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import os
import cv2
import numpy as np
import pandas as pd
from tqdm import tqdm
import shutil
from mtcnn.mtcnn import MTCNN # <-- NEW IMPORT
import tensorflow as tf
# --- Fix for MTCNN in Colab ---
# MTCNN can have issues with TF2.x, this helps
tf.get_logger().setLevel('ERROR')
try:
import config
except ImportError:
print("Error: Could not import config.py. Make sure it's in the src/ directory.")
exit(1)
def load_test_list():
"""Loads the list of test videos from the text file."""
try:
with open(config.TEST_LIST_FILE, 'r') as f:
test_videos = [line.strip().split('/')[-1] for line in f]
return set(test_videos)
except FileNotFoundError:
print(f"Error: Test list file not found at {config.TEST_LIST_FILE}")
return set()
def extract_frames(video_path, output_folder, sequence_length, detector):
"""
Extracts, face-detects, and crops 'sequence_length'
evenly spaced frames from a video.
"""
cap = cv2.VideoCapture(video_path)
if not cap.isOpened():
print(f" > Warning: Could not open video {video_path}")
return False
total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
if total_frames < sequence_length:
print(f" > Warning: Video {video_path} is too short ({total_frames} frames). Skipping.")
return False
frame_indices = np.linspace(0, total_frames - 1, sequence_length, dtype=int)
os.makedirs(output_folder, exist_ok=True)
frames_saved = 0
for i, frame_index in enumerate(frame_indices):
cap.set(cv2.CAP_PROP_POS_FRAMES, frame_index)
ret, frame = cap.read()
if not ret:
continue
# --- NEW FACE DETECTION STEP ---
try:
# Convert from BGR (OpenCV) to RGB (MTCNN)
frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
# Detect faces
results = detector.detect_faces(frame_rgb)
if results:
# Get the first and most confident face
x1, y1, width, height = results[0]['box']
# Add a 20% margin for safety
x1, y1 = max(0, x1 - int(width*0.1)), max(0, y1 - int(height*0.1))
x2, y2 = min(frame.shape[1], x1 + int(width*1.2)), min(frame.shape[0], y1 + int(height*1.2))
# Crop the face from the *original* BGR frame
face_crop = frame[y1:y2, x1:x2]
# Resize the cropped face to our model's input size
face_resized = cv2.resize(face_crop, (config.TARGET_IMAGE_SIZE, config.TARGET_IMAGE_SIZE))
# Save the frame
frame_filename = f"frame_{frames_saved:02d}.jpg"
save_path = os.path.join(output_folder, frame_filename)
cv2.imwrite(save_path, face_resized)
frames_saved += 1
except Exception as e:
# Sometimes MTCNN fails on a weird frame
print(f" > Warning: Face detection failed for a frame in {video_path}. Error: {e}")
pass
cap.release()
# Check if we actually saved enough frames
if frames_saved < sequence_length * 0.8: # Allow for a few failures
print(f" > Warning: Only saved {frames_saved} frames for {video_path}. Skipping.")
shutil.rmtree(output_folder) # Clean up partial folder
return False
return True
def main():
"""
Main function to process all raw videos into sequence folders.
"""
print("--- Starting Video Sequence Preprocessing (with Face Detection) ---")
# 0. Clean old directory
if os.path.exists(config.PROCESSED_SEQ_DIR):
print(f"Removing old directory: {config.PROCESSED_SEQ_DIR}")
shutil.rmtree(config.PROCESSED_SEQ_DIR)
# 1. Load the list of test videos
test_video_set = load_test_list()
print(f"Loaded {len(test_video_set)} videos in the test set.")
# 2. --- NEW: Initialize the Face Detector ---
print("Initializing MTCNN face detector...")
detector = MTCNN()
print("Detector initialized.")
data_sources = [
('real', config.CELEB_REAL_DIR),
('real', config.YOUTUBE_REAL_DIR),
('fake', config.CELEB_FAKE_DIR)
]
video_count = 0
for label, source_dir in data_sources:
print(f"\nProcessing directory: {source_dir} (Label: {label})")
if not os.path.exists(source_dir):
print(f" > Warning: Directory not found. Skipping.")
continue
# Use tqdm for a nice progress bar
for video_file in tqdm(os.listdir(source_dir)):
if not video_file.endswith(('.mp4', '.avi', '.mov')):
continue
video_path = os.path.join(source_dir, video_file)
video_name = os.path.splitext(video_file)[0]
if video_file in test_video_set:
output_dir_base = config.TEST_SEQ_DIR
else:
output_dir_base = config.TRAIN_SEQ_DIR
output_folder = os.path.join(output_dir_base, label, video_name)
# Pass the detector to the function
success = extract_frames(video_path, output_folder, config.SEQUENCE_LENGTH, detector)
if success:
video_count += 1
print("\n--- Preprocessing Complete ---")
print(f"Successfully processed {video_count} videos with face cropping.")
print(f"New data is located in: {config.PROCESSED_SEQ_DIR}")
if __name__ == "__main__":
main() |