Update app.py

app.py

@@ -13,197 +13,197 @@ import torch.backends.cudnn as cudnn
 from numpy import random
 import numpy as np
 
-BASE_DIR = "/home/user/app"
-os.chdir(BASE_DIR)
-os.makedirs(f"{BASE_DIR}/input",exist_ok=True)
-os.system(f"git clone https://github.com/WongKinYiu/yolov7.git {BASE_DIR}/yolov7")
-sys.path.append(f'{BASE_DIR}/yolov7')
-
-def detect(opt, save_img=False):    
-    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, load_classifier, time_synchronized, TracedModel
+# BASE_DIR = "/home/user/app"
+# os.chdir(BASE_DIR)
+# os.makedirs(f"{BASE_DIR}/input",exist_ok=True)
+# os.system(f"git clone https://github.com/WongKinYiu/yolov7.git {BASE_DIR}/yolov7")
+# sys.path.append(f'{BASE_DIR}/yolov7')
+
+# def detect(opt, save_img=False):    
+#     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, load_classifier, time_synchronized, TracedModel
     
-    bbox = {}
-    source, weights, view_img, save_txt, imgsz, trace = opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size, not opt.no_trace
-    save_img = not opt.nosave and not source.endswith('.txt')  # save inference images
-    webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith(
-        ('rtsp://', 'rtmp://', 'http://', 'https://'))
-
-    # 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)
-    half = device.type != 'cpu'  # half precision only supported on CUDA
-
-    # 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
-
-    if trace:
-        model = TracedModel(model, device, opt.img_size)
-
-    if half:
-        model.half()  # to FP16
-
-    # Second-stage classifier
-    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()
-
-    # Set Dataloader
-    vid_path, vid_writer = None, None
-    if webcam:
-        view_img = check_imshow()
-        cudnn.benchmark = True  # set True to speed up constant image size inference
-        dataset = LoadStreams(source, img_size=imgsz, stride=stride)
-    else:
-        dataset = LoadImages(source, img_size=imgsz, stride=stride)
-
-    # Get names and colors
-    names = model.module.names if hasattr(model, 'module') else model.names
-    colors = [[random.randint(0, 255) for _ in range(3)] for _ in names]
-
-    # Run inference
-    if device.type != 'cpu':
-        model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters())))  # run once
-    old_img_w = old_img_h = imgsz
-    old_img_b = 1
-
-    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)
-
-        # Warmup
-        if device.type != 'cpu' and (old_img_b != img.shape[0] or old_img_h != img.shape[2] or old_img_w != img.shape[3]):
-            old_img_b = img.shape[0]
-            old_img_h = img.shape[2]
-            old_img_w = img.shape[3]
-            for i in range(3):
-                model(img, augment=opt.augment)[0]
-
-        # Inference
-        t1 = time_synchronized()
-        with torch.no_grad():   # Calculating gradients would cause a GPU memory leak
-            pred = model(img, augment=opt.augment)[0]
-        t2 = time_synchronized()
-
-        # Apply NMS
-        pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms)
-        t3 = time_synchronized()
-
-        # Apply Classifier
-        if classify:
-            pred = apply_classifier(pred, modelc, img, im0s)
-
-        # Process detections
-        for i, det in enumerate(pred):  # detections per image
-            if webcam:  # batch_size >= 1
-                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)  # 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
-            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(f"BOXES ---->>>> {det[:, :4]}")
-                bbox[f"{txt_path.split('/')[4]}"]=(det[:, :4]).numpy()
-
-               # 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 or view_img:  # Add bbox to image
-                        label = f'{names[int(cls)]} {conf:.2f}'
-                        plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=1)
-
-            # Print time (inference + NMS)
-            print(f'{s}Done. ({(1E3 * (t2 - t1)):.1f}ms) Inference, ({(1E3 * (t3 - t2)):.1f}ms) NMS')
-
-            # Stream results
-            # if view_img:
-            #     cv2.imshow(str(p), im0)
-            #     cv2.waitKey(1)  # 1 millisecond
-
-            # Save results (image with detections)
-            if save_img:
-                if dataset.mode == 'image':
-                    # Image.fromarray(im0).show()
-                    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))
-                #         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)
-
-    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"Results saved to {save_dir}{s}")
-
-    print(f'Done. ({time.time() - t0:.3f}s)')
-    return bbox,save_path
-
-class options:
-    def __init__(self, weights, source, img_size=640, conf_thres=0.1, iou_thres=0.45, device='', 
-                 view_img=False, save_txt=False, save_conf=False, nosave=False, classes=None, 
-                 agnostic_nms=False, augment=False, update=False, project='runs/detect', name='exp', 
-                 exist_ok=False, no_trace=False):
-        self.weights=weights
-        self.source=source
-        self.img_size=img_size
-        self.conf_thres=conf_thres
-        self.iou_thres=iou_thres
-        self.device=device
-        self.view_img=view_img
-        self.save_txt=save_txt
-        self.save_conf=save_conf
-        self.nosave=nosave
-        self.classes=classes
