Update app.py
Browse files
app.py
CHANGED
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import gradio as gr
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import os
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import torch
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os.system('git clone https://github.com/WongKinYiu/yolov7')
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@@ -10,50 +17,161 @@ def detect(inp):
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otp=inp.split('/')[2]
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return f"./yolov7/runs/detect/exp/*"
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model = model['ema' if model.get('ema') else 'model'] # load model
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hub_model = Model(model.yaml).to(next(model.parameters()).device) # create
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hub_model.load_state_dict(model.float().state_dict()) # load state_dict
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hub_model.names = model.names # class names
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if autoshape:
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hub_model = hub_model.autoshape() # for file/URI/PIL/cv2/np inputs and NMS
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device = select_device('0' if torch.cuda.is_available() else 'cpu') # default to GPU if available
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return hub_model.to(device)
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#output=gr.outputs.Image(type="pil", label="Output Image")
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output = gr.
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#.outputs.Textbox()
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)
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#,examples=["Examples/img-300_jpg.rf.6b7b035dff1cda092ce3dc22be8d0135.jpg"]
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io.launch(debug=True,share=False)
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import gradio as gr
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import os
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import torch
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import torch.backends.cudnn as cudnn
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from models.experimental import attempt_load
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from utils.datasets import LoadStreams, LoadImages
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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
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from utils.plots import plot_one_box
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from utils.torch_utils import select_device, load_classifier, time_synchronized, TracedModel
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os.system('git clone https://github.com/WongKinYiu/yolov7')
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otp=inp.split('/')[2]
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return f"./yolov7/runs/detect/exp/*"
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os.system("wget https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7.pt")
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os.system("wget https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-e6e.pt")
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os.system("wget https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-e6.pt")
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def Custom_detect(img,model):
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#if model =='Yolo_v7_Custom_model':
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model='best'
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parser = argparse.ArgumentParser()
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parser.add_argument('--weights', nargs='+', type=str, default=model+".pt", help='model.pt path(s)')
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parser.add_argument('--source', type=str, default='Temp_file/', help='source')
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parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)')
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parser.add_argument('--conf-thres', type=float, default=0.25, help='object confidence threshold')
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parser.add_argument('--iou-thres', type=float, default=0.45, help='IOU threshold for NMS')
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parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
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parser.add_argument('--view-img', action='store_true', help='display results')
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parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
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parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
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parser.add_argument('--nosave', action='store_true', help='do not save images/videos')
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parser.add_argument('--classes', default=1,nargs='+', type=int, help='filter by class: --class 0, or --class 0 2 3')
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parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
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parser.add_argument('--augment', action='store_true', help='augmented inference')
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parser.add_argument('--update', action='store_true', help='update all models')
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parser.add_argument('--project', default='runs/detect', help='save results to project/name')
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parser.add_argument('--name', default='exp', help='save results to project/name')
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parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
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parser.add_argument('--trace', action='store_true', help='trace model')
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opt = parser.parse_args()
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img.save("Temp_file/test.jpg")
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source, weights, view_img, save_txt, imgsz, trace = opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size, opt.trace
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save_img = True
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webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith(
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('rtsp://', 'rtmp://', 'http://', 'https://'))
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save_dir = Path(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok))
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(save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True)
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set_logging()
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device = select_device(opt.device)
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half = device.type != 'cpu'
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model = attempt_load(weights, map_location=device)
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stride = int(model.stride.max())
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imgsz = check_img_size(imgsz, s=stride)
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if trace:
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model = TracedModel(model, device, opt.img_size)
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if half:
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model.half()
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classify = False
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if classify:
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modelc = load_classifier(name='resnet101', n=2) # initialize
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modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model']).to(device).eval()
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vid_path, vid_writer = None, None
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if webcam:
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view_img = check_imshow()
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cudnn.benchmark = True
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dataset = LoadStreams(source, img_size=imgsz, stride=stride)
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else:
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dataset = LoadImages(source, img_size=imgsz, stride=stride)
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names = model.module.names if hasattr(model, 'module') else model.names
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colors = [[random.randint(0, 255) for _ in range(3)] for _ in names]
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if device.type != 'cpu':
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model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters())))
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t0 = time.time()
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for path, img, im0s, vid_cap in dataset:
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img = torch.from_numpy(img).to(device)
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img = img.half() if half else img.float()
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img /= 255.0
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if img.ndimension() == 3:
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img = img.unsqueeze(0)
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# Inference
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t1 = time_synchronized()
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pred = model(img, augment=opt.augment)[0]
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pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms)
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t2 = time_synchronized()
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# Apply Classifier
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if classify:
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pred = apply_classifier(pred, modelc, img, im0s)
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for i, det in enumerate(pred):
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if webcam:
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p, s, im0, frame = path[i], '%g: ' % i, im0s[i].copy(), dataset.count
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else:
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p, s, im0, frame = path, '', im0s, getattr(dataset, 'frame', 0)
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p = Path(p)
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save_path = str(save_dir / p.name)
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txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # img.txt
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s += '%gx%g ' % img.shape[2:]
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gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]
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if len(det):
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det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
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for c in det[:, -1].unique():
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n = (det[:, -1] == c).sum()
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s += f"{n} {names[int(c)]}{'s' * (n > 1)}, "
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for *xyxy, conf, cls in reversed(det):
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if save_txt:
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xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()
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line = (cls, *xywh, conf) if opt.save_conf else (cls, *xywh)
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with open(txt_path + '.txt', 'a') as f:
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f.write(('%g ' * len(line)).rstrip() % line + '\n')
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if save_img or view_img:
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label = f'{names[int(cls)]} {conf:.2f}'
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plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=3)
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if view_img:
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cv2.imshow(str(p), im0)
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cv2.waitKey(1)
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if save_img:
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if dataset.mode == 'image':
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cv2.imwrite(save_path, im0)
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else:
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if vid_path != save_path:
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vid_path = save_path
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if isinstance(vid_writer, cv2.VideoWriter):
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vid_writer.release()
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if vid_cap:
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fps = vid_cap.get(cv2.CAP_PROP_FPS)
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w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
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h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
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else:
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fps, w, h = 30, im0.shape[1], im0.shape[0]
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save_path += '.mp4'
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vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
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vid_writer.write(im0)
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if save_txt or save_img:
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s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
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print(f'Done. ({time.time() - t0:.3f}s)')
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return Image.fromarray(im0[:,:,::-1])
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inp = gr.Image(type="pil")
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#gr.inputs.Image(type="filepath", label="Input")
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#output=gr.outputs.Image(type="pil", label="Output Image")
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output = gr.Image(type="pil")
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#gr.outputs.Image(type="filepath", label="Output")
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#.outputs.Textbox()
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examples=[["Examples/img-300_jpg.rf.6b7b035dff1cda092ce3dc22be8d0135.jpg",'Image1']]
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io=gr.Interface(fn=Custom_detect, inputs=inp, outputs=output, title='Pot Hole Detection With Custom YOLOv7 ',
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examples=examples
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)
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io.launch(debug=True,share=False)
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