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utils.py
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utils.py
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import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow import keras
import numpy as np
import io
import sklearn.metrics
from tensorboard.plugins import projector
import cv2
import os
import shutil
# https://github.com/aladdinpersson/Machine-Learning-Collection/blob/ac5dcd03a40a08a8af7e1a67ade37f28cf88db43/ML/TensorFlow/Basics/tutorial17-tensorboard/utils.py#L12-L32
def plot_to_image(figure):
"""Converts the matplotlib plot specified by 'figure' to a PNG image and
returns it. The supplied figure is closed and inaccessible after this call."""
# Save the plot to a PNG in memory.
buf = io.BytesIO()
plt.savefig(buf, format="png")
# Closing the figure prevents it from being displayed directly inside
# the notebook.
plt.close(figure)
buf.seek(0)
# Convert PNG buffer to TF image
image = tf.image.decode_png(buf.getvalue(), channels=4)
# Add the batch dimension
image = tf.expand_dims(image, 0)
return image
# https://github.com/aladdinpersson/Machine-Learning-Collection/blob/ac5dcd03a40a08a8af7e1a67ade37f28cf88db43/ML/TensorFlow/Basics/tutorial17-tensorboard/utils.py#L35-L56
def image_grid(data, labels, class_names):
# Data should be in (BATCH_SIZE, H, W, C)
assert data.ndim == 4
figure = plt.figure(figsize=(10, 10))
num_images = data.shape[0]
size = int(np.ceil(np.sqrt(num_images)))
for i in range(data.shape[0]):
plt.subplot(size, size, i + 1, title=class_names[labels[i]])
plt.xticks([])
plt.yticks([])
plt.grid(False)
# if grayscale
if data.shape[3] == 1:
plt.imshow(data[i], cmap=plt.cm.binary)
else:
plt.imshow(data[i])
return figure