def roi_edge_1(filename_1):
#clean_im, pts2 = main()
# for filename_1, filename_2 in zip(files_1, files_2):
#filename = 'D:\\Project-tumor-detection\\slike\\test\\roi\\canny\\.jpg'
print(filename_1)
im = cv2.imread(filename_1)
h = np.size(im, 0)
w = np.size(im, 1)
# cv2.imshow("i", im)
# cv2.waitKey(0)
PATCH_SIZE = 10 #### 10
im_2, pts2, a = main(PATCH_SIZE, im)
ret1, im_2 = cv2.threshold(im_2, 100, 255, cv2.THRESH_BINARY)
#print(pts2)
pts_new = []
for px in pts2:
#print(im_2.shape)
px_value = 0
value_list = []
#print(px)
value_1 = im_2[px[0] - 1, px[1]]
value_list.append(value_1)
#print("value_1", value_1)
value_2 = im_2[px[0] + 1, px[1]]
value_list.append(value_2)
#print("value_2", value_2)
value_3 = im_2[px[0], px[1] - 1]
value_list.append(value_3)
#print("value_3", value_3)
value_4 = im_2[px[0], px[1] + 1]
value_list.append(value_4)
#print("value_4", value_4)
for k in value_list:
px_value += k
#print("px_value", px_value)
if px_value >= 255 * 3: ###########>
pts_new.append(px)
# print("px", px)
#print("pts_new", pts_new)
clean_img = CleanImage(im_2, pts_new)
# cv2.imwrite(filename, clean_img)
# cv2.imshow("clean", clean_img)
# cv2.waitKey(0)
print("curve_fit")
# for value_list in pts_new:
# x_coord = value_list[0]
# y_coord = value_list[1]
# plt.plot(x_coord, y_coord, 'ro', ms='5')
#plt.show()
center = centroid(pts_new)
new_pts = calc_angle(pts_new, center)
########## REGRESSION #############
# POLYFIT SPLINE ##########
print(clean_img.shape)
xP, yP = curve_fit(new_pts)
print("join edge")
for x, y in zip(xP, yP):
if x <= h and y <= w:
clean_img[int(x)][int(y)] = 255
# cv2.imwrite(filename_2, clean_img)
cv2.imshow("join edge", clean_img)
cv2.waitKey(0)
cv2.destroyAllWindows()
return clean_img
def segmentation(filenames_1, filenames_2, filenames_3):
"""segment images in folder filenames_1, save mask in folder filenames_2 and save roi for each image
in folder filenames_3 """
for files_1, files_2, files_3 in zip(filenames_1, filenames_2,
filenames_3):
print(files_1)
### dobijanje 1. regiona
img = cv2.imread(files_1, 0)
img_original = cv2.imread(files_1)
### EDGE
edges = canny_img(img)
# cv2.imshow("i1",img)
# cv2.waitKey(0)
### POLYGON
PATCH_SIZE = 10 #### 10
img_edge, pts2, a = main(PATCH_SIZE, edges)
### HOUGH TRANSFORMATION
lines = StraightLineDetection(img, img_edge) #original?
### INTERPOLATION
interpolated = roi_edge_2(lines, img_original)
mask_binary = fill_area(interpolated)
# cv2.imshow("i1",im)
# cv2.waitKey(0)
## cv2.imwrite(files_2, mask_binary)
new_img = mask(mask_binary, img_original)
# cv2.imwrite(files_2, new_img)
### dobijanje 2. regiona
# new_img = cv2.imread(files_2,0)
edges_2 = canny_img(new_img) #edge_tumor
PATCH_SIZE = 10 #### 10
img_edge_2, pts2, a = main(PATCH_SIZE, edges_2)
lines_2 = StraightLineDetection(img_edge, img_edge)
# # cv2.imshow("i", lines_2)
# # cv2.waitKey(0)
interpolated_2 = roi_edge_2(lines_2, img_original)
mask_binary_2 = fill_area(interpolated_2)
### SAVE MASK ###
cv2.imwrite(files_2, mask_binary_2)
########### DILATION ########
# mask_binary_2 = dilation_func(mask_binary_2)
# cv2.imwrite(files_3, mask_binary_2)
# new_img_2 = mask(mask_binary_2, im)
# mask_binary_2 = cv2.imread(files_3,0)
new_img_2 = mask(mask_binary_2, img_original)
cv2.imwrite(files_3, new_img_2)
return
# cv2.imshow("image1", original_img)
# cv2.waitKey(0)
return image
if __name__ == "__main__":
# filename = 'D:\\Project-tumor-detection\\slike\\test\\roi\\canny\\131.jpg'
# image = roi_edge_2(filename)
# image = get_contours(image, image)
# image = fill_area(image)
files_1 = glob.glob(
'D:\\Project-tumor-detection\\slike\\training&test set-edge\\*.jpg')
files_2 = glob.glob(
'D:\\Project-tumor-detection\\slike\\training&test set\\*.jpg')
files_3 = glob.glob(
'D:\\Project-tumor-detection\\slike\\training&test set-edge\\roi\\*.jpg'
)
for filename_1, filename_2, filename_3 in zip(files_1, files_2, files_3):
img = cv2.imread(filename_1, 0)
PATCH_SIZE = 10 #### 10
img, pts2, a = main(PATCH_SIZE, img)
original_img = cv2.imread(filename_2)
im = roi_edge_2(img, original_img)
#cv2.imwrite(filename_2, im)
im = fill_area(im)
cv2.imwrite(filename_3, im)
)
for files_1, files_2, files_3, write_1, write_2, write_3 in zip(
filename_1, filename_2, filename_3, write_edge, write_hough,
write_masks):
img = cv2.imread(files_1, 0)
# img2 = cv2.imread("D:\\Project-tumor-detection\\slike\\test\\hough-polygon\\*.jpeg")
original_img = cv2.imread(files_2, 0)
original_img2 = cv2.imread(files_3)
img_2 = mask(img, original_img)
edge = edge_tumor(img_2)
PATCH_SIZE = 30 #### 10
cv2.imwrite(write_1, edge)
# try only edges
edge, pts2, a = main(PATCH_SIZE, edge)
img = StraightLineDetection(original_img, edge)
cv2.imwrite(write_2, img)
im = roi_edge_2(img, original_img2)
im = get_contours(im, im)
# roi_edge_1(edge)
im = fill_area(im)
cv2.imwrite(write_3, im)
im3 = mask(im, original_img)
cv2.destroyAllWindows()
from skimage.segmentation import active_contour
import cv2
from polygon_correct2 import main
img = cv2.imread(
'D:\\Project-tumor-detection\\slike\\test\\edge-operators\\canny\\age 40, m.jpeg',
0)
#img = rgb2gray(img)
# s = np.linspace(0, 2*np.pi, 400)
# x = 920 + 100*np.cos(s)
# y = 500 + 100*np.sin(s)
# init = np.array([x, y]).T
PATCH_SIZE = 50 #### 10
im_2, pts2, array_hull = main(PATCH_SIZE, img)
init = []
for i in array_hull:
init.append([i[0], i[1]])
print(init)
snake = active_contour(gaussian(img, 3),
init,
alpha=0.015,
beta=10,
gamma=0.001)
print(snake)
# fig, ax = plt.subplots(figsize=(7, 7))
# ax.imshow(img, cmap=plt.cm.gray)
# #ax.plot(init[:, 0], init[:, 1], '--r', lw=3)