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import os
import json
import glob
from shared import category_index
def main():
input_folder = os.path.join(".", "images")
train_folder = os.path.join(input_folder, "train")
test_folder = os.path.join(input_folder, "test")
valid_folder = os.path.join(input_folder, "valid")
output_file_name = "_annotations.coco.json"
labelme_to_coco(train_folder, os.path.join(train_folder, output_file_name))
labelme_to_coco(test_folder, os.path.join(test_folder, output_file_name))
labelme_to_coco(valid_folder, os.path.join(valid_folder, output_file_name))
def labelme_to_coco(input_folder, output_file):
"""
Convert a folder of LabelMe JSON files to COCO JSON format using a predefined category index.
Args:
- input_folder (str): Path to the folder containing LabelMe JSON files.
- output_file (str): Output file name for the COCO JSON output.
"""
images = []
annotations = []
image_id = 1
annotation_id = 1
# Process each JSON file in the input folder
files = glob.glob(os.path.join(input_folder, "Cube*.json"))
for json_file in files:
with open(json_file, 'r') as f:
data = json.load(f)
# Create COCO image entry
image_info = {
'id': image_id,
'file_name': data['imagePath'],
'height': data['imageHeight'],
'width': data['imageWidth']
}
images.append(image_info)
# Process annotations
for shape in data['shapes']:
label = shape['label']
points = shape['points']
# Find category_id based on label using category_index
category_id = None
for cat_id, cat_info in category_index.items():
if label == cat_info['name']:
category_id = cat_id
break
if category_id is None:
continue # Skip if label not found in category_index
# Calculate bounding box coordinates
x_values = [point[0] for point in points]
y_values = [point[1] for point in points]
xmin = min(x_values)
xmax = max(x_values)
ymin = min(y_values)
ymax = max(y_values)
bbox_width = xmax - xmin
bbox_height = ymax - ymin
# Create COCO annotation entry
annotation = {
'id': annotation_id,
'image_id': image_id,
'category_id': category_id,
'segmentation': [],
'area': bbox_width * bbox_height,
'bbox': [xmin, ymin, bbox_width, bbox_height],
'iscrowd': 0 # 0 for regular annotations
}
annotations.append(annotation)
annotation_id += 1
image_id += 1
# Create COCO JSON structure
coco_output = {
'info': {},
'licenses': [],
'images': images,
'annotations': annotations,
'categories': list(category_index.values()) # Use values from category_index
}
# Save to output JSON file
with open(output_file, 'w') as outfile:
json.dump(coco_output, outfile)
print(f"Conversion completed. COCO JSON saved to {output_file}")
if __name__ == "__main__":
main()
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