def visualize_pred(self, dataset_reader):
"""
Predict segmentation of images random selected from dataset_reader.
"""
valid_images, _ = dataset_reader.get_random_batch(
self.flags.batch_size)
feed_dict = {
self.image: valid_images,
self.keep_probability: 1.0,
self.phase_train: False
}
reconst_image, pred = self.sess.run(
[self.reconstruct_image, self.pred_annotation],
feed_dict=feed_dict)
pred = np.squeeze(pred, axis=3)
pred = utils.batch_colorize_ndarray(pred, 0, self.flags.num_class,
self.flags.cmap)[:, :, :, :3]
for itr in range(self.flags.batch_size):
print('itr', itr)
utils.save_image(valid_images[itr].astype(np.uint8),
self.flags.logs_dir,
name="inp_" + str(itr))
utils.save_image(reconst_image[itr].astype(np.uint8),
self.flags.logs_dir,
name="recon_" + str(itr))
utils.save_image(pred[itr].astype(np.uint8),
self.flags.logs_dir,
name="pred_" + str(itr))
print("Saved image: %d" % itr)
return valid_images, pred
def plot_segmentation_under_test_dir(self):
image_pattern = os.path.join(self.flags.test_dir, '*')
image_lst = glob(image_pattern)
data = []
if not image_lst:
print('No files found')
else:
test_images = np.stack([
misc.imresize(imageio.imread(file),
[self.flags.image_size, self.flags.image_size],
interp='bilinear') for file in image_lst
])
test_preds = self.predict_segmentation(test_images)
colorized_test_preds = utils.batch_colorize_ndarray(
test_preds, 0, self.flags.num_class,
self.flags.cmap)[:, :, :, :3]
for i, (imag,
pred) in enumerate(zip(test_images, colorized_test_preds)):
fig, axes = plt.subplots(1, 2)
axes[0].imshow(imag)
axes[1].imshow(pred)
axes[0].axis('off')
axes[1].axis('off')
filename = os.path.join(self.flags.logs_dir,
'Figure_%d.png' % i)
plt.savefig(filename, dpi=300, format="png", transparent=False)
# plt.show()
return test_images, test_preds
def plot_segmentation_on_test(self, test_dataset_reader):
if not test_dataset_reader:
print('No test data found')
else:
test_images = test_dataset_reader.images
test_gts = test_dataset_reader.annotations
test_preds = self.predict_segmentation(test_images)
colorized_test_preds = utils.batch_colorize_ndarray(test_preds,
0, self.flags.num_class, self.flags.cmap)[:,:,:,:3]
for i, (imag, pred, gt) in enumerate(zip(test_images, colorized_test_preds, test_gts)):
fig, axes = plt.subplots(1,3)
axes[0].imshow(imag)
axes[1].imshow(pred)
axes[2].imshow(gts)
axes[0].axis('off')
axes[1].axis('off')
axes[2].axis('off')
axes[0].set_title('IMG')
axes[1].set_title('PRED')
axes[2].set_title('GT')
filename = os.path.join(self.flags.logs_dir, 'Figure_%d.png'%i)
plt.savefig(filename, dpi=300, format="png", transparent=False)
# plt.show()
return test_images, test_preds
def visaulize_pred(self, dataset_reader):
"""
Predict segmentation of images random selected from dataset_reader.
"""
valid_images, valid_annotations = dataset_reader.get_random_batch(
self.flags.batch_size)
feed_dict = {
self.image: valid_images,
self.keep_probability: 1.0,
self.phase_train: False
}
pred = self.sess.run([self.pred_annotation], feed_dict=feed_dict)
pred = np.squeeze(pred, axis=3)
pred = utils.batch_colorize_ndarray(pred, 0, self.flags.num_class,
self.flags.cmap)[:, :, :, :3]
valid_annotations = np.argmax(valid_annotations, axis=-1)
valid_annotations = utils.batch_colorize_ndarray(
valid_annotations, 0, self.flags.num_class,
self.flags.cmap)[:, :, :, :3]
for itr in range(self.flags.batch_size):
utils.save_image(valid_images[itr].astype(np.uint8),
self.flags.logs_dir,
name="inp_" + str(5 + itr),
mean=1)
utils.save_image(valid_annotations[itr].astype(np.uint8),
self.flags.logs_dir,
name="gt_" + str(5 + itr),
mean=1)
utils.save_image(pred[itr].astype(np.uint8),
self.flags.logs_dir,
name="pred_" + str(5 + itr),
mean=1)
print("Saved image: %d" % itr)
return valid_images, pred
