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import argparse | |
import os | |
import platform | |
import sys | |
from pathlib import Path | |
import torch | |
FILE = Path(__file__).resolve() | |
ROOT = FILE.parents[0] # YOLO 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 | |
def run( | |
weights=ROOT / 'yolo.pt', # model path or triton URL | |
source=ROOT / 'data/images', # file/dir/URL/glob/screen/0(webcam) | |
data=ROOT / 'data/coco.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='exp', # save results to project/name | |
exist_ok=False, # existing project/name ok, do not increment | |
line_thickness=2, # 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 | |
): | |
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 = 1 # 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) | |
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()) | |
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 = pred[0][1] if isinstance(pred[0], list) else pred[0] # single model or ensemble | |
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('m','p','4','v'), 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") | |
#vid_writer.release() | |
# 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) | |
return save_path | |
def parse_opt(): | |
parser = argparse.ArgumentParser() | |
parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolo.pt', help='model path or triton URL') | |
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=[640], 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='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('--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') | |
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) | |