From 29fe90eda93424ebb6d7f31f39f62274c81018a8 Mon Sep 17 00:00:00 2001 From: wrz1-zzzzz <914921336@qq.com> Date: Sun, 24 Nov 2024 12:10:13 +0800 Subject: [PATCH] detect --- DPL/yolov5/detect_12_2.py | 373 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 373 insertions(+) create mode 100644 DPL/yolov5/detect_12_2.py diff --git a/DPL/yolov5/detect_12_2.py b/DPL/yolov5/detect_12_2.py new file mode 100644 index 0000000..2addefd --- /dev/null +++ b/DPL/yolov5/detect_12_2.py @@ -0,0 +1,373 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +""" +Run YOLOv5 detection inference on images, videos, directories, globs, YouTube, webcam, streams, etc. + +Usage - sources: + $ python detect.py --weights yolov5s.pt --source 0 # webcam + img.jpg # image + vid.mp4 # video + screen # screenshot + path/ # directory + 'path/*.jpg' # glob + 'https://youtu.be/Zgi9g1ksQHc' # YouTube + 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream + +Usage - formats: + $ python detect.py --weights yolov5s.pt # PyTorch + yolov5s.torchscript # TorchScript + yolov5s.onnx # ONNX Runtime or OpenCV DNN with --dnn + yolov5s_openvino_model # OpenVINO + yolov5s.engine # TensorRT + yolov5s.mlmodel # CoreML (macOS-only) + yolov5s_saved_model # TensorFlow SavedModel + yolov5s.pb # TensorFlow GraphDef + yolov5s.tflite # TensorFlow Lite + yolov5s_edgetpu.tflite # TensorFlow Edge TPU + yolov5s_paddle_model # PaddlePaddle +""" + +import argparse +import os +import platform +import sys +from pathlib import Path + +import torch + +FILE = Path(__file__).resolve() +ROOT = FILE.parents[0] # YOLOv5 root directory +if str(ROOT) not in sys.path: + sys.path.append(str(ROOT)) # add ROOT to PATH +ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative + +from models.common import DetectMultiBackend +from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadScreenshots, LoadStreams +from utils.general import (LOGGER, Profile, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2, + increment_path, non_max_suppression, print_args, scale_boxes, strip_optimizer, xyxy2xywh, + ) +from utils.plots import Annotator, colors, save_one_box +from utils.torch_utils import select_device, smart_inference_mode + + +@smart_inference_mode() +def run( + weights=ROOT / 'yolov5s.pt', # model path or triton URL + source=ROOT / 'data/images', # file/dir/URL/glob/screen/0(webcam) + data=ROOT / 'data/coco128.yaml', # dataset.yaml path + imgsz=(640, 640), # inference size (height, width) + conf_thres=0.25, # confidence threshold + iou_thres=0.45, # NMS IOU threshold + max_det=1000, # maximum detections per image + device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu + view_img=False, # show results + save_txt=False, # save results to *.txt + save_conf=False, # save confidences in --save-txt labels + save_crop=False, # save cropped prediction boxes + nosave=False, # do not save images/videos + classes=None, # filter by class: --class 0, or --class 0 2 3 + agnostic_nms=False, # class-agnostic NMS + augment=False, # augmented inference + visualize=False, # visualize features + update=False, # update all models + project=ROOT / 'runs/detect', # save results to project/name + name='exp11_29_12.00', # save results to project/name + exist_ok=False, # existing project/name ok, do not increment + line_thickness=3, # bounding box thickness (pixels) + hide_labels=False, # hide labels + hide_conf=False, # hide confidences + half=False, # use FP16 half-precision inference + dnn=False, # use OpenCV DNN for ONNX inference + vid_stride=1, # video frame-rate stride + batch_size=6 +): + source = str(source) + save_img = not nosave and not source.endswith('.txt') # save inference images + is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS) + is_url = source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://')) + webcam = source.isnumeric() or source.endswith('.txt') or (is_url and not is_file) + screenshot = source.lower().startswith('screen') + if is_url and is_file: + source = check_file(source) # download + + # Directories + save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run + (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir + + # Load model + device = select_device(device) + model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half) + stride, names, pt = model.stride, model.names, model.pt + imgsz = check_img_size(imgsz, s=stride) # check image size + + # Dataloader + bs = 12 # batch_size + if webcam: + view_img = check_imshow(warn=True) + dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride) + bs = len(dataset) + elif screenshot: + dataset = LoadScreenshots(source, img_size=imgsz, stride=stride, auto=pt) + else: + # dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride) + dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride,batch_size=opt.batch_size) + vid_path, vid_writer = [None] * bs, [None] * bs + + # Run inference + model.warmup(imgsz=(1 if pt or model.triton else bs, 3, *imgsz)) # warmup + seen, windows, dt = 0, [], (Profile(), Profile(), Profile()) + # Main inference loop + for paths, imgs, im0s, vid_caps, s_list in dataset: + # Preprocessing + with dt[0]: + imgs = torch.from_numpy(imgs).to(model.device) + imgs = imgs.half() if model.fp16 else imgs.float() # uint8 to fp16/32 + imgs /= 255.0 # 0 - 255 to 0.0 - 1.0 + + # Inference + with dt[1]: + visualize = increment_path(save_dir / 'visualize', mkdir=True) if visualize else False + preds = model(imgs, augment=augment, visualize=visualize) # batch inference + print(f"preds shape: {preds.shape}") + + # NMS + import time + + # NMS部分 + with dt[2]: + start_time = time.time() # 记录开始时间 + preds = non_max_suppression(preds, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det) + end_time = time.time() # 记录结束时间 + nms_time = end_time - start_time # 计算耗时 + + print(f"NMS time: {nms_time:.4f} seconds") # 输出 NMS 耗时 + + # with dt[2]: + # preds = non_max_suppression(preds, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det) + # preds = non_max_suppression(preds, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det) + + # Process each image in the batch + for i, det in enumerate(preds): # Per image in the batch + seen += 1 + path, im0, vid_cap = paths[i], im0s[i], vid_caps[i] + s = s_list[i] # Log message for current image + + p = Path(path) # to Path + save_path = str(save_dir / p.name) # Save path for the image + txt_path = str(save_dir / 'labels' / p.stem) + ( + '' if dataset.mode == 'image' else f'_{dataset.count}') # Save path for labels + s += f'{imgs.shape[2:]} ' # Add image shape to log + + gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # Normalization gain (whwh) + imc = im0.copy() if save_crop else im0 # For save_crop + annotator = Annotator(im0, line_width=line_thickness, example=str(names)) + + if len(det): + # Rescale boxes from image size to original image size + det[:, :4] = scale_boxes(imgs.shape[2:], det[:, :4], im0.shape).round() + + # Log results + for c in det[:, 5].unique(): + n = (det[:, 5] == c).sum() # Detections per class + s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # Log class count + + # Write results + for *xyxy, conf, cls in reversed(det): + if save_txt: # Write label to file + xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh + line = (cls, *xywh, conf) if save_conf else (cls, *xywh) # Label format + with open(f'{txt_path}.txt', 'a') as f: + f.write(('%g ' * len(line)).rstrip() % line + '\n') + + if save_img or save_crop or view_img: # Add bbox to image + c = int(cls) # Integer class + label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}') + annotator.box_label(xyxy, label, color=colors(c, True)) + if save_crop: + save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True) + + # Stream results + im0 = annotator.result() + if view_img: + if platform.system() == 'Linux' and p not in windows: + windows.append(p) + cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO) # Allow window resize (Linux) + cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0]) + 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[i] != save_path: # New video + vid_path[i] = save_path + if isinstance(vid_writer[i], cv2.VideoWriter): + vid_writer[i].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 = str(Path(save_path).with_suffix('.mp4')) # Force *.mp4 suffix on results videos + vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h)) + vid_writer[i].write(im0) + + # Print time (inference-only) + LOGGER.info(f"{s}{'' if len(det) else '(no detections), '}{dt[1].dt * 1E3:.1f}ms") + + # Print final results + t = tuple(x.t / seen * 1E3 for x in dt) # Speeds per image + LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}' % t) + 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 '' + LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}") + if update: + strip_optimizer(weights[0]) # Update model (to fix SourceChangeWarning) + # for path, im, im0s, vid_cap, s in dataset: + # with dt[0]: + # im = torch.from_numpy(im).to(model.device) + # im = im.half() if model.fp16 else im.float() # uint8 to fp16/32 + # im /= 255 # 0 - 255 to 0.0 - 1.0 + # if len(im.shape) == 3: + # im = im[None] # expand for batch dim + # + # # Inference + # with dt[1]: + # visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False + # pred = model(im, augment=augment, visualize=visualize) + # + # # NMS + # with dt[2]: + # pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det) + # + # # Second-stage