2.jpg │ │ │ │ ├── person1.jpg │ │ │ │ └── person2.jpg │ │ └── readme.txt │ └── Python │ │ ├── computer-vision │ │ ├── .env │ │ ├── detect-people.py │ │ └── images │ │ │ └── people.jpg │ │ ├── face-api │ │ ├── .env │ │ ├── analyze-faces.py │ │ └── images │ │ │ ├── Aisha │ │ │ ├── face1.jpg │ │ │ ├── face3.jpg │ │ │ ├── face5.jpg │ │ │ ├── face6.jpg │ │ │ └── face8.jpg │ │ │ ├── Sama │ │ │ ├── face10.jpg │ │ │ ├── face11.jpg │ │ │ ├── face2.jpg │ │ │ ├── face7.jpg │ │ │ └── face9.jpg │ │ │ ├── people.jpg │ │ │ ├── people2.jpg │ │ │ ├── person1.jpg │ │ │ └── person2.jpg │ │ └── readme.txt ├── 05-ocr │ ├── C-Sharp │ │ ├── read-text │ │ │ ├── Program.cs │ │ │ ├── appsettings.json │ │ │ ├── images │ │ │ │ ├── Lincoln.jpg │ │ │ │ └── Note.jpg │ │ │ └── read-text.csproj │ │ └── readme.txt │ └── Python │ │ ├── read-text │ │ ├── .env │ │ ├── images │ │ │ ├── Lincoln.jpg │ │ │ └── Note.jpg │ │ └── read-text.py │ │ └── readme.txt ├── 06-video-indexer │ ├── analyze-video.html │ ├── get-videos.ps1 │ └── responsible_ai.mp4 ├── 07-custom-vision-image-classification │ ├── C-Sharp │ │ ├── readme.txt │ │ ├── test-classifier │ │ │ ├── Program.cs │ │ │ ├── appsettings.json │ │ │ ├── test-classifier.csproj │ │ │ └── test-images │ │ │ │ ├── IMG_TEST_1.jpg │ │ │ │ ├── IMG_TEST_2.jpg │ │ │ │ └── IMG_TEST_3.jpg │ │ └── train-classifier │ │ │ ├── Program.cs │ │ │ ├── appsettings.json │ │ │ ├── more-training-images │ │ │ ├── apple │ │ │ │ ├── apple5.jpg │ │ │ │ └── apple7.jpg │ │ │ ├── banana │ │ │ │ ├── banana4.jpg │ │ │ │ └── banana6.jpg │ │ │ └── orange │ │ │ │ ├── orange8.jpg │ │ │ │ └── orange9.jpg │ │ │ └── train-classifier.csproj │ ├── Python │ │ ├── readme.txt │ │ ├── test-classifier │ │ │ ├── .env │ │ │ ├── test-classifier.py │ │ │ └── test-images │ │ │ │ ├── IMG_TEST_1.jpg │ │ │ │ ├── IMG_TEST_2.jpg │ │ │ │ └── IMG_TEST_3.jpg │ │ └── train-classifier │ │ │ ├── .env │ │ │ ├── more-training-images │ │ │ ├── apple │ │ │ │ ├── apple5.jpg │ │ │ │ └── apple7.jpg │ │ │ ├── banana │ │ │ │ ├── banana4.jpg │ │ │ │ └── banana6.jpg │ │ │ └── orange │ │ │ │ ├── orange8.jpg │ │ │ │ └── orange9.jpg │ │ │ └── train-classifier.py │ └── training-images │ │ ├── apple │ │ ├── IMG_20200229_164759.jpg │ │ ├── IMG_20200229_164760.jpg │ │ ├── IMG_20200229_164804.jpg │ │ ├── IMG_20200229_164811.jpg │ │ ├── IMG_20200229_164819.jpg │ │ ├── IMG_20200229_164823.jpg │ │ ├── IMG_20200229_164830.jpg │ │ ├── IMG_20200229_164851.jpg │ │ ├── IMG_20200229_164901.jpg │ │ ├── IMG_20200229_164918.jpg │ │ ├── IMG_20200229_164919.jpg │ │ ├── IMG_20200229_164925.jpg │ │ ├── IMG_20200229_164926jpg.jpg │ │ ├── IMG_20200229_164932.jpg │ │ └── IMG_20200229_164936.jpg │ │ ├── banana │ │ ├── IMG_20200229_164947.jpg │ │ ├── IMG_20200229_164952.jpg │ │ ├── IMG_20200229_164957.jpg │ │ ├── IMG_20200229_164958.jpg │ │ ├── IMG_20200229_165001.jpg │ │ ├── IMG_20200229_165002.jpg │ │ ├── IMG_20200229_165008.jpg │ │ ├── IMG_20200229_165016.jpg │ │ ├── IMG_20200229_165020.jpg │ │ ├── IMG_20200229_165021.jpg │ │ ├── IMG_20200229_165026.jpg │ │ ├── IMG_20200229_165027.jpg │ │ ├── IMG_20200229_165033.jpg │ │ ├── IMG_20200229_165046.jpg │ │ └── IMG_20200229_165047.jpg │ │ └── orange │ │ ├── IMG_20200229_165108.jpg │ │ ├── IMG_20200229_165112.jpg │ │ ├── IMG_20200229_165115.jpg │ │ ├── IMG_20200229_165126.jpg │ │ ├── IMG_20200229_165132.jpg │ │ ├── IMG_20200229_165147.jpg │ │ ├── IMG_20200229_165152.jpg │ │ ├── IMG_20200229_165157.jpg │ │ ├── IMG_20200229_165202.jpg │ │ ├── IMG_20200229_165219.jpg │ │ ├── IMG_20200229_165220.jpg │ │ ├── IMG_20200229_165223.jpg │ │ ├── IMG_20200229_165232.jpg │ │ ├── IMG_20200229_165234.jpg │ │ └── IMG_20200229_165236.jpg ├── 08-gen-ai-vision │ ├── c-sharp │ │ ├── Program.cs │ │ ├── appsettings.json │ │ ├── chat_app.csproj │ │ └── mystery-fruit.jpeg │ ├── mango.jpeg │ ├── orange.jpeg │ └── python │ │ ├── .env │ │ ├── chat-app.py │ │ └── mystery-fruit.jpeg ├── 09-dalle-client │ ├── c-sharp │ │ ├── Program.cs │ │ ├── appsettings.json │ │ └── dalle-client.csproj │ └── python │ │ ├── .env │ │ └── dalle-client.py ├── analyze-images │ ├── c-sharp │ │ ├── image-analysis │ │ │ ├── Program.cs │ │ │ ├── appsettings.json │ │ │ ├── image-analysis.csproj │ │ │ └── images │ │ │ │ ├── building.jpg │ │ │ │ ├── person.jpg │ │ │ │ └── street.jpg │ │ └── readme.txt │ └── python │ │ ├── image-analysis │ │ ├── .env │ │ ├── image-analysis.py │ │ ├── images │ │ │ ├── building.jpg │ │ │ ├── person.jpg │ │ │ └── street.jpg │ │ └── requirements.txt │ │ └── readme.txt ├── dalle-client │ ├── c-sharp │ │ ├── Program.cs │ │ ├── appsettings.json │ │ └── dalle-client.csproj │ └── python │ │ ├── .env │ │ ├── dalle-client.py │ │ └── requirements.txt ├── face │ ├── c-sharp │ │ ├── face-api │ │ │ ├── Program.cs │ │ │ ├── analyze-faces.csproj │ │ │ ├── appsettings.json │ │ │ └── images │ │ │ │ ├── face1.jpg │ │ │ │ ├── face2.jpg │ │ │ │ └── faces.jpg │ │ └── readme.txt │ └── python │ │ ├── face-api │ │ ├── .env │ │ ├── analyze-faces.py │ │ ├── images │ │ │ ├── face1.jpg │ │ │ ├── face2.jpg │ │ │ └── faces.jpg │ │ └── requirements.txt │ │ └── readme.txt ├── gen-ai-vision │ ├── c-sharp │ │ ├── Program.cs │ │ ├── appsettings.json │ │ ├── chat_app.csproj │ │ └── mystery-fruit.jpeg │ ├── mango.jpeg │ ├── orange.jpeg │ └── python │ │ ├── .env │ │ ├── chat-app.py │ │ ├── mystery-fruit.jpeg │ │ └── requirements.txt ├── image-classification │ ├── c-sharp │ │ ├── readme.txt │ │ ├── test-classifier │ │ │ ├── Program.cs │ │ │ ├── appsettings.json │ │ │ ├── test-classifier.csproj │ │ │ └── test-images │ │ │ │ ├── IMG_TEST_1.jpg │ │ │ │ ├── IMG_TEST_2.jpg │ │ │ │ └── IMG_TEST_3.jpg │ │ └── train-classifier │ │ │ ├── Program.cs │ │ │ ├── appsettings.json │ │ │ ├── more-training-images │ │ │ ├── apple │ │ │ │ ├── apple5.jpg │ │ │ │ └── apple7.jpg │ │ │ ├── banana │ │ │ │ ├── banana4.jpg │ │ │ │ └── banana6.jpg │ │ │ └── orange │ │ │ │ ├── orange8.jpg │ │ │ │ └── orange9.jpg │ │ │ └── train-classifier.csproj │ ├── python │ │ ├── readme.txt │ │ ├── test-classifier │ │ │ ├── .env │ │ │ ├── requirements.txt │ │ │ ├── test-classifier.py │ │ │ └── test-images │ │ │ │ ├── IMG_TEST_1.jpg │ │ │ │ ├── IMG_TEST_2.jpg │ │ │ │ └── IMG_TEST_3.jpg │ │ └── train-classifier │ │ │ ├── .env │ │ │ ├── more-training-images │ │ │ ├── apple │ │ │ │ ├── apple5.jpg │ │ │ │ └── apple7.jpg │ │ │ ├── banana │ │ │ │ ├── banana4.jpg │ │ │ │ └── banana6.jpg │ │ │ └── orange │ │ │ │ ├── orange8.jpg │ │ │ │ └── orange9.jpg │ │ │ ├── requirements.txt │ │ │ └── train-classifier.py │ ├── test-images │ │ ├── IMG_TEST_1.jpg │ │ ├── IMG_TEST_2.jpg │ │ └── IMG_TEST_3.jpg │ ├── training-images.zip │ └── training-images │ │ ├── apple │ │ ├── IMG_20200229_164759.jpg │ │ ├── IMG_20200229_164760.jpg │ │ ├── IMG_20200229_164804.jpg │ │ ├── IMG_20200229_164811.jpg │ │ ├── IMG_20200229_164819.jpg │ │ ├── IMG_20200229_164823.jpg │ │ ├── IMG_20200229_164830.jpg │ │ ├── IMG_20200229_164851.jpg │ │ ├── IMG_20200229_164901.jpg │ │ ├── IMG_20200229_164918.jpg │ │ ├── IMG_20200229_164919.jpg │ │ ├── IMG_20200229_164925.jpg │ │ ├── IMG_20200229_164926jpg.jpg │ │ ├── IMG_20200229_164932.jpg │ │ └── IMG_20200229_164936.jpg │ │ ├── banana │ │ ├── IMG_20200229_164947.jpg │ │ ├── IMG_20200229_164952.jpg │ │ ├── IMG_20200229_164957.jpg │ │ ├── IMG_20200229_164958.jpg │ │ ├── IMG_20200229_165001.jpg │ │ ├── IMG_20200229_165002.jpg │ │ ├── IMG_20200229_165008.jpg │ │ ├── IMG_20200229_165016.jpg │ │ ├── IMG_20200229_165020.jpg │ │ ├── IMG_20200229_165021.jpg │ │ ├── IMG_20200229_165026.jpg │ │ ├── IMG_20200229_165027.jpg │ │ ├── IMG_20200229_165033.jpg │ │ ├── IMG_20200229_165046.jpg │ │ └── IMG_20200229_165047.jpg │ │ └── orange │ │ ├── IMG_20200229_165108.jpg │ │ ├── IMG_20200229_165112.jpg │ │ ├── IMG_20200229_165115.jpg │ │ ├── IMG_20200229_165126.jpg │ │ ├── IMG_20200229_165132.jpg │ │ ├── IMG_20200229_165147.jpg │ │ ├── IMG_20200229_165152.jpg │ │ ├── IMG_20200229_165157.jpg │ │ ├── IMG_20200229_165202.jpg │ │ ├── IMG_20200229_165219.jpg │ │ ├── IMG_20200229_165220.jpg │ │ ├── IMG_20200229_165223.jpg │ │ ├── IMG_20200229_165232.jpg │ │ ├── IMG_20200229_165234.jpg │ │ └── IMG_20200229_165236.jpg ├── object-detection │ ├── IMG_TEST_OBJECTS.jpg │ ├── c-sharp │ │ ├── readme.txt │ │ ├── test-detector │ │ │ ├── Program.cs │ │ │ ├── appsettings.json │ │ │ ├── produce.jpg │ │ │ └── test-detector.csproj │ │ └── train-detector │ │ │ ├── Program.cs │ │ │ ├── appsettings.json │ │ │ ├── images │ │ │ ├── image11.jpg │ │ │ ├── image12.jpg │ │ │ ├── image13.jpg │ │ │ ├── image14.jpg │ │ │ ├── image15.jpg │ │ │ ├── image16.jpg │ │ │ ├── image17.jpg │ │ │ ├── image18.jpg │ │ │ ├── image19.jpg │ │ │ ├── image20.jpg │ │ │ ├── image21.jpg │ │ │ ├── image22.jpg │ │ │ ├── image23.jpg │ │ │ ├── image24.jpg │ │ │ ├── image25.jpg │ │ │ ├── image26.jpg │ │ │ ├── image27.jpg │ │ │ ├── image28.jpg │ │ │ ├── image29.jpg │ │ │ ├── image30.jpg │ │ │ ├── image31.jpg │ │ │ ├── image32.jpg │ │ │ └── image33.jpg │ │ │ ├── tagged-images.json │ │ │ └── train-detector.csproj │ ├── python │ │ ├── readme.txt │ │ ├── test-detector │ │ │ ├── .env │ │ │ ├── produce.jpg │ │ │ ├── requirements.txt │ │ │ └── test-detector.py │ │ └── train-detector │ │ │ ├── .env │ │ │ ├── add-tagged-images.py │ │ │ ├── images │ │ │ ├── image11.jpg │ │ │ ├── image12.jpg │ │ │ ├── image13.jpg │ │ │ ├── image14.jpg │ │ │ ├── image15.jpg │ │ │ ├── image16.jpg │ │ │ ├── image17.jpg │ │ │ ├── image18.jpg │ │ │ ├── image19.jpg │ │ │ ├── image20.jpg │ │ │ ├── image21.jpg │ │ │ ├── image22.jpg │ │ │ ├── image23.jpg │ │ │ ├── image24.jpg │ │ │ ├── image25.jpg │ │ │ ├── image26.jpg │ │ │ ├── image27.jpg │ │ │ ├── image28.jpg │ │ │ ├── image29.jpg │ │ │ ├── image30.jpg │ │ │ ├── image31.jpg │ │ │ ├── image32.jpg │ │ │ └── image33.jpg │ │ │ ├── requirements.txt │ │ │ └── tagged-images.json │ ├── training-images.zip │ └── training-images │ │ ├── image1.jpg │ │ ├── image10.jpg │ │ ├── image2.jpg │ │ ├── image3.jpg │ │ ├── image4.jpg │ │ ├── image5.jpg │ │ ├── image6.jpg │ │ ├── image7.jpg │ │ ├── image8.jpg │ │ └── image9.jpg ├── ocr │ ├── c-sharp │ │ ├── read-text │ │ │ ├── Program.cs │ │ │ ├── appsettings.json │ │ │ ├── images │ │ │ │ ├── Business-card.jpg │ │ │ │ ├── Lincoln.jpg │ │ │ │ └── Note.jpg │ │ │ └── read-text.csproj │ │ └── readme.txt │ └── python │ │ ├── read-text │ │ ├── .env │ │ ├── images │ │ │ ├── Business-card.jpg │ │ │ ├── Lincoln.jpg │ │ │ └── Note.jpg │ │ ├── read-text.py │ │ └── requirements.txt │ │ └── readme.txt └── video-indexer │ ├── analyze-video.html │ ├── get-videos.ps1 │ └── responsible_ai.mp4 ├── README.md ├── _build.yml ├── _config.yml └── index.md /.gitignore: -------------------------------------------------------------------------------- 1 | obj 2 | bin 3 | *.sln -------------------------------------------------------------------------------- /Instructions/Labs/media/ai-foundry-chat-playground.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MicrosoftLearning/mslearn-ai-vision/dc2d1beeb596267a4e77f20185debaad195ec13d/Instructions/Labs/media/ai-foundry-chat-playground.png -------------------------------------------------------------------------------- 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associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /Labfiles/01-analyze-images/C-Sharp/image-analysis/appsettings.json: -------------------------------------------------------------------------------- 1 | { 2 | "AIServicesEndpoint": "YOUR_AZURE_AI_SERVICES_ENDPOINT", 3 | "AIServicesKey": "YOUR_AZURE_AI_SERVICES_KEY" 4 | } 5 | -------------------------------------------------------------------------------- /Labfiles/01-analyze-images/C-Sharp/image-analysis/image-analysis.csproj: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | Exe 5 | netcoreapp8.0 6 | image_analysis 7 | 1998 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | PreserveNewest 19 | 20 | 21 | 22 | 23 | -------------------------------------------------------------------------------- /Labfiles/01-analyze-images/C-Sharp/image-analysis/images/building.