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Pull #418

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Pull Request Pull Request #418: Fix tests for RetinaMask and RetinaNetDataGenerator

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/deepcell/utils/plot_utils.py

# Copyright 2016-2019 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/deepcell-tf/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.
# ==============================================================================
"""Utilities plotting data"""

from __future__ import absolute_import
from __future__ import print_function
from __future__ import division

import os

import cv2
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import animation
from tensorflow.keras import backend as K


def get_js_video(images, batch=0, channel=0, cmap='jet',
                 vmin=0, vmax=0, interval=200, repeat_delay=1000):
    """Create a JavaScript video as HTML for visualizing 3D data as a movie

    Args:
        images (numpy.array): images to display as video
        batch (int): batch number of images to plot
        channel (int): channel index to plot
        vmin (int): lower end of data range covered by colormap
        vmax (int): upper end of data range covered by colormap

    Returns:
        str: JS HTML to display video
    """
    fig = plt.figure()

    ims = []
    plot_kwargs = {
        'animated': True,
        'cmap': cmap,
    }

    if vmax == 0:
        vmax = images.max()

    # TODO: do these not work for other cmaps?
    if cmap == 'cubehelix' or cmap == 'jet':
        plot_kwargs['vmin'] = vmin
        plot_kwargs['vmax'] = vmax

    for i in range(images.shape[1]):
        im = plt.imshow(images[batch, i, :, :, channel], **plot_kwargs)
        ims.append([im])

    ani = animation.ArtistAnimation(fig, ims, interval=interval, repeat_delay=repeat_delay)
    plt.close()
    return ani.to_jshtml()


def draw_box(image, box, color, thickness=2):
    """Draws a box on an image with a given color.

    Adapted from https://github.com/fizyr/keras-retinanet.

    Args:
        image (numpy.array): The image to draw on.
        box (int[]): A list of 4 elements (x1, y1, x2, y2).
        color (int[]): The color of the box.
        thickness (int): The thickness of the lines to draw a box with.
    """
    b = np.array(box).astype(int)
    cv2.rectangle(image, (b[0], b[1]), (b[2], b[3]), color, thickness, cv2.LINE_AA)


def draw_caption(image, box, caption):
    """Draws a caption above the box in an image.

    Adapted from https://github.com/fizyr/keras-retinanet.

    Args:
        image (numpy.array): The image to draw on.
        box (int[]): A list of 4 elements (x1, y1, x2, y2).
        caption (str): String containing the text to draw.
    """
    b = np.array(box).astype(int)
    cv2.putText(image, caption, (b[0], b[1] - 10),
                cv2.FONT_HERSHEY_PLAIN, 1, (0, 0, 0), 2)
    cv2.putText(image, caption, (b[0], b[1] - 10),
                cv2.FONT_HERSHEY_PLAIN, 1, (255, 255, 255), 1)


def draw_mask(image,
              box,
              mask,
              color=[31, 0, 255],
              binarize_threshold=0.5):
    """Draws a mask in a given box.

    Args:
        image (numpy.array): Three dimensional image to draw on.
        box (int[]): Vector of at least 4 values (x1, y1, x2, y2)
            representing a box in the image.
        mask (numpy.array): A 2D float mask which will be reshaped to the size
            of the box, binarized and drawn over the image.
        color (int[]): Color to draw the mask with. If the box has 5 values,
            the last value is assumed to be the label and used to
            construct a default color.
        binarize_threshold (float): Threshold used for binarizing the mask.
    """
    # resize to fit the box
    mask = mask.astype(np.float32)
    mask = cv2.resize(mask, (box[2] - box[0], box[3] - box[1]))

    # binarize the mask
    mask = (mask > binarize_threshold).astype('uint8')

    # draw the mask in the image
    mask_image = np.zeros((image.shape[0], image.shape[1]), 'uint8')
    mask_image[box[1]:box[3], box[0]:box[2]] = mask
    mask = mask_image

