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Commit 0715f7d

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czahediczahedi
authored
video: adding face detection and person detection samples for beta (GoogleCloudPlatform#2919)
* video: adding face detection and person detection samples for beta * updating requirements.txt * updating test names to faces * fixing region tag typo * responding to comments * reverted tabs to fix linting errors * responding to comments
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‎video/cloud-client/analyze/requirements.txt

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google-cloud-videointelligence==1.12.1
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google-cloud-videointelligence==1.13.0
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google-cloud-storage==1.23.0

‎video/cloud-client/analyze/video_detect_faces_beta.py

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# Copyright 2020 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# https://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# [START video_detect_faces_beta]
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import io
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from google.cloud import videointelligence_v1p3beta1 as videointelligence
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def detect_faces(local_file_path="path/to/your/video-file.mp4"):
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"""Detects faces in a video from a local file."""
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client = videointelligence.VideoIntelligenceServiceClient()
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with io.open(local_file_path, "rb") as f:
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input_content = f.read()
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# Configure the request
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config = videointelligence.types.FaceDetectionConfig(
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include_bounding_boxes=True, include_attributes=True
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)
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context = videointelligence.types.VideoContext(
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face_detection_config=config
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)
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# Start the asynchronous request
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operation = client.annotate_video(
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input_content=input_content,
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features=[videointelligence.enums.Feature.FACE_DETECTION],
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video_context=context,
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)
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print("\nProcessing video for face detection annotations.")
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result = operation.result(timeout=300)
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print("\nFinished processing.\n")
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# Retrieve the first result, because a single video was processed.
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annotation_result = result.annotation_results[0]
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for annotation in annotation_result.face_detection_annotations:
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print("Face detected:")
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for track in annotation.tracks:
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print(
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"Segment: {}s to {}s".format(
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track.segment.start_time_offset.seconds
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+ track.segment.start_time_offset.nanos / 1e9,
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track.segment.end_time_offset.seconds
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+ track.segment.end_time_offset.nanos / 1e9,
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)
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)
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# Each segment includes timestamped faces that include
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# characteristics of the face detected.
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# Grab the first timestamped face
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timestamped_object = track.timestamped_objects[0]
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box = timestamped_object.normalized_bounding_box
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print("Bounding box:")
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print("\tleft : {}".format(box.left))
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print("\ttop : {}".format(box.top))
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print("\tright : {}".format(box.right))
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print("\tbottom: {}".format(box.bottom))
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# Attributes include glasses, headwear, facial hair, smiling,
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# direction of gaze, etc.
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print("Attributes:")
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for attribute in timestamped_object.attributes:
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print(
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"\t{}:{} {}".format(
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attribute.name, attribute.value, attribute.confidence
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)
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)
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# [END video_detect_faces_beta]

‎video/cloud-client/analyze/video_detect_faces_beta_test.py

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# Copyright 2020 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import video_detect_faces_beta
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RESOURCES = os.path.join(os.path.dirname(__file__), "resources")
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def test_detect_faces(capsys):
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local_file_path = os.path.join(RESOURCES, "googlework_short.mp4")
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video_detect_faces_beta.detect_faces(local_file_path=local_file_path)
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out, _ = capsys.readouterr()
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assert "Face detected:" in out
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assert "Attributes:" in out

‎video/cloud-client/analyze/video_detect_faces_gcs_beta.py

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# Copyright 2020 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# https://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# [START video_detect_faces_gcs_beta]
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from google.cloud import videointelligence_v1p3beta1 as videointelligence
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def detect_faces(gcs_uri="gs://YOUR_BUCKET_ID/path/to/your/video.mp4"):
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"""Detects faces in a video."""
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client = videointelligence.VideoIntelligenceServiceClient()
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# Configure the request
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config = videointelligence.types.FaceDetectionConfig(
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include_bounding_boxes=True, include_attributes=True
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)
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context = videointelligence.types.VideoContext(
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face_detection_config=config
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)
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# Start the asynchronous request
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operation = client.annotate_video(
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input_uri=gcs_uri,
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features=[videointelligence.enums.Feature.FACE_DETECTION],
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video_context=context,
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)
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print("\nProcessing video for face detection annotations.")
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result = operation.result(timeout=300)
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print("\nFinished processing.\n")
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# Retrieve the first result, because a single video was processed.
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annotation_result = result.annotation_results[0]
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for annotation in annotation_result.face_detection_annotations:
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print("Face detected:")
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for track in annotation.tracks:
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print(
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"Segment: {}s to {}s".format(
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track.segment.start_time_offset.seconds
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+ track.segment.start_time_offset.nanos / 1e9,
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track.segment.end_time_offset.seconds
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+ track.segment.end_time_offset.nanos / 1e9,
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)
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)
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# Each segment includes timestamped faces that include
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# characteristics of the face detected.
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# Grab the first timestamped face
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timestamped_object = track.timestamped_objects[0]
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box = timestamped_object.normalized_bounding_box
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print("Bounding box:")
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print("\tleft : {}".format(box.left))
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print("\ttop : {}".format(box.top))
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print("\tright : {}".format(box.right))
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print("\tbottom: {}".format(box.bottom))
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# Attributes include glasses, headwear, facial hair, smiling,
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# direction of gaze, etc.
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print("Attributes:")
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for attribute in timestamped_object.attributes:
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print(
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"\t{}:{} {}".format(
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attribute.name, attribute.value, attribute.confidence
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)
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)
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# [END video_detect_faces_gcs_beta]

