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reshape_data_test.py
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reshape_data_test.py
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# Copyright 2016-2020 The Van Valen Lab at the California Institute of
# Technology (Caltech), with support from the Paul Allen Family Foundation,
# Google, & National Institutes of Health (NIH) under Grant U24CA224309-01.
# All rights reserved.
#
# Licensed under a modified 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.github.com/vanvalenlab/caliban-toolbox/LICENSE
#
# The Work provided may be used for non-commercial academic purposes only.
# For any other use of the Work, including commercial use, please contact:
# vanvalenlab@gmail.com
#
# Neither the name of Caltech nor the names of its contributors may be used
# to endorse or promote products derived from this software without specific
# prior written permission.
#
# 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 tempfile
import numpy as np
import xarray as xr
from caliban_toolbox import reshape_data
from caliban_toolbox.utils import crop_utils, io_utils
from caliban_toolbox.utils.crop_utils_test import _blank_data_xr
def test_crop_multichannel_data():
# img params
fov_len, stack_len, crop_num, slice_num, row_len = 2, 1, 1, 1, 200
col_len, channel_len = 200, 1
crop_size = (50, 50)
overlap_frac = 0.2
# test only one crop
test_X_data = _blank_data_xr(fov_len=fov_len, stack_len=stack_len, crop_num=crop_num,
slice_num=slice_num, row_len=row_len, col_len=col_len,
chan_len=channel_len)
test_y_data = _blank_data_xr(fov_len=fov_len, stack_len=stack_len, crop_num=crop_num,
slice_num=slice_num, row_len=row_len, col_len=col_len,
chan_len=channel_len)
X_data_cropped, y_data_cropped, log_data = \
reshape_data.crop_multichannel_data(X_data=test_X_data,
y_data=test_y_data,
crop_size=crop_size,
overlap_frac=overlap_frac,
test_parameters=False)
expected_crop_num = len(crop_utils.compute_crop_indices(img_len=row_len,
crop_size=crop_size[0],
overlap_frac=overlap_frac)[0]) ** 2
assert (X_data_cropped.shape == (fov_len, stack_len, expected_crop_num, slice_num,
crop_size[0], crop_size[1], channel_len))
assert log_data["num_crops"] == expected_crop_num
def test_create_slice_data():
# test output shape with even division of slice
fov_len, stack_len, num_crops, num_slices, row_len, col_len, chan_len = 1, 40, 1, 1, 50, 50, 3
slice_stack_len = 4
X_data = _blank_data_xr(fov_len=fov_len, stack_len=stack_len, crop_num=num_crops,
slice_num=num_slices, row_len=row_len, col_len=col_len,
chan_len=chan_len)
y_data = _blank_data_xr(fov_len=fov_len, stack_len=stack_len, crop_num=num_crops,
slice_num=num_slices, row_len=row_len, col_len=col_len,
chan_len=chan_len)
X_slice, y_slice, slice_indices = reshape_data.create_slice_data(X_data, y_data,
slice_stack_len)
assert X_slice.shape == (fov_len, slice_stack_len, num_crops,
int(np.ceil(stack_len / slice_stack_len)),
row_len, col_len, chan_len)
def test_reconstruct_image_stack():
with tempfile.TemporaryDirectory() as temp_dir:
# generate stack of crops from image with grid pattern
(fov_len, stack_len, crop_num,
slice_num, row_len, col_len, chan_len) = 2, 1, 1, 1, 400, 400, 4
X_data = _blank_data_xr(fov_len=fov_len, stack_len=stack_len, crop_num=crop_num,
slice_num=slice_num,
row_len=row_len, col_len=col_len, chan_len=chan_len)
y_data = _blank_data_xr(fov_len=fov_len, stack_len=stack_len, crop_num=crop_num,
slice_num=slice_num,
row_len=row_len, col_len=col_len, chan_len=1)
# create image with artificial objects to be segmented
cell_idx = 1
for i in range(12):
for j in range(11):
for fov in range(y_data.shape[0]):
y_data[fov, :, :, :, (i * 35):(i * 35 + 10 + fov * 10),
(j * 37):(j * 37 + 8 + fov * 10), 0] = cell_idx
cell_idx += 1
# Crop the data
crop_size, overlap_frac = 100, 0.2
X_cropped, y_cropped, log_data = \
reshape_data.crop_multichannel_data(X_data=X_data,
y_data=y_data,
crop_size=(crop_size, crop_size),
