Update app.py

app.py

@@ -13,136 +13,9 @@ def detect(inp):
   
   
   
-  
-import argparse
-from pathlib import Path
-import cv2
-import torch
-import numpy as np
-from numpy import random
-from . import models
-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, time_synchronized
-
-
-def letterbox(img, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True, stride=32):
-    # Resize and pad image while meeting stride-multiple constraints
-    shape = img.shape[:2]  # current shape [height, width]
-    if isinstance(new_shape, int):
-        new_shape = (new_shape, new_shape)
-
-    # Scale ratio (new / old)
-    r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
-    if not scaleup:  # only scale down, do not scale up (for better test mAP)
-        r = min(r, 1.0)
-
-    # Compute padding
-    ratio = r, r  # width, height ratios
-    new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
-    dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1]  # wh padding
-    if auto:  # minimum rectangle
-        dw, dh = np.mod(dw, stride), np.mod(dh, stride)  # wh padding
-    elif scaleFill:  # stretch
-        dw, dh = 0.0, 0.0
-        new_unpad = (new_shape[1], new_shape[0])
-        ratio = new_shape[1] / shape[1], new_shape[0] / shape[0]  # width, height ratios
-
-    dw /= 2  # divide padding into 2 sides
-    dh /= 2
-
-    if shape[::-1] != new_unpad:  # resize
-        img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR)
-    top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
-    left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
-    img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)  # add border
-    return img, ratio, (dw, dh)
-
-  
-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")
@@ -150,7 +23,7 @@ outputs=gr.outputs.Image(type="pil", label="Output Image")
 #output = gr.outputs.Image(type="filepath", label="Output")
 #.outputs.Textbox()
 
-io=gr.Interface(fn=detect2, inputs=inp, outputs=output, title='Pot Hole Detection With Custom YOLOv7 ',examples=[["Examples/img-300_jpg.rf.6b7b035dff1cda092ce3dc22be8d0135.jpg"]])
+io=gr.Interface(fn=detect, 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)