Reference for ultralytics/data/converter.py
Note
This file is available at https://github.com/ultralytics/ultralytics/blob/main/ultralytics/data/converter.py. If you spot a problem please help fix it by contributing a Pull Request 🛠️. Thank you 🙏!
ultralytics.data.converter.coco91_to_coco80_class
Converts 91-index COCO class IDs to 80-index COCO class IDs.
Returns:
Type | Description |
---|---|
list | A list of 91 class IDs where the index represents the 80-index class ID and the value is the corresponding 91-index class ID. |
Source code in ultralytics/data/converter.py
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ultralytics.data.converter.coco80_to_coco91_class
Converts 80-index (val2014) to 91-index (paper). For details see https://tech.amikelive.com/node-718/what-object-categories-labels-are-in-coco-dataset/.
Example
import numpy as np
a = np.loadtxt("data/coco.names", dtype="str", delimiter="\n")
b = np.loadtxt("data/coco_paper.names", dtype="str", delimiter="\n")
x1 = [list(a[i] == b).index(True) + 1 for i in range(80)] # darknet to coco
x2 = [list(b[i] == a).index(True) if any(b[i] == a) else None for i in range(91)] # coco to darknet
Source code in ultralytics/data/converter.py
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ultralytics.data.converter.convert_coco
convert_coco(
labels_dir="../coco/annotations/",
save_dir="coco_converted/",
use_segments=False,
use_keypoints=False,
cls91to80=True,
lvis=False,
)
Converts COCO dataset annotations to a YOLO annotation format suitable for training YOLO models.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
labels_dir | str | Path to directory containing COCO dataset annotation files. | '../coco/annotations/' |
save_dir | str | Path to directory to save results to. | 'coco_converted/' |
use_segments | bool | Whether to include segmentation masks in the output. | False |
use_keypoints | bool | Whether to include keypoint annotations in the output. | False |
cls91to80 | bool | Whether to map 91 COCO class IDs to the corresponding 80 COCO class IDs. | True |
lvis | bool | Whether to convert data in lvis dataset way. | False |
Example
Output
Generates output files in the specified output directory.
Source code in ultralytics/data/converter.py
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ultralytics.data.converter.convert_segment_masks_to_yolo_seg
Converts a dataset of segmentation mask images to the YOLO segmentation format.
This function takes the directory containing the binary format mask images and converts them into YOLO segmentation format. The converted masks are saved in the specified output directory.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
masks_dir | str | The path to the directory where all mask images (png, jpg) are stored. | required |
output_dir | str | The path to the directory where the converted YOLO segmentation masks will be stored. | required |
classes | int | Total classes in the dataset i.e. for COCO classes=80 | required |
Example
Notes
The expected directory structure for the masks is:
- masks
├─ mask_image_01.png or mask_image_01.jpg
├─ mask_image_02.png or mask_image_02.jpg
├─ mask_image_03.png or mask_image_03.jpg
└─ mask_image_04.png or mask_image_04.jpg
After execution, the labels will be organized in the following structure:
- output_dir
├─ mask_yolo_01.txt
├─ mask_yolo_02.txt
├─ mask_yolo_03.txt
└─ mask_yolo_04.txt
Source code in ultralytics/data/converter.py
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ultralytics.data.converter.convert_dota_to_yolo_obb
Converts DOTA dataset annotations to YOLO OBB (Oriented Bounding Box) format.
The function processes images in the 'train' and 'val' folders of the DOTA dataset. For each image, it reads the associated label from the original labels directory and writes new labels in YOLO OBB format to a new directory.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
dota_root_path | str | The root directory path of the DOTA dataset. | required |
Example
Notes
The directory structure assumed for the DOTA dataset:
- DOTA
├─ images
│ ├─ train
│ └─ val
└─ labels
├─ train_original
└─ val_original
After execution, the function will organize the labels into:
- DOTA
└─ labels
├─ train
└─ val
Source code in ultralytics/data/converter.py
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ultralytics.data.converter.min_index
Find a pair of indexes with the shortest distance between two arrays of 2D points.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
arr1 | ndarray | A NumPy array of shape (N, 2) representing N 2D points. | required |
arr2 | ndarray | A NumPy array of shape (M, 2) representing M 2D points. | required |
Returns:
Type | Description |
---|---|
tuple | A tuple containing the indexes of the points with the shortest distance in arr1 and arr2 respectively. |
Source code in ultralytics/data/converter.py
ultralytics.data.converter.merge_multi_segment
Merge multiple segments into one list by connecting the coordinates with the minimum distance between each segment. This function connects these coordinates with a thin line to merge all segments into one.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
segments | List[List] | Original segmentations in COCO's JSON file. Each element is a list of coordinates, like [segmentation1, segmentation2,...]. | required |
Returns:
Name | Type | Description |
---|---|---|
s | List[ndarray] | A list of connected segments represented as NumPy arrays. |
Source code in ultralytics/data/converter.py
ultralytics.data.converter.yolo_bbox2segment
Converts existing object detection dataset (bounding boxes) to segmentation dataset or oriented bounding box (OBB) in YOLO format. Generates segmentation data using SAM auto-annotator as needed.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
im_dir | str | Path | Path to image directory to convert. | required |
save_dir | str | Path | Path to save the generated labels, labels will be saved into | None |
sam_model | str | Segmentation model to use for intermediate segmentation data; optional. | 'sam_b.pt' |
Notes
The input directory structure assumed for dataset:
- im_dir
├─ 001.jpg
├─ ...
└─ NNN.jpg
- labels
├─ 001.txt
├─ ...
└─ NNN.txt
Source code in ultralytics/data/converter.py
ultralytics.data.converter.create_synthetic_coco_dataset
Creates a synthetic COCO dataset with random images based on filenames from label lists.
This function downloads COCO labels, reads image filenames from label list files, creates synthetic images for train2017 and val2017 subsets, and organizes them in the COCO dataset structure. It uses multithreading to generate images efficiently.
Examples:
>>> from ultralytics.data.converter import create_synthetic_coco_dataset
>>> create_synthetic_coco_dataset()
Notes
- Requires internet connection to download label files.
- Generates random RGB images of varying sizes (480x480 to 640x640 pixels).
- Existing test2017 directory is removed as it's not needed.
- Reads image filenames from train2017.txt and val2017.txt files.