def level_loop(cls, mask):
# 연부조직 레벨링
for i in range(0, 1):
level = i + (-300)
level_mask = magic_wand.soft_tissue(mask, level)
title = f'{level}'
cv.imshow(title, level_mask)
cv.waitKey(0)
cv.destroyAllWindows()
def level_loop(self, cv_image):
# roi 리스트 초기화
self.roi_list.clear()
# 레벨링
for i in range(0, self.level_range):
# 연부조직 level
level = i + self.level_start
# 이미지 레벨링
level_image = magic_wand.soft_tissue(cv_image, level,
self.level_window,
self.level_bone,
self.level_unit)
# 이미지 광역 쓰레숄드
_, thresh = cv.threshold(level_image, 0, 255, 0)
contours, _ = cv.findContours(thresh, cv.RETR_TREE,
cv.CHAIN_APPROX_SIMPLE)
for cnt in contours:
x, y, w, h = cv.boundingRect(cnt)
# 관심 영역 데이터를 객체에 넣고
roi = Roi()
roi.rect_level = level
roi.rect_start_x = x
roi.rect_start_y = y
roi.rect_width = w
roi.rect_height = h
roi.center()
roi.dimensions()
roi.position_list = cnt
# 관심 영역 사각형 표시
# level_image = cv.rectangle(level_image, (x, y), (x + w, y + h), (255, 255, 255), 1)
# 리스트로 보관
self.roi_list.append(roi)
return self.roi_list
def check_result(self, array, cv_image, index):
level = array[1]
roi: Roi = array[2]
level_image = magic_wand.soft_tissue(cv_image, level)
rect_image = cv.rectangle(level_image,
(roi.rect_start_x, roi.rect_start_y),
(roi.rect_start_x + roi.rect_width,
roi.rect_start_y + roi.rect_height),
(255, 255, 255), 1)
view_image = cv.rectangle(cv_image,
(roi.rect_start_x, roi.rect_start_y),
(roi.rect_start_x + roi.rect_width,
roi.rect_start_y + roi.rect_height),
(255, 255, 255), 1)
mask_group = (rect_image, index)
view_group = (view_image, index)
self.mask_list.append(mask_group)
self.best_list.append(view_group)
def level_loop(self, cv_image):
# roi 리스트 초기화
self.roi_list.clear()
# 연부조직 레벨링
for i in range(0, 100):
level = i + (-250)
# 이미지 레벨링
level_image = magic_wand.soft_tissue(cv_image, level)
# 이미지 쓰레숄드
ret, thresh = cv.threshold(level_image, 0, 255, 0)
contours, hierarchy = cv.findContours(thresh, cv.RETR_TREE,
cv.CHAIN_APPROX_SIMPLE)
for obj in contours:
cnt = obj
x, y, w, h = cv.boundingRect(cnt)
# 관심 영역 사각형 표시
level_image = cv.rectangle(level_image, (x, y), (x + w, y + h),
(255, 255, 255), 1)
# 관심 영역 데이터를 객체에 넣고
roi = Roi()
roi.rect_level = level
roi.rect_start_x = x
roi.rect_start_y = y
roi.rect_width = w
roi.rect_height = h
roi.center()
roi.dimensions()
# 리스트로 보관
self.roi_list.append(roi)
return self.roi_list
def loop_logic(self, dicom_image, threshold_value):
# 연부조직 레벨링
for i in range(0, 1600):
level = i + (-400)
level_mask = magic_wand.soft_tissue(dicom_image, level)
title = f'{level}'
cv.imshow(title, level_mask)
cv.waitKey(0)
cv.destroyAllWindows()
# 템플릿 알고리즘을 결과 좌표를 가지고 쓰레숄드 알고리즘을 적용합니다.
rectangle_list = algorithm.template(dicom_image, self.trim_dicom)
# 지역 변수 초기화
local_count: int = 0
comparative_list = []
threshold_list = []
# 템플릿 알고리즘 결과 좌표
for obj in rectangle_list:
template_x = obj[0]
template_y = obj[1]
# 템플릿 결과 좌표의 중심 좌표
trim_h, trim_w = self.trim_dicom.shape
center_x = math.floor(template_x + (trim_w / 2))
center_y = math.floor(template_y + (trim_h / 2))
threshold_image = magic_wand.get_threshold(threshold_value,
level_mask, center_x,
center_y)
# title = f'{local_count}'
# cv.imshow(title, threshold_image)
# cv.waitKey(0)
# cv.destroyAllWindows()
# 두점사이 거리 저장
distance = point2D.dot_distance(self.save_x, self.save_y, center_x,
center_y)
comparative_list.append(
[distance, local_count, center_x, center_y])
threshold_list.append(threshold_image)
local_count = local_count + 1
# 정렬
comparative_list.sort()
best_tuple = comparative_list[0]
# tuple Parsing
self.save_x = best_tuple[2]
self.save_y = best_tuple[3]
best_index = best_tuple[1]
choice_image = threshold_list[best_index]
# 리스트 비우기
comparative_list.clear()
threshold_list.clear()
rectangle_list.clear()
# threshold_image 의 ROI 영역을 정사각형 크기로 자른다.
threshold_copy = choice_image.copy()
x, y, w, h = self.cut_square(threshold_copy)
self.trim_dicom = dicom_image[y:y + h, x:x + w]
# 빈곳 체우기 로직
result_image = algorithm.fill_blank(choice_image)
# ORB 알고리즘 적용
orb = algorithm.features_orb(choice_image, self.standard_image)
print('점수 = ', orb)
cv.imshow('result_image', result_image)
cv.waitKey(0)
cv.destroyAllWindows()
# TODO: 할일
switch = False
# if orb[0] > 2000 or orb[0] < 100:
# switch = True
return result_image, switch