Create NPZ file for training (#99)

* npz build function

* added deepcell-toolbox

* padding options

* correct function name

* update for cropping generator

* Update requirements.txt

* Update caliban_toolbox/build.py

* Update caliban_toolbox/build.py

caliban_toolbox/build.py

@@ -0,0 +1,112 @@
+# 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 math
+
+import numpy as np
+
+from deepcell_toolbox.utils import resize, tile_image
+
+
+def pad_image_stack(images, crop_size):
+    """Pads an an array of images so that it is divisible by the specified crop_size
+
+    Args:
+        images: array of images to be cropped
+        crop_size: tuple specifying crop size
+
+    Returns:
+        np.array: padded image stack
+    """
+
+    row_len, col_len = images.shape[1:3]
+    row_crop, col_crop = crop_size
+    row_num = math.ceil(row_len / crop_size[0])
+    col_num = math.ceil(col_len / crop_size[1])
+
+    new_row_len = row_num * row_crop
+    new_col_len = col_num * col_crop
+
+    if new_row_len == row_len and new_col_len == col_len:
+        # don't need to pad
+        return images
+    else:
+        new_images = np.zeros((images.shape[0], new_row_len, new_col_len, images.shape[3]))
+        new_images[:, :row_len, :col_len, :] = images
+        return new_images
+
+
+def combine_npz_files(npz_list, resize_ratios, stride_ratio=1, final_size=(256, 256)):
+    """Take a series of NPZ files and combine together into single training NPZ
+
+    Args:
+        npz_list: list of NPZ files to combine. Currently only works on 2D static data
+        resize_ratios: ratio used to resize each NPZ if data is of different resolutions
+        stride_ratio: amount of overlap between crops (1 is no overlap, 0.5 is half crop size)
+        final_size: size of the final crops to be produced
+    Returns:
+        np.array: array containing resized and cropped data from all input NPZs
+    Raises:
+        ValueError: If resize ratios are not integers
+    """
+
+    combined_x = []
+    combined_y = []
+
+    for idx, npz in enumerate(npz_list):
+        current_x = npz['X']
+        current_y = npz['y']
+
+        # resize if needed
+        # TODO: Add tolerance to control when resizing happens
+        current_resize = resize_ratios[idx]
+        if current_resize != 1:
+            new_shape = (int(current_x.shape[1] * current_resize),
+                         int(current_x.shape[2] * current_resize))
+
+            current_x = resize(data=current_x, shape=new_shape)
+            current_y = resize(data=current_y, shape=new_shape, labeled_image=True)
+
+        # crop if needed
+        if current_x.shape[1:3] != final_size:
+
+            # pad image so that crops divide evenly
+            current_x = pad_image_stack(images=current_x, crop_size=final_size)
+            current_y = pad_image_stack(images=current_y, crop_size=final_size)
+
+            # create x and y crops
+            current_x, _ = tile_image(image=current_x, model_input_shape=final_size,
+                                      stride_ratio=stride_ratio)
+            current_y, _ = tile_image(image=current_y, model_input_shape=final_size,
+                                      stride_ratio=stride_ratio)
+
+        combined_x.append(current_x)
+        combined_y.append(current_y)
+
+    combined_x = np.concatenate(combined_x, axis=0)
+    combined_y = np.concatenate(combined_y, axis=0)
+
+    return combined_x, combined_y

