Convert the COCO RLE format to YOLOv5/v8 segmentation format.

[ryouchinsa]

Hi, thanks for your useful script.

We added rle2polygon() to general_json2yolo.py so that you can convert the COCO RLE format to YOLOv5/v8 segmentation format. Please let us know your opinion.
https://github.com/ryouchinsa/Rectlabel-support/blob/master/general_json2yolo.py

if use_segments:
    if len(ann['segmentation']) == 0:
        segments.append([])
        continue
    if isinstance(ann['segmentation'], dict):
        ann['segmentation'] = rle2polygon(ann['segmentation'])
    if len(ann['segmentation']) > 1:
        s = merge_multi_segment(ann['segmentation'])
        s = (np.concatenate(s, axis=0) / np.array([w, h])).reshape(-1).tolist()

def is_clockwise(contour):
    value = 0
    num = len(contour)
    for i, point in enumerate(contour):
        p1 = contour[i]
        if i < num - 1:
            p2 = contour[i + 1]
        else:
            p2 = contour[0]
        value += (p2[0][0] - p1[0][0]) * (p2[0][1] + p1[0][1]);
    return value < 0

def get_merge_point_idx(contour1, contour2):
    idx1 = 0
    idx2 = 0
    distance_min = -1
    for i, p1 in enumerate(contour1):
        for j, p2 in enumerate(contour2):
            distance = pow(p2[0][0] - p1[0][0], 2) + pow(p2[0][1] - p1[0][1], 2);
            if distance_min < 0:
                distance_min = distance
                idx1 = i
                idx2 = j
            elif distance < distance_min:
                distance_min = distance
                idx1 = i
                idx2 = j
    return idx1, idx2

def merge_contours(contour1, contour2, idx1, idx2):
    contour = []
    for i in list(range(0, idx1 + 1)):
        contour.append(contour1[i])
    for i in list(range(idx2, len(contour2))):
        contour.append(contour2[i])
    for i in list(range(0, idx2 + 1)):
        contour.append(contour2[i])
    for i in list(range(idx1, len(contour1))):
        contour.append(contour1[i])
    contour = np.array(contour)
    return contour

def merge_with_parent(contour_parent, contour):
    if not is_clockwise(contour_parent):
        contour_parent = contour_parent[::-1]
    if is_clockwise(contour):
        contour = contour[::-1]
    idx1, idx2 = get_merge_point_idx(contour_parent, contour)
    return merge_contours(contour_parent, contour, idx1, idx2)

def mask2polygon(image):
    contours, hierarchies = cv2.findContours(image, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_TC89_KCOS)
    contours_approx = []
    polygons = []
    for contour in contours:
        epsilon = 0.001 * cv2.arcLength(contour, True)
        contour_approx = cv2.approxPolyDP(contour, epsilon, True)
        contours_approx.append(contour_approx)

    contours_parent = []
    for i, contour in enumerate(contours_approx):
        parent_idx = hierarchies[0][i][3]
        if parent_idx < 0 and len(contour) >= 3:
            contours_parent.append(contour)
        else:
            contours_parent.append([])

    for i, contour in enumerate(contours_approx):
        parent_idx = hierarchies[0][i][3]
        if parent_idx >= 0 and len(contour) >= 3:
            contour_parent = contours_parent[parent_idx]
            if len(contour_parent) == 0:
                continue
            contours_parent[parent_idx] = merge_with_parent(contour_parent, contour)

    contours_parent_tmp = []
    for contour in contours_parent:
        if len(contour) == 0:
            continue
        contours_parent_tmp.append(contour)

    polygons = []
    for contour in contours_parent_tmp:
        polygon = contour.flatten().tolist()
        polygons.append(polygon)
    return polygons 

def rle2polygon(segmentation):
    if isinstance(segmentation["counts"], list):
        segmentation = mask.frPyObjects(segmentation, *segmentation["size"])
    m = mask.decode(segmentation) 
    m[m > 0] = 255
    polygons = mask2polygon(m)
    return polygons

[collinmccarthy]

Hi @ryouchinsa, I noticed you are approximating the contour in a different way than this answer here - cocodataset/cocoapi#476 (comment)

Why are you using this:

contours, _ = cv2.findContours(m, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_TC89_KCOS)
polygons = []
for contour in contours:
    epsilon = 0.001 * cv2.arcLength(contour, True)
    contour_approx = cv2.approxPolyDP(contour, epsilon, True)
    polygon = contour_approx.flatten().tolist()
    polygons.append(polygon)

instead of this, which produces significantly more polygon vertices/coordinates

contours, _ = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
polygons = []
for contour in contours:
    polygons.append(contour.astype(float).flatten().tolist())

I'm not saying your approach is wrong. I'm just curious if you chose a faster (but less accurate) method for your application, rather than a slower but more accurate method, or whether I'm misunderstanding something. Thanks.

[ryouchinsa]

Thanks for the detailed feedback.

If we combine findContours() and approxPolyDP(), we can decrease the number of polygon points from 500 points to 50 points, for example.
When we edit the polygon, 500 points are too many, we think tens of points are appropriate.
If you want to preserve the mask shape as much as possible on training, you do not have to use approxPolyDP().

[glenn-jocher]

Thanks for the detailed feedback.

We chose to use a combination of findContours() and approxPolyDP() to reduce the number of polygon points, optimizing for a decrease from 500 points to around 50. This approach balances accuracy with efficiency, ensuring a manageable number of points while retaining the essential shape of the mask.

If preserving the mask shape as closely as possible during training is a priority, it's not necessary to use approxPolyDP().

[ryouchinsa]

Using the script general_json2yolo.py, you can convert the RLE mask with holes to the YOLO segmentation format.

