def unify_chromosomes_parts(self, out_path):
img1 = common_operations.read_image(self.best_img1_path)
img2 = common_operations.read_image(self.best_img2_path)
if self.best_angle_2 > 90:
img2 = np.flipud(img2)
cut2 = 180 - self.best_angle_2
else:
cut2 = self.best_angle_2
cut2 //= 8
if self.img2.shape[0] > img2.shape[0] - cut2:
cut2 = max(0, self.img2.shape[0] - img2.shape[0])
if self.best_angle_1 > 90:
img1 = np.flipud(img1)
cut1 = 180 - self.best_angle_1
else:
cut1 = self.best_angle_1
cut1 //= 8
if self.img1.shape[0] > img1.shape[0] - cut1:
cut1 = max(0, self.img1.shape[0] - img1.shape[0])
print(img1.shape)
print(img2.shape)
h = int(img1.shape[0]) + int(img2.shape[0])
if h > cut1 + cut2:
h = h - cut1 - cut2
offset1, offset2 = self.__get_imgs_offset(img1, img2, cut1, cut2)
print(" Img1 Img2")
print("Shape: %s %s" % (str(img1.shape), str(img2.shape)))
print("Cut: %d %d" % (cut1, cut2))
print("Offset: %d %d" % (offset1, offset2))
w = max(img1.shape[1] + offset1, img2.shape[1] + offset2)
print("h=%d" % h)
print("w=%d" % w)
img3 = np.zeros((h, w, 3))
img3[:, :] = (255, 255, 255)
start_row_offset_for_2nd_img = img1.shape[0] - cut1
if cut1 > 0:
img3[:img1.shape[0] - cut1,
offset1:img1.shape[1] + offset1] = img1[:-cut1, :]
else:
img3[:img1.shape[0], offset1:img1.shape[1] + offset1] = img1[:, :]
start_row_offset_for_2nd_img = img1.shape[0]
if cut2 > 0:
img3[start_row_offset_for_2nd_img:,
offset2:img2.shape[1] + offset2] = img2[cut2:, :]
else:
img3[start_row_offset_for_2nd_img:,
offset2:img2.shape[1] + offset2] = img2[:, :]
cv2.imwrite(out_path, img3)
print("========================================================")
def __init__(self, input_image_path, invert=False):
self.image_path = input_image_path
self.image = common_operations.read_image(self.image_path, invert)
self.segments = list()
self.colored_image_path = self._get_colored_image_path()
self.logger = LOGGER.getChild("segmentation")
self.__color_index = 0
self.__current_color = [20, 0, 0]
self.__current_color_index_to_increment = 1
def __get_chromosome_len(self, file):
"""
:param file: straighten image file
:return: chromosome length
"""
if self.__straighten:
img = common_operations.read_image(file)
return img.shape[0]
else:
return self.__get_chromosome_len_using_curve_len(file)
def is_curved(self, image_to_check_path):
self.image_path = image_to_check_path
image = common_operations.read_image(self.image_path)
compute_projection_vector_obj = compute_projection_vector.ComputeProjectionVector(
)
h_vector = compute_projection_vector_obj.get_horizontal_projection_vector(
self.image_path)
ch_area = sum(h_vector)
total_area = image.shape[0] * image.shape[1]
proc = ch_area / total_area
if proc < 0.6:
return True
return False
def __init__(self,
orig_img_path,
colored_img_path,
segments_list,
inverted=False):
self.logger = LOGGER.getChild("segmentation")
self.orig_image_path = orig_img_path
self.colored_image_path = colored_img_path
self.segments_list: List[ChromosomeFrame] = segments_list
self.orig_image = common_operations.read_image(self.orig_image_path,
inverted)
self.colored_image = common_operations.read_image(
self.colored_image_path, inverted)
self.output_dir = os.path.join(
os.path.dirname(self.orig_image_path),
".".join(os.path.basename(self.orig_image_path).split(".")[:-1]) +
"_split")
self.individual_dir = os.path.join(self.output_dir, "individual")
if not os.path.exists(self.output_dir):
os.makedirs(self.output_dir)
if not os.path.exists(self.individual_dir):
os.makedirs(self.individual_dir)
def __get_chromosome_area(self, file):
