File size: 6,287 Bytes
1d4559c |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 |
# data_preprocessing_sequences.py
import os
import cv2
import dlib
import numpy as np
from imutils import face_utils
from tqdm import tqdm
import pickle
def get_facial_landmarks(detector, predictor, image):
"""
Detects facial landmarks in an image.
Args:
detector: dlib face detector.
predictor: dlib shape predictor.
image (numpy.ndarray): Input image.
Returns:
dict: Coordinates of eyes and eyebrows.
"""
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
rects = detector(gray, 1)
if len(rects) == 0:
return None # No face detected
# Assuming the first detected face is the target
rect = rects[0]
shape = predictor(gray, rect)
shape = face_utils.shape_to_np(shape)
landmarks = {}
# Define landmarks for left and right eyes and eyebrows
landmarks['left_eye'] = shape[36:42] # Left eye landmarks
landmarks['right_eye'] = shape[42:48] # Right eye landmarks
landmarks['left_eyebrow'] = shape[17:22] # Left eyebrow landmarks
landmarks['right_eyebrow'] = shape[22:27] # Right eyebrow landmarks
return landmarks
def extract_roi(image, landmarks, region='left_eye', padding=5):
"""
Extracts a region of interest (ROI) from the image based on landmarks.
Args:
image (numpy.ndarray): Input image.
landmarks (dict): Facial landmarks.
region (str): Region to extract ('left_eye', 'right_eye', 'left_eyebrow', 'right_eyebrow').
padding (int): Padding around the ROI.
Returns:
numpy.ndarray: Extracted ROI.
"""
points = landmarks.get(region)
if points is None:
return None
# Compute the bounding box
x, y, w, h = cv2.boundingRect(points)
x = max(x - padding, 0)
y = max(y - padding, 0)
w = w + 2 * padding
h = h + 2 * padding
roi = image[y:y+h, x:x+w]
return roi
def preprocess_video_sequence(sequence_dir, detector, predictor, img_size=(64, 64)):
"""
Preprocesses a sequence of frames from a video.
Args:
sequence_dir (str): Directory containing frames of a video.
detector: dlib face detector.
predictor: dlib shape predictor.
img_size (tuple): Desired image size for ROIs.
Returns:
list: List of preprocessed frames as numpy arrays.
"""
frames = sorted([f for f in os.listdir(sequence_dir) if f.endswith('.jpg') or f.endswith('.png')])
preprocessed_sequence = []
for frame_name in frames:
frame_path = os.path.join(sequence_dir, frame_name)
image = cv2.imread(frame_path)
if image is None:
continue
landmarks = get_facial_landmarks(detector, predictor, image)
if landmarks is None:
continue # Skip frames with no detected face
# Extract ROIs for eyes and eyebrows
rois = {}
rois['left_eye'] = extract_roi(image, landmarks, 'left_eye')
rois['right_eye'] = extract_roi(image, landmarks, 'right_eye')
rois['left_eyebrow'] = extract_roi(image, landmarks, 'left_eyebrow')
rois['right_eyebrow'] = extract_roi(image, landmarks, 'right_eyebrow')
# Process ROIs
roi_images = []
for region in ['left_eye', 'right_eye', 'left_eyebrow', 'right_eyebrow']:
roi = rois.get(region)
if roi is not None:
roi = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY) # Convert to grayscale
roi = cv2.resize(roi, img_size)
roi = roi.astype('float32') / 255.0 # Normalize to [0,1]
roi = np.expand_dims(roi, axis=-1) # Add channel dimension
roi_images.append(roi)
if len(roi_images) == 0:
continue # Skip if no ROIs were extracted
# Concatenate ROIs horizontally to form a single image
combined_roi = np.hstack(roi_images)
preprocessed_sequence.append(combined_roi)
return preprocessed_sequence
def preprocess_dataset(dataset_dir='dataset', output_dir='preprocessed_sequences', img_size=(64, 64)):
"""
Preprocesses the entire dataset by processing each video sequence.
Args:
dataset_dir (str): Directory containing labeled data.
output_dir (str): Directory to save preprocessed sequences.
img_size (tuple): Desired image size for ROIs.
"""
if not os.path.exists(output_dir):
os.makedirs(output_dir)
# Initialize dlib's face detector and landmark predictor
detector = dlib.get_frontal_face_detector()
predictor_path = 'shape_predictor_68_face_landmarks.dat'
if not os.path.exists(predictor_path):
print(f"Error: {predictor_path} not found. Download it from http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2")
return
predictor = dlib.shape_predictor(predictor_path)
classes = os.listdir(dataset_dir)
for cls in classes:
cls_path = os.path.join(dataset_dir, cls)
if not os.path.isdir(cls_path):
continue
output_cls_dir = os.path.join(output_dir, cls)
if not os.path.exists(output_cls_dir):
os.makedirs(output_cls_dir)
print(f"Processing class: {cls}")
sequences = os.listdir(cls_path)
for seq in tqdm(sequences, desc=f"Class {cls}"):
seq_path = os.path.join(cls_path, seq)
if not os.path.isdir(seq_path):
continue
preprocessed_sequence = preprocess_video_sequence(seq_path, detector, predictor, img_size=img_size)
if len(preprocessed_sequence) == 0:
continue # Skip sequences with no valid frames
# Stack frames to form a 3D array (frames, height, width, channels)
sequence_array = np.stack(preprocessed_sequence, axis=0)
# Save the preprocessed sequence as a numpy file
npy_filename = os.path.join(output_cls_dir, f"{seq}.npy")
np.save(npy_filename, sequence_array)
print("Data preprocessing completed.")
if __name__ == "__main__":
preprocess_dataset(dataset_dir='dataset', output_dir='preprocessed_sequences', img_size=(64, 64))
|