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utils.py
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utils.py
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import numpy as np
import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix
import itertools
import keras
from keras.preprocessing.image import ImageDataGenerator
# plots images with labels within jupyter notebook
def plots(ims, figsize=(12,6), rows=1, interp=False, titles=None):
if type(ims[0]) is np.ndarray:
ims = np.array(ims).astype(np.uint8)
if (ims.shape[-1] != 3):
ims = ims.transpose((0,2,3,1))
f = plt.figure(figsize=figsize)
cols = len(ims)//rows if len(ims) % 2 == 0 else len(ims)//rows + 1
for i in range(len(ims)):
sp = f.add_subplot(rows, cols, i+1)
sp.axis('Off')
if titles is not None:
sp.set_title(titles[i], fontsize=16)
plt.imshow(ims[i], interpolation=None if interp else 'none')
def plot_confusion_matrix(cm, classes,
normalize=False,
title='Confusion matrix',
cmap=plt.cm.Blues):
"""
This function prints and plots the confusion matrix.
Normalization can be applied by setting `normalize=True`.
"""
plt.imshow(cm, interpolation='nearest', cmap=cmap)
plt.title(title)
plt.colorbar()
tick_marks = np.arange(len(classes))
plt.xticks(tick_marks, classes, rotation=45)
plt.yticks(tick_marks, classes)
if normalize:
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
print("Normalized confusion matrix")
else:
print('Confusion matrix, without normalization')
print(cm)
thresh = cm.max() / 2.
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
plt.text(j, i, cm[i, j],
horizontalalignment="center",
color="white" if cm[i, j] > thresh else "black")
plt.tight_layout()
plt.ylabel('True label')
plt.xlabel('Predicted label')
def get_img_fit_flow(image_config, fit_smpl_size, directory, target_size, batch_size, shuffle):
'''
Sample the generators to get fit data
image_config dict holds the vars for data augmentation &
fit_smpl_size float subunit multiplier to get the sample size for normalization
directory str folder of the images
target_size tuple images processed size
batch_size str
shuffle bool
'''
if 'featurewise_std_normalization' in image_config and image_config['image_config']:
img_gen = ImageDataGenerator()
batches = img_gen.flow_from_directory(
directory=directory,
target_size=target_size,
batch_size=batch_size,
shuffle=shuffle,
)
fit_samples = np.array([])
fit_samples.resize((0, target_size[0], target_size[1], 3))
for i in range(batches.samples/batch_size):
imgs, labels = next(batches)
idx = np.random.choice(imgs.shape[0], batch_size*fit_smpl_size, replace=False)
np.vstack((fit_samples, imgs[idx]))
new_img_gen = ImageDataGenerator(**image_config)
if 'featurewise_std_normalization' in image_config and image_config['image_config']:
new_img_gen.fit(fit_samples)
return new_img_gen.flow_from_directory(
directory=directory,
target_size=target_size,
batch_size=batch_size,
shuffle=shuffle,
)