(add) detect.py

+import argparse
+import time
+from pathlib import Path
+
+import cv2
+import torch
+import torch.backends.cudnn as cudnn
+
+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, save_one_box
+from utils.plots import colors, plot_one_box
+from utils.torch_utils import select_device, load_classifier, time_synchronized
+
+import serial
+
+def detect(opt):
+    source, weights, view_img, save_txt, imgsz = opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size
+    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 = 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
+    names = model.module.names if hasattr(model, 'module') else model.names  # get class names
+    if half:
+        model.half()  # to FP16
+
+    ser = serial.Serial('/dev/ttyAMA0',115200)
+    if(ser.isOpen()):
+        print("open")
+
+
+    # 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)
+
+    # 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 = model(img, augment=opt.augment)[0]
+
+        # Apply NMS
+        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)
+
+        # Process detections
+        for i, det in enumerate(pred):  # detections per image
+            if webcam:  # batch_size >= 1
+                p, s, im0, frame = path[i], f'{i}: ', im0s[i].copy(), dataset.count
+            else:
+                p, s, im0, frame = path, '', im0s.copy(), 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
+            imc = im0.copy() if opt.save_crop else im0  # for opt.save_crop
+            
+            if len(det):  # dectection
+                # 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
+
+                # distance Measurement/Visualization and Send parameter to STM Board
+                for *xyxy, conf, cls in reversed(det):
+                    xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh
+                                        
+                    w1 = 0.4        # w1 : 1m에서 측정한 Cart b-box width
+                    h1 = 0.44       # h1 : 1m에서 측정한 Cart b-box height                               
+                    std = w1 / h1
+                    
+                    # width값의 오차율이 height보다 작을 떄
+                    if std <= xywh[2] / xywh[3]:
+                        dis = round(w1 / xywh[2] * 100)
+                    
+                    # width값의 오차율이 height보다 클 때
+                    else:
+                        dis = round(h1 / xywh[3] * 100)
+     
+                    if dis < 70:  # 전방 객체와의 거리가 0.7m 미만이면
+                        ser.write(serial.to_bytes([int('1', 16)]))  # send parameter 1 to STM Board
+                    elif 70 <= dis and dis < 100:  # 전방 객체와의 거리가 0.7m ~ 1m 이면
+                        ser.write(serial.to_bytes([int('2', 16)]))  # send parameter 2 to STM Board
+                    elif 100 <= dis and dis < 150:  # 전방 객체와의 거리가 1m ~ 1.5m 이면
+                        ser.write(serial.to_bytes([int('3', 16)]))  # send parameter 3 to STM Board
+                    else:  # 전방 객체와의 거리가 1,5m 이상이면
+                        ser.write(serial.to_bytes([int('4', 16)]))  # send parameter 4 to STM Board
+
+                    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 opt.save_crop or view_img:  # Add bbox to image
+                        c = int(cls)  # integer class
+                        
+                        # Class & Conf & Distance Visualization
+                        label = None if opt.hide_labels else (names[c] if opt.hide_conf else f'{names[c]} {conf:.2f} {dis:.2f}')
+
+                        plot_one_box(xyxy, im0, label=label, color=colors(c, True), line_thickness=opt.line_thickness)
+                        if opt.save_crop:
+                            save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True)
+            else:  # not yet detection
+                ser.write(serial.to_bytes([int('4', 16)]))  # send parameter 4 to STM Board
+
+            # Print time (inference + NMS)
+            print(f'{s}Done. ({t2 - t1:.3f}s)')
+                        
+            # 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':
+                    cv2.imwrite(save_path, im0)
+                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)')
+    
+    ser.close()
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--weights', nargs='+', type=str, default='yolov5s.pt', help='model.pt path(s)')
+    parser.add_argument('--source', type=str, default='data/images', 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('--save-crop', action='store_true', help='save cropped prediction boxes')
+    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('--line-thickness', default=3, type=int, help='bounding box thickness (pixels)')
+    parser.add_argument('--hide-labels', default=False, action='store_true', help='hide labels')
+    parser.add_argument('--hide-conf', default=False, action='store_true', help='hide confidences')
+    opt = parser.parse_args()
+    print(opt)
+    check_requirements(exclude=('tensorboard', 'pycocotools', 'thop'))
+
+    with torch.no_grad():
+        if opt.update:  # update all models (to fix SourceChangeWarning)
+            for opt.weights in ['yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.pt']:
+                detect(opt=opt)
+                strip_optimizer(opt.weights)
+        else:
+            detect(opt=opt)