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from google.cloud import videointelligence
import io
import os
def analyze_video(video_path):
# Initialize the Video Intelligence client
client = videointelligence.VideoIntelligenceServiceClient()
# Open the video file
with io.open(video_path, "rb") as file:
input_content = file.read()
# Configure the request (Label detection and Text detection)
features = [videointelligence.Feature.LABEL_DETECTION, videointelligence.Feature.TEXT_DETECTION]
# Make the request to the Video Intelligence API
operation = client.annotate_video(request={"features": features, "input_content": input_content})
print("\nProcessing video for label detection and text detection...")
# Wait for the operation to complete
result = operation.result(timeout=600)
# Process the results
print("\nLabel annotations:")
for i, annotation in enumerate(result.annotation_results[0].segment_label_annotations):
print(f"Label {i+1}: {annotation.entity.description}")
for segment in annotation.segments:
print(f"\tStart time: {segment.segment.start_time_offset.seconds} seconds")
print(f"\tEnd time: {segment.segment.end_time_offset.seconds} seconds")
# Text detection
print("\nText Detection:")
for i, text_annotation in enumerate(result.annotation_results[0].text_annotations):
print(f"Detected Text {i+1}: {text_annotation.text}")
for segment in text_annotation.segments:
start_time = segment.segment.start_time_offset.seconds + segment.segment.start_time_offset.microseconds / 1e6
end_time = segment.segment.end_time_offset.seconds + segment.segment.end_time_offset.microseconds / 1e6
print(f"\tAppears from {start_time:.2f} to {end_time:.2f} seconds")
if __name__ == "__main__":
os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = '/home/vladp/Projects/meetingUnderstanding/meetingunderstandingproject-221fb498c363.json'
# Example usage (replace with your video file path)
video_path = "temp_video.mp4"
analyze_video(video_path)
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