def first_the_first_level_rect(crop_length, x_box_length, y_box_length):
"""
:param crop_length:
:param x_box_length:
:param y_box_length:
:return: the min block top left point index from the ORIGINAL image
"""
off_set_y = 200
off_set_x = 240
rect_firt = []
crop_length
#x_box_start = 200
x_box_length
#y_box_start = 200
y_box_length
mean = np.zeros(((crop_length - x_box_length)/10, (crop_length - y_box_length)/10))
index_min_mean = np.zeros((14, 2))
real_index_min_mean = np.zeros((14, 2))
for picture in range(14):
source = 'Data\Radiographs\%02d.tif' % (picture + 1)
raw_img = cv2.imread(source, 0)
cropped_img = crop(raw_img, 1000, 500, 1000, 1000)
img = median_filter(cropped_img)
img = bilateral_filter(img)
img = top_hat_transform(img)
img = bottom_hat_transform(img)
img = sobel(img)
for i in range(0, np.shape(img)[0] - x_box_length, 10):# x range search
for j in range(0, np.shape(img)[1] - y_box_length, 10):# y range search
img_tmp = img[j: (j + y_box_length), i:(i + x_box_length)]
mean_tmp = float(np.mean(img_tmp))
mean[i/10, j/10] = mean_tmp
index_min_mean[picture] = np.where(mean == np.amin(mean, axis = (0,1)))
real_index_min_mean[picture][0] = index_min_mean[picture][0] + off_set_x
real_index_min_mean[picture][1] = index_min_mean[picture][1] + off_set_y
rect_tmp_for_show = img[int(real_index_min_mean[picture][1]): int(real_index_min_mean[picture][1] + y_box_length),
int(real_index_min_mean[picture][0]):int((real_index_min_mean[picture][0] + x_box_length))]
rect_firt.append((img[int(real_index_min_mean[picture][1]): int(real_index_min_mean[picture][1] + y_box_length),
int(real_index_min_mean[picture][0]):int((real_index_min_mean[picture][0] + x_box_length))]))
# cv2.imshow('min_mean area', rect_tmp_for_show)
#cv2.waitKey(500)
cv2.imwrite('Data\Configure\init_guess_image-%d.tif' % picture, rect_tmp_for_show)
return rect_firt, real_index_min_mean
lm = Landmarks(source).show_points()
# print lm # you can print it to check differences.
"""Initial position guess"""
ini_pos = np.array([[570, 360, 390], [620, 470, 390], [640, 570, 370],
[570, 670, 370], [640, 400, 670], [630, 480, 670],
[620, 570, 630], [640, 650, 610]])
# ini_pos = np.array([[570, 360, 390], [570, 470, 390], [570, 570, 370], [570, 670, 370], [570, 400, 670],
# [470, 500, 680], [470, 590, 630], [470, 650, 610]])
s = ini_pos[i, 0]
t = [ini_pos[i, 1], ini_pos[i, 2]]
Golden_lm = load_training_data(Nr_incisor)
Golden_lm = rescale_withoutangle(gpa(Golden_lm)[2], t, s) # Y
img = cv2.imread('Data/Radiographs/01.tif', 0)
init_guess_img = img.copy()
crop_img_ori = crop(img, 1000, 500, 1000, 1000)
crop_img_init_guess = crop(init_guess_img, 1000, 500, 1000, 1000)
crop_correct_lm = crop(init_guess_img, 1000, 500, 1000, 1000)
#=======without crop
# crop_img_ori = img
# crop_img_init_guess = init_guess_img
# crop_correct_lm = init_guess_img
"""Drawing initial guess"""
#cv2.imshow('first golden model', drawlines(crop_img_init_guess, Golden_lm))
cv2.imwrite('Data\Configure\init_guess_incisor-%d.tif' % Nr_incisor,
