Re-generate Image Analysis SDK with: tox run -e generate -c ..\..\..\eng\tox\tox.ini --root . (#33772)
* After running tox run -e generate -c ..\..\..\eng\tox\tox.ini --root . * Run again, this time with added pyproject.toml * Pick up new TypeSpec change, with prop name `list` instead of `values` * Update samples & test to use 'list' prop name instead of 'values' * No need to suppress mypy `attr-defined` error. Update README.md code snippets * Use uniform style in sample code * Hand fix emitter bug (GitHub issue filed). Remove mypy.ini (no longer needed). Fix code style I don't like * Fix pylint issues
This commit is contained in:
Darren Cohen
GitHub
Родитель
da79b36422
Коммит
9fb905c405
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@ -53,7 +53,7 @@ from azure.ai.vision.imageanalysis import ImageAnalysisClient
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from azure.ai.vision.imageanalysis.models import VisualFeatures
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from azure.core.credentials import AzureKeyCredential
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# Set the values of your computer vision endpoint and computer vision key
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# Set the values of your computer vision endpoint and computer vision key
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# as environment variables:
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try:
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endpoint = os.environ["VISION_ENDPOINT"]
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@ -65,8 +65,8 @@ except KeyError:
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# Create an Image Analysis client for synchronous operations
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client = ImageAnalysisClient(
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endpoint = endpoint,
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credential = AzureKeyCredential(key)
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endpoint=endpoint,
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credential=AzureKeyCredential(key)
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)
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```
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@ -135,14 +135,14 @@ Notes:
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```python
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# Load image to analyze into a 'bytes' object
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with open("sample.jpg", 'rb') as f:
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with open("sample.jpg", "rb") as f:
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image_data = f.read()
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# Get a caption for the image. This will be a synchronously (blocking) call.
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result = client.analyze(
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image_data = image_data,
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visual_features = [ VisualFeatures.CAPTION ],
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gender_neutral_caption = True # Optional (default is False)
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image_data=image_data,
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visual_features=[VisualFeatures.CAPTION],
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gender_neutral_caption=True, # Optional (default is False)
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)
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# Print caption results to the console
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@ -165,9 +165,9 @@ This example is similar to the above, expect it calls the `analyze` method and p
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```python
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# Get a caption for the image. This will be a synchronously (blocking) call.
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result = client.analyze(
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image_url = "https://aka.ms/azsdk/image-analysis/sample.jpg",
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visual_features = [ VisualFeatures.CAPTION ],
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gender_neutral_caption = True # Optional (default is False)
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image_url="https://aka.ms/azsdk/image-analysis/sample.jpg",
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visual_features=[VisualFeatures.CAPTION],
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gender_neutral_caption=True, # Optional (default is False)
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)
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# Print caption results to the console
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@ -187,13 +187,13 @@ This example demonstrates how to extract printed or hand-written text for the im
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```python
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# Load image to analyze into a 'bytes' object
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with open("sample.jpg", 'rb') as f:
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with open("sample.jpg", "rb") as f:
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image_data = f.read()
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# Extract text (OCR) from an image stream. This will be a synchronously (blocking) call.
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result = client.analyze(
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image_data = image_data,
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visual_features = [ VisualFeatures.READ ]
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image_data=image_data,
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visual_features=[VisualFeatures.READ]
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)
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# Print text (OCR) analysis results to the console
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@ -221,8 +221,8 @@ This example is similar to the above, expect it calls the `analyze` method and p
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```python
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# Extract text (OCR) from an image stream. This will be a synchronously (blocking) call.
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result = client.analyze(
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image_url = "https://aka.ms/azsdk/image-analysis/sample.jpg",
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visual_features = [ VisualFeatures.READ ]
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image_url="https://aka.ms/azsdk/image-analysis/sample.jpg",
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visual_features=[VisualFeatures.READ]
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)
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# Print text (OCR) analysis results to the console
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@ -277,21 +277,21 @@ The client uses the standard [Python logging library](https://docs.python.org/3/
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import sys
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import logging
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# Acquire the logger for this client library. Use 'azure' to affect both
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# Acquire the logger for this client library. Use 'azure' to affect both
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# 'azure.core` and `azure.ai.vision.imageanalysis' libraries.
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logger = logging.getLogger('azure')
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logger = logging.getLogger("azure")
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# Set the desired logging level. logging.INFO or logging.DEBUG are good options.
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logger.setLevel(logging.INFO)
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# Direct logging output to stdout (the default):
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handler = logging.StreamHandler(stream = sys.stdout)
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handler = logging.StreamHandler(stream=sys.stdout)
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# Or direct logging output to a file:
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# handler = logging.FileHandler(filename = 'sample.log')
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logger.addHandler(handler)
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# Optional: change the default logging format. Here we add a timestamp.
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formatter = logging.Formatter('%(asctime)s:%(levelname)s:%(name)s:%(message)s')
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formatter = logging.Formatter("%(asctime)s:%(levelname)s:%(name)s:%(message)s")
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handler.setFormatter(formatter)
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```
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@ -304,9 +304,9 @@ By default logs redact the values of URL query strings, the values of some HTTP
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```python
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# Create an Image Analysis client with none redacted log
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client = ImageAnalysisClient(
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endpoint = endpoint,
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credential = AzureKeyCredential(key),
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logging_enable = True
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endpoint=endpoint,
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credential=AzureKeyCredential(key),
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logging_enable=True
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)
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```
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@ -6,21 +6,17 @@
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# Changes may cause incorrect behavior and will be lost if the code is regenerated.
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# --------------------------------------------------------------------------
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from ._client import ImageAnalysisClient
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from ._patch import ImageAnalysisClient
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from ._version import VERSION
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__version__ = VERSION
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try:
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from ._patch import __all__ as _patch_all
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from ._patch import * # pylint: disable=unused-wildcard-import
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except ImportError:
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_patch_all = []
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from ._patch import patch_sdk as _patch_sdk
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__all__ = [
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"ImageAnalysisClient",
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]
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__all__.extend([p for p in _patch_all if p not in __all__])
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_patch_sdk()
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@ -147,8 +147,7 @@ class ImageAnalysisClientOperationsMixin(ImageAnalysisClientMixinABC):
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:paramtype visual_features: list[str or ~azure.ai.vision.imageanalysis.models.VisualFeatures]
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:keyword language: The desired language for result generation (a two-letter language code).
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If this option is not specified, the default value 'en' is used (English).
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See https://aka.ms/cv-languages for a list of supported languages.
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At the moment, only tags can be generated in non-English languages. Default value is None.
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See https://aka.ms/cv-languages for a list of supported languages. Default value is None.
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:paramtype language: str
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:keyword gender_neutral_caption: Boolean flag for enabling gender-neutral captioning for
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Caption and Dense Captions features.
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@ -468,8 +467,7 @@ class ImageAnalysisClientOperationsMixin(ImageAnalysisClientMixinABC):
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:paramtype visual_features: list[str or ~azure.ai.vision.imageanalysis.models.VisualFeatures]
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:keyword language: The desired language for result generation (a two-letter language code).
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If this option is not specified, the default value 'en' is used (English).
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See https://aka.ms/cv-languages for a list of supported languages.
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At the moment, only tags can be generated in non-English languages. Default value is None.
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See https://aka.ms/cv-languages for a list of supported languages. Default value is None.
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:paramtype language: str
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:keyword gender_neutral_caption: Boolean flag for enabling gender-neutral captioning for
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Caption and Dense Captions features.
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|
|
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@ -5,7 +5,8 @@
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# --------------------------------------------------------------------------
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from typing import List
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__all__: List[str] = [] # Add all objects you want publicly available to users at this package level
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__all__: List[str] = [] # Add all objects you want publicly available to users at this package level
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def patch_sdk():
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"""Do not remove from this file.
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@ -21,6 +21,7 @@ from . import models as _models
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from ._operations._operations import ImageAnalysisClientOperationsMixin
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from ._client import ImageAnalysisClient as ImageAnalysisClientGenerated
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class ImageAnalysisClient(ImageAnalysisClientGenerated):
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"""ImageAnalysisClient.
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@ -115,29 +116,32 @@ class ImageAnalysisClient(ImageAnalysisClientGenerated):
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visual_features_impl: List[Union[str, _models.VisualFeatures]] = list(visual_features)
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if image_url is not None:
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return ImageAnalysisClientOperationsMixin._analyze_from_url( # pylint: disable=protected-access
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return ImageAnalysisClientOperationsMixin._analyze_from_url( # pylint: disable=protected-access
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self,
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image_content = _models._models.ImageUrl(url = image_url), # pylint: disable=protected-access
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visual_features = visual_features_impl,
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language = language,
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gender_neutral_caption = gender_neutral_caption,
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smart_crops_aspect_ratios = smart_crops_aspect_ratios,
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model_version = model_version,
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**kwargs)
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image_content=_models._models.ImageUrl(url=image_url), # pylint: disable=protected-access
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visual_features=visual_features_impl,
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language=language,
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gender_neutral_caption=gender_neutral_caption,
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smart_crops_aspect_ratios=smart_crops_aspect_ratios,
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model_version=model_version,
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**kwargs
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)
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if image_data is not None:
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return ImageAnalysisClientOperationsMixin._analyze_from_buffer( # pylint: disable=protected-access
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return ImageAnalysisClientOperationsMixin._analyze_from_buffer( # pylint: disable=protected-access
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self,
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image_content = image_data,
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visual_features = visual_features_impl,
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language = language,
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gender_neutral_caption = gender_neutral_caption,
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smart_crops_aspect_ratios = smart_crops_aspect_ratios,
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model_version = model_version,
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**kwargs)
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image_content=image_data,
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visual_features=visual_features_impl,
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language=language,
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gender_neutral_caption=gender_neutral_caption,
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smart_crops_aspect_ratios=smart_crops_aspect_ratios,
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model_version=model_version,
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**kwargs
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)
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raise ValueError("Either image_data or image_url must be specified.")
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__all__: List[str] = [
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"ImageAnalysisClient"
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] # Add all objects you want publicly available to users at this package level
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|
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@ -6,18 +6,14 @@
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# Changes may cause incorrect behavior and will be lost if the code is regenerated.
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# --------------------------------------------------------------------------
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from ._client import ImageAnalysisClient
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from ._patch import ImageAnalysisClient
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try:
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from ._patch import __all__ as _patch_all
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from ._patch import * # pylint: disable=unused-wildcard-import
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except ImportError:
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_patch_all = []
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from ._patch import patch_sdk as _patch_sdk
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__all__ = [
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"ImageAnalysisClient",
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]
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__all__.extend([p for p in _patch_all if p not in __all__])
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_patch_sdk()
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|
|
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@ -5,7 +5,8 @@
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# --------------------------------------------------------------------------
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from typing import List
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__all__: List[str] = [] # Add all objects you want publicly available to users at this package level
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__all__: List[str] = [] # Add all objects you want publicly available to users at this package level
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def patch_sdk():
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"""Do not remove from this file.
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|
|
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@ -21,6 +21,7 @@ from .. import models as _models
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from ._operations._operations import ImageAnalysisClientOperationsMixin
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from ._client import ImageAnalysisClient as ImageAnalysisClientGenerated
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class ImageAnalysisClient(ImageAnalysisClientGenerated):
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"""ImageAnalysisClient.
