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)
149 lines
6.2 KiB
Python
Executable File
149 lines
6.2 KiB
Python
Executable File
import torch, os, cv2
|
|
from model.model import parsingNet
|
|
from utils.common import merge_config, checkpoint_state_dict
|
|
from utils.dist_utils import dist_print
|
|
import torch
|
|
import scipy.special, tqdm
|
|
import numpy as np
|
|
import torchvision.transforms as transforms
|
|
from data.dataset import LaneTestDataset
|
|
from data.constant import culane_row_anchor, tusimple_row_anchor
|
|
from scipy.optimize import curve_fit
|
|
|
|
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
|
|
|
|
|
|
# 自定义函数 e指数形式
|
|
def func(x, a, b, c):
|
|
return a * np.sqrt(x) * (b * np.square(x) + c)
|
|
|
|
|
|
def get_curve_fit(x, y):
|
|
# 非线性最小二乘法拟合
|
|
popt, pcov = curve_fit(func, x, y)
|
|
# 获取popt里面是拟合系数
|
|
# print(popt)
|
|
a = popt[0]
|
|
b = popt[1]
|
|
c = popt[2]
|
|
yvals = func(x, a, b, c) # 拟合y值
|
|
# print('popt:', popt)
|
|
# print('系数a:', a)
|
|
# print('系数b:', b)
|
|
# print('系数c:', c)
|
|
# print('系数pcov:', pcov)
|
|
# print('系数yvals:', yvals)
|
|
return yvals
|
|
|
|
|
|
if __name__ == "__main__":
|
|
torch.backends.cudnn.benchmark = True
|
|
|
|
args, cfg = merge_config()
|
|
|
|
dist_print('start testing...')
|
|
from model.backbone import SUPPORTED_BACKBONES
|
|
assert cfg.backbone in SUPPORTED_BACKBONES
|
|
|
|
if cfg.dataset == 'CULane':
|
|
cls_num_per_lane = 18
|
|
elif cfg.dataset == 'Tusimple':
|
|
cls_num_per_lane = 56
|
|
else:
|
|
raise NotImplementedError
|
|
|
|
net = parsingNet(pretrained=False, backbone=cfg.backbone, cls_dim=(cfg.griding_num+1, cls_num_per_lane, 4),
|
|
use_aux=False).to(device)
|
|
# use_aux=False).cuda() # we dont need auxiliary segmentation in testing
|
|
|
|
net.load_state_dict(checkpoint_state_dict(cfg.test_model, map_location=device), strict=False)
|
|
net.eval()
|
|
|
|
img_transforms = transforms.Compose([
|
|
transforms.Resize((288, 800)),
|
|
transforms.ToTensor(),
|
|
transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
|
|
])
|
|
if cfg.dataset == 'CULane':
|
|
splits = ['test0_normal.txt']
|
|
# 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']
|
|
datasets = [LaneTestDataset(cfg.data_root,os.path.join(cfg.data_root, 'list/test_split/'+split),img_transform = img_transforms) for split in splits]
|
|
img_w, img_h = 1280, 720
|
|
# img_w, img_h = 1640, 590
|
|
row_anchor = culane_row_anchor
|
|
elif cfg.dataset == 'Tusimple':
|
|
splits = ['test3.txt']
|
|
datasets = [LaneTestDataset(cfg.data_root,os.path.join(cfg.data_root, split),img_transform = img_transforms) for split in splits]
|
|
# img_w, img_h = 998, 560
|
|
# img_w, img_h = 960, 546
|
|
img_w, img_h = 1280, 720
|
|
row_anchor = tusimple_row_anchor
|
|
else:
|
|
raise NotImplementedError
|
|
for split, dataset in zip(splits, datasets):
|
|
loader = torch.utils.data.DataLoader(dataset, batch_size=1, shuffle=False, num_workers=1)
|
|
fourcc = cv2.VideoWriter_fourcc(*'MJPG')
|
|
print(split[:-3]+'avi')
|
|
vout = cv2.VideoWriter(split[:-3]+'avi', fourcc, 15.0, (img_w, img_h))
|
|
for i, data in enumerate(tqdm.tqdm(loader)):
|
|
imgs, names = data
|
|
# imgs = imgs.cuda()
|
|
imgs = imgs.to(device)
|
|
with torch.no_grad():
|
|
out = net(imgs)
|
|
# print(out)
|
|
col_sample = np.linspace(0, 800 - 1, cfg.griding_num)
|
|
col_sample_w = col_sample[1] - col_sample[0]
|
|
out_j = out[0].data.cpu().numpy()
|
|
out_j = out_j[:, ::-1, :]
|
|
prob = scipy.special.softmax(out_j[:-1, :, :], axis=0)
|
|
idx = np.arange(cfg.griding_num) + 1
|
|
idx = idx.reshape(-1, 1, 1)
|
|
loc = np.sum(prob * idx, axis=0)
|
|
out_j = np.argmax(out_j, axis=0)
|
|
loc[out_j == cfg.griding_num] = 0
|
|
out_j = loc
|
|
x_list = []
|
|
y_list = []
|
|
# import pdb; pdb.set_trace()
|
|
vis = cv2.imread(os.path.join(cfg.data_root, names[0]))
|
|
for i in range(out_j.shape[1]):
|
|
# print(out_j.shape[1])
|
|
if np.sum(out_j[:, i] != 0) > 2:
|
|
for k in range(out_j.shape[0]):
|
|
# print(out_j.shape[0])
|
|
if out_j[k, i] > 0:
|
|
ppp = (int(out_j[k, i] * col_sample_w * img_w / 800) - 1, int(img_h * (row_anchor[cls_num_per_lane-1-k]/288)) - 1)
|
|
# if len(x_list) >= 1:
|
|
# if abs(ppp[0] - x_list[-1]) < 12 or abs(ppp[0] - x_list[-1]) > 30:
|
|
# x_list.append(ppp[0])
|
|
# y_list.append(ppp[1])
|
|
# print('x_list[-1]', x_list[-1])
|
|
# else:
|
|
# x_list.append(ppp[0])
|
|
# y_list.append(ppp[1])
|
|
x_list.append(ppp[0])
|
|
y_list.append(ppp[1])
|
|
print(ppp)
|
|
# cv2.circle(vis, ppp, 5, (0, 255, 0), -1)
|
|
x = np.array(x_list)
|
|
yvals = np.array(y_list)
|
|
print(yvals)
|
|
# yvals = get_curve_fit(x, yvals)
|
|
# print(yvals)
|
|
start_p = (x[0], int(yvals[0]))
|
|
end_p = (x[0], int(yvals[0]))
|
|
# for i in range(len(yvals) - 1):
|
|
# if abs(int(yvals[i]) - int(yvals[i + 1])) <= 50:
|
|
# end_p = (x[i + 1], int(yvals[i + 1]))
|
|
# else:
|
|
# cv2.line(vis, start_p, end_p, (0, 0, 255), 3)
|
|
# start_p = (x[i + 1], int(yvals[i + 1]))
|
|
# if i == len(yvals) - 2:
|
|
# cv2.line(vis, start_p, end_p, (0, 0, 255), 3)
|
|
# cv2.line(vis, (x[i], int(yvals[i])), (x[i + 1], int(yvals[i + 1])), (0, 0, 255), 3)
|
|
cv2.imshow('vis', vis)
|
|
cv2.waitKey(1)
|
|
vout.write(vis)
|
|
|
|
vout.release() |