e75c4d6f07
- Remove session-ses_2f27.md (161KB raw session log) - Remove 49 ROOT_* duplicate files across REFERENCE/ - Remove 14 duplicate files between REFERENCE/ root and history/ - Remove asr_legacy.rs (dead code, replaced by asr.rs) - Remove src/core/worker/ (duplicate JobWorker) - Remove src/core/layers/ (empty directory) - Remove 4 .bak files in src/ - Remove 7 dead private methods in worker/processor.rs - Remove backup directory from git tracking
82 lines
2.4 KiB
Python
82 lines
2.4 KiB
Python
#!/opt/homebrew/bin/python3.11
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"""
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Test Florence-2 for "Stamps" Detection
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Florence-2 is superior to OWL-ViT for small objects and detailed description.
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"""
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import os
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import cv2
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from PIL import Image
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from transformers import AutoProcessor, AutoModelForCausalLM
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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("💾 Raw frame saved.")
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image_pil = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
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print("🧠 Loading Florence-2 model (this may take a moment)...")
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try:
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processor = AutoProcessor.from_pretrained(
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"microsoft/Florence-2-base", trust_remote_code=True
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)
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model = AutoModelForCausalLM.from_pretrained(
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"microsoft/Florence-2-base", trust_remote_code=True
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)
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except Exception as e:
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print(f"❌ Error loading model: {e}")
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exit()
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# Test 1: Open Vocabulary Detection
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print("🔍 Testing Open Vocabulary Detection for 'stamp'...")
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prompt = "stamp"
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inputs = processor(text=prompt, images=image_pil, return_tensors="pt")
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generated_ids = model.generate(
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input_ids=inputs["input_ids"],
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pixel_values=inputs["pixel_values"],
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max_new_tokens=1024,
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num_beams=3,
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)
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generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
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parsed_answer = processor.post_process_generation(
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generated_text,
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task="<OPEN_VOCABULARY_DETECTION>",
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image_size=(image_pil.width, image_pil.height),
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)
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print(f"📝 Florence-2 Result: {parsed_answer}")
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# Test 2: Detailed Caption (To see if it notices the stamp in context)
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print("📝 Testing Detailed Caption...")
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inputs = processor(text="<DETAILED_CAPTION>", images=image_pil, return_tensors="pt")
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generated_ids = model.generate(
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input_ids=inputs["input_ids"],
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pixel_values=inputs["pixel_values"],
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max_new_tokens=1024,
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)
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caption = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
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print(f"📝 Caption: {caption}")
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print("🏁 Done.")
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