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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license | |
""" | |
Run YOLOv5 classification inference on images, videos, directories, globs, YouTube, webcam, streams, etc. | |
Usage - sources: | |
$ python classify/predict.py --weights yolov5s-cls.pt --source 0 # webcam | |
img.jpg # image | |
vid.mp4 # video | |
screen # screenshot | |
path/ # directory | |
list.txt # list of images | |
list.streams # list of streams | |
'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 | |
yolov5s-cls.torchscript # TorchScript | |
yolov5s-cls.onnx # ONNX Runtime or OpenCV DNN with --dnn | |
yolov5s-cls_openvino_model # OpenVINO | |
yolov5s-cls.engine # TensorRT | |
yolov5s-cls.mlmodel # CoreML (macOS-only) | |
yolov5s-cls_saved_model # TensorFlow SavedModel | |
yolov5s-cls.pb # TensorFlow GraphDef | |
yolov5s-cls.tflite # TensorFlow Lite | |
yolov5s-cls_edgetpu.tflite # TensorFlow Edge TPU | |
yolov5s-cls_paddle_model # PaddlePaddle | |
""" | |
import argparse | |
import os | |
import platform | |
import sys | |
from pathlib import Path | |
import torch | |
import torch.nn.functional as F | |
FILE = Path(__file__).resolve() | |
ROOT = FILE.parents[1] # 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.augmentations import classify_transforms | |
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, print_args, strip_optimizer) | |
from utils.plots import Annotator | |
from utils.torch_utils import select_device, smart_inference_mode | |
def run( | |
weights=ROOT / 'yolov5s-cls.pt', # model.pt path(s) | |
source=ROOT / 'data/images', # file/dir/URL/glob/screen/0(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 | |
vid_stride=1, # video frame-rate stride | |
): | |
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('.streams') 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 = 1 # batch_size | |
if webcam: | |
view_img = check_imshow(warn=True) | |
dataset = LoadStreams(source, img_size=imgsz, transforms=classify_transforms(imgsz[0]), 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, transforms=classify_transforms(imgsz[0]), vid_stride=vid_stride) | |
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 = torch.Tensor(im).to(model.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]: | |
results = model(im) | |
# Post-process | |
with dt[2]: | |
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, 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 | |
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 | |
text = '\n'.join(f'{prob[j]:.2f} {names[j]}' for j in top5i) | |
if save_img or view_img: # Add bbox to image | |
annotator.text((32, 32), text, txt_color=(255, 255, 255)) | |
if save_txt: # Write to file | |
with open(f'{txt_path}.txt', 'a') as f: | |
f.write(text + '\n') | |
# 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 * 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/screen/0(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('--vid-stride', type=int, default=1, help='video frame-rate stride') | |
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) | |