Converting COCO Format to LabelMe Format - json

Question

I am trying to convert COCO json file to LabelMe json file. I used a python script called "coco2labelme.py" to convert the json file.
It successfully converts the json file, the only problem is that I get an error every time I try to load the converted json file in LabelMe. An error occurs regarding the 'imageData' of the file.
Does anyone have an idea on how to convert from COCO to LabelMe format with the image data?
below is the code for coco2labelme.py
[Source: https://gist.github.com/travishsu/6efa5c9fb92ece37b4748036026342f6]
import os
import json
import subprocess
import numpy as np
import pandas as pd
from skimage.measure import find_contours
class CocoDatasetHandler:
def __init__(self, jsonpath, imgpath):
with open(jsonpath, 'r') as jsonfile:
ann = json.load(jsonfile)
images = pd.DataFrame.from_dict(ann['images']).set_index('id')
annotations = pd.DataFrame.from_dict(ann['annotations']).set_index('id')
categories = pd.DataFrame.from_dict(ann['categories']).set_index('id')
annotations = annotations.merge(images, left_on='image_id', right_index=True)
annotations = annotations.merge(categories, left_on='category_id', right_index=True)
annotations = annotations.assign(
shapes=annotations.apply(self.coco2shape, axis=1))
self.annotations = annotations
self.labelme = {}
self.imgpath = imgpath
self.images = pd.DataFrame.from_dict(ann['images']).set_index('file_name')
def coco2shape(self, row):
if row.iscrowd == 1:
shapes = self.rle2shape(row)
elif row.iscrowd == 0:
shapes = self.polygon2shape(row)
return shapes
def rle2shape(self, row):
rle, shape = row['segmentation']['counts'], row['segmentation']['size']
mask = self._rle_decode(rle, shape)
padded_mask = np.zeros(
(mask.shape[0]+2, mask.shape[1]+2),
dtype=np.uint8,
)
padded_mask[1:-1, 1:-1] = mask
points = find_contours(mask, 0.5)
shapes = [
[[int(point[1]), int(point[0])] for point in polygon]
for polygon in points
]
return shapes
def _rle_decode(self, rle, shape):
mask = np.zeros([shape[0] * shape[1]], np.bool)
for idx, r in enumerate(rle):
if idx < 1:
s = 0
else:
s = sum(rle[:idx])
e = s + r
if e == s:
continue
assert 0 <= s < mask.shape[0]
assert 1 <= e <= mask.shape[0], "shape: {} s {} e {} r {}".format(shape, s, e, r)
if idx % 2 == 1:
mask[s:e] = 1
# Reshape and transpose
mask = mask.reshape([shape[1], shape[0]]).T
return mask
def polygon2shape(self, row):
# shapes: (n_polygons, n_points, 2)
shapes = [
[[int(points[2*i]), int(points[2*i+1])] for i in range(len(points)//2)]
for points in row.segmentation
]
return shapes
def coco2labelme(self):
fillColor = [255, 0, 0, 128]
lineColor = [0, 255, 0, 128]
groups = self.annotations.groupby('file_name')
for file_idx, (filename, df) in enumerate(groups):
record = {
'imageData': None,
'fillColor': fillColor,
'lineColor': lineColor,
'imagePath': filename,
'imageHeight': int(self.images.loc[filename].height),
'imageWidth': int(self.images.loc[filename].width),
}
record['shapes'] = []
instance = {
'line_color': None,
'fill_color': None,
'shape_type': "polygon",
}
for inst_idx, (_, row) in enumerate(df.iterrows()):
for polygon in row.shapes:
copy_instance = instance.copy()
copy_instance.update({
'label': row['name'],
'group_id': inst_idx,
'points': polygon
})
record['shapes'].append(copy_instance)
if filename not in self.labelme.keys():
self.labelme[filename] = record
def save_labelme(self, file_names, dirpath, save_json_only=False):
if not os.path.exists(dirpath):
os.makedirs(dirpath)
else:
raise ValueError(f"{dirpath} has existed")
for file in file_names:
filename = os.path.basename(os.path.splitext(file)[0])
with open(os.path.join(dirpath, filename+'.json'), 'w') as jsonfile:
json.dump(self.labelme[file], jsonfile, ensure_ascii=True, indent=2)
if not save_json_only:
subprocess.call(['cp', os.path.join(self.imgpath, file), dirpath])
ds = CocoDatasetHandler('cocodataset/annotations/instances_train2014.json', 'cocodataset/train2014/')
ds.coco2labelme()
ds.save_labelme(ds.labelme.keys(), 'cocodataset/labelme/train2014')