    # compute a nice border around the mask
    border = mask - cv2.erode(mask, np.ones((5, 5), 'uint8'), iterations=1)

    # apply color to the mask and border
    mask = (np.stack([mask] * 3, axis=2) * color).astype('uint8')
    border = (np.stack([border] * 3, axis=2) * (255, 255, 255)).astype('uint8')

    # draw the mask
    indices = np.where(mask != [0, 0, 0])
    _mask = 0.5 * image[indices[0], indices[1], :] + \
        0.5 * mask[indices[0], indices[1], :]
    image[indices[0], indices[1], :] = _mask

    # draw the border
    indices = np.where(border != [0, 0, 0])
    _border = 0.2 * image[indices[0], indices[1], :] + \
        0.8 * border[indices[0], indices[1], :]
    image[indices[0], indices[1], :] = _border


def draw_masks(image, boxes, scores, masks,
               color=[31, 0, 255],
               score_threshold=0.5,
               binarize_threshold=0.5):
    """Draws a list of masks given a list of boxes.

    Args:
        image (numpy.array): Three dimensional image to draw on.
        boxes (int[]): Matrix of shape (N, >=4)
            (at least 4 values: (x1, y1, x2, y2)) representing boxes
            in the image.
        scores (float[]): A list of N classification scores.
        masks (numpy.array): Matrix of shape (N, H, W) of N masks of shape (H, W)
            which will be reshaped to the size of the corresponding box,
            binarized and drawn over the image.
        color (int[]): Color or to draw the masks with.
        score_threshold (float): Threshold used for determining
            the masks to draw.
        binarize_threshold (float): Threshold used for binarizing the masks.
    """
    selection = np.where(scores > score_threshold)[0]

    for i in selection:
        if not any(b == -1 for b in boxes[i]):
            draw_mask(image, boxes[i].astype(int), masks[i], color=color,
                      binarize_threshold=binarize_threshold)


def draw_detections(image,
                    boxes,
                    scores,
                    labels,
                    color=[31, 0, 255],
                    label_to_name=None,
                    score_threshold=0.5):
    """Draws detections in an image.

    Adapted from https://github.com/fizyr/keras-retinanet.

    Args:
        image (numpy.array): The image to draw on.
        boxes (int[]): A [N, 4] matrix (x1, y1, x2, y2).
        scores (float[]): A list of N classification scores.
        labels (str[]): A list of N labels.
        color (int[]): The color of the boxes.
        label_to_name (function): (optional) Functor for mapping a
            label to a name.
        score_threshold (float): Threshold used for determining
            the detections to draw.
    """
    selection = np.where(scores > score_threshold)[0]

    for i in selection:
        draw_box(image, boxes[i, :], color=color)

        # draw labels
        name = label_to_name(labels[i]) if label_to_name else labels[i]
        caption = '{0}: {1:.2f}'.format(name, scores[i])
        draw_caption(image, boxes[i, :], caption)


def draw_annotations(image,
                     annotations,
                     color=[31, 0, 255],
                     label_to_name=None):
    """Draws annotations in an image.

    Adapted from https://github.com/fizyr/keras-retinanet.

    Args:
        image (numpy.array): The image to draw on.
        annotations (numpy.array): A [N, 5] matrix (x1, y1, x2, y2, label) or
            dictionary containing bboxes (shaped [N, 4])
            and labels (shaped [N]).
        color (int[]): The color of the boxes.
        label_to_name (function): (optional) Functor for mapping a
            label to a name.
    """
    if isinstance(annotations, np.ndarray):
        annotations = {'bboxes': annotations[:, :4], 'labels': annotations[:, 4]}

    assert 'bboxes' in annotations
    assert 'labels' in annotations
    assert annotations['bboxes'].shape[0] == annotations['labels'].shape[0]

    for i in range(annotations['bboxes'].shape[0]):
        label = annotations['labels'][i]
        caption = '{}'.format(label_to_name(label) if label_to_name else label)
        draw_caption(image, annotations['bboxes'][i], caption)
        draw_box(image, annotations['bboxes'][i], color=color)


def cf(x_coord, y_coord, sample_image):
    """Format x and y coordinates for printing

