feat: update Python processors and add utility scripts

- Update ASR, face, OCR, pose processors
- Add release pre-flight check script
- Add synonym generation, chunk processing scripts
- Add face recognition, stamp search utilities

scripts/magnifying_glass_analyze.py

@@ -0,0 +1,158 @@
+#!/opt/homebrew/bin/python3.11
+"""
+Magnifying Glass: Florence-2 AI analysis of extracted frames
+Uses multiple search terms to find stamps, envelopes, letters.
+"""
+
+import os
+import json
+import glob
+from PIL import Image
+import torch
+from transformers import AutoProcessor, AutoModelForCausalLM
+
+UUID = "384b0ff44aaaa1f1"
+BASE_DIR = f"output/{UUID}/magnifying_glass"
+RESULTS_DIR = f"output/{UUID}/magnifying_glass_results"
+os.makedirs(RESULTS_DIR, exist_ok=True)
+
+print("🔬 Loading Florence-2 model...")
+processor = AutoProcessor.from_pretrained(
+    "microsoft/Florence-2-base", trust_remote_code=True
+)
+model = AutoModelForCausalLM.from_pretrained(
+    "microsoft/Florence-2-base", trust_remote_code=True
+)
+model.eval()
+
+# Search terms for open vocabulary detection
+SEARCH_TERMS = [
+    "postage stamp",
+    "stamp",
+    "envelope with stamp",
+    "letter with stamp",
+    "holding a stamp",
+    "stamp album",
+    "collection of stamps",
+]
+
+
+def run_detection(image_path, search_term):
+    """Run Florence-2 detection on a single image"""
+    try:
+        image = Image.open(image_path).convert("RGB")
+
+        prompt = "<OPEN_VOCABULARY_DETECTION>"
+        text_input = f"{prompt} {search_term}"
+
+        inputs = processor(text=text_input, images=image, return_tensors="pt")
+
+        with torch.no_grad():
+            generated_ids = model.generate(
+                input_ids=inputs["input_ids"],
+                pixel_values=inputs["pixel_values"],
+                max_new_tokens=512,
+                num_beams=3,
+            )
+
+        generated_text = processor.batch_decode(
+            generated_ids, skip_special_tokens=False
+        )[0]
+
+        parsed = processor.post_process_generation(
+            generated_text,
+            task=prompt,
+            image_size=(image.width, image.height),
+        )
+
+        if parsed and "<OPEN_VOCABULARY_DETECTION>" in parsed:
+            detections = parsed["<OPEN_VOCABULARY_DETECTION>"]
+            if detections:
+                return detections
+        return []
+    except Exception as e:
+        print(f"  ⚠️  Error: {e}")
+        return []
+
+
+def analyze_scene(scene_dir, scene_name):
+    """Analyze all frames in a scene"""
+    frames = sorted(glob.glob(os.path.join(scene_dir, "frame_*.jpg")))
+    print(f"\n🔍 Analyzing {scene_name}: {len(frames)} frames")
+
+    scene_detections = []
+
+    for frame_path in frames:
+        frame_name = os.path.basename(frame_path)
+        frame_results = {}
+
+        for term in SEARCH_TERMS:
+            detections = run_detection(frame_path, term)
+            if detections:
+                frame_results[term] = detections
+
+        if frame_results:
+            sec = frame_name.replace("frame_", "").replace("s.jpg", "")
+            print(
+                f"  📍 Frame {sec}s: Found detections for {list(frame_results.keys())}"
+            )
+
+            # Save annotated image
+            try:
+                import cv2
+
+                img = cv2.imread(frame_path)
+                for term, dets in frame_results.items():
+                    for det in dets:
+                        bbox = det.get("bbox", [0, 0, 0, 0])
+                        x1, y1, x2, y2 = map(int, bbox)
+                        cv2.rectangle(img, (x1, y1), (x2, y2), (0, 255, 0), 3)
+                        label = det.get("label", term)
+                        cv2.putText(
+                            img,
+                            label,
+                            (x1, y1 - 10),
+                            cv2.FONT_HERSHEY_SIMPLEX,
+                            0.6,
+                            (0, 255, 0),
+                            2,
+                        )
+
+                        # Save crop
+                        crop = img[y1:y2, x1:x2]
+                        if crop.size > 0:
+                            crop_name = (
+                                f"{scene_name}_{sec}s_{label.replace(' ', '_')}.jpg"
+                            )
+                            cv2.imwrite(os.path.join(RESULTS_DIR, crop_name), crop)
+
+                ann_path = os.path.join(
+                    RESULTS_DIR, f"annotated_{scene_name}_{sec}s.jpg"
+                )
+                cv2.imwrite(ann_path, img)
+            except Exception as e:
+                print(f"  ⚠️  Save error: {e}")
+
+            scene_detections.append({"frame": frame_name, "detections": frame_results})
+
+    return scene_detections
+
+
+# Analyze all scenes
+all_results = {}
+scene_dirs = sorted(glob.glob(os.path.join(BASE_DIR, "*/")))
+print(f"📂 Found {len(scene_dirs)} scene directories")
+
+for scene_dir in scene_dirs:
+    scene_name = os.path.basename(os.path.dirname(scene_dir))
+    detections = analyze_scene(scene_dir, scene_name)
+    if detections:
+        all_results[scene_name] = detections
+
+# Save results
+results_path = os.path.join(RESULTS_DIR, "detection_results.json")
+with open(results_path, "w") as f:
+    json.dump(all_results, f, indent=2)
+
+print(f"\n🏁 Done. Results saved to {results_path}")
+print(f"📁 Check {RESULTS_DIR} for annotated images and crops.")