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MicrosoftLearning/mslearn-ai-vision/dc2d1beeb596267a4e77f20185debaad195ec13d/Labfiles/01-analyze-images/C-Sharp/image-analysis/images/building.jpg -------------------------------------------------------------------------------- /Labfiles/01-analyze-images/C-Sharp/image-analysis/images/person.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MicrosoftLearning/mslearn-ai-vision/dc2d1beeb596267a4e77f20185debaad195ec13d/Labfiles/01-analyze-images/C-Sharp/image-analysis/images/person.jpg -------------------------------------------------------------------------------- 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/Labfiles/01-analyze-images/Python/image-analysis/image-analysis.py: -------------------------------------------------------------------------------- 1 | from dotenv import load_dotenv 2 | import os 3 | from PIL import Image, ImageDraw 4 | import sys 5 | from matplotlib import pyplot as plt 6 | from azure.core.exceptions import HttpResponseError 7 | import requests 8 | 9 | # Import namespaces 10 | 11 | 12 | def main(): 13 | global cv_client 14 | 15 | # Clear the console 16 | os.system('cls' if os.name=='nt' else 'clear') 17 | 18 | try: 19 | # Get Configuration Settings 20 | load_dotenv() 21 | ai_endpoint = os.getenv('AI_SERVICE_ENDPOINT') 22 | ai_key = os.getenv('AI_SERVICE_KEY') 23 | 24 | # Get image 25 | image_file = 'images/street.jpg' 26 | if len(sys.argv) > 1: 27 | image_file = sys.argv[1] 28 | 29 | with open(image_file, "rb") as f: 30 | image_data = f.read() 31 | 32 | # Authenticate Azure AI Vision client 33 | 34 | 35 | # Analyze image 36 | AnalyzeImage(image_file, image_data, cv_client) 37 | 38 | except Exception as ex: 39 | print(ex) 40 | 41 | 42 | def AnalyzeImage(image_filename, image_data, cv_client): 43 | print('\nAnalyzing image...') 44 | 45 | try: 46 | # Get result with specified features to be retrieved 47 | 48 | 49 | except HttpResponseError as e: 50 | print(f"Status code: {e.status_code}") 51 | print(f"Reason: {e.reason}") 52 | print(f"Message: {e.error.message}") 53 | 54 | # Display analysis results 55 | 56 | 57 | 58 | if __name__ == "__main__": 59 | main() 60 | -------------------------------------------------------------------------------- /Labfiles/01-analyze-images/Python/image-analysis/images/building.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MicrosoftLearning/mslearn-ai-vision/dc2d1beeb596267a4e77f20185debaad195ec13d/Labfiles/01-analyze-images/Python/image-analysis/images/building.jpg -------------------------------------------------------------------------------- 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/Labfiles/04-face/C-Sharp/computer-vision/detect-people.csproj: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | Exe 5 | netcoreapp7.0 6 | detect_faces 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | PreserveNewest 17 | 18 | 19 | 20 | 21 | -------------------------------------------------------------------------------- /Labfiles/04-face/C-Sharp/computer-vision/images/people.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MicrosoftLearning/mslearn-ai-vision/dc2d1beeb596267a4e77f20185debaad195ec13d/Labfiles/04-face/C-Sharp/computer-vision/images/people.jpg -------------------------------------------------------------------------------- /Labfiles/04-face/C-Sharp/face-api/Program.cs: -------------------------------------------------------------------------------- 1 | using System; 2 | using System.IO; 3 | using System.Linq; 4 | using System.Drawing; 5 | using System.Collections.Generic; 6 | using System.Threading.Tasks; 7 | using Microsoft.Extensions.Configuration; 8 | 9 | // Import namespaces 10 | 11 | 12 | 13 | namespace analyze_faces 14 | { 15 | class Program 16 | { 17 | 18 | private static FaceClient faceClient; 19 | static async Task Main(string[] args) 20 | { 21 | try 22 | { 23 | // Get config settings from AppSettings 24 | IConfigurationBuilder builder = new ConfigurationBuilder().AddJsonFile("appsettings.json"); 25 | IConfigurationRoot configuration = builder.Build(); 26 | string cogSvcEndpoint = configuration["AIServicesEndpoint"]; 27 | string cogSvcKey = configuration["AIServiceKey"]; 28 | 29 | // Authenticate Face client 30 | 31 | 32 | // Menu for face functions 33 | Console.WriteLine("1: Detect faces\nAny other key to quit"); 34 | Console.WriteLine("Enter a number:"); 35 | string command = Console.ReadLine(); 36 | switch (command) 37 | { 38 | case "1": 39 | await DetectFaces("images/people.jpg"); 40 | break; 41 | default: 42 | break; 43 | } 44 | } 45 | catch (Exception ex) 46 | { 47 | Console.WriteLine(ex.Message); 48 | } 49 | } 50 | 51 | static async Task DetectFaces(string imageFile) 52 | { 53 | Console.WriteLine($"Detecting faces in {imageFile}"); 54 | 55 | // Specify facial features to be retrieved 56 | 57 | 58 | // Get faces 59 | 60 | 61 | } 62 | } 63 | } 64 | -------------------------------------------------------------------------------- /Labfiles/04-face/C-Sharp/face-api/analyze-faces.csproj: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | Exe 5 | netcoreapp7.0 6 | analyze_faces 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | PreserveNewest 18 | 19 | 20 | 21 | 22 | -------------------------------------------------------------------------------- /Labfiles/04-face/C-Sharp/face-api/appsettings.json: -------------------------------------------------------------------------------- 1 | { 2 | "AIServicesEndpoint": "YOUR_AI_SERVICES_ENDPOINT", 3 | 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-------------------------------------------------------------------------------- /Labfiles/04-face/Python/computer-vision/.env: -------------------------------------------------------------------------------- 1 | AI_SERVICE_ENDPOINT=your_ai_services_endpoint 2 | AI_SERVICE_KEY=your_ai_services_key -------------------------------------------------------------------------------- /Labfiles/04-face/Python/computer-vision/detect-people.py: -------------------------------------------------------------------------------- 1 | from dotenv import load_dotenv 2 | import os 3 | from