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
2026-04-30 15:07:49 +08:00
parent f4697396e4
commit 8f05a7c188
256 changed files with 60505 additions and 299 deletions
--- a/scripts/search_objects_in_hands.py
+++ b/scripts/search_objects_in_hands.py
@@ -0,0 +1,103 @@
+#!/opt/homebrew/bin/python3.11
+"""
+Find ANY Small Rectangular Object in Hands
+"""
+
+import cv2
+import numpy as np
+import os
+
+UUID = "384b0ff44aaaa1f1"
+BASE_DIR = f"output/{UUID}/florence2_results"
+
+# Frames to check
+FRAMES = [
+    "scan_6756.jpg",  # 112:36
+    "scan_6763.jpg",  # 112:43
+    "scan_6790.jpg",  # 113:10
+    "scan_6813.jpg",  # 113:33
+    "scan_6832.jpg",  # 113:52
+]
+
+print("🖐️ Searching for SMALL OBJECTS in hands...")
+
+for frame_name in FRAMES:
+    img_path = os.path.join(BASE_DIR, frame_name)
+    if not os.path.exists(img_path):
+        continue
+
+    img = cv2.imread(img_path)
+    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
+
+    # 1. Hand Detection (Skin Tone) - Adjusted for lighting
+    # Broad range to catch hands in shadow or bright light
+    skin_mask = cv2.inRange(hsv, np.array([0, 15, 40]), np.array([25, 160, 255]))
+
+    # Morphological cleaning
+    kernel = np.ones((5, 5), np.uint8)
+    skin_mask = cv2.morphologyEx(skin_mask, cv2.MORPH_CLOSE, kernel)
+    skin_mask = cv2.morphologyEx(skin_mask, cv2.MORPH_OPEN, kernel)
+
+    # 2. Find contours inside/near hands
+    # We dilate the mask slightly to include objects held IN the hand
+    skin_dilated = cv2.dilate(skin_mask, kernel, iterations=3)
+
+    # Find contours in the full image
+    contours, _ = cv2.findContours(
+        skin_dilated, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE
+    )
+
+    print(f"\n🎞️ Scanning {frame_name}...")
+    found_count = 0
+
+    for cnt in contours:
+        x, y, w, h = cv2.boundingRect(cnt)
+        area = cv2.contourArea(cnt)
+
+        # Object size filter:
+        # Too small (< 100px) = noise
+        # Too big (> 15000px) = likely the face or body part itself
+        if 100 < area < 15000:
+            # Shape filter: Rectangle-like (Aspect ratio 0.5 to 2.0)
+            aspect_ratio = float(w) / h
+
+            # Check for rectangularity (Extent)
+            rect_area = w * h
+            if rect_area > 0:
+                extent = float(area) / rect_area
+                # If extent > 0.4, it's somewhat rectangular/filled
+                if 0.5 < aspect_ratio < 2.5 and extent > 0.4:
+                    found_count += 1
+                    print(
+                        f"   ✅ Candidate Object: Area={int(area)}, Pos=({x},{y}), Size={w}x{h}"
+                    )
+
+                    # Crop with padding
+                    pad = 10
+                    crop = img[
+                        max(0, y - pad) : min(img.shape[0], y + h + pad),
+                        max(0, x - pad) : min(img.shape[1], x + w + pad),
+                    ]
+                    crop_path = os.path.join(
+                        BASE_DIR, f"object_in_hand_{frame_name}_{x}_{y}.jpg"
+                    )
+                    cv2.imwrite(crop_path, crop)
+
+                    # Draw on main image
+                    cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 3)
+                    cv2.putText(
+                        img,
+                        f"OBJ? ({int(area)})",
+                        (x, y - 10),
+                        cv2.FONT_HERSHEY_SIMPLEX,
+                        0.6,
+                        (0, 255, 0),
+                        2,
+                    )
+
+    if found_count == 0:
+        print("   ❌ No small objects found in hands.")
+    else:
+        res_path = os.path.join(BASE_DIR, f"result_objects_{frame_name}")
+        cv2.imwrite(res_path, img)
+        print(f"   🎨 Result saved to {res_path}")