Comments (2)
You should parse yolo format and convert it to labelme format. Maybe something like this, but I never tested
import os
import json
import base64
import io
import numpy as np
from PIL import Image
def read_name_file(name_path):
with open(name_path, "r") as name_file:
names = [name.strip() for name in name_file]
return names
def img_to_b64(img_path):
with open(img_path, "rb") as image_file:
return base64.b64encode(image_file.read()).decode('utf-8')
def convert_yolo_to_labelme(yolo_file, image_path, json_name=None):
if json_name is None:
json_name = yolo_file.rstrip(".txt") + ".json"
names = read_name_file('obj.names')
img = Image.open(image_path)
width, height = img.size
data = {
"version": "4.5.7",
"flags": {},
"shapes": [],
"imagePath": os.path.basename(image_path),
"imageData": img_to_b64(image_path),
"imageHeight": height,
"imageWidth": width,
}
with open(yolo_file, 'r') as f:
lines = f.readlines()
for line in lines:
parts = line.strip().split()
cls = int(parts[0])
cx, cy, w, h = [float(x) for x in parts[1:]]
x1 = (cx - w / 2) * width
y1 = (cy - h / 2) * height
x2 = (cx + w / 2) * width
y2 = (cy + h / 2) * height
shape = {
"label": names[cls],
"line_color": None,
"fill_color": None,
"points": [[x1, y1], [x2, y2]],
"shape_type": "rectangle",
"flags": {}
}
data["shapes"].append(shape)
with open(json_name, "w") as json_outfile:
json.dump(data, json_outfile, ensure_ascii=False, indent=2)
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser(description="Convert TXT to JSON")
parser.add_argument('--input', type=str, help="Path to the input TXT file", required=True)
parser.add_argument('--image', type=str, help="Path to the image associated with TXT annotations", required=True)
parser.add_argument('--output', type=str, help="Path to the output JSON file", default=None)
args = parser.parse_args()
convert_yolo_to_labelme(args.input, args.image, args.output)
from labelmetoyolosegmentation.
Check out here
yolo2labelme
from labelmetoyolosegmentation.
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