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@ -115,29 +116,32 @@ class ImageAnalysisClient(ImageAnalysisClientGenerated):
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visual_features_impl: List[Union[str, _models.VisualFeatures]] = list(visual_features)
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if image_url is not None:
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return await ImageAnalysisClientOperationsMixin._analyze_from_url( # pylint: disable=protected-access
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return await ImageAnalysisClientOperationsMixin._analyze_from_url( # pylint: disable=protected-access
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self,
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image_content = _models._models.ImageUrl(url = image_url), # pylint: disable=protected-access
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visual_features = visual_features_impl,
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language = language,
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gender_neutral_caption = gender_neutral_caption,
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smart_crops_aspect_ratios = smart_crops_aspect_ratios,
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model_version = model_version,
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**kwargs)
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image_content=_models._models.ImageUrl(url=image_url), # pylint: disable=protected-access
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visual_features=visual_features_impl,
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language=language,
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gender_neutral_caption=gender_neutral_caption,
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smart_crops_aspect_ratios=smart_crops_aspect_ratios,
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model_version=model_version,
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**kwargs
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)
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if image_data is not None:
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return await ImageAnalysisClientOperationsMixin._analyze_from_buffer( # pylint: disable=protected-access
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return await ImageAnalysisClientOperationsMixin._analyze_from_buffer( # pylint: disable=protected-access
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self,
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image_content = image_data,
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visual_features = visual_features_impl,
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language = language,
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gender_neutral_caption = gender_neutral_caption,
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smart_crops_aspect_ratios = smart_crops_aspect_ratios,
|
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model_version = model_version,
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**kwargs)
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image_content=image_data,
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visual_features=visual_features_impl,
|
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language=language,
|
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gender_neutral_caption=gender_neutral_caption,
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smart_crops_aspect_ratios=smart_crops_aspect_ratios,
|
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model_version=model_version,
|
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**kwargs
|
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)
|
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|
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raise ValueError("Either image_data or image_url must be specified.")
|
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|
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__all__: List[str] = [
|
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"ImageAnalysisClient"
|
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] # Add all objects you want publicly available to users at this package level
|
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|
|
|
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|
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@ -157,18 +157,18 @@ class DenseCaptionsResult(_model_base.Model):
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All required parameters must be populated in order to send to server.
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:ivar values: The list of image captions. Required.
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:vartype values: list[~azure.ai.vision.imageanalysis.models.DenseCaption]
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:ivar list: The list of image captions. Required.
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:vartype list: list[~azure.ai.vision.imageanalysis.models.DenseCaption]
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"""
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values: List["_models.DenseCaption"] = rest_field()
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list: List["_models.DenseCaption"] = rest_field(name="values")
|
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"""The list of image captions. Required."""
|
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|
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@overload
|
||||
def __init__(
|
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self,
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*,
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values: List["_models.DenseCaption"],
|
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list: List["_models.DenseCaption"],
|
||||
):
|
||||
...
|
||||
|
||||
|
|
@ -627,18 +627,18 @@ class ObjectsResult(_model_base.Model):
|
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|
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All required parameters must be populated in order to send to server.
|
||||
|
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:ivar values: A list of physical object detected in an image and their location. Required.
|
||||
:vartype values: list[~azure.ai.vision.imageanalysis.models.DetectedObject]
|
||||
:ivar list: A list of physical object detected in an image and their location. Required.
|
||||
:vartype list: list[~azure.ai.vision.imageanalysis.models.DetectedObject]
|
||||
"""
|
||||
|
||||
values: List["_models.DetectedObject"] = rest_field()
|
||||
list: List["_models.DetectedObject"] = rest_field(name="values")
|
||||
"""A list of physical object detected in an image and their location. Required."""
|
||||
|
||||
@overload
|
||||
def __init__(
|
||||
self,
|
||||
*,
|
||||
values: List["_models.DetectedObject"],
|
||||
list: List["_models.DetectedObject"],
|
||||
):
|
||||
...
|
||||
|
||||
|
|
@ -658,18 +658,18 @@ class PeopleResult(_model_base.Model):
|
|||
|
||||
All required parameters must be populated in order to send to server.
|
||||
|
||||
:ivar values: A list of people detected in an image and their location. Required.
|
||||
:vartype values: list[~azure.ai.vision.imageanalysis.models.DetectedPerson]
|
||||
:ivar list: A list of people detected in an image and their location. Required.
|
||||
:vartype list: list[~azure.ai.vision.imageanalysis.models.DetectedPerson]
|
||||
"""
|
||||
|
||||
values: List["_models.DetectedPerson"] = rest_field()
|
||||
list: List["_models.DetectedPerson"] = rest_field(name="values")
|
||||
"""A list of people detected in an image and their location. Required."""
|
||||
|
||||
@overload
|
||||
def __init__(
|
||||
self,
|
||||
*,
|
||||
values: List["_models.DetectedPerson"],
|
||||
list: List["_models.DetectedPerson"],
|
||||
):
|
||||
...
|
||||
|
||||
|
|
@ -725,18 +725,18 @@ class SmartCropsResult(_model_base.Model):
|
|||
|
||||
All required parameters must be populated in order to send to server.
|
||||
|
||||
:ivar values: A list of crop regions. Required.
|
||||
:vartype values: list[~azure.ai.vision.imageanalysis.models.CropRegion]
|
||||
:ivar list: A list of crop regions. Required.
|
||||
:vartype list: list[~azure.ai.vision.imageanalysis.models.CropRegion]
|
||||
"""
|
||||
|
||||
values: List["_models.CropRegion"] = rest_field()
|
||||
list: List["_models.CropRegion"] = rest_field(name="values")
|
||||
"""A list of crop regions. Required."""
|
||||
|
||||
@overload
|
||||
def __init__(
|
||||
self,
|
||||
*,
|
||||
values: List["_models.CropRegion"],
|
||||
list: List["_models.CropRegion"],
|
||||
):
|
||||
...
|
||||
|
||||
|
|
@ -758,18 +758,18 @@ class TagsResult(_model_base.Model):
|
|||
|
||||
All required parameters must be populated in order to send to server.
|
||||
|
||||
:ivar values: A list of tags. Required.
|
||||
:vartype values: list[~azure.ai.vision.imageanalysis.models.DetectedTag]
|
||||
:ivar list: A list of tags. Required.
|
||||
:vartype list: list[~azure.ai.vision.imageanalysis.models.DetectedTag]
|
||||
"""
|
||||
|
||||
values: List["_models.DetectedTag"] = rest_field()
|
||||
list: List["_models.DetectedTag"] = rest_field(name="values")
|
||||
"""A list of tags. Required."""
|
||||
|
||||
@overload
|
||||
def __init__(
|
||||
self,
|
||||
*,
|
||||
values: List["_models.DetectedTag"],
|
||||
list: List["_models.DetectedTag"],
|
||||
):
|
||||
...
|
||||
|
||||
|
|
|
|||
|
|
@ -1,13 +0,0 @@
|
|||
# Per advice from Azure SDK team, suppress the assignment error in _models.py. This file was auto-generated from TypeSpec files, and produces these MyPy errors:
|
||||
# azure\ai\vision\imageanalysis\models\_models.py:164: error: Incompatible types in assignment (expression has type "List[DenseCaption]", base class "_MyMutableMapping" defined the type as "Callable[[_MyMutableMapping], ValuesView[Any]]") [assignment]
|
||||
# azure\ai\vision\imageanalysis\models\_models.py:634: error: Incompatible types in assignment (expression has type "List[DetectedObject]", base class "_MyMutableMapping" defined the type as "Callable[[_MyMutableMapping], ValuesView[Any]]") [assignment]
|
||||
# azure\ai\vision\imageanalysis\models\_models.py:665: error: Incompatible types in assignment (expression has type "List[DetectedPerson]", base class "_MyMutableMapping" defined the type as "Callable[[_MyMutableMapping], ValuesView[Any]]") [assignment]
|
||||
# azure\ai\vision\imageanalysis\models\_models.py:732: error: Incompatible types in assignment (expression has type "List[CropRegion]", base class "_MyMutableMapping" defined the type as "Callable[[_MyMutableMapping], ValuesView[Any]]") [assignment]
|
||||
# azure\ai\vision\imageanalysis\models\_models.py:765: error: Incompatible types in assignment (expression has type "List[DetectedTag]", base class "_MyMutableMapping" defined the type as "Callable[[_MyMutableMapping], ValuesView[Any]]") [assignment]
|
||||
# See GitHub issue: https://github.com/Azure/autorest.python/issues/2317
|
||||
[mypy-azure.ai.vision.imageanalysis.models._models]
|
||||
disable_error_code = assignment
|
||||
|
||||
# Note that we also have the line `# mypy: disable-error-code="attr-defined"` at the top of every sample code file
|
||||
# under the "samples" folder. This is to suppress the MyPy error "ImageAnalysisClient has no attribute analyze [attr-defined]".
|
||||
# See GitHub issue: https://github.com/Azure/autorest.python/issues/2321
|
||||
|
|
@ -0,0 +1,2 @@
|
|||
[tool.generate]
|
||||
autorest-post-process = true
|
||||
|
|
@ -2,7 +2,6 @@
|
|||
# Copyright (c) Microsoft Corporation.
|
||||
# Licensed under the MIT License.
|
||||
# ------------------------------------
|
||||
# mypy: disable-error-code="attr-defined"
|
||||
"""
|
||||
DESCRIPTION:
|
||||
This sample demonstrates how to generate a human-readable sentence that describes the content
|
||||
|
|
@ -28,6 +27,7 @@ USAGE:
|
|||
"""
|
||||
import asyncio
|
||||
|
||||
|
||||
async def sample_caption_image_file_async():
|
||||
import os
|
||||
from azure.ai.vision.imageanalysis.aio import ImageAnalysisClient
|
||||
|
|
@ -44,19 +44,19 @@ async def sample_caption_image_file_async():
|
|||
exit()
|
||||
|
||||
# Load image to analyze into a 'bytes' object
|
||||
with open("sample.jpg", 'rb') as f:
|
||||
with open("sample.jpg", "rb") as f:
|
||||
image_data = f.read()
|
||||
|
||||
# Create an asynchronous Image Analysis client
|
||||
client = ImageAnalysisClient(
|
||||
endpoint = endpoint,
|
||||
credential = AzureKeyCredential(key)
|
||||
endpoint=endpoint,
|
||||
credential=AzureKeyCredential(key)
|
||||
)
|
||||
|
||||
# Get a caption for the image, asynchronously.
|
||||
result = await client.analyze(
|
||||
image_data = image_data,
|
||||
visual_features = [ VisualFeatures.CAPTION ]
|
||||
image_data=image_data,
|
||||
visual_features=[VisualFeatures.CAPTION]
|
||||
)
|
||||
|
||||
await client.close()
|
||||
|
|
@ -74,5 +74,6 @@ async def sample_caption_image_file_async():
|
|||
async def main():
|
||||
await sample_caption_image_file_async()
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
|
|
|
|||
|
|
@ -2,7 +2,6 @@
|
|||
# Copyright (c) Microsoft Corporation.
|
||||
# Licensed under the MIT License.
|
||||
# ------------------------------------
|
||||
# mypy: disable-error-code="attr-defined"
|
||||
"""
|
||||
DESCRIPTION:
|
||||
This sample demonstrates how to extract printed or hand-written text from a
|
||||
|
|
@ -31,6 +30,7 @@ USAGE:
|
|||
"""
|
||||
import asyncio
|
||||
|
||||
|
||||
async def sample_ocr_image_file_async():
|
||||
import os
|
||||
from azure.ai.vision.imageanalysis.aio import ImageAnalysisClient
|
||||
|
|
@ -48,14 +48,14 @@ async def sample_ocr_image_file_async():
|
|||
|
||||
# Create an asynchronous Image Analysis client
|
||||
client = ImageAnalysisClient(
|
||||
endpoint = endpoint,
|
||||
credential = AzureKeyCredential(key)
|
||||
endpoint=endpoint,
|
||||
credential=AzureKeyCredential(key)
|
||||
)
|
||||
|
||||
# Extract text (OCR) from an image URL, asynchronously.
|
||||
result = await client.analyze(
|
||||
image_url = "https://aka.ms/azsdk/image-analysis/sample.jpg",
|
||||
visual_features = [ VisualFeatures.READ ]
|
||||
image_url="https://aka.ms/azsdk/image-analysis/sample.jpg",
|
||||
visual_features=[VisualFeatures.READ]
|
||||
)
|
||||
|
||||
await client.close()
|
||||
|
|
@ -76,5 +76,6 @@ async def sample_ocr_image_file_async():
|
|||
async def main():
|
||||
await sample_ocr_image_file_async()
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
|
|
|
|||
|
|
@ -2,7 +2,6 @@
|
|||
# Copyright (c) Microsoft Corporation.
|
||||
# Licensed under the MIT License.
|
||||
# ------------------------------------
|
||||
# mypy: disable-error-code="attr-defined"
|
||||
"""
|
||||
DESCRIPTION:
|
||||
This sample demonstrates how to analyze all supported visual features from the image file sample.jpg,
|
||||
|
|
@ -26,6 +25,8 @@ USAGE:
|
|||
where `your-resource-name` is your unique Azure Computer Vision resource name.