    Args:
        x_coord (int): X coordinate
        y_coord (int): y coordinate
        sample_image (numpy.array): Sample image for numpy arrays

    Returns:
        str: formatted coordinates (x, y, and z).
    """
    numrows, numcols = sample_image.shape
    col = int(x_coord + 0.5)
    row = int(y_coord + 0.5)
    if 0 <= col < numcols and 0 <= row < numrows:
        z_coord = sample_image[row, col]
        return 'x=%1.4f, y=%1.4f, z=%1.4f' % (x_coord, y_coord, z_coord)
    return 'x=%1.4f, y=%1.4f' % (x_coord, y_coord)


def plot_training_data_2d(X, y, max_plotted=5):
    data_format = K.image_data_format()
    if max_plotted > y.shape[0]:
        max_plotted = y.shape[0]

    label_axis = 1 if K.image_data_format() == 'channels_first' else -1

    fig, ax = plt.subplots(max_plotted, y.shape[label_axis] + 1, squeeze=False)

    for i in range(max_plotted):
        X_i = X[i, 0, :, :] if data_format == 'channels_first' else X[i, :, :, 0]
        ax[i, 0].imshow(X_i, cmap=plt.get_cmap('gray'), interpolation='nearest')

        def form_coord(x_coord, y_coord):
            return cf(x_coord, y_coord, X_i)

        ax[i, 0].format_coord = form_coord
        ax[i, 0].axes.get_xaxis().set_visible(False)
        ax[i, 0].axes.get_yaxis().set_visible(False)

        for j in range(1, y.shape[label_axis] + 1):
            y_k = y[i, j - 1, :, :] if data_format == 'channels_first' else y[i, :, :, j - 1]
            ax[i, j].imshow(y_k, cmap=plt.get_cmap('gray'), interpolation='nearest')
            ax[i, j].axes.get_xaxis().set_visible(False)
            ax[i, j].axes.get_yaxis().set_visible(False)

    plt.show()


def plot_training_data_3d(X, y, num_image_stacks, frames_to_display=5):
    """Plot 3D training data

    Args:
        X (numpy.array): Raw 3D data
        y (numpy.array): Labels for 3D data
        num_image_stacks (int): number of independent 3D examples to plot
        frames_to_display (int): number of frames of X and y to display
    """
    data_format = K.image_data_format()
    fig, ax = plt.subplots(num_image_stacks, frames_to_display + 1, squeeze=False)

    for i in range(num_image_stacks):
        X_i = X[i, 0, :, :] if data_format == 'channels_first' else X[i, :, :, 0]
        ax[i, 0].imshow(X_i, cmap=plt.get_cmap('gray'), interpolation='nearest')

        def form_coord(x_coord, y_coord):
            return cf(x_coord, y_coord, X_i)

        ax[i, 0].format_coord = form_coord
        ax[i, 0].axes.get_xaxis().set_visible(False)
        ax[i, 0].axes.get_yaxis().set_visible(False)

        for j in range(frames_to_display):
            y_j = y[i, j, :, :] if data_format == 'channels_first' else y[i, :, :, j]
            ax[i, j + 1].imshow(y_j, cmap=plt.get_cmap('gray'), interpolation='nearest')
            ax[i, j + 1].axes.get_xaxis().set_visible(False)
            ax[i, j + 1].axes.get_yaxis().set_visible(False)
    plt.show()


def plot_error(loss_hist_file, saved_direc, plot_name):
    """Plot the training and validation error from the npz file

