#!/bin/env python import numpy as np import json import os import numpy as np from PIL import Image from multiprocessing import Pool, cpu_count from urllib.parse import unquote from datetime import datetime import pandas as pd import os import tempfile import argparse import glob class InputStream: def __init__(self, data): self.data = data self.i = 0 def read(self, size): out = self.data[self.i : self.i + size] self.i += size return int(out, 2) def access_bit(data, num): """from bytes array to bits by num position""" base = int(num // 8) shift = 7 - int(num % 8) return (data[base] & (1 << shift)) >> shift def bytes2bit(data): """get bit string from bytes data""" return ''.join([str(access_bit(data, i)) for i in range(len(data) * 8)]) def decode_rle(rle, print_params: bool = False): """from LS RLE to numpy uint8 3d image [width, height, channel] Args: print_params (bool, optional): If true, a RLE parameters print statement is suppressed """ input = InputStream(bytes2bit(rle)) num = input.read(32) word_size = input.read(5) + 1 rle_sizes = [input.read(4) + 1 for _ in range(4)] if print_params: print( 'RLE params:', num, 'values', word_size, 'word_size', rle_sizes, 'rle_sizes' ) i = 0 out = np.zeros(num, dtype=np.uint8) while i < num: x = input.read(1) j = i + 1 + input.read(rle_sizes[input.read(2)]) if x: val = input.read(word_size) out[i:j] = val i = j else: while i < j: val = input.read(word_size) out[i] = val i += 1 return out def log(message): timestamp = datetime.now().strftime('%Y-%m-%d %H:%M:%S') print(f"[{timestamp}] {message}") def save_image(mask_image: Image.Image, save_path: str): mask_image.save(save_path, format='PNG') log(f'Saved mask: {save_path}') def process_files_in_parallel(files_to_process, masks_save_directory, source_files): with Pool(processes=cpu_count()//2) as pool: results = pool.starmap(process_file, [(file, masks_save_directory, source_files) for file in files_to_process]) return [e for r in results for e in r] def process_file(file_path, masks_save_directory, source_files): log(f"Opening file: {file_path}") total_metadata = [] try: with open(file_path, 'r') as file: data = json.load(file) except Exception as e: log(f'Error reading file {file_path}: {e}') return total_metadata image_name = data['task']['data']['image'].split('/')[-1] if image_name not in source_files: log(f"Requested file {image_name} does not exist in source data!") return total_metadata image_name_prefix = unquote(image_name.rsplit('.', 1)[0]) log(f"Processing image: {image_name_prefix}") label_counts = {} for result in data['result']: if 'rle' not in result['value']: log(f"No 'rle' key found in result: {result.get('id', 'Unknown ID')}") continue rle_data = result['value']['rle'] rle_bytes = bytes.fromhex(''.join(format(x, '02x') for x in rle_data)) mask = decode_rle(rle_bytes) original_height = result['original_height'] original_width = result['original_width'] mask = mask.reshape((original_height, original_width, 4)) alpha_channel = mask[:, :, 3] mask_image = np.zeros((original_height, original_width, 3), dtype=np.uint8) mask_image[alpha_channel == 255] = [255, 255, 255] if 'brushlabels' in result['value']: for label in result['value']['brushlabels']: label_counts[label] = label_counts.get(label, 0) + 1 save_path = os.path.join(masks_save_directory, f"{image_name_prefix}-{label}-{label_counts[label]}.png") save_image(Image.fromarray(mask_image).convert('L'), save_path) metadata = { "original_height": result['original_height'], "original_width": result['original_width'], "image": os.path.join('sourcedata/labeled/', os.path.basename(data['task']['data']['image'])), "score": result['score'] if 'score' in result.keys() else 0, "mask": save_path, "class": label, } total_metadata.append(metadata) return total_metadata def merge_file_masks(mask_info, target_mask_dir, label2id, img): final_mask = np.zeros( np.asarray(Image.open(mask_info['mask'].iloc[0])).shape, dtype=np.uint8) for i, r in mask_info.iterrows(): mask = np.asarray(Image.open(r['mask'])) final_mask = np.where(mask == 0, final_mask, label2id[r['class']]) mask_path = os.path.join(target_mask_dir, f"{os.path.basename(img).split('.')[0]}_mask.png") Image.fromarray(final_mask).convert('L').save(mask_path, format='PNG') return { 'mask': mask_path, 'image': img, 'original_height': r['original_height'], 'original_width': r['original_width'] } def merge_masks(mask_metadata, target_mask_dir, label2id): new_metadata = [] imgs = [ ( mask_metadata[mask_metadata['image'] == img], target_mask_dir, label2id, img ) for img in mask_metadata['image'].unique()] with Pool(processes=cpu_count()//2) as pool: new_metadata = pool.starmap(merge_file_masks, imgs) return new_metadata def main(): parser = argparse.ArgumentParser('maskconvert') parser.add_argument('dataset_root') arguments = parser.parse_args() annotations_folder_path = os.path.join(arguments.dataset_root, 'labels_raw') tmp_mask_path = tempfile.mkdtemp('masks') files_to_process = glob.glob(f"{annotations_folder_path}/*") # For sanity check source_files = [os.path.basename(name) for name in glob.glob(f"sourcedata/**/*.jpg")] metadata = pd.DataFrame(process_files_in_parallel(files_to_process, tmp_mask_path, source_files)) id2label = {int(k): v for k, v in enumerate(['void', 'Fruit', 'Leaf', 'Flower', 'Stem'])} label2id = {v: k for k, v in id2label.items()} result = merge_masks(metadata, os.path.join(arguments.dataset_root, 'semantic_masks'), label2id) result = pd.DataFrame(result).drop_duplicates() result.to_csv( os.path.join(arguments.dataset_root, 'semantic_metadata.csv'), index=False) if __name__ == '__main__': main()