PIL import Image, ImageDraw 4 | import sys 5 | from matplotlib import pyplot as plt 6 | import numpy as np 7 | 8 | # import namespaces 9 | 10 | 11 | 12 | def main(): 13 | global cv_client 14 | 15 | try: 16 | # Get Configuration Settings 17 | load_dotenv() 18 | ai_endpoint = os.getenv('AI_SERVICE_ENDPOINT') 19 | ai_key = os.getenv('AI_SERVICE_KEY') 20 | 21 | # Get image 22 | image_file = 'images/people.jpg' 23 | if len(sys.argv) > 1: 24 | image_file = sys.argv[1] 25 | 26 | with open(image_file, "rb") as f: 27 | image_data = f.read() 28 | 29 | # Authenticate Azure AI Vision client 30 | 31 | 32 | # Analyze image 33 | AnalyzeImage(image_file, image_data, cv_client) 34 | 35 | except Exception as ex: 36 | print(ex) 37 | 38 | 39 | def AnalyzeImage(filename, image_data, cv_client): 40 | print('\nAnalyzing ', filename) 41 | 42 | # Get result with specified features to be retrieved (PEOPLE) 43 | 44 | 45 | # Identify people in the image 46 | if result.people is not None: 47 | print("\nPeople in image:") 48 | 49 | # Prepare image for drawing 50 | image = Image.open(filename) 51 | fig = plt.figure(figsize=(image.width/100, image.height/100)) 52 | plt.axis('off') 53 | draw = ImageDraw.Draw(image) 54 | color = 'cyan' 55 | 56 | # Draw bounding box around detected people 57 | 58 | 59 | # Save annotated image 60 | plt.imshow(image) 61 | plt.tight_layout(pad=0) 62 | outputfile = 'people.jpg' 63 | fig.savefig(outputfile) 64 | print(' Results saved in', outputfile) 65 | 66 | if __name__ == "__main__": 67 | main() -------------------------------------------------------------------------------- /Labfiles/04-face/Python/computer-vision/images/people.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MicrosoftLearning/mslearn-ai-vision/dc2d1beeb596267a4e77f20185debaad195ec13d/Labfiles/04-face/Python/computer-vision/images/people.jpg -------------------------------------------------------------------------------- /Labfiles/04-face/Python/face-api/.env: -------------------------------------------------------------------------------- 1 | AI_SERVICE_ENDPOINT=your_ai_services_endpoint 2 | AI_SERVICE_KEY=your_ai_services_key -------------------------------------------------------------------------------- /Labfiles/04-face/Python/face-api/analyze-faces.py: -------------------------------------------------------------------------------- 1 | from dotenv import load_dotenv 2 | import os 3 | from PIL import Image, ImageDraw 4 | from matplotlib import pyplot as plt 5 | 6 | # Import namespaces 7 | 8 | 9 | def main(): 10 | 11 | global face_client 12 | 13 | try: 14 | # Get Configuration Settings 15 | load_dotenv() 16 | cog_endpoint = os.getenv('AI_SERVICE_ENDPOINT') 17 | cog_key = os.getenv('AI_SERVICE_KEY') 18 | 19 | # Authenticate Face client 20 | 21 | 22 | # Menu for face functions 23 | print('1: Detect faces\nAny other key to quit') 24 | command = input('Enter a number:') 25 | if command == '1': 26 | DetectFaces(os.path.join('images','people.jpg')) 27 | 28 | except Exception as ex: 29 | print(ex) 30 | 31 | def DetectFaces(image_file): 32 | print('Detecting faces in', image_file) 33 | 34 | # Specify facial features to be retrieved 35 | 36 | 37 | # Get faces 38 | 39 | 40 | if __name__ == "__main__": 41 | main() 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| "AIServicesEndpoint": "YOUR_AZURE_AI_SERVICES_SERVICES_ENDPOINT", 3 | "AIServicesKey": "YOUR_AZURE_AI_SERVICES_KEY" 4 | } -------------------------------------------------------------------------------- /Labfiles/05-ocr/C-Sharp/read-text/images/Lincoln.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MicrosoftLearning/mslearn-ai-vision/dc2d1beeb596267a4e77f20185debaad195ec13d/Labfiles/05-ocr/C-Sharp/read-text/images/Lincoln.jpg -------------------------------------------------------------------------------- /Labfiles/05-ocr/C-Sharp/read-text/images/Note.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MicrosoftLearning/mslearn-ai-vision/dc2d1beeb596267a4e77f20185debaad195ec13d/Labfiles/05-ocr/C-Sharp/read-text/images/Note.jpg -------------------------------------------------------------------------------- /Labfiles/05-ocr/C-Sharp/read-text/read-text.csproj: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | Exe 5 | netcoreapp7.0 6 | read_text 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | PreserveNewest 17 | 18 | 19 | 20 | 21 | -------------------------------------------------------------------------------- /Labfiles/05-ocr/C-Sharp/readme.txt: -------------------------------------------------------------------------------- 1 | This folder contains C# code -------------------------------------------------------------------------------- /Labfiles/05-ocr/Python/read-text/.env: -------------------------------------------------------------------------------- 1 | AI_SERVICE_ENDPOINT=your_azure_ai_services_endpoint 2 | AI_SERVICE_KEY=your_azure_ai_services_key -------------------------------------------------------------------------------- /Labfiles/05-ocr/Python/read-text/images/Lincoln.jpg: 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| 9 | 10 | def main(): 11 | 12 | global cv_client 13 | 14 | try: 15 | # Get Configuration Settings 16 | load_dotenv() 17 | ai_endpoint = os.getenv('AI_SERVICE_ENDPOINT') 18 | ai_key = os.getenv('AI_SERVICE_KEY') 19 | 20 | # Authenticate Azure AI Vision client 21 | 22 | 23 | # Menu for text reading functions 24 | print('\n1: Use Read API for image (Lincoln.jpg)\n2: Read handwriting (Note.jpg)\nAny other key to quit\n') 25 | command = input('Enter a number:') 26 | if command == '1': 27 | image_file = os.path.join('images','Lincoln.jpg') 28 | GetTextRead(image_file) 29 | elif command =='2': 30 | image_file = os.path.join('images','Note.jpg') 31 | GetTextRead(image_file) 32 | 33 | 34 | except Exception as ex: 35 | print(ex) 36 | 37 | def GetTextRead(image_file): 38 | print('\n') 39 | 40 | # Open image file 41 | with open(image_file, "rb") as f: 42 | image_data = f.read() 43 | 44 | # Use Analyze image function to read text in image 45 | 46 | 47 | 48 | 49 | 50 | if __name__ == "__main__": 51 | main() 52 | -------------------------------------------------------------------------------- /Labfiles/05-ocr/Python/readme.txt: -------------------------------------------------------------------------------- 1 | This folder contains Python code -------------------------------------------------------------------------------- /Labfiles/06-video-indexer/analyze-video.html: -------------------------------------------------------------------------------- 1 | 2 |
