Chengfang Lu
e72bc061c5
Major changes: - New frontend (platform/web/): Vite + React 18 + TypeScript + Tailwind - 4-module navigation: 数据送标 / 模型管理 / 车队管理 / 系统管理 - Data catalog with charts (DMS/ADAS/Lane 3-tab view) - Quality review workflow (标注质检): Good/Fine/Bad scoring with auto-advance - Audit enhancements: batch operations, rejection categories, Feishu notifications - Operation audit log (操作日志) - World model simulation studio (仿真工坊) - Dataset version management with snapshots and diff - ADAS 7-class dataset integration (138K images organized + compressed) - User management with Feishu integration and pagination - CRUD/search/filter on all pages, card layout redesign - PIL-optimized image overlay rendering - Auto-snapshot on build, in_review workflow stage - Removed embedded algorithm code (now in workspace)
158 lines
6.1 KiB
Python
Executable File
158 lines
6.1 KiB
Python
Executable File
import torch, os, cv2
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from model.model import parsingNet
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from utils.common import merge_config
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from utils.dist_utils import dist_print
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import torch
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import scipy.special, tqdm
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import numpy as np
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import torchvision.transforms as transforms
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from data.dataset import LaneTestDataset
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from data.constant import culane_row_anchor, tusimple_row_anchor
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from scipy.optimize import curve_fit
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from lane_show import is_in_poly, handle_point, poly_fitting, draw_values
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import time
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# device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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# 自定义函数 e指数形式
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def func(x, a, b, c):
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return a * np.sqrt(x) * (b * np.square(x) + c)
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def get_curve_fit(x, y):
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# 非线性最小二乘法拟合
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popt, pcov = curve_fit(func, x, y)
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# 获取popt里面是拟合系数
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# print(popt)
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a = popt[0]
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b = popt[1]
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c = popt[2]
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yvals = func(x, a, b, c) # 拟合y值
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# print('popt:', popt)
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# print('系数a:', a)
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# print('系数b:', b)
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# print('系数c:', c)
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# print('系数pcov:', pcov)
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# print('系数yvals:', yvals)
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return yvals
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if __name__ == "__main__":
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torch.backends.cudnn.benchmark = True
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args, cfg = merge_config()
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dist_print('start testing...')
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from model.backbone import SUPPORTED_BACKBONES
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assert cfg.backbone in SUPPORTED_BACKBONES
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if cfg.dataset == 'CULane':
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cls_num_per_lane = 18
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elif cfg.dataset == 'Tusimple':
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cls_num_per_lane = 56
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else:
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raise NotImplementedError
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net = parsingNet(pretrained=False, backbone=cfg.backbone, cls_dim=(cfg.griding_num + 1, cls_num_per_lane, 4),
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# use_aux=False).to(device)
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use_aux=False).cuda() # we dont need auxiliary segmentation in testing
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state_dict = torch.load(cfg.test_model, map_location='cuda')['model']
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compatible_state_dict = {}
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for k, v in state_dict.items():
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if 'module.' in k:
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compatible_state_dict[k[7:]] = v
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else:
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compatible_state_dict[k] = v
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net.load_state_dict(compatible_state_dict, strict=False)
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net.eval()
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# net = torch.load(cfg.test_model, map_location='cuda')
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img_transforms = transforms.Compose([
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transforms.Resize((288, 800)),
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transforms.ToTensor(),
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transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
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])
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if cfg.dataset == 'CULane':
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splits = ['test0_normal.txt']
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# splits = ['test0_normal.txt', 'test1_crowd.txt', 'test2_hlight.txt', 'test3_shadow.txt', 'test4_noline.txt', 'test5_arrow.txt', 'test6_curve.txt', 'test7_cross.txt', 'test8_night.txt']
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datasets = [LaneTestDataset(cfg.data_root, os.path.join(cfg.data_root, 'list/test_split/' + split),
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img_transform=img_transforms) for split in splits]
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img_w, img_h = 1280, 720
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# img_w, img_h = 1640, 590
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row_anchor = culane_row_anchor
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elif cfg.dataset == 'Tusimple':
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splits = ['test1.txt']
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datasets = [LaneTestDataset(cfg.data_root, os.path.join(cfg.data_root, split), img_transform=img_transforms) for
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split in splits]
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# img_w, img_h = 998, 560
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# img_w, img_h = 960, 546
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img_w, img_h = 1280, 720
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row_anchor = tusimple_row_anchor
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else:
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raise NotImplementedError
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for split, dataset in zip(splits, datasets):
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loader = torch.utils.data.DataLoader(dataset, batch_size=1, shuffle=False, num_workers=1)
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fourcc = cv2.VideoWriter_fourcc(*'MJPG')
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print(split[:-3] + 'avi')
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vout = cv2.VideoWriter(split[:-3] + 'avi', fourcc, 5.0, (img_w, img_h))
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s = 0
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count = 0
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for i, data in enumerate(tqdm.tqdm(loader)):
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imgs, names = data
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imgs = imgs.cuda()
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# imgs = imgs.to(device)
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with torch.no_grad():
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start_t = time.time()
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out = net(imgs)
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end_t = time.time()
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count_t = end_t - start_t
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print('the pre time is : ', count_t)
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if count > 0:
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s = s + count_t
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count = count + 1
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col_sample = np.linspace(0, 800 - 1, cfg.griding_num)
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col_sample_w = col_sample[1] - col_sample[0]
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out_j = out[0].data.cpu().numpy()
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out_j = out_j[:, ::-1, :]
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prob = scipy.special.softmax(out_j[:-1, :, :], axis=0)
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idx = np.arange(cfg.griding_num) + 1
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idx = idx.reshape(-1, 1, 1)
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loc = np.sum(prob * idx, axis=0)
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out_j = np.argmax(out_j, axis=0)
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loc[out_j == cfg.griding_num] = 0
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out_j = loc
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print('out:', len(out_j), out_j.shape)
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x_list = []
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y_list = []
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# import pdb; pdb.set_trace()
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vis = cv2.imread(os.path.join(cfg.data_root, names[0]))
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for i in range(out_j.shape[1]):
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# print(out_j.shape[1])
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if np.sum(out_j[:, i] != 0) > 2:
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poly = [[50, 50], [50, 719], [1250, 50], [1250, 719]] # ROI区域
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lane_x = []
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lane_y = []
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for k in range(out_j.shape[0]):
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# print(out_j.shape[0])
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if out_j[k, i] > 0:
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ppp = (int(out_j[k, i] * col_sample_w * img_w / 800) - 1,
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int(img_h * (row_anchor[cls_num_per_lane - 1 - k] / 288)) - 1)
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is_in = is_in_poly(ppp, poly)
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if is_in == True:
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# 将处理后的点坐标添如一个空列表做拟合用
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lane_x.append(ppp[0])
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lane_y.append(ppp[1])
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cv2.circle(vis, ppp, 5, (0, 255, 0), -1)
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lx, ly, rx, ry = handle_point(lane_x, lane_y)
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# print(lx, ly, rx, ry)
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# curvature, distance_from_center = poly_fitting(lx, ly, rx, ry)
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# draw_values(vis, curvature, distance_from_center)
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# cv2.imshow('vis', vis)
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# cv2.waitKey(1)
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vout.write(vis)
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print('mean count time: ', s/count)
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vout.release()
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