镜像自地址
https://github.com/xming521/CTAI.git
已同步 2025-12-06 06:36:49 +00:00
115 行
3.5 KiB
Python
115 行
3.5 KiB
Python
import SimpleITK as sitk
|
|
import cv2
|
|
import numpy as np
|
|
|
|
from data_set.make import get_train_files
|
|
|
|
# 跑train不加第二个train
|
|
train_data_path = '../data/train/train/'
|
|
|
|
|
|
# train_data_path = '../data/CT/'
|
|
|
|
|
|
def get_roi(path):
|
|
global w
|
|
file_name = path.split('/')[-3] + '-' + path.split('/')[-1].replace('.dcm', '')
|
|
image = sitk.ReadImage(path)
|
|
image = sitk.GetArrayFromImage(image)[0, :, :]
|
|
image[image < -300] = 0
|
|
image[image > 300] = 0
|
|
img_o = image.copy()
|
|
ROI = np.zeros(image.shape, np.uint8)
|
|
slices = [image]
|
|
img = slices[int(len(slices) / 2)].copy()
|
|
img = np.uint8(img)
|
|
# kernel = np.ones((3, 3), np.uint8)
|
|
# kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1, 1))
|
|
# img = cv2.morphologyEx(img, cv2.MORPH_OPEN, kernel)
|
|
# img = cv2.dilate(img, kernel, iterations=1)
|
|
|
|
kernel = np.ones((4, 4), np.uint8)
|
|
img = cv2.dilate(img, kernel, iterations=1)
|
|
|
|
# 对图像进行阈值分割
|
|
ret, img = cv2.threshold(img, 150, 255, cv2.THRESH_BINARY_INV)
|
|
# 提取分割结果中的轮廓，并填充孔洞
|
|
im2, contours, x = cv2.findContours(img.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
|
|
area = []
|
|
for c in contours:
|
|
area.append(cv2.contourArea(c))
|
|
cparea = area.copy()
|
|
|
|
area.sort(reverse=True)
|
|
ROI_tmp = np.zeros(img.shape, np.uint8)
|
|
# for i in range(1, 10):
|
|
# # 选择最有可能的一个区域
|
|
# ROI_tmp = np.zeros(img.shape, np.uint8)
|
|
# max_idx = cparea.index(area[i])
|
|
#
|
|
# # 强度匹配 因为直接映射到dcm上不用转uint8
|
|
# cv2.drawContours(ROI_tmp, contours, max_idx, (255, 255, 255), -1)
|
|
#
|
|
# index = np.nonzero(ROI_tmp)
|
|
# mean = np.mean(img_o[index])
|
|
# std = np.std(img_o[index])
|
|
# # if mean > 90 or mean < 10 or std > 70 or std < 15:
|
|
# # continue
|
|
#
|
|
# s = pd.Series(img_o[index])
|
|
# piandu = s.skew()
|
|
# fengdu = s.kurt()
|
|
# # if piandu > 1 or fengdu < -5 or fengdu > 20:
|
|
# # continue
|
|
#
|
|
# # 生成矩
|
|
# M = cv2.moments(contours[max_idx])
|
|
#
|
|
# # 面积周长
|
|
# perimeter = cv2.arcLength(contours[max_idx], True)
|
|
# # if area[i] > 2000 or area[i] < 500 or perimeter > 250 or perimeter < 80:
|
|
# # continue
|
|
# if area[i] > 2000 or perimeter > 400 :
|
|
# continue
|
|
#
|
|
# # if area[i] > 4000 or area[i] < 500 or perimeter > 400 :
|
|
# # continue
|
|
# #
|
|
# # 椭圆拟合
|
|
# # try:
|
|
# # (x, y), (MA, ma), angle = cv2.fitEllipse(contours[max_idx])
|
|
# # if ma - MA > 25:
|
|
# # continue
|
|
# # # ellipse.append(ma-MA)
|
|
# # except:
|
|
# # continue
|
|
#
|
|
#
|
|
# # img_o[]
|
|
#
|
|
# # 加矩形框
|
|
# x, y, w, h = cv2.boundingRect(contours[max_idx])
|
|
# ROI = cv2.rectangle(ROI, (x, y), (x + w + 10, y + h + 10), (255, 255, 255), -1)
|
|
#
|
|
# # 填充
|
|
# # cv2.drawContours(ROI, contours, max_idx, (255, 255, 255), -1)
|
|
|
|
ROI_tmp[270:430, 200:300] = image[270:430, 200:300]
|
|
|
|
cv2.imshow("Image", image)
|
|
cv2.imshow("Image", ROI_tmp)
|
|
cv2.waitKey(0)
|
|
print(f"{train_data_path}ROI-{file_name}.png")
|
|
# cv2.imwrite(f"{train_data_path}ROI-{file_name}.png", ROI, [int(cv2.IMWRITE_PNG_COMPRESSION), 0])
|
|
|
|
|
|
def main():
|
|
global w
|
|
dcm_files, _ = get_train_files(train_data_path, file_type='dcm', all=False)
|
|
for i in dcm_files:
|
|
get_roi(i)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
main()
|