3 | Analyze Video 4 | 5 |
6 | 7 |

Video Analysis

8 | 9 | 10 | 14 | 18 | 19 |
11 | 12 | 13 | 15 | 16 | 17 |
20 | 21 | -------------------------------------------------------------------------------- /Labfiles/06-video-indexer/get-videos.ps1: -------------------------------------------------------------------------------- 1 | $account_id="YOUR_ACCOUNT_ID" 2 | $api_key="YOUR_API_KEY" 3 | $location="trial" 4 | 5 | # Call the AccessToken method with the API key in the header to get an access token 6 | $token = Invoke-RestMethod -Method "Get" -Uri "https://api.videoindexer.ai/auth/$location/Accounts/$account_id/AccessToken" -Headers @{'Ocp-Apim-Subscription-Key' = $api_key} 7 | 8 | # Use the access token to make an authenticated call to the Videos method to get a list of videos in the account 9 | Invoke-RestMethod -Method "Get" -Uri "https://api.videoindexer.ai/$location/Accounts/$account_id/Videos?accessToken=$token" | ConvertTo-Json -Depth 6 10 | -------------------------------------------------------------------------------- /Labfiles/06-video-indexer/responsible_ai.mp4: 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-------------------------------------------------------------------------------- /Labfiles/07-custom-vision-image-classification/Python/test-classifier/.env: -------------------------------------------------------------------------------- 1 | 2 | PredictionEndpoint=YOUR_PREDICTION_ENDPOINT 3 | PredictionKey=YOUR_PREDICTION_KEY 4 | ProjectID=YOUR_PROJECT_ID 5 | ModelName=fruit-classifier -------------------------------------------------------------------------------- /Labfiles/07-custom-vision-image-classification/Python/test-classifier/test-classifier.py: -------------------------------------------------------------------------------- 1 | from azure.cognitiveservices.vision.customvision.prediction import CustomVisionPredictionClient 2 | from msrest.authentication import ApiKeyCredentials 3 | import os 4 | 5 | def main(): 6 | from dotenv import load_dotenv 7 | 8 | try: 9 | # Get Configuration Settings 10 | load_dotenv() 11 | prediction_endpoint = os.getenv('PredictionEndpoint') 12 | prediction_key = os.getenv('PredictionKey') 13 | project_id = os.getenv('ProjectID') 14 | model_name = os.getenv('ModelName') 15 | 16 | # Authenticate a client for the training API 17 | credentials = ApiKeyCredentials(in_headers={"Prediction-key": prediction_key}) 18 | prediction_client = CustomVisionPredictionClient(endpoint=prediction_endpoint, credentials=credentials) 19 | 20 | # Classify test images 21 | for image in os.listdir('test-images'): 22 | image_data = open(os.path.join('test-images',image), "rb").read() 23 | results = prediction_client.classify_image(project_id, model_name, image_data) 24 | 25 | # Loop over each label prediction and print any with probability > 50% 26 | for prediction in results.predictions: 27 | if prediction.probability > 0.5: 28 | print(image, ': {} ({:.0%})'.format(prediction.tag_name, prediction.probability)) 29 | except Exception as ex: 30 | print(ex) 31 | 32 | if __name__ == "__main__": 33 | main() 34 | 35 | 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project_connection = os.getenv("PROJECT_CONNECTION") 19 | model_deployment = os.getenv("MODEL_DEPLOYMENT") 20 | 21 | 22 | 23 | # Initialize the project client 24 | 25 | 26 | 27 | # Get a chat client 28 | 29 | 30 | 31 | 32 | # Initialize prompts 33 | system_message = "You are an AI assistant in a grocery store that sells fruit." 34 | prompt = "" 35 | 36 | # Loop until the user types 'quit' 37 | while True: 38 | prompt = input("\nAsk a question about the image\n(or type 'quit' to exit)\n") 39 | if prompt.lower() == "quit": 40 | break 41 | elif len(prompt) == 0: 42 | print("Please enter a question.\n") 43 | else: 44 | print("Getting a response ...\n") 45 | 46 | 47 | # Get a response to image input 48 | 49 | 50 | 51 | except Exception as ex: 52 | print(ex) 53 | 54 | 55 | if __name__ == '__main__': 56 | main() -------------------------------------------------------------------------------- /Labfiles/08-gen-ai-vision/python/mystery-fruit.jpeg: 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-------------------------------------------------------------------------------- /Labfiles/09-dalle-client/python/.env: -------------------------------------------------------------------------------- 1 | PROJECT_CONNECTION="your_project_endpoint" 2 | MODEL_DEPLOYMENT="your_model_deployment" 3 | -------------------------------------------------------------------------------- /Labfiles/09-dalle-client/python/dalle-client.py: -------------------------------------------------------------------------------- 1 | import os 2 | import json 3 | 4 | # Add references 5 | 6 | 7 | def main(): 8 | 9 | # Clear the console 10 | os.system('cls' if os.name=='nt' else 'clear') 11 | 12 | try: 13 | 14 | # Get configuration settings 15 | load_dotenv() 16 | project_connection = os.getenv("PROJECT_CONNECTION") 17 | model_deployment = os.getenv("MODEL_DEPLOYMENT") 18 | 19 | # Initialize the project client 20 | 21 | 22 | ## Get an OpenAI client 23 | 24 | 25 | img_no = 0 26 | # Loop until the user types 'quit' 27 | while True: 28 | # Get input text 29 | input_text = input("Enter the prompt (or type 'quit' to exit): ") 30 | if input_text.lower() == "quit": 31 | break 32 | if len(input_text) == 0: 33 | print("Please enter a prompt.") 