‎video/cloud-client/analyze/video_detect_faces_gcs_beta_test.py

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# Copyright 2020 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import video_detect_faces_gcs_beta
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RESOURCES = os.path.join(os.path.dirname(__file__), "resources")
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def test_detect_faces(capsys):
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input_uri = "gs://cloud-samples-data/video/googlework_short.mp4"
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video_detect_faces_gcs_beta.detect_faces(gcs_uri=input_uri)
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out, _ = capsys.readouterr()
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assert "Face detected:" in out
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assert "Attributes:" in out

‎video/cloud-client/analyze/video_detect_person_beta.py

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# Copyright 2020 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# https://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# [START video_detect_person_beta]
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import io
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from google.cloud import videointelligence_v1p3beta1 as videointelligence
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def detect_person(local_file_path="path/to/your/video-file.mp4"):
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"""Detects people in a video from a local file."""
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client = videointelligence.VideoIntelligenceServiceClient()
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with io.open(local_file_path, "rb") as f:
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input_content = f.read()
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# Configure the request
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config = videointelligence.types.PersonDetectionConfig(
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include_bounding_boxes=True,
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include_attributes=True,
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include_pose_landmarks=True,
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)
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context = videointelligence.types.VideoContext(
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person_detection_config=config
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)
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# Start the asynchronous request
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operation = client.annotate_video(
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input_content=input_content,
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features=[videointelligence.enums.Feature.PERSON_DETECTION],
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video_context=context,
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)
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print("\nProcessing video for person detection annotations.")
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result = operation.result(timeout=300)
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print("\nFinished processing.\n")
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# Retrieve the first result, because a single video was processed.
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annotation_result = result.annotation_results[0]
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for annotation in annotation_result.person_detection_annotations:
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print("Person detected:")
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for track in annotation.tracks:
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print(
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"Segment: {}s to {}s".format(
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track.segment.start_time_offset.seconds
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+ track.segment.start_time_offset.nanos / 1e9,
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track.segment.end_time_offset.seconds
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+ track.segment.end_time_offset.nanos / 1e9,
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)
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)
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# Each segment includes timestamped objects that include
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# characteristic - -e.g.clothes, posture of the person detected.
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# Grab the first timestamped object
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timestamped_object = track.timestamped_objects[0]
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box = timestamped_object.normalized_bounding_box
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print("Bounding box:")
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print("\tleft : {}".format(box.left))
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print("\ttop : {}".format(box.top))
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print("\tright : {}".format(box.right))
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print("\tbottom: {}".format(box.bottom))
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# Attributes include unique pieces of clothing,
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# poses, or hair color.
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print("Attributes:")
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for attribute in timestamped_object.attributes:
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print(
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"\t{}:{} {}".format(
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attribute.name, attribute.value, attribute.confidence
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)
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)
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# Landmarks in person detection include body parts such as
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# left_shoulder, right_ear, and right_ankle
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print("Landmarks:")
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for landmark in timestamped_object.landmarks:
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print(
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"\t{}: {} (x={}, y={})".format(
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landmark.name,
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landmark.confidence,
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landmark.point.x, # Normalized vertex
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landmark.point.y, # Normalized vertex
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)
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)
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# [END video_detect_person_beta]

‎video/cloud-client/analyze/video_detect_person_beta_test.py

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# Copyright 2020 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import video_detect_person_beta
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RESOURCES = os.path.join(os.path.dirname(__file__), "resources")
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def test_detect_person(capsys):
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local_file_path = os.path.join(RESOURCES, "googlework_tiny.mp4")
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video_detect_person_beta.detect_person(local_file_path=local_file_path)
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out, _ = capsys.readouterr()
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assert "Person detected:" in out
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assert "Attributes:" in out
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assert "x=" in out
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assert "y=" in out

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