def display_batch(X_batch, y_batch):
'''
This function displays on a window multiple images with their corresponding
labels
:param X_batch: Must be the batch image samples
:param y_batch: Must be the batch categorical labels
Returns the figure and the axis of the graph
'''
from dataset import CaptchaDataset
dataset = CaptchaDataset()
texts = dataset.labels_to_text(y_batch.argmax(axis=2))
n = X_batch.shape[0]
# Number of column subplots per row
cols = ceil(sqrt(n))
# Number of rows
rows = n // cols
if n % cols > 0:
rows += 1
# Create rows x cols subplots
fig, ax = plt.subplots(rows, cols, figsize=(8, 8))
for i in range(0, rows):
for j in range(0, cols):
if i < rows - 1 or n % cols == 0 or j < n % cols:
index = i * cols + j
plt.sca(ax[i, j])
plt.imshow(X_batch[index, :, :, 0] * 255, cmap='gray')
plt.xticks([])
plt.yticks([])
title = b''.join(texts[index]).decode()
plt.title(title)
else:
ax[i, j].set_visible(False)
plt.tight_layout()
plt.show()
return fig, ax
dataset = CaptchaDataset()
X, y = dataset.X, dataset.y
# Build the model
model = OCRModel()
# The next lines will show a bunch of captcha images & the predictions made by
# the model
indices = np.random.choice(np.arange(0, dataset.num_samples), size=9)
X_batch, y_batch = next(iter(InputFlow(X, y, batch_size=9)))
# Predict texts inside images
texts = [
''.join([char.item().decode() for char in text])
for text in dataset.labels_to_text(y_batch.argmax(axis=2))
]
texts_pred = [
''.join([char.item().decode() for char in text])
for text in model.predict_text(X_batch)
]
rows, cols = X_batch.shape[0] // 3, 3
fig, ax = plt.subplots(rows, cols, figsize=(20, 10))
for i in range(0, rows):
for j in range(0, cols):
k = i * cols + j
plt.sca(ax[i, j])
plt.imshow(X_batch[k, :, :, 0], cmap='gray')
