def save_and_visualize(images, kernel_size=15, save=True):
"""Iterates through images and saves and prints both the original images, output orientation images,
and target orientation images, in that order.
Images : a list of tuples of (input_image (3x224x224), orientation_image (2x224x224),
target_orientation_image (2x224x224))
"""
for i, (input, output, target) in enumerate(images):
check_sizes(input, output)
plt.figure(figsize=(15, 5))
plt.subplot(131)
plt.imshow(np.moveaxis(input, 0, 2))
ax = plt.gca()
ax.set_axis_off()
ax.set_title("Input")
plt.subplot(132)
ax2 = show_orientation_image(output)
ax2.set_title("Decoded orientation")
try: #if target is not None:
plt.subplot(133)
ax3 = show_orientation_image(target)
ax3.set_title("Target orientation")
except:
print("Recomputing orientation image with kernel size {}".format(
kernel_size))
filts = get_quadratures(kernel_size)
# note that we invert to get the map
target = get_orientation_map(
1 - np.mean(check_on_float_scale(input), axis=0), filts)
plt.subplot(133)
ax3 = show_orientation_image(target)
ax3.set_title("Target orientation")
plt.tight_layout()
if save:
plt.savefig("Decoded_test_image_{}.png".format(i))
#save just decoded png
print(type(output), output.shape)
output_img = convert_to_orientation_image(output)
target_img = convert_to_orientation_image(target)
# matplotlib.image.imsave('only_original_img_{}'.format(i), input)
matplotlib.image.imsave('only_decoded_img_{}.png'.format(i),
output_img)
matplotlib.image.imsave('only_target_img_{}.png'.format(i),
target_img)
plt.show()
def gen_batch_random_curved_lines(red, green, blue, num_lines_per_img):
all_inputs = list()
all_targets = list()
n_samples = 10000
for n in range(n_samples):
image_array = np.zeros((3, 224, 224))
input = add_random_curved_lines(image_array, red, green, blue,
num_lines_per_img)
target = get_orientation_map(input[0], filts)
all_inputs.append(input.reshape(-1))
all_targets.append(target.reshape(-1))
return all_inputs, all_targets