|
||||
2) VISION_KEY - Your Computer Vision key (a 32-character Hexadecimal number)
|
||||
"""
|
||||
|
||||
|
||||
def sample_analyze_all_image_file():
|
||||
import os
|
||||
from azure.ai.vision.imageanalysis import ImageAnalysisClient
|
||||
|
|
@ -36,21 +37,21 @@ def sample_analyze_all_image_file():
|
|||
import sys
|
||||
import logging
|
||||
|
||||
# Acquire the logger for this client library. Use 'azure' to affect both
|
||||
# Acquire the logger for this client library. Use 'azure' to affect both
|
||||
# 'azure.core` and `azure.ai.vision.imageanalysis' libraries.
|
||||
logger = logging.getLogger('azure')
|
||||
logger = logging.getLogger("azure")
|
||||
|
||||
# Set the desired logging level. logging.INFO or logging.DEBUG are good options.
|
||||
logger.setLevel(logging.INFO)
|
||||
|
||||
# Direct logging output to stdout (the default):
|
||||
handler = logging.StreamHandler(stream = sys.stdout)
|
||||
handler = logging.StreamHandler(stream=sys.stdout)
|
||||
# Or direct logging output to a file:
|
||||
# handler = logging.FileHandler(filename = 'sample.log')
|
||||
logger.addHandler(handler)
|
||||
|
||||
# Optional: change the default logging format. Here we add a timestamp.
|
||||
formatter = logging.Formatter('%(asctime)s:%(levelname)s:%(name)s:%(message)s')
|
||||
formatter = logging.Formatter("%(asctime)s:%(levelname)s:%(name)s:%(message)s")
|
||||
handler.setFormatter(formatter)
|
||||
# [END logging]
|
||||
|
||||
|
|
@ -64,34 +65,34 @@ def sample_analyze_all_image_file():
|
|||
exit()
|
||||
|
||||
# Load image to analyze into a 'bytes' object
|
||||
with open("sample.jpg", 'rb') as f:
|
||||
with open("sample.jpg", "rb") as f:
|
||||
image_data = f.read()
|
||||
|
||||
# [START create_client_with_logging]
|
||||
# Create an Image Analysis client with none redacted log
|
||||
client = ImageAnalysisClient(
|
||||
endpoint = endpoint,
|
||||
credential = AzureKeyCredential(key),
|
||||
logging_enable = True
|
||||
endpoint=endpoint,
|
||||
credential=AzureKeyCredential(key),
|
||||
logging_enable=True
|
||||
)
|
||||
# [END create_client_with_logging]
|
||||
|
||||
# Analyze all visual features from an image stream. This will be a synchronously (blocking) call.
|
||||
result = client.analyze(
|
||||
image_data = image_data,
|
||||
visual_features = [
|
||||
image_data=image_data,
|
||||
visual_features=[
|
||||
VisualFeatures.TAGS,
|
||||
VisualFeatures.OBJECTS,
|
||||
VisualFeatures.CAPTION,
|
||||
VisualFeatures.DENSE_CAPTIONS,
|
||||
VisualFeatures.READ,
|
||||
VisualFeatures.SMART_CROPS,
|
||||
VisualFeatures.PEOPLE
|
||||
], # Mandatory. Select one or more visual features to analyze.
|
||||
smart_crops_aspect_ratios = [0.9, 1.33], # Optional. Relevant only if SMART_CROPS was specified above.
|
||||
gender_neutral_caption = True, # Optional. Relevant only if CAPTION or DENSE_CAPTIONS were specified above.
|
||||
language = "en", # Optional. Relevant only if TAGS is specified above. See https://aka.ms/cv-languages for supported languages.
|
||||
model_version = "latest" # Optional. Analysis model version to use. Defaults to "latest".
|
||||
VisualFeatures.PEOPLE,
|
||||
], # Mandatory. Select one or more visual features to analyze.
|
||||
smart_crops_aspect_ratios=[0.9, 1.33], # Optional. Relevant only if SMART_CROPS was specified above.
|
||||
gender_neutral_caption=True, # Optional. Relevant only if CAPTION or DENSE_CAPTIONS were specified above.
|
||||
language="en", # Optional. Relevant only if TAGS is specified above. See https://aka.ms/cv-languages for supported languages.
|
||||
model_version="latest", # Optional. Analysis model version to use. Defaults to "latest".
|
||||
)
|
||||
|
||||
# Print all analysis results to the console
|
||||
|
|
@ -103,7 +104,7 @@ def sample_analyze_all_image_file():
|
|||
|
||||
if result.dense_captions is not None:
|
||||
print(" Dense Captions:")
|
||||
for caption in result.dense_captions.values:
|
||||
for caption in result.dense_captions.list:
|
||||
print(f" '{caption.text}', {caption.bounding_box}, Confidence: {caption.confidence:.4f}")
|
||||
|
||||
if result.read is not None:
|
||||
|
|
@ -115,22 +116,22 @@ def sample_analyze_all_image_file():
|
|||
|
||||
if result.tags is not None:
|
||||
print(" Tags:")
|
||||
for tag in result.tags.values:
|
||||
for tag in result.tags.list:
|
||||
print(f" '{tag.name}', Confidence {tag.confidence:.4f}")
|
||||
|
||||
if result.objects is not None:
|
||||
print(" Objects:")
|
||||
for object in result.objects.values:
|
||||
for object in result.objects.list:
|
||||
print(f" '{object.tags[0].name}', {object.bounding_box}, Confidence: {object.tags[0].confidence:.4f}")
|
||||
|
||||
if result.people is not None:
|
||||
print(" People:")
|
||||
for person in result.people.values:
|
||||
for person in result.people.list:
|
||||
print(f" {person.bounding_box}, Confidence {person.confidence:.4f}")
|
||||
|
||||
if result.smart_crops is not None:
|
||||
print(" Smart Cropping:")
|
||||
for smart_crop in result.smart_crops.values:
|
||||
for smart_crop in result.smart_crops.list:
|
||||
print(f" Aspect ratio {smart_crop.aspect_ratio}: Smart crop {smart_crop.bounding_box}")
|
||||
|
||||
print(f" Image height: {result.metadata.height}")
|
||||
|
|
@ -138,5 +139,5 @@ def sample_analyze_all_image_file():
|
|||
print(f" Model version: {result.model_version}")
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
if __name__ == "__main__":
|
||||
sample_analyze_all_image_file()
|
||||
|
|
|
|||
|
|
@ -2,7 +2,6 @@
|
|||
# Copyright (c) Microsoft Corporation.
|
||||
# Licensed under the MIT License.
|
||||
# ------------------------------------
|
||||
# mypy: disable-error-code="attr-defined"
|
||||
"""
|
||||
DESCRIPTION:
|
||||
This sample demonstrates how to generate a human-readable sentence that describes the content
|
||||
|
|
@ -26,6 +25,8 @@ USAGE:
|
|||
where `your-resource-name` is your unique Azure Computer Vision resource name.
|
||||
2) VISION_KEY - Your Computer Vision key (a 32-character Hexadecimal number)
|
||||
"""
|
||||
|
||||
|
||||
def sample_caption_image_file():
|
||||
# [START create_client]
|
||||
import os
|
||||
|
|
@ -33,7 +34,7 @@ def sample_caption_image_file():
|
|||
from azure.ai.vision.imageanalysis.models import VisualFeatures
|
||||
from azure.core.credentials import AzureKeyCredential
|
||||
|
||||
# Set the values of your computer vision endpoint and computer vision key
|
||||
# Set the values of your computer vision endpoint and computer vision key
|
||||
# as environment variables:
|
||||
try:
|
||||
endpoint = os.environ["VISION_ENDPOINT"]
|
||||
|
|
@ -45,21 +46,21 @@ def sample_caption_image_file():
|
|||
|
||||
# Create an Image Analysis client for synchronous operations
|
||||
client = ImageAnalysisClient(
|
||||
endpoint = endpoint,
|
||||
credential = AzureKeyCredential(key)
|
||||
endpoint=endpoint,
|
||||
credential=AzureKeyCredential(key)
|
||||
)
|
||||
# [END create_client]
|
||||
|
||||
# [START caption]
|
||||
# Load image to analyze into a 'bytes' object
|
||||
with open("sample.jpg", 'rb') as f:
|
||||
with open("sample.jpg", "rb") as f:
|
||||
image_data = f.read()
|
||||
|
||||
# Get a caption for the image. This will be a synchronously (blocking) call.
|
||||
result = client.analyze(
|
||||
image_data = image_data,
|
||||
visual_features = [ VisualFeatures.CAPTION ],
|
||||
gender_neutral_caption = True # Optional (default is False)
|
||||
image_data=image_data,
|
||||
visual_features=[VisualFeatures.CAPTION],
|
||||
gender_neutral_caption=True, # Optional (default is False)
|
||||
)
|
||||
|
||||
# Print caption results to the console
|
||||
|
|
@ -73,5 +74,5 @@ def sample_caption_image_file():
|
|||
print(f" Model version: {result.model_version}")
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
if __name__ == "__main__":
|
||||
sample_caption_image_file()
|
||||
|
|
|
|||
|
|
@ -2,7 +2,6 @@
|
|||
# Copyright (c) Microsoft Corporation.
|
||||
# Licensed under the MIT License.
|
||||
# ------------------------------------
|
||||
# mypy: disable-error-code="attr-defined"
|
||||
"""
|
||||
DESCRIPTION:
|
||||
This sample demonstrates how to generate a human-readable sentence that describes the content
|
||||
|
|
@ -26,13 +25,15 @@ USAGE:
|
|||
where `your-resource-name` is your unique Azure Computer Vision resource name.
|
||||
2) VISION_KEY - Your Computer Vision key (a 32-character Hexadecimal number)
|
||||
"""
|
||||
|
||||
|
||||
def sample_caption_image_url():
|
||||
import os
|
||||
from azure.ai.vision.imageanalysis import ImageAnalysisClient
|
||||
from azure.ai.vision.imageanalysis.models import VisualFeatures
|
||||
from azure.core.credentials import AzureKeyCredential
|
||||
|
||||
# Set the values of your computer vision endpoint and computer vision key
|
||||
# Set the values of your computer vision endpoint and computer vision key
|
||||
# as environment variables:
|
||||
try:
|
||||
endpoint = os.environ["VISION_ENDPOINT"]
|
||||
|
|
@ -44,16 +45,16 @@ def sample_caption_image_url():
|
|||
|
||||
# Create an Image Analysis client
|
||||
client = ImageAnalysisClient(
|
||||
endpoint = endpoint,
|
||||
credential = AzureKeyCredential(key)
|
||||
endpoint=endpoint,
|
||||
credential=AzureKeyCredential(key)
|
||||
)
|
||||
|
||||
# [START caption]
|
||||
# Get a caption for the image. This will be a synchronously (blocking) call.
|
||||
result = client.analyze(
|
||||
image_url = "https://aka.ms/azsdk/image-analysis/sample.jpg",
|
||||
visual_features = [ VisualFeatures.CAPTION ],
|
||||
gender_neutral_caption = True # Optional (default is False)
|
||||
image_url="https://aka.ms/azsdk/image-analysis/sample.jpg",
|
||||
visual_features=[VisualFeatures.CAPTION],
|
||||
gender_neutral_caption=True, # Optional (default is False)
|
||||
)
|
||||
|
||||
# Print caption results to the console
|
||||
|
|
@ -67,5 +68,5 @@ def sample_caption_image_url():
|
|||
print(f" Model version: {result.model_version}")
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
if __name__ == "__main__":
|
||||
sample_caption_image_url()
|
||||
|
|
|
|||
|
|
@ -2,7 +2,6 @@
|
|||
# Copyright (c) Microsoft Corporation.
|
||||
# Licensed under the MIT License.
|
||||
# ------------------------------------
|
||||
# mypy: disable-error-code="attr-defined"
|
||||
"""
|
||||
DESCRIPTION:
|
||||
This sample demonstrates how to generate up to 10 human-readable sentences (captions) that describe
|
||||
|
|
@ -30,13 +29,15 @@ USAGE:
|
|||
where `your-resource-name` is your unique Azure Computer Vision resource name.
