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em_3d.py
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em_3d.py
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# Copyright 2022 The FastEstimator Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
import os
import shutil
from pathlib import Path
from typing import Optional, Tuple
import numpy as np
import tifffile
from fastestimator.dataset.numpy_dataset import NumpyDataset
from fastestimator.util.google_download_util import download_file_from_google_drive
color_mapping = {
0: [0, 0, 0, 0],
1: [0, 40, 255, 255],
2: [0, 212, 255, 255],
3: [124, 255, 121, 255],
4: [127, 0, 0, 255],
5: [255, 70, 0, 255],
6: [255, 229, 0, 255]
}
def generate_tiles(input_image, tile_size=256, overlap=128):
"""
This method to crop the image into smaller crops.
Args:
input_image: numpy array of input image
tile_size: The crop size
overlap: Overlap between two crops
Returns:
tiles: numpy array of image crops
"""
stride = tile_size - overlap
_, height, width = input_image.shape
tiles = []
for i in range(0, height, stride):
for j in range(0, width, stride):
if i + tile_size < height and j + tile_size < width:
tiles.append(input_image[:, i:i + tile_size, j:j + tile_size])
return np.array(tiles)
def get_encode_label(label_data):
"""
One hot encode the input mask
Args:
label_data: Color encoded input label
Returns:
encoded_label: one hot encoded label
"""
encoded_label = np.zeros(label_data.shape[:-1], np.uint8)
for i, value in color_mapping.items():
encoded_label[np.all(label_data == value, axis=-1)] = i
return encoded_label
def load_data(root_dir: Optional[str] = None, image_key: str = "image", label_key: str = "label",
tile: bool = True) -> Tuple[NumpyDataset, NumpyDataset]:
"""Load and return the 3d electron microscope platelet dataset.
Sourced from https://bio3d-vision.github.io/platelet-description.
Electronic Microscopy 3D cell dataset, consists of 2 3D images, one 800x800x50 and the other 800x800x24.
The 800x800x50 is used as training dataset and 800x800x24 is used for validation. If `tile` is True, then instead
of using the entire 800x800 images, the 800x800x50 is tiled into 256x256x24 tiles with an overlap of 128 producing
around 75 training images and similarly the 800x800x24 image is tiled to produce 25 validation images.
The method downloads the dataset from google drive and provides train and validation NumpyDataset.
While the dataset contains encoded value 0 as background, its omitted in the one hot encoded class label provided
by this method. Below indexes represent the labels in channel layer.
Index Class name
0 Cell
1 Mitochondria
2 Alpha granule
3 Canalicular vessel
4 Dense granule body
5 Dense granule core
Args:
root_dir: The path to store the downloaded data. When `path` is not provided,
the data will be saved into `fastestimator_data` under the user's home directory.
image_key: The key for image.
label_key: The key for label.
tile: Whether to tile the image into multiple smaller images, or to return the individual volumes directly.
Returns:
(train_data, eval_data)
"""
home = str(Path.home())
if root_dir is None:
root_dir = os.path.join(home, 'fastestimator_data', 'electronmicroscopy')
else:
root_dir = os.path.join(os.path.abspath(root_dir), 'electronmicroscopy')
os.makedirs(root_dir, exist_ok=True)
data_compressed_path = os.path.join(root_dir, 'images_and_labels_rgba.zip')
data_folder_path = os.path.join(root_dir, 'platelet-em/images')
download_file_from_google_drive('1OMVY1bkfssYdH11xuFdxv7nhqEjzCY7L', data_compressed_path)
shutil.unpack_archive(data_compressed_path, root_dir)
train_data = tifffile.imread(os.path.join(root_dir, 'platelet-em/images/50-images.tif'))
val_data = tifffile.imread(os.path.join(root_dir, 'platelet-em/images/24-images.tif'))
train_label_data = tifffile.imread(os.path.join(root_dir, 'platelet-em/labels-semantic/50-semantic.tif'))
val_label_data = tifffile.imread(os.path.join(root_dir, 'platelet-em/labels-semantic/24-semantic.tif'))
encoded_train_label = get_encode_label(train_label_data)
encoded_val_label = get_encode_label(val_label_data)
if not tile:
train_data = np.expand_dims(np.moveaxis(train_data, 0, -1), 0)
train_labels = np.expand_dims(np.moveaxis(encoded_train_label, 0, -1), 0)
train_labels = np.eye(7, dtype=np.float32)[train_labels].take(indices=range(1, 7), axis=-1)
val_data = np.expand_dims(np.moveaxis(val_data, 0, -1), 0)
val_labels = np.expand_dims(np.moveaxis(encoded_val_label, 0, -1), 0)
val_labels = np.eye(7, dtype=np.float32)[val_labels].take(indices=range(1, 7), axis=-1)
train_data = NumpyDataset({image_key: train_data, label_key: train_labels})
eval_data = NumpyDataset({image_key: val_data, label_key: val_labels})
return train_data, eval_data
train_data_slices = [train_data[0:24], train_data[13:37], train_data[26:50]]
train_label_slices = [encoded_train_label[0:24], encoded_train_label[13:37], encoded_train_label[26:50]]
training_data_tiles = np.moveaxis(
np.concatenate([generate_tiles(slice_data) for slice_data in train_data_slices], axis=0), 1, -1)
training_label_tiles = np.moveaxis(
np.concatenate([generate_tiles(slice_data) for slice_data in train_label_slices], axis=0), 1, -1)
val_data_tiles = np.moveaxis(generate_tiles(val_data), 1, -1)
val_label_tiles = np.moveaxis(generate_tiles(encoded_val_label), 1, -1)
val_label_tiles = np.eye(7, dtype=np.float32)[val_label_tiles].take(indices=range(1, 7), axis=-1)
training_label_tiles = np.eye(7, dtype=np.float32)[training_label_tiles].take(indices=range(1, 7), axis=-1)
train_data = NumpyDataset({image_key: training_data_tiles, label_key: training_label_tiles})
eval_data = NumpyDataset({image_key: val_data_tiles, label_key: val_label_tiles})
return train_data, eval_data