import gradio as gr import os #os.system("wget https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7.pt") os.system("wget https://github.com/hustvl/YOLOP/raw/main/weights/End-to-end.pth") #os.system("wget https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-e6e.pt") #os.system("wget https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-e6.pt") os.system("wget https://github.com/CAIC-AD/YOLOPv2/releases/download/V0.0.1/yolopv2.pt") import argparse import time from pathlib import Path import cv2 import torch import torch.backends.cudnn as cudnn from numpy import random from lib.config import cfg from lib.models import get_net import torchvision.transforms as transforms from lib.dataset.DemoDataset import LoadImages as LoadImages1 #from lib.core.general import non_max_suppression, scale_coords from lib.utils.plot import plot_one_box as plot_one_box1 from lib.utils.plot import show_seg_result as show_seg_result1 from tqdm import tqdm from utils.functions import \ time_synchronized,select_device, increment_path,\ scale_coords,xyxy2xywh,non_max_suppression,split_for_trace_model,\ driving_area_mask,lane_line_mask,plot_one_box,show_seg_result,\ AverageMeter,\ LoadImages from PIL import Image def detect(img,model): #with torch.no_grad()： parser = argparse.ArgumentParser() parser.add_argument('--weights', nargs='+', type=str, default=model+".pt", help='model.pt path(s)') parser.add_argument('--source', type=str, default='Inference/', help='source') # file/folder, 0 for webcam parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)') parser.add_argument('--conf-thres', type=float, default=0.25, help='object confidence threshold') parser.add_argument('--iou-thres', type=float, default=0.45, help='IOU threshold for NMS') parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') parser.add_argument('--view-img', action='store_true', help='display results') parser.add_argument('--save-txt', action='store_true', help='save results to *.txt') parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels') parser.add_argument('--nosave', action='store_true', help='do not save images/videos') parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --class 0, or --class 0 2 3') parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS') parser.add_argument('--augment', action='store_true', help='augmented inference') parser.add_argument('--update', action='store_true', help='update all models') parser.add_argument('--project', default='runs/detect', help='save results to project/name') parser.add_argument('--name', default='exp', help='save results to project/name') parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') parser.add_argument('--trace', action='store_true', help='trace model') opt = parser.parse_args() img.save("Inference/test.jpg") source, weights, view_img, save_txt, imgsz, trace = opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size, opt.trace save_img = True # save inference images #webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith( #('rtsp://', 'rtmp://', 'http://', 'https://')) #print(webcam) # Directories #save_dir = Path(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok)) # increment run #(save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir # Initialize #set_logging() device = select_device(opt.device) #print(device) half = device.type != 'cpu' # half precision only supported on CUDA # Load model inf_time = AverageMeter() waste_time = AverageMeter() nms_time = AverageMeter() # Load model #model = attempt_load(weights, map_location=device) # load FP32 model #stride = int(model.stride.max()) # model stride #imgsz = check_img_size(imgsz, s=stride) # check img_size if weights == 'yolopv2.pt': print(weights) stride =32 model = torch.jit.load(weights,map_location=device) model.eval() # Set Dataloader vid_path, vid_writer = None, None dataset = LoadImages(source, img_size=imgsz, stride=stride) # Run inference t0 = time.time() for path, img, im0s, vid_cap in dataset: print(img.shape) img = torch.from_numpy(img).to(device) img = img.half() if half else img.float() # uint8 to fp16/32 img /= 255.0 # 0 - 255 to 0.0 - 1.0 print(img.shape) if img.ndimension() == 3: img = img.unsqueeze(0) # Inference t1 = time_synchronized() [pred,anchor_grid],seg,ll= model(img) t2 = time_synchronized() # waste time: the incompatibility of torch.jit.trace causes extra time consumption in demo version # but this problem will not appear in offical version tw1 = time_synchronized() pred = split_for_trace_model(pred,anchor_grid) tw2 = time_synchronized() # Apply NMS t3 = time_synchronized() pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms) t4 = time_synchronized() da_seg_mask = driving_area_mask(seg) ll_seg_mask = lane_line_mask(ll) print(da_seg_mask.shape) # Process detections