def test_rotate_and_translate(self):
rotation_angle = 30
horizontal_shift = 50
vertical_shift = 40
points = np.asarray([[100., 200.]])
test_image = self.get_test_image()
shape = test_image.shape
rotated_points = image_utils.rotate_points(
points, rotation_angle=-rotation_angle)
rotated_test_image = image_utils.rotate_image(
test_image, rotation_angle=rotation_angle)
translated_points = image_utils.translate_points(
rotated_points,
horizontal_shift=horizontal_shift,
vertical_shift=vertical_shift)
translated_test_image = image_utils.translate_image(
rotated_test_image,
horizontal_shift=horizontal_shift,
vertical_shift=vertical_shift)
scaled_points = image_utils.downscale_points(points, shape=shape)
scaled_translated_points = image_utils.downscale_points(
translated_points, shape=shape)
test_image_points = image_utils.get_image_with_points(
test_image, scaled_points)
translated_test_image_points = image_utils.get_image_with_points(
translated_test_image, scaled_translated_points)
image_utils.show_multiple_images(
[test_image_points, translated_test_image_points])
def straighten_chromosomes(chromosomes, save_dir=None, popup=None):
num_chromosome = len(chromosomes)
""" Handle popup """
popup_counter = 1
if popup is not None:
popup.set_text("0/" + str(num_chromosome) +
" chromosomes processed.")
straightened_chromosomes = list()
for idx in range(num_chromosome):
chromosome = chromosomes[idx].copy()
gray = cv2.cvtColor(chromosome, cv2.COLOR_RGB2GRAY)
_, binary = cv2.threshold(gray, 254, 255, cv2.THRESH_BINARY_INV)
# get 2 end points of the best line
x1, y1, x2, y2 = image_utils.find_best_line_hough_transform(binary)
# make the first point higher than the second point
if y1 > y2:
x1, x2 = math_utils.swap(x1, x2)
y1, y2 = math_utils.swap(y1, y2)
angle = math_utils.get_angle_between_two_points([x1, y1], [x2, y2])
straightened_chromosome = image_utils.rotate_image(
chromosome, angle + 90)
if save_dir is not None:
general_utils.create_directory(save_dir)
chromosome_with_line = cv2.line(chromosome.copy(), (x1, y1),
(x2, y2), (0, 255, 0), 2)
straightened_line = image_utils.rotate_points(
[[x1, y1], [x2, y2]], angle + 90, shape=chromosome.shape)
rotated_x1, rotated_y1 = straightened_line[0]
rotated_x2, rotated_y2 = straightened_line[1]
straightened_chromosome_with_line = cv2.line(
straightened_chromosome.copy(), (rotated_x1, rotated_y1),
(rotated_x2, rotated_y2), (0, 255, 0), 2)
""" Save images """
cv2.imwrite(save_dir + "/" + str(idx) + ".bmp",
chromosome_with_line)
cv2.imwrite(save_dir + "/" + str(idx) + "_straightened.bmp",
straightened_chromosome_with_line)
straightened_chromosomes.append(straightened_chromosome)
""" Handle popup """
if popup is not None:
popup.set_text(
str(popup_counter) + "/" + str(num_chromosome) +
" chromosomes processed.")