The RLE mask is converted to a parent polygon and a child polygon using cv2.findContours().
The parent polygon points are sorted in clockwise order.
The child polygon points are sorted in counterclockwise order.
Detect the nearest point in the parent polygon and in the child polygon.
Connect those 2 points with narrow 2 lines.
So that the polygon with a hole is saved in the YOLO segmentation format.

def is_clockwise(contour):
    value = 0
    num = len(contour)
    for i, point in enumerate(contour):
        p1 = contour[i]
        if i < num - 1:
            p2 = contour[i + 1]
        else:
            p2 = contour[0]
        value += (p2[0][0] - p1[0][0]) * (p2[0][1] + p1[0][1]);
    return value < 0

def get_merge_point_idx(contour1, contour2):
    idx1 = 0
    idx2 = 0
    distance_min = -1
    for i, p1 in enumerate(contour1):
        for j, p2 in enumerate(contour2):
            distance = pow(p2[0][0] - p1[0][0], 2) + pow(p2[0][1] - p1[0][1], 2);
            if distance_min < 0:
                distance_min = distance
                idx1 = i
                idx2 = j
            elif distance < distance_min:
                distance_min = distance
                idx1 = i
                idx2 = j
    return idx1, idx2

def merge_contours(contour1, contour2, idx1, idx2):
    contour = []
    for i in list(range(0, idx1 + 1)):
        contour.append(contour1[i])
    for i in list(range(idx2, len(contour2))):
        contour.append(contour2[i])
    for i in list(range(0, idx2 + 1)):
        contour.append(contour2[i])
    for i in list(range(idx1, len(contour1))):
        contour.append(contour1[i])
    contour = np.array(contour)
    return contour

def merge_with_parent(contour_parent, contour):
    if not is_clockwise(contour_parent):
        contour_parent = contour_parent[::-1]
    if is_clockwise(contour):
        contour = contour[::-1]
    idx1, idx2 = get_merge_point_idx(contour_parent, contour)
    return merge_contours(contour_parent, contour, idx1, idx2)

def mask2polygon(image):
    contours, hierarchies = cv2.findContours(image, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_TC89_KCOS)
    contours_approx = []
    polygons = []
    for contour in contours:
        epsilon = 0.001 * cv2.arcLength(contour, True)
        contour_approx = cv2.approxPolyDP(contour, epsilon, True)
        contours_approx.append(contour_approx)

    contours_parent = []
    for i, contour in enumerate(contours_approx):
        parent_idx = hierarchies[0][i][3]
        if parent_idx < 0 and len(contour) >= 3:
            contours_parent.append(contour)
        else:
            contours_parent.append([])

    for i, contour in enumerate(contours_approx):
        parent_idx = hierarchies[0][i][3]
        if parent_idx >= 0 and len(contour) >= 3:
            contour_parent = contours_parent[parent_idx]
            if len(contour_parent) == 0:
                continue
            contours_parent[parent_idx] = merge_with_parent(contour_parent, contour)

    contours_parent_tmp = []
    for contour in contours_parent:
        if len(contour) == 0:
            continue
        contours_parent_tmp.append(contour)

    polygons = []
    for contour in contours_parent_tmp:
        polygon = contour.flatten().tolist()
        polygons.append(polygon)
    return polygons 

def rle2polygon(segmentation):
    if isinstance(segmentation["counts"], list):
        segmentation = mask.frPyObjects(segmentation, *segmentation["size"])
    m = mask.decode(segmentation) 
    m[m > 0] = 255
    polygons = mask2polygon(m)
    return polygons

The RLE mask.

The converted YOLO segmentation format.

To run the script, put the COCO JSON file coco_train.json into datasets/coco/annotations.
Run the script. python general_json2yolo.py
The converted YOLO txt files are saved in new_dir/labels/coco_train.

Edit use_segments and use_keypoints in the script.

if __name__ == '__main__':
    source = 'COCO'

    if source == 'COCO':
        convert_coco_json('../datasets/coco/annotations',  # directory with *.json
                          use_segments=True,
                          use_keypoints=False,
                          cls91to80=False)

To convert the COCO bbox format to YOLO bbox format.

use_segments=False,
use_keypoints=False,

To convert the COCO segmentation format to YOLO segmentation format.

use_segments=True,
use_keypoints=False,

To convert the COCO keypoints format to YOLO keypoints format.

use_segments=False,
use_keypoints=True,

This script originates from Ultralytics JSON2YOLO repository.
We hope this script would help your business.

[glenn-jocher]

@ryouchinsa thanks for sharing the updated script and examples of the RLE mask and the converted YOLO segmentation format. Your efforts to enhance the functionality of the script are much appreciated. It's great to see the improvements you've made and how they translate into the YOLO segmentation format. Good job!

[ryouchinsa]

We updated the general_json2yolo.py script so that the RLE mask with holes are converted to YOLO segmentation format.

We believe that this script would be beneficial for your company and users.
Could you review the script before making a PR?

[glenn-jocher]

@ryouchinsa thank you for the update and for considering our input. We appreciate your effort in enhancing the script to accommodate RLE masks with holes. We will review the script and provide feedback as soon as possible. Keep up the great work!

[ryouchinsa]

Thanks for reviewing our script.
We checked whether YOLO can train polygon masks with holes with a small dataset.

Donut images and YOLO segmentation text files to confirm that YOLO can train polygon masks with holes.

[glenn-jocher]

@ryouchinsa thank you for sharing the donut images and YOLO segmentation text files. We'll take a look and confirm that the YOLO model can effectively train polygon masks with holes using this dataset. Your contribution is valuable, and we appreciate your efforts in enhancing the YOLO functionality.

[ryouchinsa]

Hi @glenn-jocher, I submitted the PR about this update.
#61

Please let us know if there are any problems in the PR.