for i, det in enumerate(pred): # detections per image p, s, im0, frame = path, '', im0s, getattr(dataset, 'frame', 0) p = Path(p) # to Path #save_path = str(save_dir / p.name) # img.jpg #txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # img.txt s += '%gx%g ' % img.shape[2:] # print string gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh if len(det): # Rescale boxes from img_size to im0 size det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round() # Print results #for c in det[:, -1].unique(): #n = (det[:, -1] == c).sum() # detections per class #s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string # Write results for *xyxy, conf, cls in reversed(det): if save_txt: # Write to file xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh line = (cls, *xywh, conf) if opt.save_conf else (cls, *xywh) # label format if save_img : # Add bbox to image plot_one_box(xyxy, im0, line_thickness=3) # Print time (inference) print(f'{s}Done. ({t2 - t1:.3f}s)') show_seg_result(im0, (da_seg_mask,ll_seg_mask), is_demo=True) inf_time.update(t2-t1,img.size(0)) nms_time.update(t4-t3,img.size(0)) #waste_time.update(tw2-tw1,img.size(0)) print('Done. (%.3fs)' % (time.time() - t0)) print('inf : (%.4fs/frame) nms : (%.4fs/frame)' % (inf_time.avg,nms_time.avg)) if weights == 'yolop.pt': weights = 'End-to-end.pth' print(weights) normalize = transforms.Normalize( mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225] ) transform=transforms.Compose([ transforms.ToTensor(), normalize, ]) model = get_net(cfg) checkpoint = torch.load(weights, map_location= device) #print(checkpoint) model.load_state_dict(checkpoint['state_dict']) model = model.to(device) dataset = LoadImages1(source, img_size=imgsz) bs = 1 # batch_size # Get names and colors names = model.module.names if hasattr(model, 'module') else model.names colors = [[random.randint(0, 255) for _ in range(3)] for _ in range(len(names))] # Run inference t0 = time.time() vid_path, vid_writer = None, None img = torch.zeros((1, 3, imgsz, imgsz), device=device) # init img _ = model(img.half() if half else img) # run once model.eval() for i, (path, img, img_det, vid_cap,shapes) in tqdm(enumerate(dataset),total = len(dataset)): print(img.shape) img = transform(img).to(device) img = img.half() if half else img.float() # uint8 to fp16/32 if img.ndimension() == 3: img = img.unsqueeze(0) # Inference t1 = time_synchronized() det_out, da_seg_out,ll_seg_out= model(img) t2 = time_synchronized() # if i == 0: # print(det_out) inf_out, _ = det_out inf_time.update(t2-t1,img.size(0)) # Apply NMS t3 = time_synchronized() det_pred = non_max_suppression(inf_out, conf_thres=opt.conf_thres, iou_thres=opt.iou_thres, classes=None, agnostic=False) t4 = time_synchronized() nms_time.update(t4-t3,img.size(0)) det=det_pred[0] #save_path = str(save_dir +'/'+ 'img.jpg') _, _, height, width = img.shape h,w,_=img_det.shape pad_w, pad_h = shapes[1][1] pad_w = int(pad_w) pad_h = int(pad_h) ratio = shapes[1][0][1] da_predict = da_seg_out[:, :, pad_h:(height-pad_h),pad_w:(width-pad_w)] da_seg_mask = torch.nn.functional.interpolate(da_predict, scale_factor=int(1/ratio), mode='bilinear') _, da_seg_mask = torch.max(da_seg_mask, 1) da_seg_mask = da_seg_mask.int().squeeze().cpu().numpy() # da_seg_mask = morphological_process(da_seg_mask, kernel_size=7) ll_predict = ll_seg_out[:, :,pad_h:(height-pad_h),pad_w:(width-pad_w)] ll_seg_mask = torch.nn.functional.interpolate(ll_predict, scale_factor=int(1/ratio), mode='bilinear') _, ll_seg_mask = torch.max(ll_seg_mask, 1) ll_seg_mask = ll_seg_mask.int().squeeze().cpu().numpy() # Lane line post-processing #ll_seg_mask = morphological_process(ll_seg_mask, kernel_size=7, func_type=cv2.MORPH_OPEN) #ll_seg_mask = connect_lane(ll_seg_mask) img_det = show_seg_result1(img_det, (da_seg_mask, ll_seg_mask), _, _, is_demo=True) if len(det): det[:,:4] = scale_coords(img.shape[2:],det[:,:4],img_det.shape).round() for *xyxy,conf,cls in reversed(det): label_det_pred = f'{names[int(cls)]} {conf:.2f}' plot_one_box1(xyxy, img_det , label=label_det_pred, color=colors[int(cls)], line_thickness=2) im0 = img_det print('Done. (%.3fs)' % (time.time() - t0)) print('inf : (%.4fs/frame) nms : (%.4fs/frame)' % (inf_time.avg,nms_time.avg)) #inf_time.update(t2-t1,img.size(0)) #nms_time.update(t4-t3,img.size(0)) #waste_time.update(tw2-tw1,img.size(0)) #print('inf : (%.4fs/frame) nms : (%.4fs/frame)' % (inf_time.avg,nms_time.avg)) #print(f'Done. ({time.time() - t0:.3f}s)') #print(im0.shape) return Image.fromarray(im0[:,:,::-1]) gr.Interface(detect,[gr.Image(type="pil"),gr.Dropdown(choices=["yolopv2","yolop"])], gr.Image(type="pil"),title="Yolopv2",examples=[["example.jpeg", "yolopv2"]],description="demo for yolopv2 🚀: Better, Faster, Stronger for Panoptic driving Perception ").launch()