|
||||
2) VISION_KEY - Your Computer Vision key (a 32-character Hexadecimal number)
|
||||
"""
|
||||
|
||||
|
||||
def sample_dense_captions_image_file():
|
||||
import os
|
||||
from azure.ai.vision.imageanalysis import ImageAnalysisClient
|
||||
from azure.ai.vision.imageanalysis.models import VisualFeatures
|
||||
from azure.core.credentials import AzureKeyCredential
|
||||
|
||||
# Set the values of your computer vision endpoint and computer vision key
|
||||
# Set the values of your computer vision endpoint and computer vision key
|
||||
# as environment variables:
|
||||
try:
|
||||
endpoint = os.environ["VISION_ENDPOINT"]
|
||||
|
|
@ -48,20 +49,20 @@ def sample_dense_captions_image_file():
|
|||
|
||||
# Create an Image Analysis client.
|
||||
client = ImageAnalysisClient(
|
||||
endpoint = endpoint,
|
||||
credential = AzureKeyCredential(key)
|
||||
endpoint=endpoint,
|
||||
credential=AzureKeyCredential(key)
|
||||
)
|
||||
|
||||
# Load image to analyze into a 'bytes' object.
|
||||
with open("sample.jpg", 'rb') as f:
|
||||
with open("sample.jpg", "rb") as f:
|
||||
image_data = f.read()
|
||||
|
||||
# Extract multiple captions, each for a different area of the image.
|
||||
# This will be a synchronously (blocking) call.
|
||||
result = client.analyze(
|
||||
image_data = image_data,
|
||||
visual_features = [ VisualFeatures.DENSE_CAPTIONS ],
|
||||
gender_neutral_caption = True # Optional (default is False)
|
||||
image_data=image_data,
|
||||
visual_features=[VisualFeatures.DENSE_CAPTIONS],
|
||||
gender_neutral_caption=True, # Optional (default is False)
|
||||
)
|
||||
|
||||
# Print dense caption results to the console. The first caption always
|
||||
|
|
@ -69,12 +70,12 @@ def sample_dense_captions_image_file():
|
|||
print("Image analysis results:")
|
||||
print(" Dense Captions:")
|
||||
if result.dense_captions is not None:
|
||||
for caption in result.dense_captions.values:
|
||||
for caption in result.dense_captions.list:
|
||||
print(f" '{caption.text}', {caption.bounding_box}, Confidence: {caption.confidence:.4f}")
|
||||
print(f" Image height: {result.metadata.height}")
|
||||
print(f" Image width: {result.metadata.width}")
|
||||
print(f" Model version: {result.model_version}")
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
sample_dense_captions_image_file()
|
||||
if __name__ == "__main__":
|
||||
sample_dense_captions_image_file()
|
||||
|
|
|
|||
|
|
@ -2,7 +2,6 @@
|
|||
# Copyright (c) Microsoft Corporation.
|
||||
# Licensed under the MIT License.
|
||||
# ------------------------------------
|
||||
# mypy: disable-error-code="attr-defined"
|
||||
"""
|
||||
DESCRIPTION:
|
||||
This sample demonstrates how to detect physical objects in an image file sample.jpg, using a synchronous client.
|
||||
|
|
@ -23,13 +22,15 @@ USAGE:
|
|||
where `your-resource-name` is your unique Azure Computer Vision resource name.
|
||||
2) VISION_KEY - Your Computer Vision key (a 32-character Hexadecimal number)
|
||||
"""
|
||||
|
||||
|
||||
def sample_objects_image_file():
|
||||
import os
|
||||
from azure.ai.vision.imageanalysis import ImageAnalysisClient
|
||||
from azure.ai.vision.imageanalysis.models import VisualFeatures
|
||||
from azure.core.credentials import AzureKeyCredential
|
||||
|
||||
# Set the values of your computer vision endpoint and computer vision key
|
||||
# Set the values of your computer vision endpoint and computer vision key
|
||||
# as environment variables:
|
||||
try:
|
||||
endpoint = os.environ["VISION_ENDPOINT"]
|
||||
|
|
@ -41,30 +42,30 @@ def sample_objects_image_file():
|
|||
|
||||
# Create an Image Analysis client
|
||||
client = ImageAnalysisClient(
|
||||
endpoint = endpoint,
|
||||
credential = AzureKeyCredential(key)
|
||||
endpoint=endpoint,
|
||||
credential=AzureKeyCredential(key)
|
||||
)
|
||||
|
||||
# Load image to analyze into a 'bytes' object
|
||||
with open("sample.jpg", 'rb') as f:
|
||||
with open("sample.jpg", "rb") as f:
|
||||
image_data = f.read()
|
||||
|
||||
# Detect objects in an image stream. This will be a synchronously (blocking) call.
|
||||
result = client.analyze(
|
||||
image_data = image_data,
|
||||
visual_features = [ VisualFeatures.OBJECTS ]
|
||||
image_data=image_data,
|
||||
visual_features=[VisualFeatures.OBJECTS]
|
||||
)
|
||||
|
||||
# Print Objects analysis results to the console
|
||||
print("Image analysis results:")
|
||||
print(" Objects:")
|
||||
if result.objects is not None:
|
||||
for object in result.objects.values:
|
||||
for object in result.objects.list:
|
||||
print(f" '{object.tags[0].name}', {object.bounding_box}, Confidence: {object.tags[0].confidence:.4f}")
|
||||
print(f" Image height: {result.metadata.height}")
|
||||
print(f" Image width: {result.metadata.width}")
|
||||
print(f" Model version: {result.model_version}")
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
sample_objects_image_file()
|
||||
if __name__ == "__main__":
|
||||
sample_objects_image_file()
|
||||
|
|
|
|||
|
|
@ -2,7 +2,6 @@
|
|||
# Copyright (c) Microsoft Corporation.
|
||||
# Licensed under the MIT License.
|
||||
# ------------------------------------
|
||||
# mypy: disable-error-code="attr-defined"
|
||||
"""
|
||||
DESCRIPTION:
|
||||
This sample demonstrates how to extract printed or hand-written text for the image file sample.jpg
|
||||
|
|
@ -29,13 +28,15 @@ USAGE:
|
|||
where `your-resource-name` is your unique Azure Computer Vision resource name.
|
||||
2) VISION_KEY - Your Computer Vision key (a 32-character Hexadecimal number)
|
||||
"""
|
||||
|
||||
|
||||
def sample_ocr_image_file():
|
||||
import os
|
||||
from azure.ai.vision.imageanalysis import ImageAnalysisClient
|
||||
from azure.ai.vision.imageanalysis.models import VisualFeatures
|
||||
from azure.core.credentials import AzureKeyCredential
|
||||
|
||||
# Set the values of your computer vision endpoint and computer vision key
|
||||
# Set the values of your computer vision endpoint and computer vision key
|
||||
# as environment variables:
|
||||
try:
|
||||
endpoint = os.environ["VISION_ENDPOINT"]
|
||||
|
|
@ -47,19 +48,19 @@ def sample_ocr_image_file():
|
|||
|
||||
# Create an Image Analysis client
|
||||
client = ImageAnalysisClient(
|
||||
endpoint = endpoint,
|
||||
credential = AzureKeyCredential(key)
|
||||
endpoint=endpoint,
|
||||
credential=AzureKeyCredential(key)
|
||||
)
|
||||
|
||||
# [START read]
|
||||
# Load image to analyze into a 'bytes' object
|
||||
with open("sample.jpg", 'rb') as f:
|
||||
with open("sample.jpg", "rb") as f:
|
||||
image_data = f.read()
|
||||
|
||||
# Extract text (OCR) from an image stream. This will be a synchronously (blocking) call.
|
||||
result = client.analyze(
|
||||
image_data = image_data,
|
||||
visual_features = [ VisualFeatures.READ ]
|
||||
image_data=image_data,
|
||||
visual_features=[VisualFeatures.READ]
|
||||
)
|
||||
|
||||
# Print text (OCR) analysis results to the console
|
||||
|
|
@ -76,5 +77,5 @@ def sample_ocr_image_file():
|
|||
print(f" Model version: {result.model_version}")
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
sample_ocr_image_file()
|
||||
if __name__ == "__main__":
|
||||
sample_ocr_image_file()
|
||||
|
|
|
|||
|
|
@ -2,7 +2,6 @@
|
|||
# Copyright (c) Microsoft Corporation.
|
||||
# Licensed under the MIT License.
|
||||
# ------------------------------------
|
||||
# mypy: disable-error-code="attr-defined"
|
||||
"""
|
||||
DESCRIPTION:
|
||||
This sample demonstrates how to extract printed or hand-written text from a
|
||||
|
|
@ -29,13 +28,15 @@ USAGE:
|
|||
where `your-resource-name` is your unique Azure Computer Vision resource name.
|
||||
2) VISION_KEY - Your Computer Vision key (a 32-character Hexadecimal number)
|
||||
"""
|
||||
|
||||
|
||||
def sample_ocr_image_url():
|
||||
import os
|
||||
from azure.ai.vision.imageanalysis import ImageAnalysisClient
|
||||
from azure.ai.vision.imageanalysis.models import VisualFeatures
|
||||
from azure.core.credentials import AzureKeyCredential
|
||||
|
||||
# Set the values of your computer vision endpoint and computer vision key
|
||||
# Set the values of your computer vision endpoint and computer vision key
|
||||
# as environment variables:
|
||||
try:
|
||||
endpoint = os.environ["VISION_ENDPOINT"]
|
||||
|
|
@ -47,15 +48,15 @@ def sample_ocr_image_url():
|
|||
|
||||
# Create an Image Analysis client
|
||||
client = ImageAnalysisClient(
|
||||
endpoint = endpoint,
|
||||
credential = AzureKeyCredential(key)
|
||||
endpoint=endpoint,
|
||||
credential=AzureKeyCredential(key)
|
||||
)
|
||||
|
||||
# [START read]
|
||||
# Extract text (OCR) from an image stream. This will be a synchronously (blocking) call.
|
||||
result = client.analyze(
|
||||
image_url = "https://aka.ms/azsdk/image-analysis/sample.jpg",
|
||||
visual_features = [ VisualFeatures.READ ]
|
||||
image_url="https://aka.ms/azsdk/image-analysis/sample.jpg",
|
||||
visual_features=[VisualFeatures.READ]
|
||||
)
|
||||
|
||||
# Print text (OCR) analysis results to the console
|
||||
|
|
@ -72,5 +73,5 @@ def sample_ocr_image_url():
|
|||
print(f" Model version: {result.model_version}")
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
sample_ocr_image_url()
|
||||
if __name__ == "__main__":
|
||||
sample_ocr_image_url()
|
||||
|
|
|
|||
|
|
@ -2,7 +2,6 @@
|
|||
# Copyright (c) Microsoft Corporation.
|
||||
# Licensed under the MIT License.
|
||||
# ------------------------------------
|
||||
# mypy: disable-error-code="attr-defined"
|
||||
"""
|
||||
DESCRIPTION:
|
||||
This sample demonstrates how to detect people in the image file sample.jpg using a synchronous client.
|
||||
|
|
@ -22,13 +21,15 @@ USAGE:
|
|||
where `your-resource-name` is your unique Azure Computer Vision resource name.
