Update app.py

app.py

@@ -36,153 +36,156 @@ from PIL import Image
 
 
 def detect(img,model):
-    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='Inference/', help='source')  # file/folder, 0 for webcam
-    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("Inference/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  # save inference images
-    #webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith(
-        #('rtsp://', 'rtmp://', 'http://', 'https://'))
-    #print(webcam)
-    # Directories
-    save_dir = Path(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok))  # increment run
-    (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True)  # make dir
-
-    # Initialize
-    set_logging()
-    device = select_device(opt.device)
-    print(device)
-    half = device.type != 'cpu'  # half precision only supported on CUDA
-
-    # Load model
-    inf_time = AverageMeter()
-    waste_time = AverageMeter()
-    nms_time = AverageMeter()
-
-    # Load model
-    #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
-    print(weights)
-    stride =32
-    model  = torch.jit.load(weights,map_location=device)
-    model.eval()
-
-    # Set Dataloader
-    vid_path, vid_writer = None, None
-    dataset = LoadImages(source, img_size=imgsz, stride=stride)
-
-    # Run inference
-    if device.type != 'cpu':
-        model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters())))  # run once
-    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()  # 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,anchor_grid],seg,ll= model(img)
-        t2 = time_synchronized()
-
-        # waste time: the incompatibility of  torch.jit.trace causes extra time consumption in demo version 
-        # but this problem will not appear in offical version 
-        tw1 = time_synchronized()
-        pred = split_for_trace_model(pred,anchor_grid)
-        tw2 = time_synchronized()
-
-        # Apply NMS
-        t3 = time_synchronized()
-        pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms)
-        t4 = time_synchronized()
-
-        da_seg_mask = driving_area_mask(seg)
-        ll_seg_mask = lane_line_mask(ll)
-
-        # Process detections
-        for i, det in enumerate(pred):  # detections per image
-          
-            p, s, im0, frame = path, '', im0s, getattr(dataset, 'frame', 0)
-
-            p = Path(p)  # to Path
-            save_path = str(save_dir / p.name)  # img.jpg
-            txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}')  # img.txt
-            s += '%gx%g ' % img.shape[2:]  # print string
-            gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwh
-            if len(det):
-                # Rescale boxes from img_size to im0 size
-                det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
-
-                # Print results
-                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
-
-                # Write results
-                for *xyxy, conf, cls in reversed(det):
-                    if save_txt:  # Write to file
-                        xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh
-                        line = (cls, *xywh, conf) if opt.save_conf else (cls, *xywh)  # label format
-                        with open(txt_path + '.txt', 'a') as f:
-                            f.write(('%g ' * len(line)).rstrip() % line + '\n')
-
-                    if save_img :  # Add bbox to image
-                        plot_one_box(xyxy, im0, line_thickness=3)
-
-            # Print time (inference)
-            print(f'{s}Done. ({t2 - t1:.3f}s)')
-            show_seg_result(im0, (da_seg_mask,ll_seg_mask), is_demo=True)
-
-            # Save results (image with detections)
-            if save_img:
-                if dataset.mode == 'image':
-                    cv2.imwrite(save_path, im0)
-                    print(f" The image with the result is saved in: {save_path}")
-                else:  # 'video' or 'stream'
-                    if vid_path != save_path:  # new video
-                        vid_path = save_path
-                        if isinstance(vid_writer, cv2.VideoWriter):
-                            vid_writer.release()  # release previous video writer
-                        if vid_cap:  # video
-                            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))
-                            w,h = im0.shape[1], im0.shape[0]
-                        else:  # stream
-                            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)
-
-    inf_time.update(t2-t1,img.size(0))
-    nms_time.update(t4-t3,img.size(0))
-    waste_time.update(tw2-tw1,img.size(0))
-    print('inf : (%.4fs/frame)   nms : (%.4fs/frame)' % (inf_time.avg,nms_time.avg))
-    print(f'Done. ({time.time() - t0:.3f}s)')
-    print(im0.shape)
-
+    with torch.no_grad()：
+        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='Inference/', help='source')  # file/folder, 0 for webcam
+        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("Inference/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  # save inference images
