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)
153 lines
4.3 KiB
Python
Executable File
153 lines
4.3 KiB
Python
Executable File
# Thanks for the contribution of KopiSoftware https://github.com/KopiSoftware
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import torch
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import time
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import numpy as np
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from model.model import parsingNet
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import torchvision.transforms as transforms
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import cv2
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from matplotlib import pyplot as plt
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from PIL import Image
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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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def resize(x, y):
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global cap
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cap.set(3,x)
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cap.set(4,y)
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def test_practical_without_readtime():
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global cap
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for i in range(10):
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_,img = cap.read()
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img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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img2 = Image.fromarray(img)
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x = img_transforms(img2)
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x = x.unsqueeze(0).cuda()+1
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y = net(x)
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print("pracrical image input size:",img.shape)
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print("pracrical tensor input size:",x.shape)
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t_all = []
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for i in range(100):
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_,img = cap.read()
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img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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img2 = Image.fromarray(img)
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x = img_transforms(img2)
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x = x.unsqueeze(0).cuda()+1
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t1 = time.time()
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y = net(x)
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t2 = time.time()
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t_all.append(t2 - t1)
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print("practical with out read time:")
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print('\taverage time:', np.mean(t_all) / 1)
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print('\taverage fps:',1 / np.mean(t_all))
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# print('fastest time:', min(t_all) / 1)
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# print('fastest fps:',1 / min(t_all))
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# print('slowest time:', max(t_all) / 1)
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# print('slowest fps:',1 / max(t_all))
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def test_practical():
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global cap
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for i in range(10):
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_,img = cap.read()
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img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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img2 = Image.fromarray(img)
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x = img_transforms(img2)
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x = x.unsqueeze(0).cuda()+1
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y = net(x)
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print("pracrical image input size:",img.shape)
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print("pracrical tensor input size:",x.shape)
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t_all = []
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t_capture = []
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t_preprocessing = []
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t_net = []
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for i in range(100):
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t1 = time.time()
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t3 = time.time()
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_,img = cap.read()
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t4 = time.time()
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t5 = time.time()
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img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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img2 = Image.fromarray(img)
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x = img_transforms(img2)
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x = x.unsqueeze(0).cuda()+1
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t6 = time.time()
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y = net(x)
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t2 = time.time()
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t_all.append(t2 - t1)
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t_capture.append(t4 - t3)
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t_preprocessing.append(t6 - t5)
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t_net.append(t2 - t6)
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print("practical with read time:")
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print('\taverage time:', np.mean(t_all) / 1)
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print('\taverage fps:',1 / np.mean(t_all))
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print('\tcapture time:', np.mean(t_capture) / 1)
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print('\tpre-processing time:', np.mean(t_preprocessing) / 1)
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print('\tdetect time:', np.mean(t_net) / 1)
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# print('fastest time:', min(t_all) / 1)
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# print('fastest fps:',1 / min(t_all))
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# print('slowest time:', max(t_all) / 1)
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# print('slowest fps:',1 / max(t_all))
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###x = torch.zeros((1,3,288,800)).cuda() + 1
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def test_theoretical():
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x = torch.zeros((1,3,288,800)).cuda() + 1
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for i in range(10):
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y = net(x)
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t_all = []
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for i in range(100):
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t1 = time.time()
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y = net(x)
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t2 = time.time()
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t_all.append(t2 - t1)
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print("theortical")
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print('\taverage time:', np.mean(t_all) / 1)
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print('\taverage fps:',1 / np.mean(t_all))
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# print('fastest time:', min(t_all) / 1)
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# print('fastest fps:',1 / min(t_all))
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# print('slowest time:', max(t_all) / 1)
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# print('slowest fps:',1 / max(t_all))
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if __name__ == "__main__":
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###captrue data from camera or video
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#cap = cv2.VideoCapture("video.mp4") #uncommen to activate a video input
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cap = cv2.VideoCapture(0) #uncommen to activate a camera imput
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#resize(480, 640) #ucommen to change input size
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# torch.backends.cudnn.deterministic = False
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torch.backends.cudnn.benchmark = True
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net = parsingNet(pretrained = False, backbone='18',cls_dim = (100+1,56,4),use_aux=False).cuda()
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# net = parsingNet(pretrained = False, backbone='18',cls_dim = (200+1,18,4),use_aux=False).cuda()
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net.eval()
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test_practical_without_readtime()
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test_practical()
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cap.release()
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test_theoretical() |