|
||||
2) VISION_KEY - Your Computer Vision key (a 32-character Hexadecimal number)
|
||||
"""
|
||||
|
||||
|
||||
def sample_people_image_file():
|
||||
import os
|
||||
from azure.ai.vision.imageanalysis import ImageAnalysisClient
|
||||
from azure.ai.vision.imageanalysis.models import VisualFeatures
|
||||
from azure.core.credentials import AzureKeyCredential
|
||||
|
||||
# Set the values of your computer vision endpoint and computer vision key
|
||||
# Set the values of your computer vision endpoint and computer vision key
|
||||
# as environment variables:
|
||||
try:
|
||||
endpoint = os.environ["VISION_ENDPOINT"]
|
||||
|
|
@ -40,31 +41,30 @@ def sample_people_image_file():
|
|||
|
||||
# Create an Image Analysis client
|
||||
client = ImageAnalysisClient(
|
||||
endpoint = endpoint,
|
||||
credential = AzureKeyCredential(key)
|
||||
endpoint=endpoint,
|
||||
credential=AzureKeyCredential(key)
|
||||
)
|
||||
|
||||
# Load image to analyze into a 'bytes' object
|
||||
with open("sample.jpg", 'rb') as f:
|
||||
with open("sample.jpg", "rb") as f:
|
||||
image_data = f.read()
|
||||
|
||||
# Find people in an image stream. This will be a synchronously (blocking) call.
|
||||
result = client.analyze(
|
||||
image_data = image_data,
|
||||
visual_features = [ VisualFeatures.PEOPLE ]
|
||||
image_data=image_data,
|
||||
visual_features=[VisualFeatures.PEOPLE]
|
||||
)
|
||||
|
||||
# Print People analysis results to the console
|
||||
print("Image analysis results:")
|
||||
print(" People:")
|
||||
if result.people is not None:
|
||||
for person in result.people.values:
|
||||
for person in result.people.list:
|
||||
print(f" {person.bounding_box}, Confidence {person.confidence:.4f}")
|
||||
print(f" Image height: {result.metadata.height}")
|
||||
print(f" Image width: {result.metadata.width}")
|
||||
print(f" Model version: {result.model_version}")
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
if __name__ == "__main__":
|
||||
sample_people_image_file()
|
||||
|
||||
|
|
|
|||
|
|
@ -2,7 +2,6 @@
|
|||
# Copyright (c) Microsoft Corporation.
|
||||
# Licensed under the MIT License.
|
||||
# ------------------------------------
|
||||
# mypy: disable-error-code="attr-defined"
|
||||
"""
|
||||
DESCRIPTION:
|
||||
This sample demonstrates how to find representatives sub-regions of the image file sample.jpg,
|
||||
|
|
@ -31,13 +30,15 @@ USAGE:
|
|||
where `your-resource-name` is your unique Azure Computer Vision resource name.
|
||||
2) VISION_KEY - Your Computer Vision key (a 32-character Hexadecimal number)
|
||||
"""
|
||||
|
||||
|
||||
def sample_smart_crops_image_file():
|
||||
import os
|
||||
from azure.ai.vision.imageanalysis import ImageAnalysisClient
|
||||
from azure.ai.vision.imageanalysis.models import VisualFeatures
|
||||
from azure.core.credentials import AzureKeyCredential
|
||||
|
||||
# Set the values of your computer vision endpoint and computer vision key
|
||||
# Set the values of your computer vision endpoint and computer vision key
|
||||
# as environment variables:
|
||||
try:
|
||||
endpoint = os.environ["VISION_ENDPOINT"]
|
||||
|
|
@ -49,31 +50,31 @@ def sample_smart_crops_image_file():
|
|||
|
||||
# Create an Image Analysis client
|
||||
client = ImageAnalysisClient(
|
||||
endpoint = endpoint,
|
||||
credential = AzureKeyCredential(key)
|
||||
endpoint=endpoint,
|
||||
credential=AzureKeyCredential(key)
|
||||
)
|
||||
|
||||
# Load image to analyze into a 'bytes' object
|
||||
with open("sample.jpg", 'rb') as f:
|
||||
with open("sample.jpg", "rb") as f:
|
||||
image_data = f.read()
|
||||
|
||||
# Do Smart Cropping analysis on an image stream. This will be a synchronously (blocking) call.
|
||||
result = client.analyze(
|
||||
image_data = image_data,
|
||||
visual_features = [ VisualFeatures.SMART_CROPS ],
|
||||
smart_crops_aspect_ratios = [0.9, 1.33] # Optional. Specify one more desired aspect ratios
|
||||
image_data=image_data,
|
||||
visual_features=[VisualFeatures.SMART_CROPS],
|
||||
smart_crops_aspect_ratios=[0.9, 1.33], # Optional. Specify one more desired aspect ratios
|
||||
)
|
||||
|
||||
# Print smart crop analysis results to the console
|
||||
print("Image analysis results:")
|
||||
print(" Smart Cropping:")
|
||||
if result.smart_crops is not None:
|
||||
for smart_crop in result.smart_crops.values:
|
||||
for smart_crop in result.smart_crops.list:
|
||||
print(f" Aspect ratio {smart_crop.aspect_ratio}: Smart crop {smart_crop.bounding_box}")
|
||||
print(f" Image height: {result.metadata.height}")
|
||||
print(f" Image width: {result.metadata.width}")
|
||||
print(f" Model version: {result.model_version}")
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
sample_smart_crops_image_file()
|
||||
if __name__ == "__main__":
|
||||
sample_smart_crops_image_file()
|
||||
|
|
|
|||
|
|
@ -2,7 +2,6 @@
|
|||
# Copyright (c) Microsoft Corporation.
|
||||
# Licensed under the MIT License.
|
||||
# ------------------------------------
|
||||
# mypy: disable-error-code="attr-defined"
|
||||
"""
|
||||
DESCRIPTION:
|
||||
This sample demonstrates how to extract content tags in an image file sample.jpg, using a synchronous client.
|
||||
|
|
@ -24,13 +23,15 @@ USAGE:
|
|||
where `your-resource-name` is your unique Azure Computer Vision resource name.
|
||||
2) VISION_KEY - Your Computer Vision key (a 32-character Hexadecimal number)
|
||||
"""
|
||||
|
||||
|
||||
def sample_tags_image_file():
|
||||
import os
|
||||
from azure.ai.vision.imageanalysis import ImageAnalysisClient
|
||||
from azure.ai.vision.imageanalysis.models import VisualFeatures
|
||||
from azure.core.credentials import AzureKeyCredential
|
||||
|
||||
# Set the values of your computer vision endpoint and computer vision key
|
||||
# Set the values of your computer vision endpoint and computer vision key
|
||||
# as environment variables:
|
||||
try:
|
||||
endpoint = os.environ["VISION_ENDPOINT"]
|
||||
|
|
@ -42,31 +43,31 @@ def sample_tags_image_file():
|
|||
|
||||
# Create an Image Analysis client
|
||||
client = ImageAnalysisClient(
|
||||
endpoint = endpoint,
|
||||
credential = AzureKeyCredential(key)
|
||||
endpoint=endpoint,
|
||||
credential=AzureKeyCredential(key)
|
||||
)
|
||||
|
||||
# Load image to analyze into a 'bytes' object
|
||||
with open("sample.jpg", 'rb') as f:
|
||||
with open("sample.jpg", "rb") as f:
|
||||
image_data = f.read()
|
||||
|
||||
# Do 'Tags' analysis on an image stream. This will be a synchronously (blocking) call.
|
||||
result = client.analyze(
|
||||
image_data = image_data,
|
||||
visual_features = [ VisualFeatures.TAGS ],
|
||||
language = "en" # Optional. See https://aka.ms/cv-languages for supported languages.
|
||||
image_data=image_data,
|
||||
visual_features=[VisualFeatures.TAGS],
|
||||
language="en", # Optional. See https://aka.ms/cv-languages for supported languages.
|
||||
)
|
||||
|
||||
# Print Tags analysis results to the console
|
||||
print("Image analysis results:")
|
||||
print(" Tags:")
|
||||
if result.tags is not None:
|
||||
for tag in result.tags.values:
|
||||
for tag in result.tags.list:
|
||||
print(f" '{tag.name}', Confidence {tag.confidence:.4f}")
|
||||
print(f" Image height: {result.metadata.height}")
|
||||
print(f" Image width: {result.metadata.width}")
|
||||
print(f" Model version: {result.model_version}")
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
if __name__ == "__main__":
|
||||
sample_tags_image_file()
|
||||
|
|
|
|||
|
|
@ -42,7 +42,6 @@ setup(
|
|||
"Programming Language :: Python",
|
||||
"Programming Language :: Python :: 3 :: Only",
|
||||
"Programming Language :: Python :: 3",
|
||||
"Programming Language :: Python :: 3.7",
|
||||
"Programming Language :: Python :: 3.8",
|
||||
"Programming Language :: Python :: 3.9",
|
||||
"Programming Language :: Python :: 3.10",
|
||||
|
|
@ -66,8 +65,8 @@ setup(
|
|||
},
|
||||
install_requires=[
|
||||
"isodate<1.0.0,>=0.6.1",
|
||||
"azure-core<2.0.0,>=1.28.0",
|
||||
"azure-core<2.0.0,>=1.29.5",
|
||||
"typing-extensions>=4.3.0; python_version<'3.8.0'",
|
||||
],
|
||||
python_requires=">=3.7",
|
||||
python_requires=">=3.8",
|
||||
)
|
||||
|
|
|
|||
|
|
@ -21,7 +21,7 @@ LOGGING_ENABLED = True
|
|||
if LOGGING_ENABLED:
|
||||
# Create a logger for the 'azure' SDK
|
||||
# See https://docs.python.org/3/library/logging.html
|
||||
logger = logging.getLogger('azure')
|
||||
logger = logging.getLogger("azure")
|
||||
logger.setLevel(logging.INFO) # INFO or DEBUG
|
||||
|
||||
# Configure a console output
|
||||
|
|
@ -31,8 +31,8 @@ if LOGGING_ENABLED:
|
|||
ServicePreparer = functools.partial(
|
||||
EnvironmentVariableLoader,
|
||||
"vision",
|
||||
vision_endpoint = "https://fake-resource-name.cognitiveservices.azure.com",
|
||||
vision_key = "00000000000000000000000000000000",
|
||||
vision_endpoint="https://fake-resource-name.cognitiveservices.azure.com",
|
||||
vision_key="00000000000000000000000000000000",
|
||||
)
|
||||
|
||||
|
||||
|
|
@ -55,214 +55,212 @@ class ImageAnalysisTestBase(AzureRecordedTestCase):
|
|||
key = kwargs.pop("vision_key")
|
||||
self._create_client(endpoint, key, sync, get_connection_url)
|
||||
|
||||
|
||||
def _create_client_for_authentication_failure(self, sync: bool, **kwargs):
|
||||
endpoint = kwargs.pop("vision_endpoint")
|
||||
key = "00000000000000000000000000000000"
|
||||
self._create_client(endpoint, key, sync, False)
|
||||
|
||||
|
||||
def _create_client(self, endpoint: str, key: str, sync: bool, get_connection_url: bool):
|
||||
credential = AzureKeyCredential(key)
|
||||
if sync:
|
||||
self.client = sdk.ImageAnalysisClient(
|
||||
endpoint = endpoint,
|
||||
credential = credential,
|
||||
logging_enable = LOGGING_ENABLED,
|
||||
raw_request_hook = self._raw_request_check if get_connection_url else None)
|
||||
endpoint=endpoint,
|
||||
credential=credential,
|
||||
logging_enable=LOGGING_ENABLED,
|
||||
raw_request_hook=self._raw_request_check if get_connection_url else None,
|
||||
)
|
||||
assert self.client is not None
|
||||
else:
|
||||
self.async_client = async_sdk.ImageAnalysisClient(
|
||||
endpoint = endpoint,
|
||||
credential = credential,
|
||||
logging_enable = LOGGING_ENABLED,
|
||||
raw_request_hook = self._raw_request_check if get_connection_url else None)
|
||||
endpoint=endpoint,
|
||||
credential=credential,
|
||||
logging_enable=LOGGING_ENABLED,
|
||||
raw_request_hook=self._raw_request_check if get_connection_url else None,
|
||||
)
|
||||
assert self.async_client is not None
|
||||
|
||||
|
||||
def _raw_request_check(self, request: PipelineRequest):
|
||||
self.connection_url = request.http_request.url
|
||||
print(f"Connection URL: {request.http_request.url}")
|
||||
|
||||
|
||||
def _do_analysis(
|
||||
self,
|
||||
image_source: str,
|
||||
visual_features: List[sdk.models.VisualFeatures],
|
||||
language: Optional[str] = None,
|
||||
gender_neutral_caption: Optional[bool] = None,
|
||||
smart_crops_aspect_ratios: Optional[List[float]] = None,
|
||||
model_version: Optional[str] = None,
|
||||
query_params: Optional[dict] = None,
|
||||
**kwargs):
|
||||
self,
|
||||
image_source: str,
|
||||
visual_features: List[sdk.models.VisualFeatures],
|
||||
language: Optional[str] = None,
|
||||
gender_neutral_caption: Optional[bool] = None,
|
||||
smart_crops_aspect_ratios: Optional[List[float]] = None,
|
||||
model_version: Optional[str] = None,
|
||||
query_params: Optional[dict] = None,
|
||||
**kwargs,
|
||||
):
|
||||
|
||||
image_content: Union[str, bytes]
|
||||
|
||||
if "http" in image_source:
|
||||
result = self.client.analyze(
|
||||
image_url = image_source,
|
||||
visual_features = visual_features,
|
||||
language = language,
|
||||
gender_neutral_caption = gender_neutral_caption,
|
||||
