diff --git a/our_scripts/detect.py b/our_scripts/detect.py deleted file mode 100644 index b4aaf229..00000000 --- a/our_scripts/detect.py +++ /dev/null @@ -1,209 +0,0 @@ -import argparse - -from models import * # set ONNX_EXPORT in models.py -from utils.datasets import * -from utils.utils import * - - -def detect(save_img=False): - imgsz = (320, 192) if ONNX_EXPORT else opt.img_size # (320, 192) or (416, 256) or (608, 352) for (height, width) - out, source, weights, half, view_img, save_txt = opt.output, opt.source, opt.weights, opt.half, opt.view_img, opt.save_txt - webcam = source == '0' or source.startswith('rtsp') or source.startswith('http') or source.endswith('.txt') - - # Initialize - device = torch_utils.select_device(device='cpu' if ONNX_EXPORT else opt.device) - if os.path.exists(out): - shutil.rmtree(out) # delete output folder - os.makedirs(out) # make new output folder - - # Initialize model - model = Darknet(opt.cfg, imgsz) - - # Load weights - attempt_download(weights) - if weights.endswith('.pt'): # pytorch format - model.load_state_dict(torch.load(weights, map_location=device)['model']) - else: # darknet format - load_darknet_weights(model, weights) - - # Second-stage classifier - classify = False - if classify: - modelc = torch_utils.load_classifier(name='resnet101', n=2) # initialize - modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model']) # load weights - modelc.to(device).eval() - - # Eval mode - model.to(device).eval() - - # Fuse Conv2d + BatchNorm2d layers - # model.fuse() - - # Export mode - if ONNX_EXPORT: - model.fuse() - img = torch.zeros((1, 3) + imgsz) # (1, 3, 320, 192) - f = opt.weights.replace(opt.weights.split('.')[-1], 'onnx') # *.onnx filename - torch.onnx.export(model, img, f, verbose=False, opset_version=11, - input_names=['images'], output_names=['classes', 'boxes']) - - # Validate exported model - import onnx - model = onnx.load(f) # Load the ONNX model - onnx.checker.check_model(model) # Check that the IR is well formed - print(onnx.helper.printable_graph(model.graph)) # Print a human readable representation of the graph - return - - # Half precision - half = half and device.type != 'cpu' # half precision only supported on CUDA - if half: - model.half() - - # Set Dataloader - vid_path, vid_writer = None, None - if webcam: - view_img = True - torch.backends.cudnn.benchmark = True # set True to speed up constant image size inference - dataset = LoadStreams(source, img_size=imgsz) - else: - save_img = True - dataset = LoadImages(source, img_size=imgsz) - - # Get names and colors - names = load_classes(opt.names) - colors = [[random.randint(0, 255) for _ in range(3)] for _ in range(len(names))] - output_json_file = open(opt.json, 'w') - - # Run inference - t0 = time.time() - img = torch.zeros((1, 3, imgsz, imgsz), device=device) # init img - _ = model(img.half() if half else img.float()) if device.type != 'cpu' else None # run once - for i, (path, img, im0s, vid_cap) in enumerate(dataset): - json = {"detections": []} - 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 = torch_utils.time_synchronized() - pred = model(img, augment=opt.augment)[0] - t2 = torch_utils.time_synchronized() - - # to float - if half: - pred = pred.float() - - # Apply NMS - pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, - multi_label=False, classes=opt.classes, agnostic=opt.agnostic_nms) - - # Apply Classifier - if classify: - pred = apply_classifier(pred, modelc, img, im0s) - - # Process detections - for i, det in enumerate(pred): # detections for image i - if webcam: # batch_size >= 1 - p, s, im0 = path[i], '%g: ' % i, im0s[i].copy() - else: - p, s, im0 = path, '', im0s - - save_path = str(Path(out) / Path(p).name) - s += '%gx%g ' % img.shape[2:] # print string - gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] #  normalization gain whwh - if det is not None and len(det): - # Rescale boxes from imgsz 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 += '%g %ss, ' % (n, names[int(c)]) # add to string - - # Write results - for *xyxy, conf, cls in det: - if save_txt: # Write to file - xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh - with open(save_path[:save_path.rfind('.')] + '.txt', 'a') as file: - file.write(('%g ' * 5 + '\n') % (cls, *xywh)) # label format - - label_to_display = '%s %.2f' % (names[int(cls)], conf) - name = '%s' % names[int(cls)] - probability = float('%.3f' % conf) - left, top, right, bottom = plot_one_box(xyxy, im0, label=label_to_display, color=colors[int(cls)]) - json["detections"].append({"classes": [{"name": name, "probability": probability}], - "rect": {"bottom": bottom, "left": left, "right": right, "top": top}}) - - if save_img or view_img: # Add bbox to image - label = '%s %.2f' % (names[int(cls)], conf) - plot_one_box(xyxy, im0, label=label, color=colors[int(cls)]) - - # Print time (inference + NMS) - print('%sDone. (%.3fs)' % (s, t2 - t1)) - - json["frameId"] = i - line = str(json).replace("\'", "\"") - - output_json_file.write(line) - output_json_file.write("\n") - - # Stream results - if view_img: - cv2.imshow(p, im0) - if cv2.waitKey(1) == ord('q'): # q to quit - raise StopIteration - - # Save results (image with detections) - if save_img: - if dataset.mode == 'images': - cv2.imwrite(save_path, im0) - else: - if vid_path != save_path: # new video - vid_path = save_path - if isinstance(vid_writer, cv2.VideoWriter): - vid_writer.release() # release previous video writer - - 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)) - vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*opt.fourcc), fps, (w, h)) - vid_writer.write(im0) - - output_json_file.close() - if save_txt or save_img: - print('Results saved to %s' % os.getcwd() + os.sep + out) - if platform == 'darwin': # MacOS - os.system('open ' + save_path) - - print('Done. (%.3fs)' % (time.time() - t0)) - - -if __name__ == '__main__': - parser = argparse.ArgumentParser() - parser.add_argument('--cfg', type=str, default='cfg/yolov3-spp.cfg', help='*.cfg path') - parser.add_argument('--names', type=str, default='data/coco.names', help='*.names path') - parser.add_argument('--weights', type=str, default='weights/yolov3-spp-ultralytics.pt', help='weights path') - parser.add_argument('--source', type=str, default='data/samples', help='source') # input file/folder, 0 for webcam - parser.add_argument('--output', type=str, default='output', help='output folder') # output folder - parser.add_argument('--img-size', type=int, default=512, help='inference size (pixels)') - parser.add_argument('--conf-thres', type=float, default=0.3, help='object confidence threshold') - parser.add_argument('--iou-thres', type=float, default=0.6, help='IOU threshold for NMS') - parser.add_argument('--fourcc', type=str, default='mp4v', help='output video codec (verify ffmpeg support)') - parser.add_argument('--half', action='store_true', help='half precision FP16 inference') - parser.add_argument('--device', default='', help='device id (i.e. 0 or 0,1) 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('--classes', nargs='+', type=int, help='filter by class') - 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('--json', type=str, default='output.txt', help='output json file ') - parser.add_argument('--save-video', type=bool, default=True, help='saving output video') - opt = parser.parse_args() - opt.cfg = check_file(opt.cfg) # check file - opt.names = check_file(opt.names) # check file - print(opt) - - with torch.no_grad(): - detect(opt.save_video)