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
143 lines
4.1 KiB
Python
143 lines
4.1 KiB
Python
#!/opt/homebrew/bin/python3.11
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"""
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Find ALL Stamps in the Image using Florence-2
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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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OUTPUT_DIR = f"output/{UUID}/florence2_results"
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INPUT_IMG = os.path.join(OUTPUT_DIR, f"raw_6846.jpg")
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OUTPUT_IMG = os.path.join(OUTPUT_DIR, f"all_stamps_detected.jpg")
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# Patch for compatibility (Same as before)
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import types
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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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print(f"📷 Loading image from {INPUT_IMG}...")
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if not os.path.exists(INPUT_IMG):
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print("❌ Image not found.")
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exit()
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image = Image.open(INPUT_IMG).convert("RGB")
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print(f"📐 Image Size: {image.width}x{image.height}")
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print("🧠 Loading Florence-2 model...")
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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, 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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text_input = "stamp"
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print(f"🔍 Scanning for '{text_input}'...")
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inputs = processor(text=prompt, images=image, 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=2048,
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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 result
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parsed_answer = processor.post_process_generation(
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generated_text, task=prompt, image_size=(image.width, image.height)
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)
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print(f"📦 Raw Parsed Data: {parsed_answer}")
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results = parsed_answer.get("<OPEN_VOCABULARY_DETECTION>", {})
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bboxes = results.get("bboxes", [])
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labels = results.get("bboxes_labels", [])
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print(f"✅ Found {len(bboxes)} stamp(s)!")
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# Draw results
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img_cv = cv2.imread(INPUT_IMG)
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colors = [
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(0, 255, 0),
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(255, 0, 0),
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(0, 0, 255),
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(255, 255, 0),
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] # Green, Blue, Red, Yellow
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for i, (box, label) in enumerate(zip(bboxes, labels)):
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x1, y1, x2, y2 = map(int, box)
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color = colors[i % len(colors)]
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# Draw box
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cv2.rectangle(img_cv, (x1, y1), (x2, y2), color, 4)
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# Draw label background
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text = f"{label} {i + 1}"
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(tw, th), _ = cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, 1, 2)
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cv2.rectangle(img_cv, (x1, y1 - th - 10), (x1 + tw + 10, y1), color, -1)
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# Draw text
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cv2.putText(
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img_cv, text, (x1 + 5, y1 - 5), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 0), 2
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)
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print(f" 📍 Stamp #{i + 1} at ({x1}, {y1}) -> ({x2}, {y2})")
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cv2.imwrite(OUTPUT_IMG, img_cv)
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print(f"\n🎨 Image with all detections saved to: {OUTPUT_IMG}")
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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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