smart_crops_aspect_ratios = smart_crops_aspect_ratios,
|
||||
model_version = model_version,
|
||||
params = query_params)
|
||||
image_url=image_source,
|
||||
visual_features=visual_features,
|
||||
language=language,
|
||||
gender_neutral_caption=gender_neutral_caption,
|
||||
smart_crops_aspect_ratios=smart_crops_aspect_ratios,
|
||||
model_version=model_version,
|
||||
params=query_params,
|
||||
)
|
||||
else:
|
||||
# Load image to analyze into a 'bytes' object
|
||||
with open(image_source, 'rb') as f:
|
||||
with open(image_source, "rb") as f:
|
||||
image_data = bytes(f.read())
|
||||
|
||||
result = self.client.analyze(
|
||||
image_data = image_data,
|
||||
visual_features = visual_features,
|
||||
language = language,
|
||||
gender_neutral_caption = gender_neutral_caption,
|
||||
smart_crops_aspect_ratios = smart_crops_aspect_ratios,
|
||||
model_version = model_version,
|
||||
params = query_params)
|
||||
image_data=image_data,
|
||||
visual_features=visual_features,
|
||||
language=language,
|
||||
gender_neutral_caption=gender_neutral_caption,
|
||||
smart_crops_aspect_ratios=smart_crops_aspect_ratios,
|
||||
model_version=model_version,
|
||||
params=query_params,
|
||||
)
|
||||
|
||||
# Optional: console printout of all results
|
||||
if ImageAnalysisTestBase.PRINT_ANALYSIS_RESULTS:
|
||||
ImageAnalysisTestBase._print_analysis_results(result)
|
||||
|
||||
# Validate all results
|
||||
ImageAnalysisTestBase._validate_result(result, visual_features, gender_neutral_caption, smart_crops_aspect_ratios)
|
||||
ImageAnalysisTestBase._validate_result(
|
||||
result, visual_features, gender_neutral_caption, smart_crops_aspect_ratios
|
||||
)
|
||||
|
||||
# Validate that additional query parameters exists in the connection URL, if specify
|
||||
if query_params is not None:
|
||||
ImageAnalysisTestBase._validate_query_parameters(query_params, self.connection_url)
|
||||
|
||||
|
||||
async def _do_async_analysis(
|
||||
self,
|
||||
image_source: str,
|
||||
visual_features: List[sdk.models.VisualFeatures],
|
||||
language: Optional[str] = None,
|
||||
gender_neutral_caption: Optional[bool] = None,
|
||||
smart_crops_aspect_ratios: Optional[List[float]] = None,
|
||||
model_version: Optional[str] = None,
|
||||
query_params: Optional[dict] = None,
|
||||
**kwargs):
|
||||
self,
|
||||
image_source: str,
|
||||
visual_features: List[sdk.models.VisualFeatures],
|
||||
language: Optional[str] = None,
|
||||
gender_neutral_caption: Optional[bool] = None,
|
||||
smart_crops_aspect_ratios: Optional[List[float]] = None,
|
||||
model_version: Optional[str] = None,
|
||||
query_params: Optional[dict] = None,
|
||||
**kwargs,
|
||||
):
|
||||
|
||||
image_content: Union[str, bytes]
|
||||
|
||||
if "http" in image_source:
|
||||
result = await self.async_client.analyze(
|
||||
image_url = image_source,
|
||||
visual_features = visual_features,
|
||||
language = language,
|
||||
gender_neutral_caption = gender_neutral_caption,
|
||||
smart_crops_aspect_ratios = smart_crops_aspect_ratios,
|
||||
model_version = model_version,
|
||||
params = query_params)
|
||||
image_url=image_source,
|
||||
visual_features=visual_features,
|
||||
language=language,
|
||||
gender_neutral_caption=gender_neutral_caption,
|
||||
smart_crops_aspect_ratios=smart_crops_aspect_ratios,
|
||||
model_version=model_version,
|
||||
params=query_params,
|
||||
)
|
||||
|
||||
else:
|
||||
# Load image to analyze into a 'bytes' object
|
||||
with open(image_source, 'rb') as f:
|
||||
with open(image_source, "rb") as f:
|
||||
image_data = bytes(f.read())
|
||||
|
||||
result = await self.async_client.analyze(
|
||||
image_data = image_data,
|
||||
visual_features = visual_features,
|
||||
language = language,
|
||||
gender_neutral_caption = gender_neutral_caption,
|
||||
smart_crops_aspect_ratios = smart_crops_aspect_ratios,
|
||||
model_version = model_version,
|
||||
params = query_params)
|
||||
image_data=image_data,
|
||||
visual_features=visual_features,
|
||||
language=language,
|
||||
gender_neutral_caption=gender_neutral_caption,
|
||||
smart_crops_aspect_ratios=smart_crops_aspect_ratios,
|
||||
model_version=model_version,
|
||||
params=query_params,
|
||||
)
|
||||
|
||||
# Optional: console printout of all results
|
||||
if ImageAnalysisTestBase.PRINT_ANALYSIS_RESULTS:
|
||||
ImageAnalysisTestBase._print_analysis_results(result)
|
||||
|
||||
# Validate all results
|
||||
ImageAnalysisTestBase._validate_result(result, visual_features, gender_neutral_caption, smart_crops_aspect_ratios)
|
||||
ImageAnalysisTestBase._validate_result(
|
||||
result, visual_features, gender_neutral_caption, smart_crops_aspect_ratios
|
||||
)
|
||||
|
||||
# Validate that additional query parameters exists in the connection URL, if specify
|
||||
if query_params is not None:
|
||||
ImageAnalysisTestBase._validate_query_parameters(query_params, self.connection_url)
|
||||
|
||||
|
||||
def _do_analysis_with_error(
|
||||
self,
|
||||
image_source: str,
|
||||
visual_features: List[sdk.models.VisualFeatures],
|
||||
expected_status_code: int,
|
||||
expected_message_contains: str,
|
||||
**kwargs):
|
||||
self,
|
||||
image_source: str,
|
||||
visual_features: List[sdk.models.VisualFeatures],
|
||||
expected_status_code: int,
|
||||
expected_message_contains: str,
|
||||
**kwargs,
|
||||
):
|
||||
|
||||
image_content: Union[str, bytes]
|
||||
try:
|
||||
if "http" in image_source:
|
||||
result = self.client.analyze(
|
||||
image_url = image_source,
|
||||
visual_features = visual_features)
|
||||
result = self.client.analyze(image_url=image_source, visual_features=visual_features)
|
||||
else:
|
||||
# Load image to analyze into a 'bytes' object
|
||||
with open(image_source, 'rb') as f:
|
||||
with open(image_source, "rb") as f:
|
||||
image_data = bytes(f.read())
|
||||
|
||||
result = self.client.analyze(
|
||||
image_data = image_data,
|
||||
visual_features = visual_features)
|
||||
result = self.client.analyze(image_data=image_data, visual_features=visual_features)
|
||||
|
||||
except AzureError as e:
|
||||
print(e)
|
||||
assert hasattr(e, 'status_code')
|
||||
assert hasattr(e, "status_code")
|
||||
assert e.status_code == expected_status_code
|
||||
assert expected_message_contains in e.message
|
||||
return
|
||||
assert False # We should not get here
|
||||
|
||||
assert False # We should not get here
|
||||
|
||||
async def _do_async_analysis_with_error(
|
||||
self,
|
||||
image_source: str,
|
||||
visual_features: List[sdk.models.VisualFeatures],
|
||||
expected_status_code: int,
|
||||
expected_message_contains: str,
|
||||
**kwargs):
|
||||
self,
|
||||
image_source: str,
|
||||
visual_features: List[sdk.models.VisualFeatures],
|
||||
expected_status_code: int,
|
||||
expected_message_contains: str,
|
||||
**kwargs,
|
||||
):
|
||||
|
||||
image_content: Union[str, bytes]
|
||||
|
||||
try:
|
||||
if "http" in image_source:
|
||||
result = await self.async_client.analyze(
|
||||
image_url = image_source,
|
||||
visual_features = visual_features)
|
||||
result = await self.async_client.analyze(image_url=image_source, visual_features=visual_features)
|
||||
else:
|
||||
# Load image to analyze into a 'bytes' object
|
||||
with open(image_source, 'rb') as f:
|
||||
with open(image_source, "rb") as f:
|
||||
image_data = bytes(f.read())
|
||||
|
||||
result = await self.async_client.analyze(
|
||||
image_data = image_data,
|
||||
visual_features = visual_features)
|
||||
result = await self.async_client.analyze(image_data=image_data, visual_features=visual_features)
|
||||
|
||||
except AzureError as e:
|
||||
print(e)
|
||||
assert hasattr(e, 'status_code')
|
||||
assert hasattr(e, "status_code")
|
||||
assert e.status_code == expected_status_code
|
||||
assert expected_message_contains in e.message
|
||||
return
|
||||
assert False # We should not get here
|
||||
|
||||
assert False # We should not get here
|
||||
|
||||
@staticmethod
|
||||
def _validate_query_parameters(query_params: dict, connection_url: str):
|
||||
assert len(query_params) > 0
|
||||
query_string = ''
|
||||
query_string = ""
|
||||
for key, value in query_params.items():
|
||||
query_string += '&' + key + '=' + value
|
||||
query_string = '?' + query_string[1:]
|
||||
query_string += "&" + key + "=" + value
|
||||
query_string = "?" + query_string[1:]
|
||||
assert query_string in connection_url
|
||||
|
||||
|
||||
@staticmethod
|
||||
def _validate_result(
|
||||
result: sdk.models.ImageAnalysisResult,
|
||||
expected_features: List[sdk.models.VisualFeatures],
|
||||
gender_neutral_caption: Optional[bool] = None,
|
||||
smart_crops_aspect_ratios: Optional[List[float]] = None):
|
||||
result: sdk.models.ImageAnalysisResult,
|
||||
expected_features: List[sdk.models.VisualFeatures],
|
||||
gender_neutral_caption: Optional[bool] = None,
|
||||
smart_crops_aspect_ratios: Optional[List[float]] = None,
|
||||
):
|
||||
ImageAnalysisTestBase._validate_metadata(result)
|
||||
ImageAnalysisTestBase._validate_model_version(result)
|
||||
|
||||
|
|
@ -301,20 +299,17 @@ class ImageAnalysisTestBase(AzureRecordedTestCase):
|
|||
else:
|
||||
assert result.read is None
|
||||
|
||||
|
||||
@staticmethod
|
||||
def _validate_metadata(result: sdk.models.ImageAnalysisResult):
|
||||
assert result.metadata is not None
|
||||
assert result.metadata.height == 576
|
||||
assert result.metadata.width == 864
|
||||
|
||||
|
||||
@staticmethod
|
||||
def _validate_model_version(result: sdk.models.ImageAnalysisResult):
|
||||
assert result.model_version is not None
|
||||
assert result.model_version == "2023-10-01"
|
||||
|
||||
|
||||
@staticmethod
|
||||
def _validate_caption(result: sdk.models.ImageAnalysisResult, gender_neutral_caption: Optional[bool] = None):
|
||||
assert result.caption is not None
|
||||
|
|
@ -327,14 +322,13 @@ class ImageAnalysisTestBase(AzureRecordedTestCase):
|
|||
assert "laptop" in result.caption.text.lower()
|
||||
assert 0.0 < result.caption.confidence < 1.0
|
||||
|
||||
|
||||
@staticmethod
|
||||
def _validate_dense_captions(result: sdk.models.ImageAnalysisResult):
|
||||
assert result.dense_captions is not None
|
||||
assert len(result.dense_captions.values) > 1
|
||||
assert len(result.dense_captions.list) > 1
|
||||
|
||||
# First dense caption should apply to the whole image, and be identical to the caption found in CaptionResult
|
||||
first_dense_caption = result.dense_captions.values[0]
|
||||
first_dense_caption = result.dense_captions.list[0]
|
||||
assert first_dense_caption is not None
|
||||
assert first_dense_caption.text is not None
|
||||
if result.caption is not None:
|
||||
|
|
@ -349,7 +343,7 @@ class ImageAnalysisTestBase(AzureRecordedTestCase):
|
|||
assert first_dense_caption.bounding_box.width == result.metadata.width
|
||||
|
||||
# Sanity checks on all dense captions
|
||||
for dense_caption in result.dense_captions.values:
|
||||
for dense_caption in result.dense_captions.list:
|
||||
assert dense_caption is not None
|
||||
assert dense_caption.text is not None
|
||||
assert len(dense_caption.text) > 0
|
||||
|
|
@ -362,24 +356,25 @@ class ImageAnalysisTestBase(AzureRecordedTestCase):
|
|||
assert dense_caption.bounding_box.width <= result.metadata.width - dense_caption.bounding_box.x
|
||||
|
||||
# Make sure each dense caption is unique
|
||||
for i, dense_caption in enumerate(result.dense_captions.values):
|
||||
for other_dense_caption in result.dense_captions.values[i + 1:]:
|
||||
for i, dense_caption in enumerate(result.dense_captions.list):
|
||||
for other_dense_caption in result.dense_captions.list[i + 1 :]:
|
||||
# Do not include the check below. It's okay to have two identical dense captions since they have different bounding boxes.
