cwm/eval/Segmentation/archive/merge_results_into_json.py

import os
import json
import glob
import torch
import datetime
import argparse
import torch.nn.functional as F
import numpy as np
import pycocotools.mask as mask_util
def create_image_info(image_id, file_name, image_size,
                      date_captured=datetime.datetime.utcnow().isoformat(' '),
                      license_id=1, coco_url="", flickr_url=""):
    """Return image_info in COCO style
    Args:
        image_id: the image ID
        file_name: the file name of each image
        image_size: image size in the format of (width, height)
        date_captured: the date this image info is created
        license: license of this image
        coco_url: url to COCO images if there is any
        flickr_url: url to flickr if there is any
    """
    image_info = {
            "id": image_id,
            "file_name": file_name,
            "width": image_size[0],
            "height": image_size[1],
            "date_captured": date_captured,
            "license": license_id,
            "coco_url": coco_url,
            "flickr_url": flickr_url
    }
    return image_info


def create_annotation_info(annotation_id, image_id, category_info, binary_mask,
                           image_size=None, bounding_box=None):
    """Return annotation info in COCO style
    Args:
        annotation_id: the annotation ID
        image_id: the image ID
        category_info: the information on categories
        binary_mask: a 2D binary numpy array where '1's represent the object
        file_name: the file name of each image
        image_size: image size in the format of (width, height)
        bounding_box: the bounding box for detection task. If bounding_box is not provided,
        we will generate one according to the binary mask.
    """
    upper = np.max(binary_mask)
    lower = np.min(binary_mask)
    thresh = upper / 2.0
    binary_mask[binary_mask > thresh] = upper
    binary_mask[binary_mask <= thresh] = lower
    if image_size is not None:
        binary_mask = resize_binary_mask(binary_mask.astype(np.uint8), image_size)

    binary_mask_encoded = mask_util.encode(np.asfortranarray(binary_mask.astype(np.uint8)))

    area = mask_util.area(binary_mask_encoded)
    if area < 1:
        return None

    if bounding_box is None:
        bounding_box = mask_util.toBbox(binary_mask_encoded)

    rle = mask_util.encode(np.array(binary_mask[...,None], order="F", dtype="uint8"))[0]
    rle['counts'] = rle['counts'].decode('ascii')
    segmentation = rle

    annotation_info = {
        "id": annotation_id,
        "image_id": image_id,
        "category_id": category_info["id"],
        "iscrowd": 0,
        "area": area.tolist(),
        "bbox": bounding_box.tolist(),
        "segmentation": segmentation,
        "width": binary_mask.shape[1],
        "height": binary_mask.shape[0],
    }

    return annotation_info

# necessay info used for coco style annotations
INFO = {
    "description": "ImageNet-1K: pseudo-masks with MaskCut",
    "url": "https://github.com/facebookresearch/CutLER",
    "version": "1.0",
    "year": 2023,
    "contributor": "Xudong Wang",
    "date_created": datetime.datetime.utcnow().isoformat(' ')
}

LICENSES = [
    {
        "id": 1,
        "name": "Apache License",
        "url": "https://github.com/facebookresearch/CutLER/blob/main/LICENSE"
    }
]

# only one class, i.e. foreground
CATEGORIES = [
    {
        'id': 1,
        'name': 'fg',
        'supercategory': 'fg',
    },
]

convert = lambda text: int(text) if text.isdigit() else text.lower()
natrual_key = lambda key: [ convert(c) for c in re.split('([0-9]+)', key) ]

output = {
        "info": INFO,
        "licenses": LICENSES,
        "categories": CATEGORIES,
        "images": [],
        "annotations": []}

category_info = {
    "is_crowd": 0,
    "id": 1
}

if __name__ == "__main__":

    parser = argparse.ArgumentParser('Merge pytorch results file into json')

    parser.add_argument('--base-dir', type=str,
                        default='annotations/',
                        help='Dir to the generated annotation .pt files with CWM')
    parser.add_argument('--save-path', type=str, default="coco_train_fixsize480_N3.json",
                        help='Path to save the merged annotation file')
    args = parser.parse_args()

    file_list = glob.glob(os.path.join(args.base_dir, '*', '*'))

    ann_file = '/ccn2/u/honglinc/datasets/coco/annotations/instances_train2017.json'
    with open(ann_file, 'r') as file:
        gt_json = json.load(file)

    image_id, segmentation_id = 1, 1
    image_names = []
    for file_name in file_list:
        print('processing file name', file_name)

        data = torch.load(file_name)

        for img_name, mask_list in data.items():

            for img in gt_json['images']:
                if img['file_name'] == img_name:
                    height = img['height']
                    width = img['width']
                    break

            flag = img_name not in image_names
            if flag:
                image_info = create_image_info(
                    image_id, img_name, (height, width, 3))
                output["images"].append(image_info)
                image_names.append(img_name)


            for mask in mask_list:
                # create coco-style annotation info

                if mask.sum() == 0:
                    continue
                pseudo_mask = F.interpolate(mask.float(), size=(height, width), mode='bicubic') > 0.5
                pseudo_mask = pseudo_mask[0,0].numpy()
                annotation_info = create_annotation_info(
                    segmentation_id, image_id, category_info, pseudo_mask.astype(np.uint8), None)
                if annotation_info is not None:
                    output["annotations"].append(annotation_info)
                    segmentation_id += 1
            if flag:
                image_id += 1
        print(image_id, segmentation_id)

    # save annotations
    with open(args.save_path, 'w') as output_json_file:
        json.dump(output, output_json_file)
    print(f'dumping {args.save_path}')
    print("Done: {} images; {} anns.".format(len(output['images']), len(output['annotations'])))