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sample |
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image-analysis-samples |
These are runnable console Python programs that show how to use the Image Analysis client library. They cover all the supported visual features. Most use the a synchronous client to analyze an image file or image URL. Two samples use the asynchronous client. The concepts are similar, you can easily modify any of the samples to your needs.
| File Name | Description |
|---|---|
| sample_analyze_all_image_file.py | Extract all 7 visual features from an image file, using a synchronous client. Logging turned on. |
| sample_caption_image_file.py and sample_caption_image_url.py | Generate a human-readable sentence that describes the content of an image file or image URL, using a synchronous client. |
| sample_dense_captions_image_file.py | Generating a human-readable caption for up to 10 different regions in the image, including one for the whole image, using a synchronous client. |
| sample_ocr_image_file.py and sample_ocr_image_url.py | Extract printed or handwritten text from an image file or image URL, using a synchronous client. |
| sample_tags_image_file.py | Extract content tags for thousands of recognizable objects, living beings, scenery, and actions that appear in an image file, using a synchronous client. |
| sample_objects_image_file.py | Detect physical objects in an image file and return their location, using a synchronous client. |
| sample_smart_crops_image_file.py | Find a representative sub-region of the image for thumbnail generation, using a synchronous client . |
| sample_people_image_file.py | Locate people in the image and return their location, using a synchronous client. |
| File Name | Description |
|---|---|
| sample_caption_image_file_async.py | Generate a human-readable sentence that describes the content of an image file, using an asynchronous client. |
| sample_ocr_image_url_async.py | Extract printed or handwritten text from an image URL, using an asynchronous client. |
See Prerequisites here.
- Clone or download this sample repository
- Open a command prompt / terminal window in this samples folder
- Install the Image Analysis client library for Python with pip:
pip install azure-ai-vision-imageanalysis
- If you plan to run the asynchronous client samples, insall the additional package aiohttp:
pip install aiohttp
See Set environment variables here.
To run the first sample, type:
python sample_analyze_all_image_file.pysimilarly for the other samples.
The sample sample_analyze_all_image_file.py analyzes the image sample.jpg in this folder:
And produces an output similar to the following:
Image analysis results:
Caption:
'a person wearing a mask sitting at a table with a laptop', Confidence 0.8498
Dense Captions:
'a person wearing a mask sitting at a table with a laptop', {'x': 0, 'y': 0, 'w': 864, 'h': 576}, Confidence: 0.8498
'a person using a laptop', {'x': 293, 'y': 383, 'w': 195, 'h': 100}, Confidence: 0.7724
'a person wearing a face mask', {'x': 383, 'y': 233, 'w': 275, 'h': 336}, Confidence: 0.8209
'a close-up of a green chair', {'x': 616, 'y': 211, 'w': 164, 'h': 249}, Confidence: 0.8763
'a person wearing a colorful cloth face mask', {'x': 473, 'y': 294, 'w': 68, 'h': 56}, Confidence: 0.7086
'a person using a laptop', {'x': 288, 'y': 211, 'w': 151, 'h': 244}, Confidence: 0.7642
'a person wearing a colorful fabric face mask', {'x': 433, 'y': 240, 'w': 180, 'h': 236}, Confidence: 0.7734
'a close-up of a laptop on a table', {'x': 115, 'y': 443, 'w': 476, 'h': 125}, Confidence: 0.8537
'a person wearing a mask and using a laptop', {'x': 0, 'y': 0, 'w': 774, 'h': 432}, Confidence: 0.7816
'a close up of a text', {'x': 714, 'y': 493, 'w': 130, 'h': 80}, Confidence: 0.6407
Read:
Line: 'Sample text', Bounding box [{'x': 721, 'y': 502}, {'x': 843, 'y': 502}, {'x': 843, 'y': 519}, {'x': 721, 'y': 519}]
Word: 'Sample', Bounding polygon [{'x': 722, 'y': 503}, {'x': 785, 'y': 503}, {'x': 785, 'y': 520}, {'x': 722, 'y': 520}], Confidence 0.9930
Word: 'text', Bounding polygon [{'x': 800, 'y': 503}, {'x': 842, 'y': 502}, {'x': 842, 'y': 519}, {'x': 800, 'y': 520}], Confidence 0.9890
Line: 'Hand writing', Bounding box [{'x': 720, 'y': 525}, {'x': 819, 'y': 526}, {'x': 819, 'y': 544}, {'x': 720, 'y': 543}]
Word: 'Hand', Bounding polygon [{'x': 721, 'y': 526}, {'x': 759, 'y': 526}, {'x': 759, 'y': 544}, {'x': 721, 'y': 543}], Confidence 0.9890
Word: 'writing', Bounding polygon [{'x': 765, 'y': 526}, {'x': 819, 'y': 527}, {'x': 819, 'y': 545}, {'x': 765, 'y': 544}], Confidence 0.9940
Line: '123 456', Bounding box [{'x': 721, 'y': 548}, {'x': 791, 'y': 548}, {'x': 791, 'y': 563}, {'x': 721, 'y': 564}]
Word: '123', Bounding polygon [{'x': 723, 'y': 548}, {'x': 750, 'y': 548}, {'x': 750, 'y': 564}, {'x': 723, 'y': 564}], Confidence 0.9940
Word: '456', Bounding polygon [{'x': 761, 'y': 548}, {'x': 788, 'y': 549}, {'x': 787, 'y': 564}, {'x': 760, 'y': 564}], Confidence 0.9990
Tags:
'furniture', Confidence 0.9874
'clothing', Confidence 0.9793
'person', Confidence 0.9427
'houseplant', Confidence 0.9400
'desk', Confidence 0.9183
'indoor', Confidence 0.8964
'laptop', Confidence 0.8782
'computer', Confidence 0.8482
'sitting', Confidence 0.8135
'wall', Confidence 0.7512
'woman', Confidence 0.7411
'table', Confidence 0.6811
'plant', Confidence 0.6445
'using', Confidence 0.5359
Objects:
'chair', {'x': 603, 'y': 225, 'w': 152, 'h': 224}, Confidence: 0.6180
'person', {'x': 399, 'y': 244, 'w': 249, 'h': 325}, Confidence: 0.8810
'Laptop', {'x': 295, 'y': 387, 'w': 211, 'h': 102}, Confidence: 0.7670
'chair', {'x': 441, 'y': 436, 'w': 256, 'h': 136}, Confidence: 0.5810
'dining table', {'x': 123, 'y': 437, 'w': 460, 'h': 125}, Confidence: 0.6060
People:
{'x': 395, 'y': 241, 'w': 261, 'h': 333}, Confidence 0.9603
{'x': 831, 'y': 246, 'w': 31, 'h': 255}, Confidence 0.0017
Smart Cropping:
Aspect ratio 0.9: Smart crop {'x': 238, 'y': 0, 'w': 511, 'h': 568}
Aspect ratio 1.33: Smart crop {'x': 54, 'y': 0, 'w': 760, 'h': 571}
Image height: 576
Image width: 864
Model version: 2023-10-01
See Troubleshooting here.