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yolov8先训练生成best.pt文件，用这个生成的模型进行视频的测试
因为本来用的代码生成的测试视频打不开，格式应该是损坏了，或者部分帧没有正常保存吧。
修改了一下代码，现状可以正常打开生成的视频了。
1、训练代码train.py
import os# os.environ["CUDA_VISIBLE_DEVICES"] = "3" # 同样是选择第3块GPUfrom ultralytics import YOLO# Load a model
# model = YOLO("yolov8n.yaml") # build a new model from YAML
# model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training)# ffs = os.listdir("cfg1116/new_cfg")
# for ff in ffs:
model = YOLO(f"cfg1116/yolov8n.yaml") # build from YAML and transfer weights
# Train the model
# results = model.train(data=r"/mnt/disk3/sunjiahui/CV-code/v8_all/data.yaml", epochs=5, imgsz=1280, workers=0, batch=2, device=[2])
results = model.train(data=r"/mnt/disk3/sunjiahui/CV-code/v8_all/data.yaml",epochs=500,imgsz=1280,workers=0,batch=2,device=[0],hsv_h=0.015, # HSV色调变化hsv_s=0.7, # HSV饱和度变化hsv_v=0.4, # HSV亮度变化degrees=0.0, # 旋转角度translate=0.1, # 平移比例scale=0.5, # 缩放比例shear=0.0, # 剪切变换perspective=0.0, # 透视变换flipud=0.0, # 上下翻转概率fliplr=0.5, # 左右翻转概率mosaic=1.0, # Mosaic增强的概率mixup=0.0 # MixUp增强的概率
)
model.val(imgsz=[1280,1280])2、测试代码：视频
from ultralytics import YOLO
import cv2
import osos.environ["CUDA_VISIBLE_DEVICES"] = "2" # 同样是选择第3块GPUdef process_video():# 初始化模型model = YOLO("runs/detect/train2/weights/best.pt")# 输入输出路径input_path = "/mnt/disk3/sunjiahui/CV-code/v8_all/XIONG_AN/shipin.mp4"output_path = "/mnt/disk3/sunjiahui/CV-code/v8_all/XIONG_AN/output_video15.mp4"# 尝试不同编解码器组合codec_options = ['mp4v', 'avc1', 'X264', 'MJPG']success = Falsefor codec in codec_options:try:cap = cv2.VideoCapture(input_path)fps = int(cap.get(cv2.CAP_PROP_FPS)) or 30 # 处理fps为0的情况width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))fourcc = cv2.VideoWriter_fourcc(*codec)out = cv2.VideoWriter(output_path, fourcc, fps, (width, height))print(f"尝试使用编解码器 {codec}...")while cap.isOpened():ret, frame = cap.read()if not ret:breakresults = model.predict(frame, conf=0.15)annotated_frame = results[0].plot()# 确保帧格式正确if annotated_frame.shape[:2] != (height, width):annotated_frame = cv2.resize(annotated_frame, (width, height))out.write(annotated_frame)success = Truebreakexcept Exception as e:print(f"编解码器 {codec} 失败: {str(e)}")if os.path.exists(output_path):os.remove(output_path)continuefinally:cap.release()out.release()if success:print(f"视频生成成功！保存路径：{os.path.abspath(output_path)}")print("如果仍无法播放，请尝试以下方案：")print("1. 使用 VLC 播放器（兼容性最佳）")print("2. 执行命令：ffmpeg -i output_video.mp4 -c:v libx264 final.mp4")else:print("所有编解码器尝试失败，改用图像序列方案...")save_as_image_sequence(model, input_path)def save_as_image_sequence(model, input_path):"""备用方案：保存为图片序列"""output_dir = "video_frames"os.makedirs(output_dir, exist_ok=True)cap = cv2.VideoCapture(input_path)frame_count = 0while cap.isOpened():ret, frame = cap.read()if not ret:breakresults = model.predict(frame)annotated_frame = results[0].plot()cv2.imwrite(f"{output_dir}/frame_{frame_count:04d}.jpg", annotated_frame)frame_count += 1cap.release()print(f"图像序列已保存至 {output_dir}，可用以下命令合成视频：")print(f"ffmpeg -framerate 30 -i {output_dir}/frame_%04d.jpg -c:v libx264 output.mp4")if __name__ == "__main__":process_video()