    Args:
        loss_hist_file (str): full path to .npz loss history file
        saved_direc (str): full path to directory where the plot is saved
        plot_name (str): the name of plot
    """
    loss_history = np.load(loss_hist_file)
    loss_history = loss_history['loss_history'][()]

    err = np.subtract(1, loss_history['acc'])
    val_err = np.subtract(1, loss_history['val_acc'])

    epoch = np.arange(1, len(err) + 1, 1)
    plt.plot(epoch, err)
    plt.plot(epoch, val_err)
    plt.title('Model Error')
    plt.xlabel('Epoch')
    plt.ylabel('Model Error')
    plt.legend(['Training error', 'Validation error'], loc='upper right')

    filename = os.path.join(saved_direc, plot_name)
    plt.savefig(filename, format='pdf')

1	# Copyright 2016-2019 The Van Valen Lab at the California Institute of
2	# Technology (Caltech), with support from the Paul Allen Family Foundation,
3	# Google, & National Institutes of Health (NIH) under Grant U24CA224309-01.
4	# All rights reserved.
5	#
6	# Licensed under a modified Apache License, Version 2.0 (the "License");
7	# you may not use this file except in compliance with the License.
8	# You may obtain a copy of the License at
9	#
10	# http://www.github.com/vanvalenlab/deepcell-tf/LICENSE
11	#
12	# The Work provided may be used for non-commercial academic purposes only.
13	# For any other use of the Work, including commercial use, please contact:
14	# vanvalenlab@gmail.com
15	#
16	# Neither the name of Caltech nor the names of its contributors may be used
17	# to endorse or promote products derived from this software without specific
18	# prior written permission.
19	#
20	# Unless required by applicable law or agreed to in writing, software
21	# distributed under the License is distributed on an "AS IS" BASIS,
22	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
23	# See the License for the specific language governing permissions and
24	# limitations under the License.
25	# ==============================================================================
26	"""Utilities plotting data"""	4✔
27
28	from __future__ import absolute_import	4✔
29	from __future__ import print_function	4✔
30	from __future__ import division	4✔
31
32	import os	4✔
33
34	import cv2	4✔
35	import numpy as np	4✔
36	import matplotlib.pyplot as plt	4✔
37	from matplotlib import animation	4✔
38	from tensorflow.keras import backend as K	4✔
39
40
41	def get_js_video(images, batch=0, channel=0, cmap='jet',	4✔
42	vmin=0, vmax=0, interval=200, repeat_delay=1000):
43	"""Create a JavaScript video as HTML for visualizing 3D data as a movie
44
45	Args:
46	images (numpy.array): images to display as video
47	batch (int): batch number of images to plot
48	channel (int): channel index to plot
49	vmin (int): lower end of data range covered by colormap
50	vmax (int): upper end of data range covered by colormap
51
52	Returns:
53	str: JS HTML to display video
54	"""
55	fig = plt.figure()	×
56
57	ims = []	×
58	plot_kwargs = {	×
59	'animated': True,
60	'cmap': cmap,
61	}
62
63	if vmax == 0:	×
64	vmax = images.max()	×
65
66	# TODO: do these not work for other cmaps?
67	if cmap == 'cubehelix' or cmap == 'jet':	×
68	plot_kwargs['vmin'] = vmin	×
69	plot_kwargs['vmax'] = vmax	×
70
71	for i in range(images.shape[1]):	×
72	im = plt.imshow(images[batch, i, :, :, channel], **plot_kwargs)	×
73	ims.append([im])	×
74
75	ani = animation.ArtistAnimation(fig, ims, interval=interval, repeat_delay=repeat_delay)	×
