镜像自地址
https://github.com/xming521/CTAI.git
已同步 2025-12-06 14:46:48 +00:00
115 行
3.5 KiB
Python
115 行
3.5 KiB
Python
import SimpleITK as sitk
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import cv2
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import numpy as np
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from data_set.make import get_train_files
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# 跑train不加第二个train
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train_data_path = '../data/train/train/'
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# train_data_path = '../data/CT/'
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def get_roi(path):
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global w
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file_name = path.split('/')[-3] + '-' + path.split('/')[-1].replace('.dcm', '')
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image = sitk.ReadImage(path)
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image = sitk.GetArrayFromImage(image)[0, :, :]
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image[image < -300] = 0
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image[image > 300] = 0
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img_o = image.copy()
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ROI = np.zeros(image.shape, np.uint8)
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slices = [image]
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img = slices[int(len(slices) / 2)].copy()
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img = np.uint8(img)
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# kernel = np.ones((3, 3), np.uint8)
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# kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1, 1))
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# img = cv2.morphologyEx(img, cv2.MORPH_OPEN, kernel)
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# img = cv2.dilate(img, kernel, iterations=1)
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kernel = np.ones((4, 4), np.uint8)
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img = cv2.dilate(img, kernel, iterations=1)
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# 对图像进行阈值分割
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ret, img = cv2.threshold(img, 150, 255, cv2.THRESH_BINARY_INV)
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# 提取分割结果中的轮廓，并填充孔洞
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im2, contours, x = cv2.findContours(img.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
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area = []
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for c in contours:
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area.append(cv2.contourArea(c))
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cparea = area.copy()
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area.sort(reverse=True)
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ROI_tmp = np.zeros(img.shape, np.uint8)
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# for i in range(1, 10):
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# # 选择最有可能的一个区域
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# ROI_tmp = np.zeros(img.shape, np.uint8)
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# max_idx = cparea.index(area[i])
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#
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# # 强度匹配 因为直接映射到dcm上不用转uint8
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# cv2.drawContours(ROI_tmp, contours, max_idx, (255, 255, 255), -1)
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#
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# index = np.nonzero(ROI_tmp)
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# mean = np.mean(img_o[index])
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# std = np.std(img_o[index])
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# # if mean > 90 or mean < 10 or std > 70 or std < 15:
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# # continue
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#
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# s = pd.Series(img_o[index])
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# piandu = s.skew()
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# fengdu = s.kurt()
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# # if piandu > 1 or fengdu < -5 or fengdu > 20:
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# # continue
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#
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# # 生成矩
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# M = cv2.moments(contours[max_idx])
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#
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# # 面积周长
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# perimeter = cv2.arcLength(contours[max_idx], True)
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# # if area[i] > 2000 or area[i] < 500 or perimeter > 250 or perimeter < 80:
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# # continue
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# if area[i] > 2000 or perimeter > 400 :
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# continue
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#
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# # if area[i] > 4000 or area[i] < 500 or perimeter > 400 :
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# # continue
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# #
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# # 椭圆拟合
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# # try:
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# # (x, y), (MA, ma), angle = cv2.fitEllipse(contours[max_idx])
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# # if ma - MA > 25:
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# # continue
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# # # ellipse.append(ma-MA)
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# # except:
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# # continue
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#
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#
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# # img_o[]
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#
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# # 加矩形框
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# x, y, w, h = cv2.boundingRect(contours[max_idx])
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# ROI = cv2.rectangle(ROI, (x, y), (x + w + 10, y + h + 10), (255, 255, 255), -1)
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#
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# # 填充
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# # cv2.drawContours(ROI, contours, max_idx, (255, 255, 255), -1)
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ROI_tmp[270:430, 200:300] = image[270:430, 200:300]
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cv2.imshow("Image", image)
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cv2.imshow("Image", ROI_tmp)
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cv2.waitKey(0)
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print(f"{train_data_path}ROI-{file_name}.png")
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# cv2.imwrite(f"{train_data_path}ROI-{file_name}.png", ROI, [int(cv2.IMWRITE_PNG_COMPRESSION), 0])
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def main():
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global w
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dcm_files, _ = get_train_files(train_data_path, file_type='dcm', all=False)
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for i in dcm_files:
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get_roi(i)
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if __name__ == '__main__':
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main()
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