def visualize_image_with_mask(line_id):
'''
Function to visualize the image and the mask.
INPUT:
line_id - id of the line to visualize the masks
'''
# replace null values with '-1'
im_df = train_df.fillna('-1')
# get segmentation mask
np_mask = get_mask(line_id)
# open the image
image = Image.open(TRAIN_PATH + im_df.loc[line_id]['Image'])
# create segmentation map
segmap = SegmentationMapOnImage(np_mask, np_mask.shape, nb_classes=2)
# visualize the image and map
side_by_side = np.hstack([segmap.draw_on_image(np.asarray(image))
]).reshape(np.asarray(image).shape)
fig, ax = plt.subplots(figsize=(6, 4))
ax.axis('off')
plt.title(im_df.loc[line_id]['Label'])
ax.imshow(side_by_side)
def plot_training_images_and_masks(n_images=3):
'''
Function to plot several random images with segmentation masks.
INPUT:
n_images - number of images to visualize
'''
# get a list of images from training set
images = sorted(glob(TRAIN_PATH + '*.jpg'))
fig, ax = plt.subplots(n_images, 4, figsize=(20, 10))
# create a list of random indices
rnd_indices = [
np.random.choice(range(0, len(images))) for i in range(n_images)
]
for im in range(0, n_images):
# open image with a random index
image = Image.open(images[rnd_indices[im]])
# get segmentation masks
fish = get_mask_by_image_id(images[rnd_indices[im]], 'Fish')
flower = get_mask_by_image_id(images[rnd_indices[im]], 'Flower')
gravel = get_mask_by_image_id(images[rnd_indices[im]], 'Gravel')
sugar = get_mask_by_image_id(images[rnd_indices[im]], 'Sugar')
# draw masks on images
shape = (np.asarray(image).shape[0], np.asarray(image).shape[1])
if np.sum(fish) > 0:
segmap_fish = SegmentationMapOnImage(fish,
shape=shape,
nb_classes=2)
im_fish = np.array(segmap_fish.draw_on_image(
np.asarray(image))).reshape(np.asarray(image).shape)
else:
im_fish = np.asarray(image)
if np.sum(flower) > 0:
segmap_flower = SegmentationMapOnImage(flower,
shape=shape,
nb_classes=2)
im_flower = np.array(segmap_flower.draw_on_image(
np.asarray(image))).reshape(np.asarray(image).shape)
else:
im_flower = np.asarray(image)
if np.sum(gravel) > 0:
segmap_gravel = SegmentationMapOnImage(gravel,
shape=shape,
nb_classes=2)
im_gravel = np.array(segmap_gravel.draw_on_image(
np.asarray(image))).reshape(np.asarray(image).shape)
else:
im_gravel = np.asarray(image)
if np.sum(sugar) > 0:
segmap_sugar = SegmentationMapOnImage(sugar,
shape=shape,
nb_classes=2)
im_sugar = np.array(segmap_sugar.draw_on_image(
np.asarray(image))).reshape(np.asarray(image).shape)
else:
im_sugar = np.asarray(image)
# plot images and masks
ax[im, 0].imshow(im_fish)
ax[im, 0].axis('off')
ax[im, 0].set_title('Fish')
# plot images and masks
ax[im, 1].imshow(im_flower)
ax[im, 1].axis('off')
ax[im, 1].set_title('Flower')
# plot images and masks
ax[im, 2].imshow(im_gravel)
ax[im, 2].axis('off')
ax[im, 2].set_title('Gravel')
# plot images and masks
ax[im, 3].imshow(im_sugar)
ax[im, 3].axis('off')
ax[im, 3].set_title('Sugar')
plt.suptitle('Sample images from the train set')
plt.show()
cv2.imwrite(os.path.join('./mask_aug','aug2_'+str(k)+'_'+img.split('.')[0] + '_color_mask.png'),
segmaps_aug_i.draw(size=(h,w)))
#images_aug.append(images_aug_i)
#segmaps_aug.append(segmaps_aug_i)
# We want to generate an image of original input images and segmaps
# before/after augmentation.
# It is supposed to have five columns: (1) original image, (2) original
# image with segmap, (3) augmented image, (4) augmented
# segmap on augmented image, (5) augmented segmap on its own in.
# We now generate the cells of these columns.
if images_aug:
cells = []
for image_aug, segmap_aug in zip(images_aug, segmaps_aug):
cells.append(image) # column 1
cells.append(segmap.draw_on_image(image)) # column 2
cells.append(image_aug) # column 3
cells.append(segmap_aug.draw_on_image(image_aug)) # column 4
cells.append(segmap_aug.draw(size=image_aug.shape[:2])) # column 5
# Convert cells to grid image and save.
grid_image = ia.draw_grid(cells, cols=5)
ia.imshow(grid_image)
#imageio.imwrite(os.path.join('./test','aug'+img), grid_image)