drawlines(crop_img_init_guess, Golden_lm))
cv2.imwrite('Data\Configure\correct_lm_incisor-%d.tif' % Nr_incisor,
drawlines(crop_correct_lm, lm))
#cv2.waitKey(0)
import numpy as np
import cv2
from Figure_denoise import crop
from pre_process import Landmarks
if __name__ == '__main__':
img_raw = cv2.imread('Data/Radiographs/01.tif', 0)
print(np.shape(img_raw))
crop_img_ori = crop(img_raw, 1000, 500, 1000, 1000) # (y, x, xx, yy)
#cv2.imshow('cropped', crop_img_ori)
# cv2.waitKey(0)
mask = np.zeros((1000, 1000), dtype='uint8')
for incisor in range(8):
Nr_incisor = incisor + 1
source = 'Data\Landmarks\c_landmarks\landmarks1-%d.txt' % Nr_incisor
lm = Landmarks(source).show_points()
# print(lm)
for i in range(np.shape(lm)[0]):
lm[i, 0] = int(lm[i, 0] - 1000)
lm[i, 1] = int(lm[i, 1] - 500)
# print lm[i, 0]
# print lm[i, 1]
for i in range(np.shape(lm)[0]):
mask[int(lm[i, 1]), int(lm[i, 0])] = 1
# for i in range(1000):# x
# for j in range(1000): # y
# if i < lm[]
for i in range(np.shape(lm)[0] - 1):
mask = cv2.line(mask, (int(lm[i, 0]), int(lm[i, 1])),
(int(lm[(i + 1), 0]), int(lm[(i + 1), 1])),
return Landmarks(np.mean(mat, axis=0))
#def evaluation(X, Goldenlm)
if __name__ == '__main__':
Nr_incisor = 1
s = 500
t = [1370, 890]
Golden_lm = load_training_data(
Nr_incisor
) # the function in util refers to the file which only contains 02-14tif.
Golden_lm = rescale_withoutangle(gpa(Golden_lm)[2], t, s)
lm_objects = load_training_data(Nr_incisor)
landmarks_pca = PCA.ASM(lm_objects)
#print(np.shape(landmarks_pca.mu_value))
landmarks_pca_value = rescale_withoutangle(landmarks_pca.mu_value, t, s)
img = cv2.imread('Data/Radiographs/01.tif', 0)
img = img.copy()
img_withlm = drawlines(img, landmarks_pca_value)
crop_img = crop(img_withlm, 1000, 500, 1000, 1000)
img = cv2.resize(crop_img, (0, 0), fx=0.5, fy=0.5)
cv2.imshow('cropped_raw', crop_img)
#sobel = sobel(crop_img)
#cv2.imshow('sobel', sobel)
#median = median_filter(crop_img)
#cv2.imshow('median', crop_img)
cv2.waitKey(0)
x_02 = np.shape(14)
x_03 = np.shape(14)
y_0 = np.shape(14)
y_1 = np.shape(14)
print np.shape(real_start_point) # since here it is right
final_middle_point = np.zeros((14, 2))
"""This is the final result of middle points"""
for picture in range(14):
final_middle_point[picture] = [real_start_point[picture, 0] + 220, real_start_point[picture, 1] + 100]
for picture in range(14):
source = 'Data\Radiographs\%02d.tif' % (picture + 1)
raw_img = cv2.imread(source, 0)
cropped_img = crop(raw_img, 1000, 500, 1000, 1000)
img = median_filter(cropped_img)
img = bilateral_filter(img)
img = top_hat_transform(img)
img = bottom_hat_transform(img)
img = sobel(img)
#cv2.circle(img, (int(final_middle_point[picture, 0]), int(final_middle_point[picture, 1])), 10, (255, 0, 0), 1)
#cv2.imshow('center circle', img)
#cv2.waitKey(500)
#cv2.imwrite('Data\Configure\init_guess_center_point-%d.tif' % picture, img)
x_length_top = 400
x_length_bot = 300
y_length = 800
for i in range(14):
x_00 = int(final_middle_point[i, 0] - (x_length_top/10)*4)