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# ------------------------------------
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
# ------------------------------------
"""
DESCRIPTION:
This sample demonstrates how to detect physical objects in an image file sample.jpg, using a synchronous client.
The synchronous (blocking) `analyze` method call returns an `ImageAnalysisResult` object.
Its `objects` property (a `ObjectsResult` object) contains a list of `DetectedObject` objects. Each has:
- The object name, for example: "chair", "laptop".
- A confidence score in the range [0, 1], with higher values indicating greater confidences in the detection.
- A `BoundingBox` coordinates in pixels, for a rectangular surrounding the object in the image.
Object names are only supported in English at the moment.
USAGE:
python sample_objects_image_file.py
Set these two environment variables before running the sample:
1) VISION_ENDPOINT - Your endpoint URL, in the form https://your-resource-name.cognitiveservices.azure.com
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
# as environment variables:
try:
endpoint = os.environ["VISION_ENDPOINT"]
key = os.environ["VISION_KEY"]
except KeyError:
print("Missing environment variable 'VISION_ENDPOINT' or 'VISION_KEY'")
print("Set them before running this sample.")
exit()
# Create an Image Analysis client
client = ImageAnalysisClient(
endpoint=endpoint,
credential=AzureKeyCredential(key)
)
# Load image to analyze into a 'bytes' object
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]
)
# Print Objects analysis results to the console
print("Image analysis results:")
print(" Objects:")
if result.objects is not None:
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()
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