Improved mask conversion script
Browse filesSigned-off-by: Jiri Podivin <jpodivin@gmail.com>
- utils/convert_masks.py +31 -5
utils/convert_masks.py
CHANGED
@@ -1,5 +1,8 @@
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#!/bin/env python
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import numpy as np
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import json
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import os
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@@ -13,6 +16,8 @@ import os
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import tempfile
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import argparse
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import glob
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class InputStream:
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def __init__(self, data):
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@@ -174,25 +179,46 @@ def merge_masks(mask_metadata, target_mask_dir, label2id):
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def main():
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parser = argparse.ArgumentParser('maskconvert')
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parser.add_argument('dataset_root')
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arguments = parser.parse_args()
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files_to_process = glob.glob(f"{annotations_folder_path}/*")
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#
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metadata = pd.DataFrame(process_files_in_parallel(files_to_process, tmp_mask_path, source_files))
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id2label = {int(k): v for k, v in enumerate(['void', 'Fruit', 'Leaf', 'Flower', 'Stem'])}
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label2id = {v: k for k, v in id2label.items()}
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result = merge_masks(metadata, os.path.join(arguments.dataset_root, 'semantic_masks'), label2id)
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result = pd.DataFrame(result).drop_duplicates()
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result.to_csv(
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os.path.join(arguments.dataset_root, 'semantic_metadata.csv'),
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index=False)
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#!/bin/env python
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# Mask conversion for plantorgans
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# 2024 by Jiri Podivin
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import numpy as np
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import json
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import os
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import tempfile
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import argparse
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import glob
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import shutil
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import tarfile
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class InputStream:
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def __init__(self, data):
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def main():
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parser = argparse.ArgumentParser('maskconvert')
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parser.add_argument('dataset_root', help="Root directory of the dataset repo.")
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arguments = parser.parse_args()
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# Unzip raw labels to temporary location
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tmp_raw_label_pth = tempfile.mkdtemp('_labels_raw')
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with tarfile.open(os.path.join(arguments.dataset_root, 'labels_raw.tar.gz'), mode='r:gz') as archive:
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archive.extractall(tmp_raw_label_pth)
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annotations_folder_path = os.path.join(tmp_raw_label_pth, 'labels_raw')
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tmp_mask_path = tempfile.mkdtemp('_masks')
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files_to_process = glob.glob(f"{annotations_folder_path}/*")
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# Image name -> path dict
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images = {os.path.basename(x): x for x in glob.glob('sourcedata/**/*.jpg')}
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# Image names
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source_files = [k for k in images.keys()]
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metadata = pd.DataFrame(process_files_in_parallel(files_to_process, tmp_mask_path, source_files))
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# Label map order IS important
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id2label = {int(k): v for k, v in enumerate(['void', 'Fruit', 'Leaf', 'Flower', 'Stem'])}
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label2id = {v: k for k, v in id2label.items()}
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result = merge_masks(metadata, os.path.join(arguments.dataset_root, 'semantic_masks'), label2id)
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result = pd.DataFrame(result).drop_duplicates()
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# Moving newly labeled images to right dir
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images = {os.path.basename(x): x for x in glob.glob('sourcedata/**/*.jpg')}
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for img in metadata['image']:
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if 'unlabeled' in images[img]:
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print("Moving {img} to labeled!")
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shutil.move(images[img], os.path.join('sourcedata/labeled/', img))
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result.to_csv(
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os.path.join(arguments.dataset_root, 'semantic_metadata.csv'),
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index=False)
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