It is quite easy to do if you have the image in a memory buffer. Below, I show an example, where a pyplot is saved to a buffer and then converted to a TF image representation which is then sent to an image summary.
import io
import matplotlib.pyplot as plt
import tensorflow as tf
def gen_plot():
"""Create a pyplot plot and save to buffer."""
plt.figure()
plt.plot([1, 2])
plt.title("test")
buf = io.BytesIO()
plt.savefig(buf, format="png")
buf.seek(0)
return buf
# Prepare the plot
plot_buf = gen_plot()
# Convert PNG buffer to TF image
image = tf.image.decode_png(plot_buf.getvalue(), channels=4)
# Add the batch dimension
image = tf.expand_dims(image, 0)
# Add image summary
summary_op = tf.summary.image("plot", image)
# Session
with tf.Session() as sess:
# Run
summary = sess.run(summary_op)
# Write summary
writer = tf.train.SummaryWriter('./logs')
writer.add_summary(summary)
writer.close()
This gives the following TensorBoard visualization:
