YOLO로 이것저것 하다보면 제일 귀찮고 망설여지는게 데이터셋 라벨링 작업이다.
일단 최소 클래스당 3000장은 해야 신뢰성이 가는 수치가 나오는데
그 많은걸 어느세월에 하나 싶은 찰나에 기막힌 생각이 떠올랐습니다.
1. 실시간 or 유트브 영상
2. custom 모델을 사용해 detect
3. detect될 때 jpg로 저장
4. --save-txt 인자를 활용해 3번의 저장 된 jpg의 라벨을 만들자
YOLOv5에서는 detect를 할 때 인식된 클래스의 라벨 좌표가 나오는 기능을 제공하고 있습니다.
detect.py를 살펴보면
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')
이런 부분이 있는걸 볼 수 있습니다.
detect.py를 실행할 때 --save 인자를 넣어주면 자동으로 /run/detect폴더에 생성이 될 것입니다.
세부 코드는 다음과 같습니다.
더보기
# YOLOv5 🚀 by Ultralytics, AGPL-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
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 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=1, # maximum detections per image
device=0, # 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=True, # 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=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
):
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
(save_dir / "labels").mkdir(parents=True, exist_ok=True)
(save_dir / "images").mkdir(parents=True, exist_ok=True)
# 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 = 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 / "images" / p.stem) + f"0_{frame}" + ".jpg"
txt_path = str(save_dir / "labels" / p.stem) + (
"" if dataset.mode == "image" else f"0_{frame}"
) # im.txt
save_image = False
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 cls == 0:
save_image = True
xywh = (
(xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn)
.view(-1)
.tolist()
) # normalized xywh
line = (0, *xywh)
# line = (cls, *xywh, conf) if save_conf else (cls, *xywh) # label format
with open(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_image:
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)
cv2.imwrite(save_path, 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 / "yolov5s.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(ROOT / "requirements.txt", exclude=("tensorboard", "thop"))
run(**vars(opt))
if __name__ == "__main__":
opt = parse_opt()
main(opt)
저장되는 파일의 파일명 부분만 바꿔주면 되겠습니다.
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