caliban_toolbox/build_test.py

@@ -0,0 +1,135 @@
+# 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 numpy as np
+
+from caliban_toolbox import build
+
+
+def _make_npzs(size, num):
+    npz_list = []
+
+    for i in range(num):
+        x = np.zeros((1, ) + size + (4, ))
+        y = np.zeros((1,) + size + (1,))
+        npz = {'X': x, 'y': y}
+
+        npz_list.append(npz)
+
+    return npz_list
+
+
+def test_pad_image_stack():
+    # rows and cols both need to be modified
+    input_stack = np.zeros((2, 55, 55, 2))
+    tags = [1, 2]
+    input_stack[:, 0, 0, 0] = tags
+    crop_size = (10, 10)
+    padded_stack = build.pad_image_stack(images=input_stack, crop_size=crop_size)
+    assert padded_stack.shape == (2, 60, 60, 2)
+    assert np.all(padded_stack[:, 0, 0, 0] == tags)
+
+    # just rows need to be modified
+    input_stack = np.zeros((2, 50, 35, 2))
+    input_stack[:, 0, 0, 0] = tags
+    crop_size = (10, 10)
+    padded_stack = build.pad_image_stack(images=input_stack, crop_size=crop_size)
+    assert padded_stack.shape == (2, 50, 40, 2)
+    assert np.all(padded_stack[:, 0, 0, 0] == tags)
+
+    # neither needs to be modified
+    input_stack = np.zeros((2, 30, 50, 2))
+    input_stack[:, 0, 0, 0] = tags
+    crop_size = (10, 10)
+    padded_stack = build.pad_image_stack(images=input_stack, crop_size=crop_size)
+    assert padded_stack.shape == input_stack.shape
+    assert np.all(padded_stack[:, 0, 0, 0] == tags)
+
+
+def test_combine_npz_files():
+    # NPZ files are appropriate size and resolution
+    npz_list = _make_npzs((256, 256), 2)
+    resize_ratios = [1] * 2
+    final_size = (256, 256)
+
+    combined_npz = build.combine_npz_files(npz_list=npz_list, resize_ratios=resize_ratios,
+                                           final_size=final_size)
+
+    combined_x, combined_y = combined_npz
+
+    # check that correct number of NPZs present
+    assert combined_x.shape[0] == len(npz_list)
+
+    # check correct size of NPZs
+    assert combined_x.shape[1:3] == final_size
+
+    # NPZ files need to be cropped
+    npz_crop_list = _make_npzs((512, 512), 3)
+    resize_ratios = [1] * 3
+    final_size = (256, 256)
+
+    combined_npz = build.combine_npz_files(npz_list=npz_crop_list, resize_ratios=resize_ratios,
+                                           final_size=final_size)
+
+    combined_x, combined_y = combined_npz
+
+    # check that correct number of NPZs present
+    assert combined_x.shape[0] == len(npz_crop_list) * 4
+
+    # check correct size of NPZs
+    assert combined_x.shape[1:3] == final_size
+
+    # NPZ files need to be resized
+    npz_resize_list = _make_npzs((256, 256), 5)
+    resize_ratios = [3] * 5
+    final_size = (256, 256)
+
+    combined_npz = build.combine_npz_files(npz_list=npz_resize_list, resize_ratios=resize_ratios,
+                                           final_size=final_size)
+
+    combined_x, combined_y = combined_npz
+
+    # check that correct number of NPZs present
+    assert combined_x.shape[0] == len(npz_resize_list) * (resize_ratios[0] ** 2)
+
+    # check correct size of NPZs
+    assert combined_x.shape[1:3] == final_size
+
+    # some need to be cropped, some need to be resized
+    npz_list = npz_crop_list + npz_resize_list
+    resize_ratios = [1] * 3 + [3] * 5
+    final_size = (256, 256)
+
+    combined_npz = build.combine_npz_files(npz_list=npz_list, resize_ratios=resize_ratios,
+                                           final_size=final_size)
+
+    combined_x, combined_y = combined_npz
+
+    # check that correct number of NPZs present
+    assert combined_x.shape[0] == (len(npz_crop_list) * 4 +
+                                   len(npz_resize_list) * (resize_ratios[4] ** 2))
+
+    # check correct size of NPZs
+    assert combined_x.shape[1:3] == final_size

requirements.txt

@@ -11,3 +11,4 @@ boto3>=1.9.0
 xarray==0.13.0
 netCDF4==1.5.3
 pathlib==1.0.1
+deepcell-toolbox>=0.6.1