196 lines
8.2 KiB
Python
196 lines
8.2 KiB
Python
import os
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import sys
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import subprocess
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import time
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import cv2
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import numpy as np
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from pathlib import Path
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from datetime import datetime, timedelta
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# Try to import YOLO
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try:
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from ultralytics import YOLO
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except ImportError:
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print("Error: 'ultralytics' library not found. Please install it using: pip install ultralytics")
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sys.exit(1)
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class VideoCleaner:
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def __init__(self, model_path='yolo26n.pt', brightness_threshold=25, age_days=30):
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print(f"Initializing YOLO model: {model_path}...")
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try:
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self.model = YOLO(model_path)
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except Exception as e:
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print(f"Failed to load YOLO model: {e}")
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sys.exit(1)
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self.brightness_threshold = brightness_threshold
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self.age_limit = timedelta(days=age_days)
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self.supported_extensions = ('.mp4', '.avi', '.mov', '.mkv', '.flv', '.wmv')
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def is_frame_color_and_bright(self, frame):
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"""Checks if a single frame is in 'Day/Lights-on' mode."""
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# 1. Brightness check
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gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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if np.mean(gray) < self.brightness_threshold:
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return False
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# 2. Color (Saturation) check
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hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
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sat_95th = np.percentile(hsv[:, :, 1], 95)
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if sat_95th < 20: # Infrared/Grayscale mode
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return False
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return True
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def should_keep_video(self, video_path, save_preview_path=None):
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"""
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Scans the video to see if it should be kept.
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Kept if: (It's not a night-only video) AND (Human is detected).
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"""
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cap = cv2.VideoCapture(str(video_path))
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fps = cap.get(cv2.CAP_PROP_FPS)
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frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
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if fps <= 0 or frame_count <= 0:
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cap.release()
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return False, "Invalid video"
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step = max(1, int(fps * 2))
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print(f"Analyzing {video_path.name}...")
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has_shown_color = False
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prev_gray = None
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for i in range(0, frame_count, step):
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cap.set(cv2.CAP_PROP_POS_FRAMES, i)
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ret, frame = cap.read()
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if not ret:
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break
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# Check if this frame is 'Normal Mode' (Lights on)
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is_color = self.is_frame_color_and_bright(frame)
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if is_color:
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has_shown_color = True
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# --- MOTION DETECTION ---
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gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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gray = cv2.GaussianBlur(gray, (21, 21), 0)
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if prev_gray is not None:
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frame_delta = cv2.absdiff(prev_gray, gray)
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thresh = cv2.threshold(frame_delta, 25, 255, cv2.THRESH_BINARY)[1]
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if (np.sum(thresh) / thresh.size) < 0.005:
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continue
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prev_gray = gray
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# --- AI HUMAN DETECTION ---
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# We only care about humans if the lights are on (as per your rules)
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# OR we can scan anyway if you want to find people in IR mode too.
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# Following your rules: "If it's night and no lights, delete".
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# So we only need to detect humans to justify keeping the video.
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small_frame = cv2.resize(frame, (640, 360))
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results = self.model(small_frame, classes=[0], verbose=False, conf=0.7)
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debugmode=True
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if len(results[0].boxes) > 0:
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if is_color:
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# --- DEBUG: Save the frame that triggered detection ---
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if debugmode or save_preview_path:
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# Draw boxes on the frame for visualization
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annotated_frame = results[0].plot()
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if debugmode:
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debug_dir = Path("debug_detections")
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debug_dir.mkdir(exist_ok=True)
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debug_filename = debug_dir / f"detected_{video_path.stem}_frame_{i}.jpg"
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cv2.imwrite(str(debug_filename), annotated_frame)
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print(f"DEBUG: Saved detection image to {debug_filename}")
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if save_preview_path:
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save_preview_path.parent.mkdir(parents=True, exist_ok=True)
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cv2.imwrite(str(save_preview_path), annotated_frame)
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print(f"INFO: Saved preview image to {save_preview_path}")
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cap.release()
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return True, "Human detected in color mode"
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cap.release()
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# Final decision after scanning the whole video:
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if has_shown_color:
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# If lights were turned on but we found no people during the whole video
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# Based on your rule #2: "If no humans in the whole video, delete."
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return False, "Lights were on, but no humans detected"
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return False, "Entirely night/IR mode or no humans"
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def process_video_file(self, video_path, processed_base_dir, input_base_dir):
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video_path = Path(video_path).resolve()
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mtime = datetime.fromtimestamp(os.path.getmtime(video_path))
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if datetime.now() - mtime < self.age_limit:
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return False # 表示因为时间太新没处理
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# Calculate target path for preview image
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rel_path = video_path.relative_to(input_base_dir)
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output_path = processed_base_dir / rel_path
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preview_path = output_path.with_suffix('.jpg')
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keep, reason = self.should_keep_video(video_path, save_preview_path=preview_path)
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if keep:
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print(f"Action: KEEP {video_path.name} - Reason: {reason}")
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self.move_to_processed(video_path, processed_base_dir, input_base_dir)
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else:
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print(f"Action: DELETE {video_path.name} - Reason: {reason}")
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os.remove(video_path)
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return True # 表示处理了
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def move_to_processed(self, video_path, processed_base_dir, input_base_dir):
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rel_path = video_path.relative_to(input_base_dir)
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output_path = processed_base_dir / rel_path
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output_path.parent.mkdir(parents=True, exist_ok=True)
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if os.path.exists(output_path): os.remove(output_path)
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os.rename(video_path, output_path)
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def scan_and_process(self, input_dir):
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input_path = Path(input_dir).resolve()
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if not input_path.exists(): return
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processed_dir = input_path.parent / "processed"
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print(f"Scanning {input_path}...")
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while True:
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all_videos = []
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# 每次循环都获取最新列表
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for root, dirs, files in os.walk(input_path):
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if "processed" in dirs: dirs.remove("processed")
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for file in files:
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if file.lower().endswith(self.supported_extensions):
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full_path = Path(root) / file
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all_videos.append(full_path)
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if not all_videos:
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print("No more files to process. Exiting.")
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break
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# 按修改时间排序，取最旧的一个
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all_videos.sort(key=lambda x: os.path.getmtime(x))
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processed_in_this_loop = False
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for target_file in all_videos:
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if self.process_video_file(target_file, processed_dir, input_path):
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processed_in_this_loop = True
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break # 处理了一个，重新获取列表
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if not processed_in_this_loop:
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# 如果列表里剩下的所有文件都还没到 30 天，那就退出
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print("All remaining files are newer than the age limit. Exiting.")
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break
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if __name__ == "__main__":
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import argparse
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parser = argparse.ArgumentParser()
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parser.add_argument("dir", help="Target directory")
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parser.add_argument("--days", type=int, default=30)
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parser.add_argument("--model", type=str, default='yolo26n.pt', help="Path to YOLO model or model name")
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args = parser.parse_args()
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cleaner = VideoCleaner(model_path=args.model, age_days=args.days)
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cleaner.scan_and_process(args.dir)
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