File size: 2,721 Bytes
178f61f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 | import cv2
import numpy as np
import os
import sys
from pathlib import Path
# Add project root to path
project_root = str(Path(__file__).parent.parent.parent)
if project_root not in sys.path:
sys.path.append(project_root)
from src.utils.preprocess import GazePreprocessor
def debug_filters(img_path, output_dir='data/verification/denoise_test'):
os.makedirs(output_dir, exist_ok=True)
# Load original image
frame = cv2.imread(img_path)
if frame is None:
print(f"Error: Could not load image at {img_path}")
return
preprocessor = GazePreprocessor()
landmarks = preprocessor.get_landmarks(frame)
if landmarks is None:
print("No landmarks detected.")
return
# 1. Test Raw Normalization (No additional filters - simulate old state)
# We bypass the current preprocess.py by doing a manual warp for comparison
def raw_normalize(eye_side='left'):
h, w, _ = frame.shape
indices = preprocessor.LEFT_CORNERS if eye_side == 'left' else preprocessor.RIGHT_CORNERS
p1 = np.array([landmarks[indices[0]].x * w, landmarks[indices[0]].y * h])
p2 = np.array([landmarks[indices[1]].x * w, landmarks[indices[1]].y * h])
center = (p1 + p2) / 2
dx, dy = p2 - p1
angle = np.degrees(np.arctan2(dy, dx))
dist = np.linalg.norm(p2 - p1)
scale = (64 * 0.7) / (dist + 1e-6)
M = cv2.getRotationMatrix2D(tuple(center), angle, scale)
M[0, 2] += (64 / 2) - center[0]
M[1, 2] += (32 / 2) - center[1]
raw = cv2.warpAffine(frame, M, (64, 32), flags=cv2.INTER_LINEAR)
return cv2.cvtColor(raw, cv2.COLOR_BGR2GRAY)
# 2. Get Processed result (Current code with Bilateral + CLAHE)
processed_eye, _ = preprocessor.normalize_eye(frame, landmarks, 'left')
# Save for comparison
raw_eye = raw_normalize('left')
# Magnify for inspection (16x16 zoom)
raw_zoom = cv2.resize(raw_eye, (256, 128), interpolation=cv2.INTER_NEAREST)
proc_zoom = cv2.resize(processed_eye, (256, 128), interpolation=cv2.INTER_NEAREST)
cv2.imwrite(os.path.join(output_dir, '0_raw_eye.png'), raw_eye)
cv2.imwrite(os.path.join(output_dir, '1_processed_eye.png'), processed_eye)
cv2.imwrite(os.path.join(output_dir, '2_raw_zoom.png'), raw_zoom)
cv2.imwrite(os.path.join(output_dir, '3_processed_zoom.png'), proc_zoom)
print(f"Denoising debug images saved to {output_dir}")
if __name__ == "__main__":
# Sử dụng ảnh test Gaze360 có sẵn
test_img = 'data/verification/test_gaze.jpg'
if os.path.exists(test_img):
debug_filters(test_img)
else:
print(f"Please provide a valid test image at {test_img}")
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