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
129 lines
3.8 KiB
Python
129 lines
3.8 KiB
Python
#!/opt/homebrew/bin/python3.11
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"""
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Crop the detected stamp from the 112:36 frame (with Patch).
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"""
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from PIL import Image
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import os
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import cv2
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import types
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from transformers import AutoProcessor, AutoModelForCausalLM
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UUID = "384b0ff44aaaa1f1"
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BASE_DIR = f"output/{UUID}/florence2_results"
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IMG_NAME = "frame_6756.jpg"
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img_path = os.path.join(BASE_DIR, IMG_NAME)
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print(f"📷 Loading image: {img_path}")
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if not os.path.exists(img_path):
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print("❌ Image not found.")
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exit()
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# Patch for compatibility
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def patch_model(model):
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inner_model = model.language_model
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original_prepare = inner_model.prepare_inputs_for_generation
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def patched_prepare(
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self,
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input_ids,
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past_key_values=None,
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attention_mask=None,
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inputs_embeds=None,
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**kwargs,
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):
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is_valid_cache = False
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if past_key_values is not None:
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if isinstance(past_key_values, (list, tuple)) and len(past_key_values) > 0:
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first_layer = past_key_values[0]
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if first_layer is not None and (
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not isinstance(first_layer, (list, tuple)) or len(first_layer) > 0
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):
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is_valid_cache = True
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if not is_valid_cache:
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return {
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"input_ids": input_ids,
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"attention_mask": attention_mask,
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"past_key_values": None,
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"use_cache": True,
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}
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else:
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return original_prepare(
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input_ids,
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past_key_values=past_key_values,
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attention_mask=attention_mask,
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inputs_embeds=inputs_embeds,
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**kwargs,
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)
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inner_model.prepare_inputs_for_generation = types.MethodType(
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patched_prepare, inner_model
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)
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try:
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img = Image.open(img_path).convert("RGB")
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print(f"📐 Image Size: {img.width}x{img.height}")
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print("🧠 Running detection to get coordinates...")
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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, attn_implementation="eager"
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)
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patch_model(model)
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prompt = "<OPEN_VOCABULARY_DETECTION>"
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inputs = processor(text=prompt, images=img, return_tensors="pt")
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# Generate
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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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# Parse
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parsed_answer = processor.post_process_generation(
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generated_text, task=prompt, image_size=(img.width, img.height)
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)
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results = parsed_answer.get("<OPEN_VOCABULARY_DETECTION>", {})
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bboxes = results.get("bboxes", [])
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if bboxes:
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box = bboxes[0] # Take the first detected stamp
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print(f"📦 Detected Box: {box}")
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# Crop
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box_int = [int(x) for x in box]
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cropped = img.crop(box_int)
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out_path = os.path.join(BASE_DIR, "stamp_from_112_36.jpg")
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cropped.save(out_path)
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print(f"✅ Successfully saved cropped stamp to {out_path}")
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# Also save a visualization
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img_cv = cv2.imread(img_path)
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x1, y1, x2, y2 = map(int, box)
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cv2.rectangle(img_cv, (x1, y1), (x2, y2), (0, 255, 0), 3)
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cv2.putText(
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img_cv, "STAMP", (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2
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)
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vis_path = os.path.join(BASE_DIR, "stamp_detection_112_36.jpg")
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cv2.imwrite(vis_path, img_cv)
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print(f"🎨 Visualization saved to {vis_path}")
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else:
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print("❌ No stamp found in this frame.")
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except Exception as e:
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print(f"❌ Error: {e}")
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import traceback
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traceback.print_exc()
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