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
python
import os
# os.environ["CUDA_VISIBLE_DEVICES"] = "3" # 同样是选择第3块GPU
from 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
python
from ultralytics import YOLO
import cv2
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "2" # 同样是选择第3块GPU
def 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 = False
for 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:
break
results = 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 = True
break
except Exception as e:
print(f"编解码器 {codec} 失败: {str(e)}")
if os.path.exists(output_path):
os.remove(output_path)
continue
finally:
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 = 0
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
results = model.predict(frame)
annotated_frame = results[0].plot()
cv2.imwrite(f"{output_dir}/frame_{frame_count:04d}.jpg", annotated_frame)
frame_count += 1
cap.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()