207 lines
8.5 KiB
Python
207 lines
8.5 KiB
Python
import os
|
||
import sys
|
||
import subprocess
|
||
import time
|
||
import cv2
|
||
import numpy as np
|
||
from pathlib import Path
|
||
from datetime import datetime, timedelta
|
||
|
||
# Try to import YOLO
|
||
try:
|
||
from ultralytics import YOLO
|
||
except ImportError:
|
||
print("Error: 'ultralytics' library not found. Please install it using: pip install ultralytics")
|
||
sys.exit(1)
|
||
|
||
class VideoCleaner:
|
||
def __init__(self, model_path='yolo26n.pt', brightness_threshold=25, age_days=30, workers=4):
|
||
self.model_path = model_path
|
||
self.brightness_threshold = brightness_threshold
|
||
self.age_limit = timedelta(days=age_days)
|
||
self.workers = workers
|
||
self.supported_extensions = ('.mp4', '.avi', '.mov', '.mkv', '.flv', '.wmv')
|
||
|
||
# We'll initialize the model inside each process for thread/process safety
|
||
self.model = None
|
||
|
||
def _get_model(self):
|
||
"""Lazy initialization of the model for each process."""
|
||
if self.model is None:
|
||
# print(f"[{os.getpid()}] Initializing YOLO model: {self.model_path}...")
|
||
try:
|
||
self.model = YOLO(self.model_path)
|
||
# Ensure it's on GPU if available
|
||
if torch.cuda.is_available():
|
||
self.model.to('cuda')
|
||
except Exception as e:
|
||
print(f"Failed to load YOLO model in process {os.getpid()}: {e}")
|
||
sys.exit(1)
|
||
return self.model
|
||
|
||
def is_frame_color_and_bright(self, frame):
|
||
"""Checks if a single frame is in 'Day/Lights-on' mode."""
|
||
# 1. Brightness check
|
||
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
|
||
if np.mean(gray) < self.brightness_threshold:
|
||
return False
|
||
|
||
# 2. Color (Saturation) check
|
||
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
|
||
sat_95th = np.percentile(hsv[:, :, 1], 95)
|
||
if sat_95th < 20: # Infrared/Grayscale mode
|
||
return False
|
||
|
||
return True
|
||
|
||
def should_keep_video(self, video_path, save_preview_path=None):
|
||
"""
|
||
Scans the video to see if it should be kept.
|
||
Kept if: (It's not a night-only video) AND (Human is detected).
|
||
"""
|
||
cap = cv2.VideoCapture(str(video_path))
|
||
fps = cap.get(cv2.CAP_PROP_FPS)
|
||
frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
|
||
|
||
if fps <= 0 or frame_count <= 0:
|
||
cap.release()
|
||
return False, "Invalid video"
|
||
|
||
step = max(1, int(fps * 2))
|
||
print(f"Analyzing {video_path.name}...")
|
||
|
||
has_shown_color = False
|
||
prev_gray = None
|
||
|
||
for i in range(0, frame_count, step):
|
||
cap.set(cv2.CAP_PROP_POS_FRAMES, i)
|
||
ret, frame = cap.read()
|
||
if not ret:
|
||
break
|
||
|
||
# Check if this frame is 'Normal Mode' (Lights on)
|
||
is_color = self.is_frame_color_and_bright(frame)
|
||
if is_color:
|
||
has_shown_color = True
|
||
|
||
# --- MOTION DETECTION ---
|
||
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
|
||
gray = cv2.GaussianBlur(gray, (21, 21), 0)
|
||
if prev_gray is not None:
|
||
frame_delta = cv2.absdiff(prev_gray, gray)
|
||
thresh = cv2.threshold(frame_delta, 25, 255, cv2.THRESH_BINARY)[1]
|
||
if (np.sum(thresh) / thresh.size) < 0.005:
|
||
continue
|
||
prev_gray = gray
|
||
|
||
# --- AI HUMAN DETECTION ---
|
||
model = self._get_model()
|
||
# 直接传原图，省去 CPU resize，开启半精度和指定推理尺寸
|
||
results = model(frame, classes=[0], verbose=False, conf=0.7, half=True, imgsz=640)
|
||
debugmode=True
|
||
if len(results[0].boxes) > 0:
|
||
if is_color:
|
||
# --- DEBUG: Save the frame that triggered detection ---
|
||
if debugmode or save_preview_path:
|
||
# Draw boxes on the frame for visualization
|
||
annotated_frame = results[0].plot()
|
||
|
||
if debugmode:
|
||
debug_dir = Path("debug_detections")
|
||
debug_dir.mkdir(exist_ok=True)
|
||
debug_filename = debug_dir / f"detected_{video_path.stem}_frame_{i}.jpg"
|
||
cv2.imwrite(str(debug_filename), annotated_frame)
|
||
print(f"DEBUG: Saved detection image to {debug_filename}")
|
||
|
||
if save_preview_path:
|
||
save_preview_path.parent.mkdir(parents=True, exist_ok=True)
|
||
cv2.imwrite(str(save_preview_path), annotated_frame)
|
||
print(f"INFO: Saved preview image to {save_preview_path}")
|
||
|
||
cap.release()
|
||
return True, "Human detected in color mode"
|
||
|
||
cap.release()
|
||
|
||
# Final decision after scanning the whole video:
|
||
if has_shown_color:
