Image annotation conversion from json format to YOLOv7 format - object-detection

Question

I am trying object detection in YOLOv7. As I have downloaded some public dataset for training, I got annotations in JSON format.
{
"width": 4608,
"height": 3456,
"ispano": false,
"objects": [
{
"key": "o5a4uvvzxdp6dod5uiexhl",
"label": "regulatory--stop--g1",
"bbox": {
"xmin": 2496.375,
"ymin": 2449.40625,
"xmax": 2557.125,
"ymax": 2548.125
},
"properties": {
"barrier": false,
"occluded": false,
"out-of-frame": false,
"exterior": false,
"ambiguous": false,
"included": false,
"direction-or-information": false,
"highway": false,
"dummy": false
}
},
{
"key": "0f05h7prxavgpvxre68ml0",
"label": "warning--wild-animals--g4",
"bbox": {
"xmin": 1957.5,
"ymin": 2652.75,
"xmax": 1978.875,
"ymax": 2674.6875
},
"properties": {
"barrier": false,
"occluded": false,
"out-of-frame": false,
"exterior": false,
"ambiguous": false,
"included": false,
"direction-or-information": false,
"highway": false,
"dummy": false
}
},
{
"key": "bd43wshq6ayz0jowsxgp23",
"label": "other-sign",
"bbox": {
"xmin": 3211.875,
"ymin": 2170.96875,
"xmax": 3704.625,
"ymax": 2261.25
},
"properties": {
"barrier": false,
"occluded": false,
"out-of-frame": false,
"exterior": false,
"ambiguous": false,
"included": false,
"direction-or-information": true,
"highway": false,
"dummy": false
}
},
{
"key": "qzoqx6j766ovaw4x7zd6wi",
"label": "regulatory--reversible-lanes--g2",
"bbox": {
"xmin": 2250.0,
"ymin": 2481.46875,
"xmax": 2311.875,
"ymax": 2570.90625
},
"properties": {
"barrier": false,
"occluded": false,
"out-of-frame": false,
"exterior": false,
"ambiguous": false,
"included": false,
"direction-or-information": false,
"highway": false,
"dummy": false
}
},
{
"key": "q0g8osx48bubijrosgfpda",
"label": "warning--junction-with-a-side-road-perpendicular-left--g3",
"bbox": {
"xmin": 1990.125,
"ymin": 2609.71875,
"xmax": 2032.875,
"ymax": 2651.90625
},
"properties": {
"barrier": false,
"occluded": false,
"out-of-frame": false,
"exterior": false,
"ambiguous": false,
"included": false,
"direction-or-information": false,
"highway": false,
"dummy": false
}
},
{
"key": "do3wb1gql83q76xg9zmnkr",
"label": "other-sign",
"bbox": {
"xmin": 1929.375,
"ymin": 2662.03125,
"xmax": 1944.0,
"ymax": 2678.0625
},
"properties": {
"barrier": false,
"occluded": false,
"out-of-frame": false,
"exterior": false,
"ambiguous": true,
"included": false,
"direction-or-information": false,
"highway": false,
"dummy": false
}
}
]
}
I need the annotations in YOLOv7 PyTorch version. I tried the conversion in roboflow, but as I uploaded the the files with the annotations, it was saying none of the images are annotated. Is there a way I can convert these annotations to YOLOv7 PyTorch version?

Answer 1

I developed a library in order to convert from LabelMe to Yolov7 format you can download it here:
https://github.com/Tlaloc-Es/labelme2yolov7segmentation
And you can see how I do the conversion in order to that you can develop your version.

Answer 2

import json
# Load the JSON file
with open('lables.json', 'r') as f:
data = json.load(f)
# Extract the necessary information from the JSON file
images = data['images']
annotations = data['annotations']
categories = {c['id']: c['name'] for c in data['categories']}
# Iterate through the images
for image in images:
# Get the file name for the image
file_name = image['file_name']
# Create an empty list of bounding boxes for category 1
bounding_boxes = []
# Iterate through the annotations
for annotation in annotations:
# If the annotation's image ID matches the current image's ID
if annotation['image_id'] == image['id']:
# Get the category ID for the annotation
category_id = annotation['category_id']
# If the category ID is 1
if category_id == 1:
# Get the bounding box coordinates
x, y, w, h = annotation['bbox']
# Convert the bounding box coordinates to normalized xywh
x = x / image['width']
y = y / image['height']
w = w / image['width']
h = h / image['height']
# Create a string with the class ID, center x, center y, width, and height values, separated by spaces
bounding_box_str = f"{category_id} {x + w/2} {y + h/2} {w} {h}"
# Add the string to the list of bounding boxes
bounding_boxes.append(bounding_box_str)
# Create a new text file with the same name as the image file, but with a .txt extension
with open(f"{file_name}.txt", 'w') as f:
# Write the bounding boxes to the text file
for bounding_box in bounding_boxes:
f.write(f"{bounding_box}\n")
1)This script only convert boundary box annotations of .json ot yolov7 pytorch format
2) in (if category_id == 1) "1" is for person class only you can change
according to your requirement