From 51c9f9229731021f55a9ceb9f9504abfc979a54b Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Tue, 23 Aug 2022 17:54:51 +0200 Subject: [PATCH] Streaming Classification support (#9106) * Streaming Classification support * Streaming Classification support * Streaming Classification support --- classify/predict.py | 168 +++++++++++++++++++++++++++++++---------- detect.py | 2 +- utils/augmentations.py | 1 + 3 files changed, 131 insertions(+), 40 deletions(-) diff --git a/classify/predict.py b/classify/predict.py index 135470fd36ed..b430c0645f21 100644 --- a/classify/predict.py +++ b/classify/predict.py @@ -1,12 +1,15 @@ # YOLOv5 🚀 by Ultralytics, GPL-3.0 license """ -Run YOLOv5 classification inference on images, videos, directories, and globs. +Run YOLOv5 classification inference on images, videos, directories, globs, YouTube, webcam, streams, etc. Usage - sources: - $ python classify/predict.py --weights yolov5s.pt --source img.jpg # image - vid.mp4 # video - path/ # directory - 'path/*.jpg' # glob + $ python classify/predict.py --weights yolov5s-cls.pt --source 0 # webcam + img.jpg # image + vid.mp4 # video + path/ # directory + 'path/*.jpg' # glob + 'https://youtu.be/Zgi9g1ksQHc' # YouTube + 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream Usage - formats: $ python classify/predict.py --weights yolov5s-cls.pt # PyTorch @@ -23,9 +26,11 @@ import argparse import os +import platform import sys from pathlib import Path +import torch.backends.cudnn as cudnn import torch.nn.functional as F FILE = Path(__file__).resolve() @@ -36,45 +41,70 @@ from models.common import DetectMultiBackend from utils.augmentations import classify_transforms -from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages -from utils.general import LOGGER, Profile, check_file, check_requirements, colorstr, increment_path, print_args +from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadStreams +from utils.general import (LOGGER, Profile, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2, + increment_path, print_args, strip_optimizer) +from utils.plots import Annotator from utils.torch_utils import select_device, smart_inference_mode @smart_inference_mode() def run( weights=ROOT / 'yolov5s-cls.pt', # model.pt path(s) - source=ROOT / 'data/images', # file/dir/URL/glob - imgsz=224, # inference size + source=ROOT / 'data/images', # file/dir/URL/glob, 0 for webcam + data=ROOT / 'data/coco128.yaml', # dataset.yaml path + imgsz=(224, 224), # inference size (height, width) 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 + nosave=False, # do not save images/videos + augment=False, # augmented inference + visualize=False, # visualize features + update=False, # update all models + project=ROOT / 'runs/predict-cls', # save results to project/name + name='exp', # save results to project/name + exist_ok=False, # existing project/name ok, do not increment half=False, # use FP16 half-precision inference dnn=False, # use OpenCV DNN for ONNX inference - project=ROOT / 'runs/predict-cls', # save to project/name - name='exp', # save to project/name - exist_ok=False, # existing project/name ok, do not increment ): 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) if is_url and is_file: source = check_file(source) # download - dt = Profile(), Profile(), Profile() - device = select_device(device) - # Directories save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run - save_dir.mkdir(parents=True, exist_ok=True) # make dir + (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir # Load model - model = DetectMultiBackend(weights, device=device, dnn=dnn, fp16=half) - model.warmup(imgsz=(1, 3, imgsz, imgsz)) # warmup - dataset = LoadImages(source, img_size=imgsz, transforms=classify_transforms(imgsz)) - for seen, (path, im, im0s, vid_cap, s) in enumerate(dataset): - # Image + 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 + if webcam: + view_img = check_imshow() + cudnn.benchmark = True # set True to speed up constant image size inference + dataset = LoadStreams(source, img_size=imgsz, transforms=classify_transforms(imgsz[0])) + bs = len(dataset) # batch_size + else: + dataset = LoadImages(source, img_size=imgsz, transforms=classify_transforms(imgsz[0])) + bs = 1 # batch_size + vid_path, vid_writer = [None] * bs, [None] * bs + + # Run inference + model.warmup(imgsz=(1 if pt else bs, 3, *imgsz)) # warmup + seen, windows, dt = 0, [], (Profile(), Profile(), Profile()) + for path, im, im0s, vid_cap, s in dataset: with dt[0]: - im = im.unsqueeze(0).to(device) - im = im.half() if model.fp16 else im.float() + im = im.to(device) + im = im.half() if model.fp16 else im.float() # uint8 to fp16/32 + if len(im.shape) == 3: + im = im[None] # expand for batch dim # Inference with dt[1]: @@ -82,33 +112,93 @@ def run( # Post-process with dt[2]: - p = F.softmax(results, dim=1) # probabilities - i = p.argsort(1, descending=True)[:, :5].squeeze().tolist() # top 5 indices - # if save: - # imshow_cls(im, f=save_dir / Path(path).name, verbose=True) - LOGGER.info( - f"{s}{imgsz}x{imgsz} {', '.join(f'{model.names[j]} {p[0, j]:.2f}' for j in i)}, {dt[1].dt * 1E3:.1f}ms") + pred = F.softmax(results, dim=1) # probabilities + + # Process predictions + for i, prob in enumerate(pred): # per image + seen += 1 + if webcam: # batch_size >= 1 + p, im0 = path[i], im0s[i].copy() + s += f'{i}: ' + else: + p, im0 = path, im0s.copy() + + p = Path(p) # to Path + save_path = str(save_dir / p.name) # im.jpg + s += '%gx%g ' % im.shape[2:] # print string + annotator = Annotator(im0, example=str(names), pil=True) + + # Print results + top5i = prob.argsort(0, descending=True)[:5].tolist() # top 5 indices + s += f"{', '.join(f'{names[j]} {prob[j]:.2f}' for j in top5i)}, " + + # Write results + if save_img or view_img: # Add bbox to image + text = '\n'.join(f'{prob[j]:.2f} {names[j]}' for j in top5i) + annotator.text((64, 64), text, txt_color=(255, 255, 255)) + + # 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}{dt[1].dt * 1E3:.1f}ms") # Print results - t = tuple(x.t / (seen + 1) * 1E3 for x in dt) # speeds per image - shape = (1, 3, imgsz, imgsz) - LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms post-process per image at shape {shape}' % t) - LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}") - return p + 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 / 'yolov5s-cls.pt', help='model path(s)') - parser.add_argument('--source', type=str, default=ROOT / 'data/images', help='file/dir/URL/glob') - parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=224, help='train, val image size (pixels)') + parser.add_argument('--source', type=str, default=ROOT / 'data/images', help='file/dir/URL/glob, 0 for webcam') + parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='(optional) dataset.yaml path') + parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[224], help='inference size h,w') 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('--nosave', action='store_true', help='do not save images/videos') + 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/predict-cls', 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('--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('--project', default=ROOT / 'runs/predict-cls', help='save to project/name') - parser.add_argument('--name', default='exp', help='save to project/name') - parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') opt = parser.parse_args() + opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand print_args(vars(opt)) return opt diff --git a/detect.py b/detect.py index 541ad90e051d..60a821b59a03 100644 --- a/detect.py +++ b/detect.py @@ -1,6 +1,6 @@ # YOLOv5 🚀 by Ultralytics, GPL-3.0 license """ -Run YOLOv5 detection inference on images, videos, directories, streams, etc. +Run YOLOv5 detection inference on images, videos, directories, globs, YouTube, webcam, streams, etc. Usage - sources: $ python detect.py --weights yolov5s.pt --source 0 # webcam diff --git a/utils/augmentations.py b/utils/augmentations.py index a55fefa68a76..c8499b3fc8ae 100644 --- a/utils/augmentations.py +++ b/utils/augmentations.py @@ -344,4 +344,5 @@ def classify_albumentations(augment=True, def classify_transforms(size=224): # Transforms to apply if albumentations not installed + assert isinstance(size, int), f'ERROR: classify_transforms size {size} must be integer, not (list, tuple)' return T.Compose([T.ToTensor(), T.Resize(size), T.CenterCrop(size), T.Normalize(IMAGENET_MEAN, IMAGENET_STD)])