CLRNet 车道线检测_clrnet代码实现

import torch
import cv2
from clrnet.engine.runner import Runner
from clrnet.utils.config import Config
import cv2
import os
import time
import torch.backends.cudnn as cudnn
 
cfg = Config.fromfile("configs/clrnet/clr_resnet18_culane.py")
cfg.gpus = 1
cfg.load_from = 'culane_r18.pth'
cfg.resume_from = ''
cfg.finetune_from = ''
cfg.seed = 0
 
def pre_process_image(path):
    img_w = 800
    img_h = 320
    cut_height = 270 
    image = cv2.imread(path)
    image = image[cut_height:, :, :]
    image = cv2.resize(image, (img_w, img_h))
    image = torch.Tensor([image])
    image = image.permute(0, 3, 1, 2)
    return image
 
def view(predictions):
        for lanes in predictions:
            img = cv2.imread("/root/CLRNet/v2-b40621ccca1d9a432adf1442dcc69540_b.jpg")
            out_file = "1.jpg"
            lanes = [lane.to_array(cfg) for lane