init flask

CTAI_flask/core/get_feature.py

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+import inspect
+
+import SimpleITK as sitk
+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']
+
+
+# 最后俩偏度 峰度
+
+
+# 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
+    im2, 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 = sitk.ReadImage(image)
+    image_arrary = sitk.GetArrayFromImage(image)[0, :, :]
+    # 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]]
+
+    get_feature(f'tmp/ct/{pid}.dcm', f'tmp/mask/{pid}_mask.png')
+
+    for j in c_features:
+        if j == 'id':
+            continue
+        c_features[j][1] = np.round(np.mean(c_features[j][1]), 4)
+
+    return c_features
+
+
+if __name__ == '__main__':
+    main()

CTAI_flask/core/main.py

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+from core import process, predict, get_feature
+
+
+def c_main(path,model):
+    image_data = process.pre_process(path)
+    # print(image_data)
+    predict.predict(image_data,model)
+    process.last_process(image_data[1])
+    image_info = get_feature.main(image_data[1])
+
+    return image_data[1] + '.png', image_info
+
+
+if __name__ == '__main__':
+    pass