def image(self, tag, image, step=None):
"""Saves RGB image summary from onp.ndarray [H,W], [H,W,1], or [H,W,3].
Args:
tag: str: label for this data
image: ndarray: [H,W], [H,W,1], [H,W,3] save image in greyscale or colors/
step: int: training step
"""
image = onp.array(image)
if step is None:
step = self._step
else:
self._step = step
if len(onp.shape(image)) == 2:
image = image[:, :, onp.newaxis]
if onp.shape(image)[-1] == 1:
image = onp.repeat(image, 3, axis=-1)
image_strio = io.BytesIO()
plt.imsave(image_strio, image, format='png')
image_summary = Summary.Image(
encoded_image_string=image_strio.getvalue(),
colorspace=3,
height=image.shape[0],
width=image.shape[1])
summary = Summary(value=[Summary.Value(tag=tag, image=image_summary)])
self.add_summary(summary, step)
def plot(self, tag, mpl_plt, step=None, close_plot=True):
"""Saves matplotlib plot output to summary image.
Args:
tag: str: label for this data
mpl_plt: matplotlib stateful pyplot object with prepared plotting state
step: int: training step
close_plot: bool: automatically closes plot
"""
if step is None:
step = self._step
else:
self._step = step
fig = mpl_plt.get_current_fig_manager()
img_w, img_h = fig.canvas.get_width_height()
image_buf = io.BytesIO()
mpl_plt.savefig(image_buf, format='png')
image_summary = Summary.Image(
encoded_image_string=image_buf.getvalue(),
colorspace=4, # RGBA
height=img_h,
width=img_w)
summary = Summary(value=[Summary.Value(tag=tag, image=image_summary)])
self.add_summary(summary, step)
if close_plot:
mpl_plt.close()
def log_colorimages(tag, images, tagsuffix=''):
img = images
s = StringIO()
plt.imsave(s, img, format='png')
img_sum = Summary.Image(encoded_image_string=s.getvalue(),
height=img.shape[0],
width=img.shape[1])
return Summary(
value=[Summary.Value(tag='%s%s' % (tag, tagsuffix), image=img_sum)])
def log_images(tag, images, tagsuffix=''):
"""
log_images
Logs a list of images.
"""
def convert_to_uint8(img):
return np.uint8(img * 255)
if not type(images) == list:
img = images
s = StringIO()
Image.fromarray(convert_to_uint8(img), mode='L').save(s, 'png')
# Create an Image object
img_res = Summary.Image(encoded_image_string=s.getvalue(),
height=img.shape[0],
width=img.shape[1],
colorspace=1)
return Summary(value=[
Summary.Value(tag='%s%s' % (tag, tagsuffix), image=img_res)
])
else:
im_summaries = []
for nr, img in enumerate(images):
# Write the image to a string
s = StringIO()
Image.fromarray(convert_to_uint8(img), mode='L').save(s, 'png')
img_sum = Summary.Image(
encoded_image_string=s.getvalue(),
height=img.shape[0],
width=img.shape[1],
colorspace=1
) #https://github.com/tensorflow/tensorflow/blob/r1.3/tensorflow/core/framework/summary.proto
# Create a Summary value
im_summaries.append(
Summary.Value(tag='%s/%d%s' % (tag, nr, tagsuffix),
image=img_sum))
return Summary(value=im_summaries)
def to_summary(fig, tag):
"""
Convert a matplotlib figure ``fig`` into a TensorFlow Summary object
that can be directly fed into ``Summary.FileWriter``.
Example:
>>> fig, ax = ... # (as above)
>>> summary = to_summary(fig, tag='MyFigure/image')
>>> type(summary)
tensorflow.core.framework.summary_pb2.Summary
>>> summary_writer.add_summary(summary, global_step=global_step)
Args:
fig: A ``matplotlib.figure.Figure`` object.
tag (string): The tag name of the created summary.
Returns:
A TensorFlow ``Summary`` protobuf object containing the plot image
as a image summary.
"""
if not isinstance(tag, six.string_types):
raise TypeError("tag must be a string type")
# attach a new agg canvas
_old_canvas = fig.canvas
try:
canvas = FigureCanvasAgg(fig)
canvas.draw()
w, h = canvas.get_width_height()
# get PNG data from the figure
png_buffer = BytesIO()
canvas.print_png(png_buffer)
png_encoded = png_buffer.getvalue()
png_buffer.close()
summary_image = Summary.Image(
height=h,
width=w,
colorspace=4, # RGB-A
encoded_image_string=png_encoded)
summary = Summary(value=[Summary.Value(tag=tag, image=summary_image)])
return summary
finally:
fig.canvas = _old_canvas
def log_plot(self, tag, figure, global_step):
plot_buf = io.BytesIO()
figure.savefig(plot_buf, format='png')
plot_buf.seek(0)
img = Image.open(plot_buf)
img_ar = np.array(img)
img_summary = Summary.Image(encoded_image_string=plot_buf.getvalue(),
height=img_ar.shape[0],
width=img_ar.shape[1])
summary = Summary()
summary.value.add(tag=tag, image=img_summary)
self.writer.add_summary(summary, global_step=global_step)
self.writer.flush()
def log_image(file_writer, tensor, epoch_no, tag):
height, width, channel = tensor.shape
tensor = ((tensor + 1) * 255)
tensor = tensor.astype('uint8')
image = Image.fromarray(tensor)
import io
output = io.BytesIO()
image.save(output, format='PNG')
image_string = output.getvalue()
output.close()
tf_img = Summary.Image(height=height,
width=width,
colorspace=channel,
encoded_image_string=image_string)
summary = Summary(value=[Summary.Value(tag=tag, image=tf_img)])
file_writer.add_summary(summary, epoch_no)
file_writer.flush()
