Prompt Title: Add negative images COCO.

To add negative images for a single custom class to a COCO dataset, you would need to follow these steps:

1. Collect a set of images that do not contain any instances of your custom class. These images should be representative of the background or the negative cases that you want to detect.

2. Create a new category ID for the negative case class. This can be any number that is not used by any other category in the COCO dataset.

3. Add the negative case category to the COCO dataset's categories list. You can do this by appending a new dictionary with the id of your negative case category and the name of your choice.

4. For each negative image, create a new annotation dictionary with the following keys: id, image_id, category_id, bbox, area, iscrowd. Set the category_id to the ID of your negative case category, and set the iscrowd key to 0.

5. Add the annotation dictionaries for the negative images to the COCO dataset's annotations list.

6. Save the updated COCO dataset file.

Here is an example Python code snippet that illustrates how to add negative images for a custom class to a COCO dataset:

pythonCopy code
import json

# Load the existing COCO dataset file
with open('coco_dataset.json', 'r') as f:
    coco_data = json.load(f)

# Define the ID and name for the negative case category
neg_cat_id = 100
neg_cat_name = 'negative_case'

# Add the negative case category to the COCO dataset's categories list
coco_data['categories'].append({'id': neg_cat_id, 'name': neg_cat_name})

# For each negative image, create a new annotation dictionary
for neg_img_path in neg_images:
    neg_img_id = get_image_id(neg_img_path)  # get the image ID
    neg_ann_id = get_new_ann_id()  # generate a new annotation ID
    neg_ann_dict = {
        'id': neg_ann_id,
        'image_id': neg_img_id,
        'category_id': neg_cat_id,
        'bbox': [0, 0, 0, 0],  # set the bbox to [0, 0, 0, 0] for negative cases
        'area': 0,  # set the area to 0 for negative cases
        'iscrowd': 0  # set iscrowd to 0 for negative cases
    }
    coco_data['annotations'].append(neg_ann_dict)

# Save the updated COCO dataset file
with open('coco_dataset_with_neg.json', 'w') as f:
    json.dump(coco_data, f)

Note that in this example, get_image_id() and get_new_ann_id() are functions that you would need to implement to retrieve the image ID and generate a new annotation ID, respectively, based on the COCO dataset's existing image IDs and annotation IDs.