76	plt.close()	×
77	return ani.to_jshtml()	×
78
79
80	def draw_box(image, box, color, thickness=2):	4✔
81	"""Draws a box on an image with a given color.
82
83	Adapted from https://github.com/fizyr/keras-retinanet.
84
85	Args:
86	image (numpy.array): The image to draw on.
87	box (int[]): A list of 4 elements (x1, y1, x2, y2).
88	color (int[]): The color of the box.
89	thickness (int): The thickness of the lines to draw a box with.
90	"""
91	b = np.array(box).astype(int)	×
92	cv2.rectangle(image, (b[0], b[1]), (b[2], b[3]), color, thickness, cv2.LINE_AA)	×
93
94
95	def draw_caption(image, box, caption):	4✔
96	"""Draws a caption above the box in an image.
97
98	Adapted from https://github.com/fizyr/keras-retinanet.
99
100	Args:
101	image (numpy.array): The image to draw on.
102	box (int[]): A list of 4 elements (x1, y1, x2, y2).
103	caption (str): String containing the text to draw.
104	"""
105	b = np.array(box).astype(int)	×
106	cv2.putText(image, caption, (b[0], b[1] - 10),	×
107	cv2.FONT_HERSHEY_PLAIN, 1, (0, 0, 0), 2)
108	cv2.putText(image, caption, (b[0], b[1] - 10),	×
109	cv2.FONT_HERSHEY_PLAIN, 1, (255, 255, 255), 1)
110
111
112	def draw_mask(image,	4✔
113	box,
114	mask,
115	color=[31, 0, 255],
116	binarize_threshold=0.5):
117	"""Draws a mask in a given box.
118
119	Args:
120	image (numpy.array): Three dimensional image to draw on.
121	box (int[]): Vector of at least 4 values (x1, y1, x2, y2)
122	representing a box in the image.
123	mask (numpy.array): A 2D float mask which will be reshaped to the size
124	of the box, binarized and drawn over the image.
125	color (int[]): Color to draw the mask with. If the box has 5 values,
126	the last value is assumed to be the label and used to
127	construct a default color.
128	binarize_threshold (float): Threshold used for binarizing the mask.
129	"""
130	# resize to fit the box
131	mask = mask.astype(np.float32)	×
132	mask = cv2.resize(mask, (box[2] - box[0], box[3] - box[1]))	×
133
134	# binarize the mask
135	mask = (mask > binarize_threshold).astype('uint8')	×
136
137	# draw the mask in the image
138	mask_image = np.zeros((image.shape[0], image.shape[1]), 'uint8')	×
139	mask_image[box[1]:box[3], box[0]:box[2]] = mask	×
140	mask = mask_image	×
141
142	# compute a nice border around the mask
143	border = mask - cv2.erode(mask, np.ones((5, 5), 'uint8'), iterations=1)	×
144
145	# apply color to the mask and border
146	mask = (np.stack([mask] * 3, axis=2) * color).astype('uint8')	×
147	border = (np.stack([border] * 3, axis=2) * (255, 255, 255)).astype('uint8')	×
148
149	# draw the mask
150	indices = np.where(mask != [0, 0, 0])	×
151	_mask = 0.5 * image[indices[0], indices[1], :] + \	×
152	0.5 * mask[indices[0], indices[1], :]
153	image[indices[0], indices[1], :] = _mask	×
154
155	# draw the border
156	indices = np.where(border != [0, 0, 0])	×
157	_border = 0.2 * image[indices[0], indices[1], :] + \	×
158	0.8 * border[indices[0], indices[1], :]
159	image[indices[0], indices[1], :] = _border	×
160
161
162	def draw_masks(image, boxes, scores, masks,	4✔
163	color=[31, 0, 255],
164	score_threshold=0.5,
165	binarize_threshold=0.5):
166	"""Draws a list of masks given a list of boxes.
167
168	Args:
169	image (numpy.array): Three dimensional image to draw on.
170	boxes (int[]): Matrix of shape (N, >=4)
171	(at least 4 values: (x1, y1, x2, y2)) representing boxes
172	in the image.
