2cfcfdd1af
Phase 2.6.1: co_occurrence_edges migration - build_co_occurrence_edges_from_qdrant() - Qdrant embeddings → frame grouping → YOLO objects - Result: 6679 edges (vs 6701 PostgreSQL) Phase 2.6.2: face_face_edges migration - build_face_face_edges_from_qdrant() - Qdrant embeddings → frame grouping → face pairs - mutual_gaze detection preserved - Result: 6 edges (exact match) Phase 2.6.3: speaker_face_edges migration - build_speaker_face_edges_from_qdrant() - Qdrant embeddings → trace_id frame ranges - SPEAKS_AS edge creation Architecture: - All edges use Qdrant payload (no face_detections queries) - PostgreSQL fallback for empty Qdrant - Estimated 3.6x performance improvement Testing: - Playground (3003): ✓ All Phase 2.6 logs verified - Edge counts: ✓ Close match with PostgreSQL - Fallback: ✓ Working Docs: - docs_v1.0/DESIGN/TKG_PHASE2_6_EDGES_MIGRATION.md - docs_v1.0/M4_workspace/2026-06-21_phase2_6_test.md
137 lines
4.3 KiB
Python
137 lines
4.3 KiB
Python
#!/opt/homebrew/bin/python3.11
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"""
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Refined Search for "Postage Stamp" in the Image
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"""
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import os
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import cv2
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import types
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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, "raw_6846.jpg")
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# Patch for compatibility (Required for this environment)
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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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# Try more specific terms
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search_terms = ["postage stamp", "envelope", "letter"]
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img_cv = cv2.imread(INPUT_IMG)
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all_found = []
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for term in search_terms:
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print(f"🔍 Scanning for '{term}'...")
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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=1024,
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num_beams=3,
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)
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generated_text = processor.batch_decode(
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generated_ids, skip_special_tokens=False
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)[0]
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try:
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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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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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if bboxes:
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print(f"✅ Found {len(bboxes)} '{term}'! Labels: {labels}")
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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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# Crop and save
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crop = img_cv[y1:y2, x1:x2]
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crop_path = os.path.join(
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OUTPUT_DIR, f"crop_{term.replace(' ', '_')}_{i}.jpg"
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)
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cv2.imwrite(crop_path, crop)
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print(f" 💾 Saved crop to {crop_path}")
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# Also draw on main image
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cv2.rectangle(img_cv, (x1, y1), (x2, y2), (0, 255, 0), 2)
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all_found.append((box, label))
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else:
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print(f" ❌ No '{term}' found.")
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except Exception as e:
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print(f" ⚠️ Error processing '{term}': {e}")
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final_out = os.path.join(OUTPUT_DIR, "refined_detection.jpg")
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cv2.imwrite(final_out, img_cv)
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print(f"\n🎨 Main image with detections saved to: {final_out}")
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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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