34 | continue 35 | 36 | # Generate an image 37 | 38 | 39 | # save the image 40 | img_no += 1 41 | file_name = f"image_{img_no}.png" 42 | save_image (image_url, file_name) 43 | 44 | 45 | except Exception as ex: 46 | print(ex) 47 | 48 | def save_image (image_url, file_name): 49 | # Set the directory for the stored image 50 | image_dir = os.path.join(os.getcwd(), 'images') 51 | 52 | # If the directory doesn't exist, create it 53 | if not os.path.isdir(image_dir): 54 | os.mkdir(image_dir) 55 | 56 | # Initialize the image path (note the filetype should be png) 57 | image_path = os.path.join(image_dir, file_name) 58 | 59 | # Retrieve the generated image 60 | generated_image = requests.get(image_url).content # download the image 61 | with open(image_path, "wb") as image_file: 62 | image_file.write(generated_image) 63 | print (f"Image saved as {image_path}") 64 | 65 | 66 | if __name__ == '__main__': 67 | main() -------------------------------------------------------------------------------- /Labfiles/analyze-images/c-sharp/image-analysis/appsettings.json: -------------------------------------------------------------------------------- 1 | { 2 | "AIServicesEndpoint": "YOUR_AZURE_AI_SERVICES_ENDPOINT", 3 | "AIServicesKey": "YOUR_AZURE_AI_SERVICES_KEY" 4 | } 5 | -------------------------------------------------------------------------------- /Labfiles/analyze-images/c-sharp/image-analysis/image-analysis.csproj: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | Exe 5 | netcoreapp8.0 6 | image_analysis 7 | 1998 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | PreserveNewest 19 | 20 | 21 | 22 | 23 | -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | Exe 5 | net8.0 6 | dalle_client 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | PreserveNewest 17 | 18 | 19 | 20 | 21 | -------------------------------------------------------------------------------- /Labfiles/dalle-client/python/.env: -------------------------------------------------------------------------------- 1 | PROJECT_CONNECTION="your_project_endpoint" 2 | MODEL_DEPLOYMENT="your_model_deployment" 3 | -------------------------------------------------------------------------------- /Labfiles/dalle-client/python/dalle-client.py: -------------------------------------------------------------------------------- 1 | import os 2 | import json 3 | 4 | # Add references 5 | 6 | 7 | def main(): 8 | 9 | # Clear the console 10 | os.system('cls' if os.name=='nt' else 'clear') 11 | 12 | try: 13 | 14 | # Get configuration settings 15 | load_dotenv() 16 | project_connection = os.getenv("PROJECT_CONNECTION") 17 | model_deployment = os.getenv("MODEL_DEPLOYMENT") 18 | 19 | # Initialize the OpenAI client1 20 | 21 | 22 | 23 | 24 | img_no = 0 25 | # Loop until the user types 'quit' 26 | while True: 27 | # Get input text 28 | input_text = input("Enter the prompt (or type 'quit' to exit): ") 29 | if input_text.lower() == "quit": 30 | break 31 | if len(input_text) == 0: 32 | print("Please enter a prompt.") 33 | continue 34 | 35 | # Generate an image 36 | 37 | 38 | # save the image 39 | img_no += 1 40 | file_name = f"image_{img_no}.png" 41 | save_image (image_url, file_name) 42 | 43 | 44 | except Exception as ex: 45 | print(ex) 46 | 47 | def save_image (image_url, file_name): 48 | # Set the directory for the stored image 49 | image_dir = os.path.join(os.getcwd(), 'images') 50 | 51 | # If the directory doesn't exist, create it 52 | if not os.path.isdir(image_dir): 53 | os.mkdir(image_dir) 54 | 55 | # Initialize the image path (note the filetype should be png) 56 | image_path = os.path.join(image_dir, file_name) 57 | 58 | # Retrieve the generated image 59 | generated_image = requests.get(image_url).content # download the image 60 | with open(image_path, "wb") as image_file: 61 | image_file.write(generated_image) 62 | print (f"Image saved as {image_path}") 63 | 64 | 65 | if __name__ == '__main__': 66 | main() -------------------------------------------------------------------------------- /Labfiles/dalle-client/python/requirements.txt: -------------------------------------------------------------------------------- 1 | dotenv 2 | -------------------------------------------------------------------------------- /Labfiles/face/c-sharp/face-api/analyze-faces.csproj: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | Exe 5 | netcoreapp8.0 6 | analyze_faces 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | PreserveNewest 18 | 19 | 20 | 21 | 22 | -------------------------------------------------------------------------------- /Labfiles/face/c-sharp/face-api/appsettings.json: -------------------------------------------------------------------------------- 1 | { 2 | "AIServicesEndpoint": "YOUR_AI_SERVICES_ENDPOINT", 3 | "AIServiceKey": "YOUR_AI_SERVICES_KEY" 4 | } -------------------------------------------------------------------------------- /Labfiles/face/c-sharp/face-api/images/face1.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MicrosoftLearning/mslearn-ai-vision/dc2d1beeb596267a4e77f20185debaad195ec13d/Labfiles/face/c-sharp/face-api/images/face1.jpg -------------------------------------------------------------------------------- /Labfiles/face/c-sharp/face-api/images/face2.jpg: -------------------------------------------------------------------------------- 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AI_SERVICE_ENDPOINT=your_ai_services_endpoint 2 | AI_SERVICE_KEY=your_ai_services_key -------------------------------------------------------------------------------- /Labfiles/face/python/face-api/analyze-faces.py: -------------------------------------------------------------------------------- 1 | from dotenv import load_dotenv 2 | import os 3 | import sys 4 | from PIL import Image, ImageDraw 5 | from matplotlib import pyplot as plt 6 | 7 | # Import namespaces 8 | 9 | 10 | def main(): 11 | 12 | # Clear the console 13 | os.system('cls' if os.name=='nt' else 'clear') 14 | 15 | try: 16 | # Get Configuration Settings 17 | load_dotenv() 18 | cog_endpoint = os.getenv('AI_SERVICE_ENDPOINT') 19 | cog_key = os.getenv('AI_SERVICE_KEY') 20 | 21 | # Get image 22 | image_file = 'images/face1.jpg' 23 | if len(sys.argv) > 1: 24 | image_file = sys.argv[1] 25 | 26 | 27 | # Authenticate Face client 28 | 29 | 30 | 31 | # Specify facial features to be retrieved 32 | 33 | 34 | # Get faces 35 | 36 | 37 | 38 | except Exception as ex: 39 | print(ex) 40 | 41 | def annotate_faces(image_file, detected_faces): 42 | print('\nAnnotating faces in image...') 