-        self.agnostic_nms=agnostic_nms
-        self.augment=augment
-        self.update=update
-        self.project=project
-        self.name=name
-        self.exist_ok=exist_ok
-        self.no_trace=no_trace
+#     bbox = {}
+#     source, weights, view_img, save_txt, imgsz, trace = opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size, not opt.no_trace
+#     save_img = not opt.nosave and not source.endswith('.txt')  # save inference images
+#     webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith(
+#         ('rtsp://', 'rtmp://', 'http://', 'https://'))
+
+#     # 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)
+#     half = device.type != 'cpu'  # half precision only supported on CUDA
+
+#     # 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
+
+#     if trace:
+#         model = TracedModel(model, device, opt.img_size)
+
+#     if half:
+#         model.half()  # to FP16
+
+#     # Second-stage classifier
+#     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()
+
+#     # Set Dataloader
+#     vid_path, vid_writer = None, None
+#     if webcam:
+#         view_img = check_imshow()
+#         cudnn.benchmark = True  # set True to speed up constant image size inference
+#         dataset = LoadStreams(source, img_size=imgsz, stride=stride)
+#     else:
+#         dataset = LoadImages(source, img_size=imgsz, stride=stride)
+
+#     # Get names and colors
+#     names = model.module.names if hasattr(model, 'module') else model.names
+#     colors = [[random.randint(0, 255) for _ in range(3)] for _ in names]
+
+#     # Run inference
+#     if device.type != 'cpu':
+#         model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters())))  # run once
+#     old_img_w = old_img_h = imgsz
+#     old_img_b = 1
+
+#     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)
+
+#         # Warmup
+#         if device.type != 'cpu' and (old_img_b != img.shape[0] or old_img_h != img.shape[2] or old_img_w != img.shape[3]):
+#             old_img_b = img.shape[0]
+#             old_img_h = img.shape[2]
+#             old_img_w = img.shape[3]
+#             for i in range(3):
+#                 model(img, augment=opt.augment)[0]
+
+#         # Inference
+#         t1 = time_synchronized()
+#         with torch.no_grad():   # Calculating gradients would cause a GPU memory leak
+#             pred = model(img, augment=opt.augment)[0]
+#         t2 = time_synchronized()
+
+#         # Apply NMS
+#         pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms)
+#         t3 = time_synchronized()
+
+#         # Apply Classifier
+#         if classify:
+#             pred = apply_classifier(pred, modelc, img, im0s)
+
+#         # Process detections
+#         for i, det in enumerate(pred):  # detections per image
+#             if webcam:  # batch_size >= 1
+#                 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)  # 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
+#             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(f"BOXES ---->>>> {det[:, :4]}")
+#                 bbox[f"{txt_path.split('/')[4]}"]=(det[:, :4]).numpy()
+
+#                # 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 or view_img:  # Add bbox to image
+#                         label = f'{names[int(cls)]} {conf:.2f}'
+#                         plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=1)
+
+#             # Print time (inference + NMS)
+#             print(f'{s}Done. ({(1E3 * (t2 - t1)):.1f}ms) Inference, ({(1E3 * (t3 - t2)):.1f}ms) NMS')
+
+#             # Stream results
+#             # if view_img:
+#             #     cv2.imshow(str(p), im0)
+#             #     cv2.waitKey(1)  # 1 millisecond
+
+#             # Save results (image with detections)
+#             if save_img:
+#                 if dataset.mode == 'image':
+#                     # Image.fromarray(im0).show()
+#                     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))
+#                 #         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)
+
+#     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"Results saved to {save_dir}{s}")
+
+#     print(f'Done. ({time.time() - t0:.3f}s)')
+#     return bbox,save_path
+
+# class options:
+#     def __init__(self, weights, source, img_size=640, conf_thres=0.1, iou_thres=0.45, device='', 
+#                  view_img=False, save_txt=False, save_conf=False, nosave=False, classes=None, 
+#                  agnostic_nms=False, augment=False, update=False, project='runs/detect', name='exp', 
+#                  exist_ok=False, no_trace=False):
+#         self.weights=weights
+#         self.source=source
+#         self.img_size=img_size
+#         self.conf_thres=conf_thres
+#         self.iou_thres=iou_thres
+#         self.device=device
+#         self.view_img=view_img
+#         self.save_txt=save_txt
+#         self.save_conf=save_conf
+#         self.nosave=nosave
+#         self.classes=classes
+#         self.agnostic_nms=agnostic_nms
+#         self.augment=augment
+#         self.update=update
+#         self.project=project
+#         self.name=name
+#         self.exist_ok=exist_ok
+#         self.no_trace=no_trace
 
 def get_output(image):
-    image.save(f"{BASE_DIR}/input/image.jpg")
+    # image.save(f"{BASE_DIR}/input/image.jpg")
     # source = f"{BASE_DIR}/input"
     # opt = options(weights='logo_detection.pt',source=source)
     # bbox = None