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# YOLOv5 🚀 by Ultralytics, GPL-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) | |