43 | 44 | # Prepare image for drawing 45 | fig = plt.figure(figsize=(8, 6)) 46 | plt.axis('off') 47 | image = Image.open(image_file) 48 | draw = ImageDraw.Draw(image) 49 | color = 'lightgreen' 50 | 51 | # Annotate each face in the image 52 | face_count = 0 53 | for face in detected_faces: 54 | face_count += 1 55 | r = face.face_rectangle 56 | bounding_box = ((r.left, r.top), (r.left + r.width, r.top + r.height)) 57 | draw = ImageDraw.Draw(image) 58 | draw.rectangle(bounding_box, outline=color, width=5) 59 | annotation = 'Face number {}'.format(face_count) 60 | plt.annotate(annotation,(r.left, r.top), backgroundcolor=color) 61 | 62 | # Save annotated image 63 | plt.imshow(image) 64 | outputfile = 'detected_faces.jpg' 65 | fig.savefig(outputfile) 66 | print(f' Results saved in {outputfile}\n') 67 | 68 | 69 | if __name__ == "__main__": 70 | 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1 | import os 2 | from urllib.request import urlopen, Request 3 | import base64 4 | from pathlib import Path 5 | 6 | # Add references 7 | 8 | 9 | def main(): 10 | 11 | # Clear the console 12 | os.system('cls' if os.name=='nt' else 'clear') 13 | 14 | try: 15 | 16 | # Get configuration settings 17 | load_dotenv() 18 | project_connection = os.getenv("PROJECT_CONNECTION") 19 | model_deployment = os.getenv("MODEL_DEPLOYMENT") 20 | 21 | 22 | 23 | # Initialize the project client 24 | 25 | 26 | 27 | # Get a chat client 28 | 29 | 30 | 31 | 32 | # Initialize prompts 33 | system_message = "You are an AI assistant in a grocery store that sells fruit." 34 | prompt = "" 35 | 36 | # Loop until the user types 'quit' 37 | while True: 38 | prompt = input("\nAsk a question about the image\n(or type 'quit' to exit)\n") 39 | if prompt.lower() == "quit": 40 | break 41 | elif len(prompt) == 0: 42 | print("Please enter a question.\n") 43 | else: 44 | print("Getting a response ...\n") 45 | 46 | 47 | # Get a response to image input 48 | 49 | 50 | 51 | except Exception as ex: 52 | print(ex) 53 | 54 | 55 | if __name__ == '__main__': 56 | main() -------------------------------------------------------------------------------- /Labfiles/gen-ai-vision/python/mystery-fruit.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MicrosoftLearning/mslearn-ai-vision/dc2d1beeb596267a4e77f20185debaad195ec13d/Labfiles/gen-ai-vision/python/mystery-fruit.jpeg -------------------------------------------------------------------------------- /Labfiles/gen-ai-vision/python/requirements.txt: -------------------------------------------------------------------------------- 1 | dotenv 2 | -------------------------------------------------------------------------------- /Labfiles/image-classification/c-sharp/readme.txt: -------------------------------------------------------------------------------- 1 | This folder contains C# code 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PreserveNewest 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | -------------------------------------------------------------------------------- /Labfiles/image-classification/python/readme.txt: -------------------------------------------------------------------------------- 1 | This folder contains Python code -------------------------------------------------------------------------------- /Labfiles/image-classification/python/test-classifier/.env: -------------------------------------------------------------------------------- 1 | PredictionEndpoint=YOUR_PREDICTION_ENDPOINT 2 | PredictionKey=YOUR_PREDICTION_KEY 3 | ProjectID=YOUR_PROJECT_ID 4 | ModelName=fruit-classifier -------------------------------------------------------------------------------- /Labfiles/image-classification/python/test-classifier/requirements.txt: -------------------------------------------------------------------------------- 1 | dotenv 2 | 3 | -------------------------------------------------------------------------------- /Labfiles/image-classification/python/test-classifier/test-classifier.py: -------------------------------------------------------------------------------- 1 | from azure.cognitiveservices.vision.customvision.prediction import CustomVisionPredictionClient 2 | from msrest.authentication import ApiKeyCredentials 3 | import os 4 | 5 | def main(): 6 | from dotenv import load_dotenv 7 | 8 | # Clear the console 9 | os.system('cls' if os.name=='nt' else 'clear') 10 | 11 | try: 12 | # Get Configuration Settings 13 | load_dotenv() 14 | prediction_endpoint = os.getenv('PredictionEndpoint') 15 | prediction_key = os.getenv('PredictionKey') 16 | project_id = os.getenv('ProjectID') 17 | model_name = os.getenv('ModelName') 18 | 19 | # Authenticate a client for the prediction API 20 | credentials = ApiKeyCredentials(in_headers={"Prediction-key": prediction_key}) 21 | prediction_client = CustomVisionPredictionClient(endpoint=prediction_endpoint, credentials=credentials) 22 | 23 | # Classify test images 24 | for image in os.listdir('test-images'): 25 | image_data = open(os.path.join('test-images',image), "rb").read() 26 | results = prediction_client.classify_image(project_id, model_name, image_data) 27 | 28 | # Loop over each label prediction and print any with probability > 50% 29 | for prediction in results.predictions: 30 | if prediction.probability > 0.5: 31 | print(image, ': {} ({:.0%})'.format(prediction.tag_name, prediction.probability)) 32 | except Exception as ex: 33 | print(ex) 34 | 35 | if __name__ == "__main__": 36 | main() 37 | 38 | -------------------------------------------------------------------------------- /Labfiles/image-classification/python/test-classifier/test-images/IMG_TEST_1.jpg: -------------------------------------------------------------------------------- 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3 | Analyze Video 4 | 5 |