|
||
# If lights were turned on but we found no people during the whole video
|
||
# Based on your rule #2: "If no humans in the whole video, delete."
|
||
return False, "Lights were on, but no humans detected"
|
||
|
||
return False, "Entirely night/IR mode or no humans"
|
||
|
||
def process_video_file(self, video_path, processed_base_dir, input_base_dir):
|
||
video_path = Path(video_path).resolve()
|
||
mtime = datetime.fromtimestamp(os.path.getmtime(video_path))
|
||
if datetime.now() - mtime < self.age_limit:
|
||
return False # 表示因为时间太新没处理
|
||
|
||
# Calculate target path for preview image
|
||
rel_path = video_path.relative_to(input_base_dir)
|
||
output_path = processed_base_dir / rel_path
|
||
preview_path = output_path.with_suffix('.jpg')
|
||
|
||
keep, reason = self.should_keep_video(video_path, save_preview_path=preview_path)
|
||
if keep:
|
||
print(f"Action: KEEP {video_path.name} - Reason: {reason}")
|
||
self.move_to_processed(video_path, processed_base_dir, input_base_dir)
|
||
else:
|
||
print(f"Action: DELETE {video_path.name} - Reason: {reason}")
|
||
os.remove(video_path)
|
||
return True # 表示处理了
|
||
|
||
def move_to_processed(self, video_path, processed_base_dir, input_base_dir):
|
||
rel_path = video_path.relative_to(input_base_dir)
|
||
output_path = processed_base_dir / rel_path
|
||
output_path.parent.mkdir(parents=True, exist_ok=True)
|
||
if os.path.exists(output_path): os.remove(output_path)
|
||
os.rename(video_path, output_path)
|
||
|
||
def scan_and_process(self, input_dir):
|
||
input_path = Path(input_dir).resolve()
|
||
if not input_path.exists(): return
|
||
processed_dir = input_path.parent / "processed"
|
||
|
||
print(f"Scanning {input_path}...")
|
||
|
||
all_videos = []
|
||
for root, dirs, files in os.walk(input_path):
|
||
if "processed" in dirs:
|
||
dirs.remove("processed")
|
||
|
||
for file in files:
|
||
if file.lower().endswith(self.supported_extensions):
|
||
all_videos.append(Path(root) / file)
|
||
|
||
if not all_videos:
|
||
print("No videos found.")
|
||
return
|
||
|
||
# Sort by mtime
|
||
all_videos.sort(key=lambda x: os.path.getmtime(x))
|
||
|
||
print(f"Found {len(all_videos)} videos. Starting parallel processing with {self.workers} workers...")
|
||
|
||
with ProcessPoolExecutor(max_workers=self.workers) as executor:
|
||
# We pass the method and its arguments.
|
||
# Note: self will be pickled. Since self.model is None, it should work.
|
||
futures = [executor.submit(self.process_video_file, v, processed_dir, input_path) for v in all_videos]
|
||
|
||
for future in as_completed(futures):
|
||
try:
|
||
future.result()
|
||
except Exception as e:
|
||
print(f"Error in worker process: {e}")
|
||
|
||
if __name__ == "__main__":
|
||
import argparse
|
||
parser = argparse.ArgumentParser()
|
||
parser.add_argument("dir", help="Target directory")
|
||
parser.add_argument("--days", type=int, default=30)
|
||
parser.add_argument("--model", type=str, default='yolo26n.pt', help="Path to YOLO model or model name")
|
||
parser.add_argument("--workers", type=int, default=4, help="Number of parallel workers")
|
||
args = parser.parse_args()
|
||
cleaner = VideoCleaner(model_path=args.model, age_days=args.days, workers=args.workers)
|
||
cleaner.scan_and_process(args.dir)
|