|
||||
#assert other_dense_caption.text != dense_caption.text
|
||||
assert not (other_dense_caption.bounding_box.x == dense_caption.bounding_box.x
|
||||
# assert other_dense_caption.text != dense_caption.text
|
||||
assert not (
|
||||
other_dense_caption.bounding_box.x == dense_caption.bounding_box.x
|
||||
and other_dense_caption.bounding_box.y == dense_caption.bounding_box.y
|
||||
and other_dense_caption.bounding_box.height == dense_caption.bounding_box.height
|
||||
and other_dense_caption.bounding_box.width == dense_caption.bounding_box.width)
|
||||
|
||||
and other_dense_caption.bounding_box.width == dense_caption.bounding_box.width
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def _validate_objects(result: sdk.models.ImageAnalysisResult):
|
||||
objects = result.objects
|
||||
assert objects is not None
|
||||
assert len(objects.values) > 1
|
||||
assert len(objects.list) > 1
|
||||
|
||||
found1 = False
|
||||
for object in objects.values:
|
||||
for object in objects.list:
|
||||
assert object is not None
|
||||
assert object.tags is not None
|
||||
assert len(object.tags) == 1
|
||||
|
|
@ -394,25 +389,26 @@ class ImageAnalysisTestBase(AzureRecordedTestCase):
|
|||
assert found1
|
||||
|
||||
# Make sure each object box is unique
|
||||
for i in range(len(objects.values)):
|
||||
for j in range(i + 1, len(objects.values)):
|
||||
box_i = objects.values[i].bounding_box
|
||||
box_j= objects.values[j].bounding_box
|
||||
assert not (box_i.x ==box_j.x
|
||||
for i in range(len(objects.list)):
|
||||
for j in range(i + 1, len(objects.list)):
|
||||
box_i = objects.list[i].bounding_box
|
||||
box_j = objects.list[j].bounding_box
|
||||
assert not (
|
||||
box_i.x == box_j.x
|
||||
and box_i.y == box_j.y
|
||||
and box_i.height == box_j.height
|
||||
and box_i.width == box_j.width)
|
||||
|
||||
and box_i.width == box_j.width
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def _validate_tags(result: sdk.models.ImageAnalysisResult):
|
||||
tags = result.tags
|
||||
assert tags is not None
|
||||
assert tags.values is not None
|
||||
assert len(tags.values) > 1
|
||||
assert tags.list is not None
|
||||
assert len(tags.list) > 1
|
||||
|
||||
found1, found2 = False, False
|
||||
for tag in tags.values:
|
||||
for tag in tags.list:
|
||||
assert tag.name is not None
|
||||
assert len(tag.name) > 0
|
||||
assert 0.0 < tag.confidence < 1.0
|
||||
|
|
@ -425,17 +421,16 @@ class ImageAnalysisTestBase(AzureRecordedTestCase):
|
|||
assert found2
|
||||
|
||||
# Make sure each tag is unique
|
||||
for i in range(len(tags.values)):
|
||||
for j in range(i + 1, len(tags.values)):
|
||||
assert tags.values[j].name != tags.values[i].name
|
||||
|
||||
for i in range(len(tags.list)):
|
||||
for j in range(i + 1, len(tags.list)):
|
||||
assert tags.list[j].name != tags.list[i].name
|
||||
|
||||
@staticmethod
|
||||
def _validate_people(result: sdk.models.ImageAnalysisResult):
|
||||
assert result.people is not None
|
||||
assert len(result.people.values) > 0
|
||||
assert len(result.people.list) > 0
|
||||
|
||||
for person in result.people.values:
|
||||
for person in result.people.list:
|
||||
assert 0.0 < person.confidence < 1.0
|
||||
assert person.bounding_box.x >= 0
|
||||
assert person.bounding_box.y >= 0
|
||||
|
|
@ -443,32 +438,31 @@ class ImageAnalysisTestBase(AzureRecordedTestCase):
|
|||
assert person.bounding_box.width <= result.metadata.width - person.bounding_box.x
|
||||
|
||||
# Make sure each person is unique
|
||||
for i, person in enumerate(result.people.values):
|
||||
for other_person in result.people.values[i + 1:]:
|
||||
for i, person in enumerate(result.people.list):
|
||||
for other_person in result.people.list[i + 1 :]:
|
||||
assert not (
|
||||
other_person.bounding_box.x == person.bounding_box.x
|
||||
and other_person.bounding_box.y == person.bounding_box.y
|
||||
and other_person.bounding_box.height == person.bounding_box.height
|
||||
and other_person.bounding_box.width == person.bounding_box.width)
|
||||
|
||||
and other_person.bounding_box.width == person.bounding_box.width
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def _validate_smart_crops(
|
||||
result: sdk.models.ImageAnalysisResult,
|
||||
smart_crops_aspect_ratios: Optional[List[float]] = None):
|
||||
result: sdk.models.ImageAnalysisResult, smart_crops_aspect_ratios: Optional[List[float]] = None
|
||||
):
|
||||
|
||||
assert result.smart_crops is not None
|
||||
crop_regions = result.smart_crops.values
|
||||
crop_regions = result.smart_crops.list
|
||||
|
||||
if smart_crops_aspect_ratios is None:
|
||||
assert(len(crop_regions) == 1)
|
||||
assert(crop_regions[0].aspect_ratio >= 0.5 and crop_regions[0].aspect_ratio <= 2.0)
|
||||
assert len(crop_regions) == 1
|
||||
assert crop_regions[0].aspect_ratio >= 0.5 and crop_regions[0].aspect_ratio <= 2.0
|
||||
else:
|
||||
assert len(crop_regions) == len(smart_crops_aspect_ratios)
|
||||
for i, region in enumerate(crop_regions):
|
||||
assert region.aspect_ratio == smart_crops_aspect_ratios[i]
|
||||
assert(region.aspect_ratio >= 0.75 and region.aspect_ratio <= 1.8)
|
||||
|
||||
assert region.aspect_ratio >= 0.75 and region.aspect_ratio <= 1.8
|
||||
|
||||
for region in crop_regions:
|
||||
assert region.bounding_box.x >= 0
|
||||
|
|
@ -478,13 +472,13 @@ class ImageAnalysisTestBase(AzureRecordedTestCase):
|
|||
|
||||
# Make sure each bounding box is unique
|
||||
for i, region in enumerate(crop_regions):
|
||||
for other_region in crop_regions[i+1:]:
|
||||
for other_region in crop_regions[i + 1 :]:
|
||||
assert not (
|
||||
other_region.bounding_box.x == region.bounding_box.x
|
||||
and other_region.bounding_box.y == region.bounding_box.y
|
||||
and other_region.bounding_box.height == region.bounding_box.height
|
||||
and other_region.bounding_box.width == region.bounding_box.width)
|
||||
|
||||
and other_region.bounding_box.width == region.bounding_box.width
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def _validate_read(result: sdk.models.ImageAnalysisResult):
|
||||
|
|
@ -541,7 +535,6 @@ class ImageAnalysisTestBase(AzureRecordedTestCase):
|
|||
assert polygon[i].x > 0.0
|
||||
assert polygon[i].y > 0.0
|
||||
|
||||
|
||||
@staticmethod
|
||||
def _print_analysis_results(result: sdk.models.ImageAnalysisResult):
|
||||
|
||||
|
|
@ -555,33 +548,38 @@ class ImageAnalysisTestBase(AzureRecordedTestCase):
|
|||
|
||||
if result.dense_captions is not None:
|
||||
print(" Dense Captions:")
|
||||
for caption in result.dense_captions.values:
|
||||
for caption in result.dense_captions.list:
|
||||
print(" '{}', {}, Confidence: {:.4f}".format(caption.text, caption.bounding_box, caption.confidence))
|
||||
|
||||
if result.objects is not None:
|
||||
print(" Objects:")
|
||||
for object in result.objects.values:
|
||||
print(" '{}', {}, Confidence: {:.4f}".format(object.tags[0].name, object.bounding_box, object.tags[0].confidence))
|
||||
for object in result.objects.list:
|
||||
print(
|
||||
" '{}', {}, Confidence: {:.4f}".format(
|
||||
object.tags[0].name, object.bounding_box, object.tags[0].confidence
|
||||
)
|
||||
)
|
||||
|
||||
if result.tags is not None:
|
||||
print(" Tags:")
|
||||
for tag in result.tags.values:
|
||||
for tag in result.tags.list:
|
||||
print(" '{}', Confidence {:.4f}".format(tag.name, tag.confidence))
|
||||
|
||||
if result.people is not None:
|
||||
print(" People:")
|
||||
for person in result.people.values:
|
||||
for person in result.people.list:
|
||||
print(" {}, Confidence {:.4f}".format(person.bounding_box, person.confidence))
|
||||
|
||||
if result.smart_crops is not None:
|
||||
print(" Smart Cropping:")
|
||||
for smart_crop in result.smart_crops.values:
|
||||
print(" Aspect ratio {}: Smart crop {}" .format(smart_crop.aspect_ratio, smart_crop.bounding_box))
|
||||
for smart_crop in result.smart_crops.list:
|
||||
print(" Aspect ratio {}: Smart crop {}".format(smart_crop.aspect_ratio, smart_crop.bounding_box))
|
||||
|
||||
if result.read is not None:
|
||||
print(" Read:")
|
||||
for line in result.read.blocks[0].lines:
|
||||
print(f" Line: '{line.text}', Bounding box {line.bounding_polygon}")
|
||||
for word in line.words:
|
||||
print(f" Word: '{word.text}', Bounding polygon {word.bounding_polygon}, Confidence {word.confidence:.4f}")
|
||||
|
||||
print(
|
||||
f" Word: '{word.text}', Bounding polygon {word.bounding_polygon}, Confidence {word.confidence:.4f}"
|
||||
)
|
||||
|
|
|
|||
|
|
@ -11,31 +11,32 @@ from devtools_testutils.aio import recorded_by_proxy_async
|
|||
# The test class name needs to start with "Test" to get collected by pytest
|
||||
class TestImageAnalysisAsyncClient(ImageAnalysisTestBase):
|
||||
|
||||
#**********************************************************************************
|
||||
# **********************************************************************************
|
||||
#
|
||||
# HAPPY PATH TESTS
|
||||
#
|
||||
