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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 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 | import os
import cv2
import numpy as np
import h5py
import scipy.io
from tqdm import tqdm
import sys
from pathlib import Path
from dataclasses import dataclass
# Add src to path
sys.path.append(str(Path(__file__).parent.parent.parent))
from src.utils.preprocess import GazePreprocessor
@dataclass
class MockLandmark:
x: float
y: float
z: float = 0.0
class RobustGazePreprocessor(GazePreprocessor):
def get_landmarks_robust(self, frame):
"""
Thử tìm mặt ở nhiều góc xoay khác nhau (0, 180, 90, 270)
để đảm bảo không bỏ sót ảnh nào MediaPipe có thể đọc được.
"""
rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
h, w = frame.shape[:2]
# 1. Thử góc thẳng
landmarks = self.get_landmarks(frame)
if landmarks:
return landmarks, 0
# 2. Thử các góc xoay (ưu tiên 180 độ cho trường hợp lộn đầu)
for angle in [180, 90, 270]:
if angle == 180:
rotated = cv2.rotate(rgb_frame, cv2.ROTATE_180)
elif angle == 90:
rotated = cv2.rotate(rgb_frame, cv2.ROTATE_90_CLOCKWISE)
else:
rotated = cv2.rotate(rgb_frame, cv2.ROTATE_90_COUNTERCLOCKWISE)
# Gọi hàm detect gốc của MediaPipe
import mediapipe as mp
mp_image = mp.Image(image_format=mp.ImageFormat.SRGB, data=rotated)
res = self.detector.detect(mp_image)
if res.face_landmarks:
# Nắn tọa độ landmark về khung hình gốc
lms = res.face_landmarks[0]
unrotated = []
for lm in lms:
x, y = lm.x, lm.y
if angle == 180: nx, ny = 1.0 - x, 1.0 - y
elif angle == 90: nx, ny = y, 1.0 - x
elif angle == 270: nx, ny = 1.0 - y, x
unrotated.append(MockLandmark(x=nx, y=ny, z=lm.z))
return unrotated, angle
return None, 0
def run_robust_processing(output_prefix='data/processed/gaze360_v16', method='new', train_ids=None, test_ids=None):
mat_path = 'data/raw/metadata.mat'
img_root = 'data/raw/imgs'
rec_idx = 6
patch_size = 16
print(f"Loading metadata... [Method: {method}]")
mat = scipy.io.loadmat(mat_path)
indices = np.where(mat['recording'].flatten() == rec_idx)[0]
rec_name = mat['recordings'][0, rec_idx][0]
preprocessor = RobustGazePreprocessor()
os.makedirs(os.path.dirname(output_prefix), exist_ok=True)
# Train and Test H5 files
train_path = f"{output_prefix}_train_A.h5"
test_path = f"{output_prefix}_test_B.h5"
with h5py.File(train_path, 'w') as h5_train, h5py.File(test_path, 'w') as h5_test:
datasets = {}
for mode, h5f in zip(['train', 'test'], [h5_train, h5_test]):
datasets[mode] = {
'lp': h5f.create_dataset('left_patches', (0, 4, patch_size, patch_size), maxshape=(None, 4, patch_size, patch_size), dtype='uint8', compression='gzip'),
'rp': h5f.create_dataset('right_patches', (0, 4, patch_size, patch_size), maxshape=(None, 4, patch_size, patch_size), dtype='uint8', compression='gzip'),
'gaze': h5f.create_dataset('gaze', (0, 2), maxshape=(None, 2), dtype='float32'),
'lm': h5f.create_dataset('landmarks', (0, 478, 2), maxshape=(None, 478, 2), dtype='float32'),
'idx': 0
}
for i in tqdm(indices, desc=f"Robust Splitting ({method})"):
person_id = mat['person_identity'][0, i]
# Determine which set this subject belongs to
if train_ids is not None and person_id in train_ids:
mode = 'train'
elif test_ids is not None and person_id in test_ids:
mode = 'test'
else:
continue # Skip subjects not in our targeted split
frame_num = mat['frame'][0, i]
gaze_3d = mat['gaze_dir'][i]
img_path = os.path.join(img_root, rec_name, 'head', f'{person_id:06d}', f'{frame_num:06d}.jpg')
if not os.path.exists(img_path): continue
frame = cv2.imread(img_path)
if frame is None: continue
landmarks, det_angle = preprocessor.get_landmarks_robust(frame)
if landmarks is None: continue
try:
left_eye_img, left_angle = preprocessor.normalize_eye(frame, landmarks, 'left', method=method)
left_patches = preprocessor.extract_patches(left_eye_img, patch_size=patch_size)
right_eye_img, right_angle = preprocessor.normalize_eye(frame, landmarks, 'right', method=method)
right_patches = preprocessor.extract_patches(right_eye_img, patch_size=patch_size)
avg_angle = (left_angle + right_angle) / 2
gaze_rot = preprocessor.rotate_gaze(gaze_3d, avg_angle)
gaze_rad = preprocessor.gaze_3d_to_mag(gaze_rot)
# Landmarks zero-centering
lms_arr = np.array([[lm.x, lm.y] for lm in landmarks])
left_c = np.mean([[landmarks[idx].x, landmarks[idx].y] for idx in preprocessor.LEFT_CORNERS], axis=0)
right_c = np.mean([[landmarks[idx].x, landmarks[idx].y] for idx in preprocessor.RIGHT_CORNERS], axis=0)
face_center = (left_c + right_c) / 2
landmarks_centered = lms_arr - face_center
# Save to specific H5
ds = datasets[mode]
cur_idx = ds['idx']
ds['lp'].resize((cur_idx + 1, 4, patch_size, patch_size))
ds['lp'][cur_idx] = left_patches
ds['rp'].resize((cur_idx + 1, 4, patch_size, patch_size))
ds['rp'][cur_idx] = right_patches
ds['gaze'].resize((cur_idx + 1, 2))
ds['gaze'][cur_idx] = gaze_rad
ds['lm'].resize((cur_idx + 1, 478, 2))
ds['lm'][cur_idx] = landmarks_centered
ds['idx'] += 1
except Exception as e:
continue
print(f"\nDone! Method '{method}'")
print(f"Train A: {datasets['train']['idx']} samples")
print(f"Test B: {datasets['test']['idx']} samples")
if __name__ == "__main__":
# Define Split (Based on IDs identified for Recording 6)
# IDs: [0, 1, 17, 25, 49, 60, 61, 62]
# Balanced split to give more data to Train A (ID 61 is heavy)
TRAIN_IDS = [0, 1, 17, 25, 49, 61]
TEST_IDS = [60, 62]
# Generate NEW (Huy's 4-step) version with Splitting
run_robust_processing(
output_prefix='data/processed/gaze360_robust_v16',
method='new',
train_ids=TRAIN_IDS,
test_ids=TEST_IDS
)
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