pycvt
Install
pip install pycvt --upgrade
dev
uv sync
Usage
from pycvt import (
load_yolo_annotations,
save_yolo_annotations,
load_yolo_names,
convert_yolo_dataset_to_coco,
prepare_yolo_dataset_for_coco,
draw_bounding_boxes,
xyxy2xywh,
xywh2xyxy,
xyxy2xywhn,
xywhn2xyxy,
box_iou,
generate_sliding_windows,
crop_with_bbox,
sliding_crop,
scale_boxes,
get_color,
example_file,
)
"load_yolo_annotations", # to load yolo format annotations from a file
"save_yolo_annotations", # to save yolo format annotations to a file
"load_yolo_names", # to load class names from a yolo names file
"convert_yolo_dataset_to_coco", # convert a YOLO data.yaml dataset into RF-DETR/COCO layout
"prepare_yolo_dataset_for_coco", # prepare a shared converted dataset directory with file locking
"draw_bounding_boxes", # to draw bounding boxes on an image
"xyxy2xywh", # convert bounding box from (x1, y1, x2, y2) to (x_center, y_center, width, height)
"xywh2xyxy", # convert bounding box from (x_center, y_center, width, height) to (x1, y1, x2, y2)
"xyxy2xywhn", # convert bounding box from (x1, y1, x2, y2) to normalized (x_center, y_center, width, height)
"xywhn2xyxy", # convert bounding box from normalized (x_center, y_center, width, height) to (x1, y1, x2, y2)
"box_iou", # calculate Intersection over Union (IoU) between two sets of boxes
"generate_sliding_windows", # generate sliding window coordinates for an image
"crop_with_bbox", # crop image regions with bounding boxes and adjust boxes accordingly
"sliding_crop", # crop an image using sliding windows
"scale_boxes", # scale bounding boxes by a factor
"get_color", # get a color for a given class id
"example_file", # one example file for testing purposes
YOLO Predict CLI
pycvt 提供了一个基于 Ray 的 YOLO 数据集预测工具，详细用法见 doc/yolo-pred.md。
YOLO To COCO
from pycvt import convert_yolo_dataset_to_coco
summary = convert_yolo_dataset_to_coco(
yaml_path="data.yaml",
output_dir="output/rfdetr-dataset",
copy_images=False,
overwrite=False,
include_test=True,
num_workers=8,
)
print(summary)
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