"""
:param file: straighten image file
:return: chromosome area
"""
colored_individual_ch_file_path = self.__get_individual_colored_path_from_individual_straighten_path(
file)
img = common_operations.read_image(colored_individual_ch_file_path)
area = 0
for i in img:
for j in i:
if any(j != 0):
area += 1
return area
def get_horizontal_projection_vector(self, image_path):
self.img_path = image_path
image = common_operations.read_image(self.img_path)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
ret, img1 = cv2.threshold(gray, 245, 255, cv2.THRESH_BINARY_INV)
h_projection_vector = list()
for i in range(img1.shape[0]):
value = 0
for j in range(img1.shape[1]):
if img1[i][j] == 0:
continue
value += 1
h_projection_vector.append(value)
return h_projection_vector
def is_valid_curve(curve_path):
img = common_operations.read_image(curve_path)
ret, threshold_img = cv2.threshold(img, 245, 255, cv2.THRESH_BINARY)
k = 0
x_points, y_points = list(), list()
for i in range(threshold_img.shape[0]):
for j in range(threshold_img.shape[1]):
if all(threshold_img[i][j] == 255):
x_points.append(i)
y_points.append(j)
k += 1
if len(set(x_points)) <= img.shape[0] / VALID_CURVE_POINT_PERCENT \
or len(set(y_points)) <= img.shape[1] / VALID_CURVE_POINT_PERCENT:
return False
return True
def __compute_polynomial_function(self):
if self.__img is None:
self.__img = common_operations.read_image(self.__img_path)
ret, self.orig_threshold_img = cv2.threshold(self.__img, 245, 255,
cv2.THRESH_BINARY)
ret, self.threshold_img = cv2.threshold(self.__img, 245, 255,
cv2.THRESH_BINARY)
k = 0
for i in range(self.threshold_img.shape[0]):
for j in range(self.threshold_img.shape[1]):
if all(self.threshold_img[i][j] == 255):
self.x_points.append(i)
self.y_points.append(j)
k += 1
if len(set(self.x_points)) == 1 or len(set(self.y_points)) == 1:
self.__polynomial_function_grade = 1
self.polynomial_function = np.poly1d(
np.polyfit(self.x_points, self.y_points,
self.__polynomial_function_grade))
def straight_curved_chromosome(self, image_path, cut_point,
ready_dir_path):
self.image_path = image_path
self.ready_dir_path = ready_dir_path
self.img = common_operations.read_image(self.image_path)
self.img1 = self.img[:cut_point, :]
self.img2 = self.img[cut_point:, :]
img_dir_path = os.path.join(
os.path.dirname(self.image_path),
os.path.splitext(os.path.basename(self.image_path))[0])
img1_dir_path = os.path.join(img_dir_path, "img1")
img2_dir_path = os.path.join(img_dir_path, 'img2')
if not os.path.exists(img_dir_path):
os.makedirs(img_dir_path)
if not os.path.exists(img1_dir_path):
os.makedirs(img1_dir_path)
if not os.path.exists(img2_dir_path):
os.makedirs(img2_dir_path)
self.img1_path = os.path.join(
img1_dir_path,
os.path.splitext(os.path.basename(self.image_path))[0] + "_1" +
os.path.splitext(os.path.basename(self.image_path))[1])
self.img2_path = os.path.join(
img2_dir_path,
os.path.splitext(os.path.basename(self.image_path))[0] + "_2" +
os.path.splitext(os.path.basename(self.image_path))[1])
cv2.imwrite(self.img1_path, self.img1)
cv2.imwrite(self.img2_path, self.img2)
self.best_img1_path, self.best_angle_1 = self.straight_half_chromosome(
self.img1_path)
self.best_img2_path, self.best_angle_2 = self.straight_half_chromosome(
self.img2_path)
self.unify_chromosomes_parts(
os.path.join(self.ready_dir_path,
os.path.basename(self.image_path)))
def __trim_and_save_image(image_path, x1, x2, y1, y2):
image = common_operations.read_image(image_path)
new_image = image[y1:y2, x1:x2]
cv2.imwrite(image_path, new_image)