popup_counter += 1
return straightened_chromosomes
def test_generate_chromosome_cluster(self):
image_dir = data_dir + "/chromosome/1"
image_files = general_utils.get_all_files(image_dir)
num_chromosome = 15
contours = list()
chromosomes = list()
chosen_files = list()
# error_case = ['/home/lntk/Desktop/Karyotype/data/chromosome/1/xx_karyotype_225_0.bmp',
# '/home/lntk/Desktop/Karyotype/data/chromosome/1/xy_karyotype_213_1.bmp',
# '/home/lntk/Desktop/Karyotype/data/chromosome/1/xy_karyotype_190_1.bmp',
# '/home/lntk/Desktop/Karyotype/data/chromosome/1/xx_karyotype_064_0.bmp',
# '/home/lntk/Desktop/Karyotype/data/chromosome/1/xx_karyotype_017_0.bmp',
# '/home/lntk/Desktop/Karyotype/data/chromosome/1/xy_karyotype_055_0.bmp',
# '/home/lntk/Desktop/Karyotype/data/chromosome/1/xy_karyotype_208_0.bmp',
# '/home/lntk/Desktop/Karyotype/data/chromosome/1/xy_karyotype_266_1.bmp',
# '/home/lntk/Desktop/Karyotype/data/chromosome/1/xy_karyotype_132_0.bmp',
# '/home/lntk/Desktop/Karyotype/data/chromosome/1/xy_karyotype_233_1.bmp']
for _ in range(num_chromosome):
""" Randomly get chromosome image from directory """
idx = np.random.randint(len(image_files))
chromosome = image_utils.read_image(image_dir + "/" +
image_files[idx])
chosen_files.append(image_dir + "/" + image_files[idx])
# for image_file in error_case:
# chromosome = image_utils.read_image(image_file)
""" Resize """
chromosome = cv2.resize(chromosome, (64, 64))
""" Rotate """
angle = np.random.randint(90)
rotated_chromosome = image_utils.rotate_image(chromosome, angle)
""" Extract contour """
contour = image_utils.get_chromosome_contour(rotated_chromosome)
contours.append(contour)
chromosomes.append(rotated_chromosome)
print(chosen_files)
contours, boxes, initial_boxes = chromosome_utils.generate_chromosome_cluster(
contours)
for contour in contours:
print(contour.shape)
# silhouette_image = chromosome_utils.get_chromosome_silhouette(contours, boxes)
cluster_image = chromosome_utils.get_chromosome_cluster_image(
boxes, initial_boxes, chromosomes)
image_utils.show_multiple_images([cluster_image])
def generate_image(image,
points=None,
rotation_angle=60,
horizontal_shift=10,
vertical_shift=10):
r = np.random.randint(rotation_angle) + 1
h = np.random.randint(horizontal_shift) + 1
v = np.random.randint(vertical_shift) + 1
new_image = image_utils.translate_image(image, h, v)
new_image = image_utils.rotate_image(new_image, r)
if points is None:
return new_image
else:
new_points = learning_utils.points_augmentation(
points, -r, h, v, image.shape)
return new_image, new_points
def data_augmentation(X,
y_skeleton,
y_clf,
rotation_angle,
horizontal_shift,
vertical_shift,
iteration=10):
new_X = list()
new_y_skeleton = list()
new_y_clf = list()
num_data = X.shape[0]
data_shape = (X.shape[1], X.shape[2])
# Add current data
for idx in range(num_data):
new_X.append(X[idx].copy())
new_y_skeleton.append(y_skeleton[idx])
new_y_clf.append(y_clf[idx])
for i in range(iteration):
for idx in range(num_data):
image = X[idx].copy()
class_id = y_clf[idx]
points = y_skeleton[idx]
# process image of X
r = np.random.randint(rotation_angle) + 1
h = np.random.randint(horizontal_shift) + 1
v = np.random.randint(vertical_shift) + 1
new_image = image_utils.translate_image(image, h, v)
new_image = image_utils.rotate_image(new_image, r)
new_X.append(new_image)
# process points of y_skeleton
new_points = points_augmentation(points, -r, h, v, data_shape)
new_y_skeleton.append(new_points)
# process class of y_clf
new_y_clf.append(class_id)
new_X = np.asarray(new_X)
new_y_skeleton = np.asarray(new_y_skeleton)
new_y_clf = np.asarray(new_y_clf)
return new_X, new_y_skeleton, new_y_clf
def test_rotate_points(self):
rotation_angle = 30
points = np.asarray([[100., 200.]])
test_image = self.get_test_image()
shape = test_image.shape
rotated_points = image_utils.rotate_points(
points, rotation_angle=rotation_angle)
rotated_test_image = image_utils.rotate_image(
test_image, rotation_angle=rotation_angle)
scaled_points = image_utils.downscale_points(points, shape=shape)
scaled_rotated_points = image_utils.downscale_points(rotated_points,
shape=shape)
test_image_points = image_utils.get_image_with_points(
test_image, scaled_points)
rotated_test_image_points = image_utils.get_image_with_points(
rotated_test_image, scaled_rotated_points)
image_utils.show_multiple_images(
[test_image_points, rotated_test_image_points])