import inspect from random import random import cv2 import numpy as np import pandas as pd from numba import jit np.set_printoptions(suppress=True) # 输出时禁止科学表示法，直接输出小数值 column_all_c = ['面积', '周长', '重心x', '重心y', '似圆度', '灰度均值', '灰度方差', '灰度偏度', '灰度峰态', '小梯度优势', '大梯度优势', '灰度分布不均匀性', '梯度分布不均匀性', '能量', '灰度平均', '梯度平均', '灰度均方差', '梯度均方差', '相关', '灰度熵', '梯度熵', '混合熵', '惯性', '逆差矩'] features_list = ['area', 'perimeter', 'focus_x', 'focus_y', 'ellipse', 'mean', 'std', 'piandu', 'fengdu', 'small_grads_dominance', 'big_grads_dominance', 'gray_asymmetry', 'grads_asymmetry', 'energy', 'gray_mean', 'grads_mean', 'gray_variance', 'grads_variance', 'corelation', 'gray_entropy', 'grads_entropy', 'entropy', 'inertia', 'differ_moment'] # 获取变量的名 def get_variable_name(variable): callers_local_vars = inspect.currentframe().f_back.f_locals.items() return [var_name for var_name, var_val in callers_local_vars if var_val is variable] def glcm(img_gray, ngrad=16, ngray=16): '''Gray Level-Gradient Co-occurrence Matrix,取归一化后的灰度值、梯度值分别为16、16''' # 利用sobel算子分别计算x-y方向上的梯度值 gsx = cv2.Sobel(img_gray, cv2.CV_64F, 1, 0, ksize=3) gsy = cv2.Sobel(img_gray, cv2.CV_64F, 0, 1, ksize=3) height, width = img_gray.shape grad = (gsx ** 2 + gsy ** 2) ** 0.5 # 计算梯度值 grad = np.asarray(1.0 * grad * (ngrad - 1) / grad.max(), dtype=np.int16) gray = np.asarray(1.0 * img_gray * (ngray - 1) / img_gray.max(), dtype=np.int16) # 0-255变换为0-15 gray_grad = np.zeros([ngray, ngrad]) # 灰度梯度共生矩阵 for i in range(height): for j in range(width): gray_value = gray[i][j] grad_value = grad[i][j] gray_grad[gray_value][grad_value] += 1 gray_grad = 1.0 * gray_grad / (height * width) # 归一化灰度梯度矩阵，减少计算量 get_glcm_features(gray_grad) @jit def get_gray_feature(): # 灰度特征提取算法 hist = cv2.calcHist([image_ROI_uint8[index]], [0], None, [256], [0, 256]) # 假的 还没用灰度直方图 c_features['mean'].append(np.mean(image_ROI[index])) c_features['std'].append(np.std(image_ROI[index])) s = pd.Series(image_ROI[index]) c_features['piandu'].append(s.skew()) c_features['fengdu'].append(s.kurt()) def get_glcm_features(mat): '''根据灰度梯度共生矩阵计算纹理特征量，包括小梯度优势，大梯度优势，灰度分布不均匀性，梯度分布不均匀性，能量，灰度平均，梯度平均， 灰度方差，梯度方差，相关，灰度熵，梯度熵，混合熵，惯性，逆差矩''' sum_mat = mat.sum() small_grads_dominance = big_grads_dominance = gray_asymmetry = grads_asymmetry = energy = gray_mean = grads_mean = 0 gray_variance = grads_variance = corelation = gray_entropy = grads_entropy = entropy = inertia = differ_moment = 0 for i in range(mat.shape[0]): gray_variance_temp = 0 for j in range(mat.shape[1]): small_grads_dominance += mat[i][j] / ((j + 1) ** 2) big_grads_dominance += mat[i][j] * j ** 2 energy += mat[i][j] ** 2 if mat[i].sum() != 0: gray_entropy -= mat[i][j] * np.log(mat[i].sum()) if mat[:, j].sum() != 0: grads_entropy -= mat[i][j] * np.log(mat[:, j].sum()) if mat[i][j] != 0: entropy -= mat[i][j] * np.log(mat[i][j]) inertia += (i - j) ** 2 * np.log(mat[i][j]) differ_moment += mat[i][j] / (1 + (i - j) ** 2) gray_variance_temp += mat[i][j] ** 0.5 gray_asymmetry += mat[i].sum() ** 2 gray_mean += i * mat[i].sum() ** 2 gray_variance += (i - gray_mean) ** 2 * gray_variance_temp for j in range(mat.shape[1]): grads_variance_temp = 0 for i in range(mat.shape[0]): grads_variance_temp += mat[i][j] ** 0.5 grads_asymmetry += mat[:, j].sum() ** 2 grads_mean += j * mat[:, j].sum() ** 2 grads_variance += (j - grads_mean) ** 2 * grads_variance_temp small_grads_dominance /= sum_mat big_grads_dominance /= sum_mat gray_asymmetry /= sum_mat grads_asymmetry /= sum_mat gray_variance = gray_variance ** 0.5 grads_variance = grads_variance ** 0.5 for i in range(mat.shape[0]): for j in range(mat.shape[1]): corelation += (i - gray_mean) * (j - grads_mean) * mat[i][j] glgcm_features = [small_grads_dominance, big_grads_dominance, gray_asymmetry, grads_asymmetry, energy, gray_mean, grads_mean, gray_variance, grads_variance, corelation, gray_entropy, grads_entropy, entropy, inertia, differ_moment] for i in range(len(glgcm_features)): t = get_variable_name(glgcm_features[i])[0] c_features[t].append(np.round(glgcm_features[i], 4)) def get_geometry_feature(): # 形态特征 分割mask获得一些特征 contours, x = cv2.findContours(mask_array.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE) tarea = [] tperimeter = [] for c in contours: # 生成矩 try: M = cv2.moments(c) cx = int(M['m10'] / M['m00']) cy = int(M['m01'] / M['m00']) c_features['focus_x'].append(cx) c_features['focus_y'].append(cy) except: print('error') # 椭圆拟合 try: (x, y), (MA, ma), angle = cv2.fitEllipse(c) c_features['ellipse'].append((ma - MA)) except: continue # 面积周长 tarea.append(cv2.contourArea(c)) tperimeter.append(cv2.arcLength(c, True)) # 将mask里的最大值追加 有黑洞 try: c_features['area'].append(max(tarea)) c_features['perimeter'].append(round(max(tperimeter), 4)) except: print('area error') # 提取肿瘤特征 def get_feature(image, mask): global w global image_ROI_uint8, index, image_ROI_mini, image_ROI, mask_array mask_array = cv2.imread(mask, 0) image_arrary = cv2.imread(image) # 映射到CT获得特征 image_ROI = np.zeros(shape=image_arrary.shape) index = np.nonzero(mask_array) if not index[0].any(): # c_features['no'] = True return None image_ROI[index] = image_arrary[index] image_ROI_uint8 = np.uint8(image_ROI) # 获得只有肿瘤的图片 x, y, w, h = cv2.boundingRect(mask_array) image_ROI_mini = np.uint8(image_arrary[y:y + h, x:x + w]) w = image_ROI_mini # 灰度梯度共生矩阵提取纹理特征 get_geometry_feature() # get_gray_feature() glcm(image_ROI_mini, 15, 15) return c_features def main(pid): global w person_id = pid global c_features c_features = {} for i in range(len(features_list)): c_features[features_list[i]] = [column_all_c[i], np.round(random(), 3)] return c_features