173	scores (float[]): A list of N classification scores.
174	masks (numpy.array): Matrix of shape (N, H, W) of N masks of shape (H, W)
175	which will be reshaped to the size of the corresponding box,
176	binarized and drawn over the image.
177	color (int[]): Color or to draw the masks with.
178	score_threshold (float): Threshold used for determining
179	the masks to draw.
180	binarize_threshold (float): Threshold used for binarizing the masks.
181	"""
182	selection = np.where(scores > score_threshold)[0]	×
183
184	for i in selection:	×
185	if not any(b == -1 for b in boxes[i]):	×
186	draw_mask(image, boxes[i].astype(int), masks[i], color=color,	×
187	binarize_threshold=binarize_threshold)
188
189
190	def draw_detections(image,	4✔
191	boxes,
192	scores,
193	labels,
194	color=[31, 0, 255],
195	label_to_name=None,
196	score_threshold=0.5):
197	"""Draws detections in an image.
198
199	Adapted from https://github.com/fizyr/keras-retinanet.
200
201	Args:
202	image (numpy.array): The image to draw on.
203	boxes (int[]): A [N, 4] matrix (x1, y1, x2, y2).
204	scores (float[]): A list of N classification scores.
205	labels (str[]): A list of N labels.
206	color (int[]): The color of the boxes.
207	label_to_name (function): (optional) Functor for mapping a
208	label to a name.
209	score_threshold (float): Threshold used for determining
210	the detections to draw.
211	"""
212	selection = np.where(scores > score_threshold)[0]	×
213
214	for i in selection:	×
215	draw_box(image, boxes[i, :], color=color)	×
216
217	# draw labels
218	name = label_to_name(labels[i]) if label_to_name else labels[i]	×
219	caption = '{0}: {1:.2f}'.format(name, scores[i])	×
220	draw_caption(image, boxes[i, :], caption)	×
221
222
223	def draw_annotations(image,	4✔
224	annotations,
225	color=[31, 0, 255],
226	label_to_name=None):
227	"""Draws annotations in an image.
228
229	Adapted from https://github.com/fizyr/keras-retinanet.
230
231	Args:
232	image (numpy.array): The image to draw on.
233	annotations (numpy.array): A [N, 5] matrix (x1, y1, x2, y2, label) or
234	dictionary containing bboxes (shaped [N, 4])
235	and labels (shaped [N]).
236	color (int[]): The color of the boxes.
237	label_to_name (function): (optional) Functor for mapping a
238	label to a name.
239	"""
240	if isinstance(annotations, np.ndarray):	×
241	annotations = {'bboxes': annotations[:, :4], 'labels': annotations[:, 4]}	×
242
243	assert 'bboxes' in annotations	×
244	assert 'labels' in annotations	×
245	assert annotations['bboxes'].shape[0] == annotations['labels'].shape[0]	×
246
247	for i in range(annotations['bboxes'].shape[0]):	×
248	label = annotations['labels'][i]	×
249	caption = '{}'.format(label_to_name(label) if label_to_name else label)	×
250	draw_caption(image, annotations['bboxes'][i], caption)	×
251	draw_box(image, annotations['bboxes'][i], color=color)	×
252
253
254	def cf(x_coord, y_coord, sample_image):	4✔
255	"""Format x and y coordinates for printing
256
257	Args:
258	x_coord (int): X coordinate
259	y_coord (int): y coordinate
260	sample_image (numpy.array): Sample image for numpy arrays
261
262	Returns:
263	str: formatted coordinates (x, y, and z).
264	"""
265	numrows, numcols = sample_image.shape	4✔
266	col = int(x_coord + 0.5)	4✔
267	row = int(y_coord + 0.5)	4✔
268	if 0 <= col < numcols and 0 <= row < numrows:	4✔
269	z_coord = sample_image[row, col]	4✔
270	return 'x=%1.4f, y=%1.4f, z=%1.4f' % (x_coord, y_coord, z_coord)	4✔
