import gradio as gr import os import torch import torch.backends.cudnn as cudnn from models.experimental import attempt_load from utils.datasets import LoadStreams, LoadImages from utils.general import check_img_size, check_requirements, check_imshow, non_max_suppression, apply_classifier,scale_coords, xyxy2xywh, strip_optimizer, set_logging, increment_path from utils.plots import plot_one_box from utils.torch_utils import select_device, load_classifier, time_synchronized, TracedModel os.system('git clone https://github.com/WongKinYiu/yolov7') def detect(inp): os.system('python ./yolov7/detect.py --weights best.pt --conf 0.25 --img-size 640 --source f{inp} --project ./yolov7/runs/detect ') otp=inp.split('/')[2] return f"./yolov7/runs/detect/exp/*" ''' os.system("wget https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7.pt") 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") ''' def Custom_detect(img,model): #if model =='Yolo_v7_Custom_model': model='best' 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='Temp_file/', help='source') 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("Temp_file/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 webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith( ('rtsp://', 'rtmp://', 'http://', 'https://')) save_dir = Path(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok)) (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) set_logging() device = select_device(opt.device) half = device.type != 'cpu' model = attempt_load(weights, map_location=device) stride = int(model.stride.max()) imgsz = check_img_size(imgsz, s=stride) if trace: model = TracedModel(model, device, opt.img_size) if half: model.half() classify = False if classify: modelc = load_classifier(name='resnet101', n=2) # initialize modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model']).to(device).eval() vid_path, vid_writer = None, None if webcam: view_img = check_imshow() cudnn.benchmark = True dataset = LoadStreams(source, img_size=imgsz, stride=stride) else: dataset = LoadImages(source, img_size=imgsz, stride=stride) names = model.module.names if hasattr(model, 'module') else model.names colors = [[random.randint(0, 255) for _ in range(3)] for _ in names] if device.type != 'cpu': model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters()))) t0 = time.time() for path, img, im0s, vid_cap in dataset: img = torch.from_numpy(img).to(device) img = img.half() if half else img.float() img /= 255.0 if img.ndimension() == 3: img = img.unsqueeze(0) # Inference t1 = time_synchronized() pred = model(img, augment=opt.augment)[0] pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms) t2 = time_synchronized() # Apply Classifier if classify: pred = apply_classifier(pred, modelc, img, im0s) for i, det in enumerate(pred): if webcam: p, s, im0, frame = path[i], '%g: ' % i, im0s[i].copy(), dataset.count else: p, s, im0, frame = path, '', im0s, getattr(dataset, 'frame', 0) p = Path(p) save_path = str(save_dir / p.name) txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # img.txt s += '%gx%g ' % img.shape[2:] gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] if len(det): det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round() for c in det[:, -1].unique(): n = (det[:, -1] == c).sum() s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " for *xyxy, conf, cls in reversed(det): if save_txt: xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() line = (cls, *xywh, conf) if opt.save_conf else (cls, *xywh) with open(txt_path + '.txt', 'a') as f: f.write(('%g ' * len(line)).rstrip() % line + '\n') if save_img or view_img: label = f'{names[int(cls)]} {conf:.2f}' plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=3) if view_img: cv2.imshow(str(p), im0) cv2.waitKey(1) if save_img: if dataset.mode == 'image': cv2.imwrite(save_path, im0) else: if vid_path != save_path: vid_path = save_path if isinstance(vid_writer, cv2.VideoWriter): vid_writer.release() if vid_cap: fps = vid_cap.get(cv2.CAP_PROP_FPS) w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH)) h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) else: fps, w, h = 30, im0.shape[1], im0.shape[0] save_path += '.mp4' vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h)) vid_writer.write(im0) if save_txt or save_img: s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else '' print(f'Done. ({time.time() - t0:.3f}s)') return Image.fromarray(im0[:,:,::-1]) inp = gr.Image(type="pil") #gr.inputs.Image(type="filepath", label="Input") #output=gr.outputs.Image(type="pil", label="Output Image") output = gr.Image(type="pil") #gr.outputs.Image(type="filepath", label="Output") #.outputs.Textbox() examples=[["Examples/img-300_jpg.rf.6b7b035dff1cda092ce3dc22be8d0135.jpg",'Image1']] io=gr.Interface(fn=Custom_detect, inputs=inp, outputs=output, title='Pot Hole Detection With Custom YOLOv7 ', examples=examples ) io.launch(debug=True,share=False)