6 | 7 |

Video Analysis

8 | 9 | 10 | 14 | 18 | 19 |
11 | 12 | 13 | 15 | 16 | 17 |
20 | 21 | -------------------------------------------------------------------------------- /Labfiles/video-indexer/get-videos.ps1: -------------------------------------------------------------------------------- 1 | $account_id="YOUR_ACCOUNT_ID" 2 | $api_key="YOUR_API_KEY" 3 | $location="trial" 4 | 5 | # Clear the console window 6 | cls 7 | 8 | # Call the AccessToken method with the API key in the header to get an access token 9 | $token = Invoke-RestMethod -Method "Get" -Uri "https://api.videoindexer.ai/auth/$location/Accounts/$account_id/AccessToken" -Headers @{'Ocp-Apim-Subscription-Key' = $api_key} 10 | 11 | # Use the access token to make an authenticated call to the Videos method to get a list of videos in the account 12 | Invoke-RestMethod -Method "Get" -Uri "https://api.videoindexer.ai/$location/Accounts/$account_id/Videos?accessToken=$token" | ConvertTo-Json -Depth 6 13 | -------------------------------------------------------------------------------- /Labfiles/video-indexer/responsible_ai.mp4: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MicrosoftLearning/mslearn-ai-vision/dc2d1beeb596267a4e77f20185debaad195ec13d/Labfiles/video-indexer/responsible_ai.mp4 -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Develop computer vision solutions in Azure 2 | 3 | The exercises in this repo are designed to provide you with a hands-on learning experience in which you'll explore common tasks that developers perform when creating computer vision solutions on Microsoft Azure. 4 | 5 | > **Note**: To complete the exercises, you'll need an Azure subscription in which you have sufficient permissions and quota to provision the necessary Azure resources and generative AI models. If you don't already have one, you can sign up for an [Azure account](https://azure.microsoft.com/free). There's a free trial option for new users that includes credits for the first 30 days. 6 | 7 | [View the exercises in the GitHub Pages site for this repo](https://go.microsoft.com/fwlink/?linkid=2318640) 8 | 9 | > **Note**: While you can complete these exercises on their own, they're designed to complement modules on [Microsoft Learn](https://learn.microsoft.com/training/paths/create-computer-vision-solutions-azure-ai/); in which you'll find a deeper dive into some of the underlying concepts on which these exercises are based. 10 | 11 | -------------------------------------------------------------------------------- /_build.yml: -------------------------------------------------------------------------------- 1 | name: '$(Date:yyyyMMdd)$(Rev:.rr)' 2 | jobs: 3 | - job: build_markdown_content 4 | displayName: 'Build Markdown Content' 5 | workspace: 6 | clean: all 7 | pool: 8 | vmImage: 'Ubuntu 16.04' 9 | container: 10 | image: 'microsoftlearning/markdown-build:latest' 11 | steps: 12 | - task: Bash@3 13 | displayName: 'Build Content' 14 | inputs: 15 | targetType: inline 16 | script: | 17 | cp /{attribution.md,template.docx,package.json,package.js} . 18 | npm install 19 | node package.js --version $(Build.BuildNumber) 20 | - task: GitHubRelease@0 21 | displayName: 'Create GitHub Release' 22 | inputs: 23 | gitHubConnection: 'github-microsoftlearning-organization' 24 | repositoryName: '$(Build.Repository.Name)' 25 | tagSource: manual 26 | tag: 'v$(Build.BuildNumber)' 27 | title: 'Version $(Build.BuildNumber)' 28 | releaseNotesSource: input 29 | releaseNotes: '# Version $(Build.BuildNumber) Release' 30 | assets: '$(Build.SourcesDirectory)/out/*.zip' 31 | assetUploadMode: replace 32 | - task: PublishBuildArtifacts@1 33 | displayName: 'Publish Output Files' 34 | inputs: 35 | pathtoPublish: '$(Build.SourcesDirectory)/out/' 36 | artifactName: 'Lab Files' 37 | -------------------------------------------------------------------------------- /_config.yml: -------------------------------------------------------------------------------- 1 | remote_theme: MicrosoftLearning/Jekyll-Theme 2 | exclude: 3 | - readme.md 4 | - .github/ 5 | header_pages: 6 | - index.html 7 | author: Microsoft Learning 8 | twitter_username: mslearning 9 | github_username: MicrosoftLearning 10 | plugins: 11 | - jekyll-sitemap 12 | - jekyll-mentions 13 | - jemoji 14 | markdown: kramdown 15 | kramdown: 16 | syntax_highlighter_opts: 17 | disable : true 18 | title: Develop computer vision solutions in Azure 19 | -------------------------------------------------------------------------------- /index.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: Develop computer vision solutions in Azure 3 | permalink: index.html 4 | layout: home 5 | --- 6 | 7 | The following exercises are designed to provide you with a hands-on learning experience in which you'll explore common tasks that developers perform when creating computer vision solutions on Microsoft Azure. 8 | 9 | > **Note**: To complete the exercises, you'll need an Azure subscription in which you have sufficient permissions and quota to provision the necessary Azure resources and generative AI models. If you don't already have one, you can sign up for an [Azure account](https://azure.microsoft.com/free). There's a free trial option for new users that includes credits for the first 30 days. 10 | 11 | ## Exercises 12 | 13 | {% assign labs = site.pages | where_exp:"page", "page.url contains '/Instructions/Labs'" %} 14 | {% for activity in labs %} 15 |
16 | ### [{{ activity.lab.title }}]({{ site.github.url }}{{ activity.url }}) 17 | {{activity.lab.description}} 18 | {% endfor %} 19 | 20 | > **Note**: While you can complete these exercises on their own, they're designed to complement modules on [Microsoft Learn](https://learn.microsoft.com/training/paths/create-computer-vision-solutions-azure-ai/); in which you'll find a deeper dive into some of the underlying concepts on which these exercises are based. 21 | --------------------------------------------------------------------------------