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#!/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() |