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
96 lines
3.2 KiB
Python
96 lines
3.2 KiB
Python
#!/opt/homebrew/bin/python3.11
|
|
"""
|
|
Detect stamp-like rectangular regions with Blue+Red colors in full frames
|
|
"""
|
|
|
|
import cv2
|
|
import numpy as np
|
|
import os
|
|
import glob
|
|
|
|
UUID = "384b0ff44aaaa1f1"
|
|
BASE_DIR = f"output/{UUID}/florence2_results"
|
|
|
|
print("🔍 Searching for stamp-like rectangles in full frames...")
|
|
|
|
scan_frames = sorted(glob.glob(os.path.join(BASE_DIR, "scan_*.jpg")))
|
|
print(f"Found {len(scan_frames)} scan frames.")
|
|
|
|
for frame_path in scan_frames:
|
|
img = cv2.imread(frame_path)
|
|
if img is None:
|
|
continue
|
|
|
|
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
|
|
|
|
# Detect Blue regions
|
|
blue_mask = cv2.inRange(hsv, np.array([90, 30, 30]), np.array([130, 255, 255]))
|
|
|
|
# Detect Red regions
|
|
red_mask1 = cv2.inRange(hsv, np.array([0, 30, 30]), np.array([10, 255, 255]))
|
|
red_mask2 = cv2.inRange(hsv, np.array([170, 30, 30]), np.array([179, 255, 255]))
|
|
red_mask = red_mask1 + red_mask2
|
|
|
|
# Combine: areas that have BOTH blue and red nearby
|
|
combined = cv2.bitwise_and(blue_mask, red_mask)
|
|
|
|
# Actually, let's find contours in blue areas and check if they contain red inside
|
|
contours, _ = cv2.findContours(
|
|
blue_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE
|
|
)
|
|
|
|
stamp_candidates = []
|
|
|
|
for contour in contours:
|
|
# Filter by area (stamps are medium-sized)
|
|
area = cv2.contourArea(contour)
|
|
if area < 500 or area > 50000:
|
|
continue
|
|
|
|
# Get bounding rectangle
|
|
x, y, w, h = cv2.boundingRect(contour)
|
|
aspect_ratio = w / h if h > 0 else 0
|
|
|
|
# Stamps are roughly rectangular (aspect ratio 0.5-2.0)
|
|
if aspect_ratio < 0.4 or aspect_ratio > 2.5:
|
|
continue
|
|
|
|
# Check if this blue region contains red pixels inside
|
|
roi_red = red_mask[y : y + h, x : x + w]
|
|
red_pixels = cv2.countNonZero(roi_red)
|
|
red_ratio = red_pixels / (w * h) if w * h > 0 else 0
|
|
|
|
# If there's significant red inside the blue region, it's a stamp candidate
|
|
if red_ratio > 0.05:
|
|
stamp_candidates.append((x, y, w, h, area, red_ratio))
|
|
# Draw rectangle on the image
|
|
cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 3)
|
|
cv2.putText(
|
|
img,
|
|
f"Red:{red_ratio:.1%}",
|
|
(x, y - 10),
|
|
cv2.FONT_HERSHEY_SIMPLEX,
|
|
0.6,
|
|
(0, 255, 0),
|
|
2,
|
|
)
|
|
|
|
if stamp_candidates:
|
|
print(
|
|
f"\n📍 {os.path.basename(frame_path)}: Found {len(stamp_candidates)} candidates"
|
|
)
|
|
for x, y, w, h, area, red_ratio in stamp_candidates:
|
|
print(f" ({x},{y}) size={w}x{h} area={area} red={red_ratio:.1%}")
|
|
|
|
# Save annotated image
|
|
out_name = "STAMP_DETECTED_" + os.path.basename(frame_path)
|
|
cv2.imwrite(os.path.join(BASE_DIR, out_name), img)
|
|
|
|
# Also extract and save each candidate region
|
|
for i, (x, y, w, h, area, red_ratio) in enumerate(stamp_candidates):
|
|
crop = img[y : y + h, x : x + w]
|
|
crop_name = f"STAMP_CROP_{os.path.basename(frame_path)[:-4]}_{i}.jpg"
|
|
cv2.imwrite(os.path.join(BASE_DIR, crop_name), crop)
|
|
|
|
print("\n🏁 Done. Check files named 'STAMP_DETECTED_*' and 'STAMP_CROP_*'")
|