Update app.py

app.py

@@ -5,17 +5,103 @@ os.system('git clone https://github.com/WongKinYiu/yolov7.git')
 
 
 def detect(inp):
-  os.system('python ./yolov7/detect.py --weights ./yolov7/runs/train/yolov7/weights/best.pt --conf 0.25 --img-size 640 --source f{inp} "--project","./yolov7/runs/detect ')
+  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/exp2/*"
+  return f"./yolov7/runs/detect/exp/*"
    
   #f"./yolov7/runs/detect/exp/{otp}"
+  
+  
+opt  = {
+    
+    "weights": "best.pt", # Path to weights file default weights are for nano model
+    "yaml"   : "custom.yaml",
+    "img-size": 640, # default image size
+    "conf-thres": 0.25, # confidence threshold for inference.
+    "iou-thres" : 0.45, # NMS IoU threshold for inference.
+    "device" : '0',  # device to run our model i.e. 0 or 0,1,2,3 or cpu
+    "classes" : classes_to_filter  # list of classes to filter or None
+
+}  
+  
+def detect2(inp):
+  with torch.no_grad():
+    weights, imgsz = opt['weights'], opt['img-size']
+    set_logging()
+    device = select_device(opt['device'])
+    half = device.type != 'cpu'
+    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 half:
+      model.half()
+  
+    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())))
+  
+    img0 = cv2.imread(inp)
+    img = letterbox(img0, imgsz, stride=stride)[0]
+    img = img[:, :, ::-1].transpose(2, 0, 1)  # BGR to RGB, to 3x416x416
+    img = np.ascontiguousarray(img)
+    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
+    if img.ndimension() == 3:
+      img = img.unsqueeze(0)
+  
+    # Inference
+    t1 = time_synchronized()
+    pred = model(img, augment= False)[0]
+  
+    # Apply NMS
+    classes = None
+    if opt['classes']:
+      classes = []
+      for class_name in opt['classes']:
+  
+        classes.append(names.index(class_name))
+  
+    if classes:
+      
+      classes = [i for i in range(len(names)) if i not in classes]
+    
+    
+    pred = non_max_suppression(pred, opt['conf-thres'], opt['iou-thres'], classes= [17], agnostic= False)
+    t2 = time_synchronized()
+    for i, det in enumerate(pred):
+      s = ''
+      s += '%gx%g ' % img.shape[2:]  # print string
+      gn = torch.tensor(img0.shape)[[1, 0, 1, 0]]
+      if len(det):
+        det[:, :4] = scale_coords(img.shape[2:], det[:, :4], img0.shape).round()
+  
+        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
+      
+        for *xyxy, conf, cls in reversed(det):
+  
+          label = f'{names[int(cls)]} {conf:.2f}'
+          plot_one_box(xyxy, img0, label=label, color=colors[int(cls)], line_thickness=3)
+      return img0
+      
+
+
+   
+  
+  
+  
+  
+  
 
 inp = gr.inputs.Image(type="filepath", label="Input")
-output = gr.outputs.Image(type="filepath", label="Output")
+outputs=gr.outputs.Image(type="pil", label="Output Image")
+#output = gr.outputs.Image(type="filepath", label="Output")
 #.outputs.Textbox()
 
-io=gr.Interface(fn=detect, inputs=inp, outputs=output, title='Pot Hole Detection With Custom YOLOv7 ',examples=[["Examples/img-300_jpg.rf.6b7b035dff1cda092ce3dc22be8d0135.jpg"]])
+io=gr.Interface(fn=detect2, inputs=inp, outputs=output, title='Pot Hole Detection With Custom YOLOv7 ',examples=[["Examples/img-300_jpg.rf.6b7b035dff1cda092ce3dc22be8d0135.jpg"]])
 #,examples=["Examples/img-300_jpg.rf.6b7b035dff1cda092ce3dc22be8d0135.jpg"]
 io.launch(debug=True,share=False)