8f05a7c188
- Update ASR, face, OCR, pose processors - Add release pre-flight check script - Add synonym generation, chunk processing scripts - Add face recognition, stamp search utilities
58 lines
1.6 KiB
Python
58 lines
1.6 KiB
Python
#!/opt/homebrew/bin/python3.11
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"""
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Test Florence-2 for "Stamps" Detection using Pipeline
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"""
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import os
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import cv2
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from transformers import pipeline
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UUID = "384b0ff44aaaa1f1"
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VIDEO_PATH = f"output/{UUID}/{UUID}.mp4"
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OUTPUT_DIR = f"output/{UUID}/florence2_results"
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os.makedirs(OUTPUT_DIR, exist_ok=True)
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# Frame where "stamp" is heavily discussed
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TIMESTAMP = 6846.0
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print(f"📽️ Extracting frame at {TIMESTAMP}s...")
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cap = cv2.VideoCapture(VIDEO_PATH)
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cap.set(cv2.CAP_PROP_POS_MSEC, TIMESTAMP * 1000)
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ret, frame = cap.read()
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cap.release()
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if not ret:
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print("❌ Failed to read frame.")
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exit()
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# Save raw frame
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raw_path = os.path.join(OUTPUT_DIR, f"raw_{int(TIMESTAMP)}.jpg")
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cv2.imwrite(raw_path, frame)
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print(f"💾 Raw frame saved.")
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print("🧠 Loading Florence-2 model via pipeline...")
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try:
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# Using pipeline handles model configuration automatically
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pipe = pipeline(
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"image-to-text", model="microsoft/Florence-2-base", trust_remote_code=True
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)
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print("🔍 Running detection on 'stamp'...")
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# Florence-2 tasks: '<OPEN_VOCABULARY_DETECTION>', '<CAPTION>', etc.
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# We want to see if there is a stamp, so let's use caption first to see what it sees.
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result = pipe(raw_path, prompt="<CAPTION>")
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print(f"📝 Caption Result: {result}")
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# Let's try open vocabulary detection for 'stamp'
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print("🔍 Running Open Vocabulary Detection for 'stamp'...")
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result_ood = pipe(
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raw_path, prompt="<OPEN_VOCABULARY_DETECTION>", text_input="stamp"
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)
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print(f"📦 OOD Result: {result_ood}")
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except Exception as e:
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print(f"❌ Error: {e}")
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print("🏁 Done.")
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