#**********************************************************************************
|
||||
# **********************************************************************************
|
||||
|
||||
# Test all visual features from a local image, using default settings
|
||||
@ServicePreparer()
|
||||
@recorded_by_proxy_async
|
||||
async def test_analyze_async_all_features_from_file(self, **kwargs):
|
||||
|
||||
self._create_client_for_standard_analysis(sync = False, **kwargs)
|
||||
self._create_client_for_standard_analysis(sync=False, **kwargs)
|
||||
|
||||
await self._do_async_analysis(
|
||||
image_source = self.IMAGE_FILE,
|
||||
visual_features = [
|
||||
image_source=self.IMAGE_FILE,
|
||||
visual_features=[
|
||||
sdk.models.VisualFeatures.TAGS,
|
||||
sdk.models.VisualFeatures.OBJECTS,
|
||||
sdk.models.VisualFeatures.CAPTION,
|
||||
sdk.models.VisualFeatures.DENSE_CAPTIONS,
|
||||
sdk.models.VisualFeatures.READ,
|
||||
sdk.models.VisualFeatures.SMART_CROPS,
|
||||
sdk.models.VisualFeatures.PEOPLE
|
||||
sdk.models.VisualFeatures.PEOPLE,
|
||||
],
|
||||
**kwargs)
|
||||
**kwargs
|
||||
)
|
||||
|
||||
await self.async_client.close()
|
||||
|
||||
|
|
@ -44,50 +45,50 @@ class TestImageAnalysisAsyncClient(ImageAnalysisTestBase):
|
|||
@recorded_by_proxy_async
|
||||
async def test_analyze_async_single_feature_from_url(self, **kwargs):
|
||||
|
||||
self._create_client_for_standard_analysis(sync = False, **kwargs)
|
||||
self._create_client_for_standard_analysis(sync=False, **kwargs)
|
||||
|
||||
await self._do_async_analysis(
|
||||
image_source = self.IMAGE_URL,
|
||||
visual_features = [ sdk.models.VisualFeatures.DENSE_CAPTIONS ],
|
||||
gender_neutral_caption = True,
|
||||
**kwargs)
|
||||
image_source=self.IMAGE_URL,
|
||||
visual_features=[sdk.models.VisualFeatures.DENSE_CAPTIONS],
|
||||
gender_neutral_caption=True,
|
||||
**kwargs
|
||||
)
|
||||
|
||||
await self._do_async_analysis(
|
||||
image_source = self.IMAGE_URL,
|
||||
visual_features = [ sdk.models.VisualFeatures.SMART_CROPS ],
|
||||
smart_crops_aspect_ratios = [0.9, 1.33],
|
||||
**kwargs)
|
||||
image_source=self.IMAGE_URL,
|
||||
visual_features=[sdk.models.VisualFeatures.SMART_CROPS],
|
||||
smart_crops_aspect_ratios=[0.9, 1.33],
|
||||
**kwargs
|
||||
)
|
||||
|
||||
await self._do_async_analysis(
|
||||
image_source = self.IMAGE_URL,
|
||||
visual_features = [ sdk.models.VisualFeatures.TAGS ],
|
||||
language = "en",
|
||||
**kwargs)
|
||||
image_source=self.IMAGE_URL, visual_features=[sdk.models.VisualFeatures.TAGS], language="en", **kwargs
|
||||
)
|
||||
|
||||
await self._do_async_analysis(
|
||||
image_source = self.IMAGE_URL,
|
||||
visual_features = [ sdk.models.VisualFeatures.PEOPLE ],
|
||||
**kwargs)
|
||||
image_source=self.IMAGE_URL, visual_features=[sdk.models.VisualFeatures.PEOPLE], **kwargs
|
||||
)
|
||||
|
||||
await self.async_client.close()
|
||||
|
||||
#**********************************************************************************
|
||||
# **********************************************************************************
|
||||
#
|
||||
# ERROR TESTS
|
||||
#
|
||||
#**********************************************************************************
|
||||
# **********************************************************************************
|
||||
|
||||
@ServicePreparer()
|
||||
@recorded_by_proxy_async
|
||||
async def test_analyze_async_authentication_failure(self, **kwargs):
|
||||
|
||||
self._create_client_for_authentication_failure(sync = False, **kwargs)
|
||||
self._create_client_for_authentication_failure(sync=False, **kwargs)
|
||||
|
||||
await self._do_async_analysis_with_error(
|
||||
image_source = self.IMAGE_URL,
|
||||
visual_features = [ sdk.models.VisualFeatures.TAGS ],
|
||||
expected_status_code = 401,
|
||||
expected_message_contains = "Access denied",
|
||||
**kwargs)
|
||||
image_source=self.IMAGE_URL,
|
||||
visual_features=[sdk.models.VisualFeatures.TAGS],
|
||||
expected_status_code=401,
|
||||
expected_message_contains="Access denied",
|
||||
**kwargs
|
||||
)
|
||||
|
||||
await self.async_client.close()
|
||||
|
|
|
|||
|
|
@ -12,81 +12,77 @@ from devtools_testutils import recorded_by_proxy
|
|||
# The test class name needs to start with "Test" to get collected by pytest
|
||||
class TestImageAnalysisClient(ImageAnalysisTestBase):
|
||||
|
||||
#**********************************************************************************
|
||||
# **********************************************************************************
|
||||
#
|
||||
# HAPPY PATH TESTS
|
||||
#
|
||||
#**********************************************************************************
|
||||
# **********************************************************************************
|
||||
|
||||
# Test all visual features from an image URL, which settings specified
|
||||
@ServicePreparer()
|
||||
@recorded_by_proxy
|
||||
def test_analyze_sync_all_features_from_url(self, **kwargs):
|
||||
|
||||
self._create_client_for_standard_analysis(sync = True, **kwargs)
|
||||
self._create_client_for_standard_analysis(sync=True, **kwargs)
|
||||
|
||||
self._do_analysis(
|
||||
image_source = self.IMAGE_URL,
|
||||
visual_features = [
|
||||
image_source=self.IMAGE_URL,
|
||||
visual_features=[
|
||||
sdk.models.VisualFeatures.TAGS,
|
||||
sdk.models.VisualFeatures.OBJECTS,
|
||||
sdk.models.VisualFeatures.CAPTION,
|
||||
sdk.models.VisualFeatures.DENSE_CAPTIONS,
|
||||
sdk.models.VisualFeatures.READ,
|
||||
sdk.models.VisualFeatures.SMART_CROPS,
|
||||
sdk.models.VisualFeatures.PEOPLE
|
||||
sdk.models.VisualFeatures.PEOPLE,
|
||||
],
|
||||
language = "en",
|
||||
gender_neutral_caption = True,
|
||||
smart_crops_aspect_ratios = [0.9, 1.33],
|
||||
model_version = "latest",
|
||||
**kwargs)
|
||||
language="en",
|
||||
gender_neutral_caption=True,
|
||||
smart_crops_aspect_ratios=[0.9, 1.33],
|
||||
model_version="latest",
|
||||
**kwargs
|
||||
)
|
||||
|
||||
self.client.close()
|
||||
|
||||
|
||||
# Test some visual features, one after the other, from file, using default settings
|
||||
@ServicePreparer()
|
||||
@recorded_by_proxy
|
||||
def test_analyze_sync_single_feature_from_file(self, **kwargs):
|
||||
|
||||
self._create_client_for_standard_analysis(sync = True, get_connection_url = True, **kwargs)
|
||||
self._create_client_for_standard_analysis(sync=True, get_connection_url=True, **kwargs)
|
||||
|
||||
self._do_analysis(
|
||||
image_source = self.IMAGE_FILE,
|
||||
visual_features = [ sdk.models.VisualFeatures.CAPTION ],
|
||||
query_params = {'key1': 'value1', 'key2': 'value2'},
|
||||
**kwargs)
|
||||
image_source=self.IMAGE_FILE,
|
||||
visual_features=[sdk.models.VisualFeatures.CAPTION],
|
||||
query_params={"key1": "value1", "key2": "value2"},
|
||||
**kwargs
|
||||
)
|
||||
|
||||
self._do_analysis(
|
||||
image_source = self.IMAGE_FILE,
|
||||
visual_features = [ sdk.models.VisualFeatures.READ ],
|
||||
**kwargs)
|
||||
self._do_analysis(image_source=self.IMAGE_FILE, visual_features=[sdk.models.VisualFeatures.READ], **kwargs)
|
||||
|
||||
self._do_analysis(
|
||||
image_source = self.IMAGE_FILE,
|
||||
visual_features = [ sdk.models.VisualFeatures.TAGS ],
|
||||
**kwargs)
|
||||
self._do_analysis(image_source=self.IMAGE_FILE, visual_features=[sdk.models.VisualFeatures.TAGS], **kwargs)
|
||||
|
||||
self.client.close()
|
||||
|
||||
#**********************************************************************************
|
||||
# **********************************************************************************
|
||||
#
|
||||
# ERROR TESTS
|
||||
#
|
||||
#**********************************************************************************
|
||||
# **********************************************************************************
|
||||
|
||||
@ServicePreparer()
|
||||
@recorded_by_proxy
|
||||
def test_analyze_sync_image_url_does_not_exist(self, **kwargs):
|
||||
|
||||
self._create_client_for_standard_analysis(sync = True, **kwargs)
|
||||
self._create_client_for_standard_analysis(sync=True, **kwargs)
|
||||
|
||||
self._do_analysis_with_error(
|
||||
image_source = "https://www.this.is.a.bad.url.com/for/sure.jpg",
|
||||
visual_features = [ sdk.models.VisualFeatures.CAPTION ],
|
||||
expected_status_code = 400,
|
||||
expected_message_contains = "image url is not accessible",
|
||||
**kwargs)
|
||||
|
||||
image_source="https://www.this.is.a.bad.url.com/for/sure.jpg",
|
||||
visual_features=[sdk.models.VisualFeatures.CAPTION],
|
||||
expected_status_code=400,
|
||||
expected_message_contains="image url is not accessible",
|
||||
**kwargs
|
||||
)
|
||||
|
||||
self.client.close()
|
||||
|
|
|
|||
|
|
@ -1,5 +1,6 @@
|
|||
additionalDirectories: []
|
||||
repo: Azure/azure-rest-api-specs
|
||||
directory: specification/ai/ImageAnalysis
|
||||
commit: 49a78b92e9ef42da134e833e36123ca9bb20994f
|
||||
commit: f2d0e7863a591e538366b251cfd85cc725153384
|
||||
|
||||
|
||||
|
|
|
|||
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