+        #webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith(
+            #('rtsp://', 'rtmp://', 'http://', 'https://'))
+        #print(webcam)
+        # Directories
+        save_dir = Path(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok))  # increment run
+        (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True)  # make dir
+    
+        # Initialize
+        set_logging()
+        device = select_device(opt.device)
+        #print(device)
+        half = device.type != 'cpu'  # half precision only supported on CUDA
+    
+        # Load model
+        inf_time = AverageMeter()
+        waste_time = AverageMeter()
+        nms_time = AverageMeter()
+    
+        # Load model
+        #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
+        #print(weights)
+        stride =32
+        model  = torch.jit.load(weights,map_location=device)
+        model.eval()
+    
+        # Set Dataloader
+        vid_path, vid_writer = None, None
+        dataset = LoadImages(source, img_size=imgsz, stride=stride)
+    
+        # Run inference
+        if device.type != 'cpu':
+            model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters())))  # run once
+        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()  # uint8 to fp16/32
+            img /= 255.0  # 0 - 255 to 0.0 - 1.0
+            print(img.shape)
+    
+            if img.ndimension() == 3:
+                img = img.unsqueeze(0)
+    
+            # Inference
+            t1 = time_synchronized()
+            [pred,anchor_grid],seg,ll= model(img)
+            t2 = time_synchronized()
+    
+            # waste time: the incompatibility of  torch.jit.trace causes extra time consumption in demo version 
+            # but this problem will not appear in offical version 
+            tw1 = time_synchronized()
+            pred = split_for_trace_model(pred,anchor_grid)
+            tw2 = time_synchronized()
+    
+            # Apply NMS
+            t3 = time_synchronized()
+            pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms)
+            t4 = time_synchronized()
+    
+            da_seg_mask = driving_area_mask(seg)
+            ll_seg_mask = lane_line_mask(ll)
+                
+            print(da_seg_mask.shape)
+            # Process detections
+            for i, det in enumerate(pred):  # detections per image
+              
+                p, s, im0, frame = path, '', im0s, getattr(dataset, 'frame', 0)
+    
+                p = Path(p)  # to Path
+                save_path = str(save_dir / p.name)  # img.jpg
+                txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}')  # img.txt
+                s += '%gx%g ' % img.shape[2:]  # print string
+                gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwh
+                if len(det):
+                    # Rescale boxes from img_size to im0 size
+                    det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
+    
+                    # Print results
+                    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
+    
+                    # Write results
+                    for *xyxy, conf, cls in reversed(det):
+                        if save_txt:  # Write to file
+                            xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh
+                            line = (cls, *xywh, conf) if opt.save_conf else (cls, *xywh)  # label format
+                            with open(txt_path + '.txt', 'a') as f:
+                                f.write(('%g ' * len(line)).rstrip() % line + '\n')
+    
+                        if save_img :  # Add bbox to image
+                            plot_one_box(xyxy, im0, line_thickness=3)
+    
+                # Print time (inference)
+                print(f'{s}Done. ({t2 - t1:.3f}s)')
+                show_seg_result(im0, (da_seg_mask,ll_seg_mask), is_demo=True)
+    
+                # Save results (image with detections)
+                if save_img:
+                    if dataset.mode == 'image':
+                        cv2.imwrite(save_path, im0)
+                        print(f" The image with the result is saved in: {save_path}")
+                    else:  # 'video' or 'stream'
+                        if vid_path != save_path:  # new video
+                            vid_path = save_path
+                            if isinstance(vid_writer, cv2.VideoWriter):
+                                vid_writer.release()  # release previous video writer
+                            if vid_cap:  # video
+                                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))
+                                w,h = im0.shape[1], im0.shape[0]
+                            else:  # stream
+                                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)
+    
+        inf_time.update(t2-t1,img.size(0))
+        nms_time.update(t4-t3,img.size(0))
+        waste_time.update(tw2-tw1,img.size(0))
+        print('inf : (%.4fs/frame)   nms : (%.4fs/frame)' % (inf_time.avg,nms_time.avg))
+        print(f'Done. ({time.time() - t0:.3f}s)')
+        print(im0.shape)
+    
     return Image.fromarray(im0[:,:,::-1])