overlap_frac=overlap_frac)
io_utils.save_npzs_for_caliban(X_data=X_cropped, y_data=y_cropped, original_data=X_data,
log_data=log_data, save_dir=temp_dir)
reshape_data.reconstruct_image_stack(crop_dir=temp_dir)
stitched_imgs = xr.open_dataarray(os.path.join(temp_dir, 'stitched_images.xr'))
# dims are the same
assert np.all(stitched_imgs.shape == y_data.shape)
# all the same pixels are marked
assert (np.all(np.equal(stitched_imgs[:, :, 0] > 0, y_data[:, :, 0] > 0)))
# there are the same number of cells
assert (len(np.unique(stitched_imgs)) == len(np.unique(y_data)))
with tempfile.TemporaryDirectory() as temp_dir:
# generate data with the corner tagged
fov_len, stack_len, crop_num, slice_num = 1, 40, 1, 1
row_len, col_len, chan_len = 50, 50, 3
slice_stack_len = 4
X_data = _blank_data_xr(fov_len=fov_len, stack_len=stack_len, crop_num=crop_num,
slice_num=slice_num,
row_len=row_len, col_len=col_len, chan_len=chan_len)
y_data = _blank_data_xr(fov_len=fov_len, stack_len=stack_len, crop_num=crop_num,
slice_num=slice_num,
row_len=row_len, col_len=col_len, chan_len=1)
# tag upper left hand corner of the label in each image
tags = np.arange(stack_len)
y_data[0, :, 0, 0, 0, 0, 0] = tags
X_slice, y_slice, slice_log_data = \
reshape_data.create_slice_data(X_data=X_data,
y_data=y_data,
slice_stack_len=slice_stack_len)
io_utils.save_npzs_for_caliban(X_data=X_slice, y_data=y_slice, original_data=X_data,
log_data={**slice_log_data}, save_dir=temp_dir,
blank_labels="include",
save_format="npz", verbose=False)
reshape_data.reconstruct_image_stack(temp_dir)
stitched_imgs = xr.open_dataarray(os.path.join(temp_dir, 'stitched_images.xr'))
assert np.all(stitched_imgs.shape == y_data.shape)
assert np.all(np.equal(stitched_imgs[0, :, 0, 0, 0, 0, 0], tags))
with tempfile.TemporaryDirectory() as temp_dir:
# generate data with both corners tagged and images labeled
(fov_len, stack_len, crop_num,
slice_num, row_len, col_len, chan_len) = 1, 8, 1, 1, 400, 400, 4
X_data = _blank_data_xr(fov_len=fov_len, stack_len=stack_len, crop_num=crop_num,
slice_num=slice_num,
row_len=row_len, col_len=col_len, chan_len=chan_len)
y_data = _blank_data_xr(fov_len=fov_len, stack_len=stack_len, crop_num=crop_num,
slice_num=slice_num,
row_len=row_len, col_len=col_len, chan_len=1)
# create image with artificial objects to be segmented
cell_idx = 1
for i in range(1, 12):
for j in range(1, 11):
for stack in range(stack_len):
y_data[:, stack, :, :, (i * 35):(i * 35 + 10 + stack * 2),
(j * 37):(j * 37 + 8 + stack * 2), 0] = cell_idx
cell_idx += 1
# tag upper left hand corner of each image with squares of increasing size
for stack in range(stack_len):
y_data[0, stack, 0, 0, :stack, :stack, 0] = 1
# Crop the data
crop_size, overlap_frac = 100, 0.2
X_cropped, y_cropped, log_data = \
reshape_data.crop_multichannel_data(X_data=X_data,
y_data=y_data,
crop_size=(crop_size, crop_size),
overlap_frac=overlap_frac)
X_slice, y_slice, slice_log_data = \
reshape_data.create_slice_data(X_data=X_cropped,
y_data=y_cropped,
slice_stack_len=slice_stack_len)
io_utils.save_npzs_for_caliban(X_data=X_slice, y_data=y_slice, original_data=X_data,
log_data={**slice_log_data, **log_data},
save_dir=temp_dir,
blank_labels="include",
save_format="npz", verbose=False)
reshape_data.reconstruct_image_stack(temp_dir)
stitched_imgs = xr.open_dataarray(os.path.join(temp_dir, 'stitched_images.xr'))
assert np.all(stitched_imgs.shape == y_data.shape)
# dims are the same
assert np.all(stitched_imgs.shape == y_data.shape)
# all the same pixels are marked
assert (np.all(np.equal(stitched_imgs[:, :, 0] > 0, y_data[:, :, 0] > 0)))
# there are the same number of cells
assert (len(np.unique(stitched_imgs)) == len(np.unique(y_data)))
# check mark in upper left hand corner of image
for stack in range(stack_len):
original = np.zeros((10, 10))
original[:stack, :stack] = 1
new = stitched_imgs[0, stack, 0, 0, :10, :10, 0]
assert np.array_equal(original > 0, new > 0)