classifier (optional) + # # pred = utils.general.apply_classifier(pred, classifier_model, im, im0s) + # + # # Process predictions + # for i, det in enumerate(pred): # per image + # seen += 1 + # if webcam: # batch_size >= 1 + # p, im0, frame = path[i], im0s[i].copy(), dataset.count + # s += f'{i}: ' + # else: + # p, im0, frame = path, im0s.copy(), getattr(dataset, 'frame', 0) + # + # p = Path(p) # to Path + # save_path = str(save_dir / p.name) # im.jpg + # txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # im.txt + # s += '%gx%g ' % im.shape[2:] # print string + # gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh + # imc = im0.copy() if save_crop else im0 # for save_crop + # annotator = Annotator(im0, line_width=line_thickness, example=str(names)) + # if len(det): + # # Rescale boxes from img_size to im0 size + # det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round() + # + # # Print results + # for c in det[:, 5].unique(): + # n = (det[:, 5] == 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 save_conf else (cls, *xywh) # label format + # with open(f'{txt_path}.txt', 'a') as f: + # f.write(('%g ' * len(line)).rstrip() % line + '\n') + # + # if save_img or save_crop or view_img: # Add bbox to image + # c = int(cls) # integer class + # label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}') + # annotator.box_label(xyxy, label, color=colors(c, True)) + # if save_crop: + # save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True) + # + # # Stream results + # im0 = annotator.result() + # if view_img: + # if platform.system() == 'Linux' and p not in windows: + # windows.append(p) + # cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO) # allow window resize (Linux) + # cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0]) + # 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[i] != save_path: # new video + # vid_path[i] = save_path + # if isinstance(vid_writer[i], cv2.VideoWriter): + # vid_writer[i].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 = str(Path(save_path).with_suffix('.mp4')) # force *.mp4 suffix on results videos + # vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h)) + # vid_writer[i].write(im0) + # + # # Print time (inference-only) + # LOGGER.info(f"{s}{'' if len(det) else '(no detections), '}{dt[1].dt * 1E3:.1f}ms") + # + # # Print results + # t = tuple(x.t / seen * 1E3 for x in dt) # speeds per image + # LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}' % t) + # 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 '' + # LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}") + # if update: + # strip_optimizer(weights[0]) # update model (to fix SourceChangeWarning) + + +def parse_opt(): + parser = argparse.ArgumentParser() + parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'runs/train/exp11_29_17.00/weights/12.2_bt12_1280.onnx', help='model path or triton URL') + parser.add_argument('--source', type=str, default=ROOT / 'datasets/xixian/images', help='file/dir/URL/glob/screen/0(webcam)') + parser.add_argument('--data', type=str, default=ROOT / 'data/yixian.yaml', help='(optional) dataset.yaml path') + parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[1280], help='inference size h,w') + parser.add_argument('--conf-thres', type=float, default=0.25, help='confidence threshold') + parser.add_argument('--iou-thres', type=float, default=0.45, help='NMS IoU threshold') + parser.add_argument('--max-det', type=int, default=1000, help='maximum detections per image') + 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='show 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: --classes 0, or --classes 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('--visualize', action='store_true', help='visualize features') + parser.add_argument('--update', action='store_true', help='update all models') + parser.add_argument('--project', default=ROOT / 'runs/detect', help='save results to project/name') + parser.add_argument('--name', default='exp11_29_12.00', 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') + parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference') + parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference') + parser.add_argument('--vid-stride', type=int, default=1, help='video frame-rate stride') + parser.add_argument('--batch_size', type=int, default=12, help='batch size for inference') + opt = parser.parse_args() + opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand + print_args(vars(opt)) + return opt + + +def main(opt): + check_requirements(exclude=('tensorboard', 'thop')) + run(**vars(opt)) + + +if __name__ == "__main__": + opt = parse_opt() + main(opt)