271	return 'x=%1.4f, y=%1.4f' % (x_coord, y_coord)	4✔
272
273
274	def plot_training_data_2d(X, y, max_plotted=5):	4✔
275	data_format = K.image_data_format()	×
276	if max_plotted > y.shape[0]:	×
277	max_plotted = y.shape[0]	×
278
279	label_axis = 1 if K.image_data_format() == 'channels_first' else -1	×
280
281	fig, ax = plt.subplots(max_plotted, y.shape[label_axis] + 1, squeeze=False)	×
282
283	for i in range(max_plotted):	×
284	X_i = X[i, 0, :, :] if data_format == 'channels_first' else X[i, :, :, 0]	×
285	ax[i, 0].imshow(X_i, cmap=plt.get_cmap('gray'), interpolation='nearest')	×
286
287	def form_coord(x_coord, y_coord):	×
288	return cf(x_coord, y_coord, X_i)	×
289
290	ax[i, 0].format_coord = form_coord	×
291	ax[i, 0].axes.get_xaxis().set_visible(False)	×
292	ax[i, 0].axes.get_yaxis().set_visible(False)	×
293
294	for j in range(1, y.shape[label_axis] + 1):	×
295	y_k = y[i, j - 1, :, :] if data_format == 'channels_first' else y[i, :, :, j - 1]	×
296	ax[i, j].imshow(y_k, cmap=plt.get_cmap('gray'), interpolation='nearest')	×
297	ax[i, j].axes.get_xaxis().set_visible(False)	×
298	ax[i, j].axes.get_yaxis().set_visible(False)	×
299
300	plt.show()	×
301
302
303	def plot_training_data_3d(X, y, num_image_stacks, frames_to_display=5):	4✔
304	"""Plot 3D training data
305
306	Args:
307	X (numpy.array): Raw 3D data
308	y (numpy.array): Labels for 3D data
309	num_image_stacks (int): number of independent 3D examples to plot
310	frames_to_display (int): number of frames of X and y to display
311	"""
312	data_format = K.image_data_format()	×
313	fig, ax = plt.subplots(num_image_stacks, frames_to_display + 1, squeeze=False)	×
314
315	for i in range(num_image_stacks):	×
316	X_i = X[i, 0, :, :] if data_format == 'channels_first' else X[i, :, :, 0]	×
317	ax[i, 0].imshow(X_i, cmap=plt.get_cmap('gray'), interpolation='nearest')	×
318
319	def form_coord(x_coord, y_coord):	×
320	return cf(x_coord, y_coord, X_i)	×
321
322	ax[i, 0].format_coord = form_coord	×
323	ax[i, 0].axes.get_xaxis().set_visible(False)	×
324	ax[i, 0].axes.get_yaxis().set_visible(False)	×
325
326	for j in range(frames_to_display):	×
327	y_j = y[i, j, :, :] if data_format == 'channels_first' else y[i, :, :, j]	×
328	ax[i, j + 1].imshow(y_j, cmap=plt.get_cmap('gray'), interpolation='nearest')	×
329	ax[i, j + 1].axes.get_xaxis().set_visible(False)	×
330	ax[i, j + 1].axes.get_yaxis().set_visible(False)	×
331	plt.show()	×
332
333
334	def plot_error(loss_hist_file, saved_direc, plot_name):	4✔
335	"""Plot the training and validation error from the npz file
336
337	Args:
338	loss_hist_file (str): full path to .npz loss history file
339	saved_direc (str): full path to directory where the plot is saved
340	plot_name (str): the name of plot
341	"""
342	loss_history = np.load(loss_hist_file)	×
343	loss_history = loss_history['loss_history'][()]	×
344
345	err = np.subtract(1, loss_history['acc'])	×
346	val_err = np.subtract(1, loss_history['val_acc'])	×
347
348	epoch = np.arange(1, len(err) + 1, 1)	×
349	plt.plot(epoch, err)	×
350	plt.plot(epoch, val_err)	×
351	plt.title('Model Error')	×
352	plt.xlabel('Epoch')	×
353	plt.ylabel('Model Error')	×
354	plt.legend(['Training error', 'Validation error'], loc='upper right')	×
355
356	filename = os.path.join(saved_direc, plot_name)	×
357	plt.savefig(filename, format='pdf')	×

vanvalenlab / deepcell-tf / 2016

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