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app.py

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+import torch
+import argparse
+import gradio as gr
+from PIL import Image
+from numpy import random
+from pathlib import Path
+import os
+import time
+import torch.backends.cudnn as cudnn
+from models.experimental import attempt_load
+import cv2
+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 Custom_detect(img):
+    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=100, 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")
+output = gr.Image(type="pil")
+
+examples=[["Examples/Image1.jpg","Image1"],["Examples/Image2.jpg","Image2"]]
+
+io=gr.Interface(fn=Custom_detect, inputs=inp, outputs=output, title='Pot Hole Detection With Custom YOLOv7',examples=examples,cache_examples=False)
+io.launch()
+