preprocess_3D_to_2D/create_customer_datasets.py

import glob
from tqdm import tqdm
import pandas as pd

from create_annotations import *


# provide the path to the dataset. There should be train, train_mask, test, test_mask under this folder

image_size = 1024


### Load Biomed Label Base
# provide path to predefined label base
with open('label_base.json', 'r') as f:
    label_base = json.load(f)
    

# get parent class for the names
parent_class = {}
for i in label_base:
    subnames = [label_base[i]['name']] + label_base[i].get('child', [])
    for label in subnames:
        parent_class[label] = int(i)
    
# Label ids of the dataset
category_ids = {label_base[i]['name']: int(i) for i in label_base if 'name' in label_base[i]}

# Get "images" and "annotations" info 
def images_annotations_info(maskpath, keyword):
    
    imagepath = maskpath.replace('_mask', '')
    # This id will be automatically increased as we go
    annotation_id = 0
    
    sent_id = 0
    ref_id = 0
    
    annotations = []
    images = []
    image_to_id = {}
    n_total = len(glob.glob(maskpath + "*.png"))
    n_errors = 0
    
    def extra_annotation(ann, file_name, target):
        nonlocal sent_id, ref_id
        ann['file_name'] = file_name
        ann['split'] = keyword
        
        ### modality
        mod = file_name.split('.')[0].split('_')[-2]
        ### site
        site = file_name.split('.')[0].split('_')[-1]
        
        task = {'target': target, 'modality': mod, 'site': site}
        if 'T1' in mod or 'T2' in mod or 'FLAIR' in mod or 'ADC' in mod:
            task['modality'] = 'MRI'
            if 'MRI' not in mod:
                task['sequence'] = mod
            else:
                task['sequence'] = mod[4:]
            
        prompts = [f'{target} in {site} {mod}']
        
        ann['sentences'] = []
        for p in prompts:
            ann['sentences'].append({'raw': p, 'sent': p, 'sent_id': sent_id})
            sent_id += 1
        ann['sent_ids'] = [s['sent_id'] for s in ann['sentences']]
        
        ann['ann_id'] = ann['id']
        ann['ref_id'] = ref_id
        ref_id += 1
        
        return ann
    
    for mask_image in tqdm(glob.glob(maskpath + "*.png")):
        # The mask image is *.png but the original image is *.jpg.
        # We make a reference to the original file in the COCO JSON file
        filename_parsed = os.path.basename(mask_image).split("_")
        target_name = filename_parsed[-1].split(".")[0].replace("+", " ")
        
        original_file_name = "_".join(filename_parsed[:-1]) + ".png"
        
        if original_file_name not in os.listdir(imagepath):
            print("Original file not found: {}".format(original_file_name))
            n_errors += 1
            print(n_errors)
            continue
        
        if original_file_name not in image_to_id:
            image_to_id[original_file_name] = len(image_to_id)

            # "images" info 
            image_id = image_to_id[original_file_name]
            image = create_image_annotation(original_file_name, image_size, image_size, image_id)
            images.append(image)
            
        
        annotation = {
            "mask_file": os.path.basename(mask_image),
            "iscrowd": 0,
            "image_id": image_to_id[original_file_name],
            "category_id": parent_class[target_name],
            "id": annotation_id,
        }

        annotation = extra_annotation(annotation, original_file_name, target_name)
                
        annotations.append(annotation)
        annotation_id += 1
            
    #print(f"Number of errors in conversion: {n_errors}/{n_total}")
    return images, annotations, annotation_id


def create(targetpath):
    # Get the standard COCO JSON format
    coco_format = get_coco_json_format()

    for keyword in ['train', 'test']:
        mask_path = os.path.join(targetpath, "{}_mask/".format(keyword))
        
        # Create category section
        coco_format["categories"] = create_category_annotation(category_ids)
    
        # Create images and annotations sections
        coco_format["images"], coco_format["annotations"], annotation_cnt = images_annotations_info(mask_path, keyword)

        # post-process file
        images_with_ann = set()
        for ann in coco_format['annotations']:
            images_with_ann.add(ann['file_name'])
        for im in coco_format['images']:
            if im["file_name"] not in images_with_ann:
                coco_format['images'].remove(im)

        with open(os.path.join(targetpath, "{}.json".format(keyword)),"w") as outfile:
            json.dump(coco_format, outfile)
        
        print("Created %d annotations for %d images in folder: %s" % (annotation_cnt, len(coco_format['images']), mask_path))