From 983f77e86f010eb3fd188c50c3e56c3909694d3b Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E5=B0=8F=E9=93=AD?= <1223398803@qq.com>
Date: Mon, 17 Feb 2020 16:04:55 +0800
Subject: [PATCH 1/2] init flask

---
 CTAI_flask/core/get_feature.py | 200 +++++++++++++++++++++++++++++++++
 CTAI_flask/core/main.py        |  15 +++
 2 files changed, 215 insertions(+)
 create mode 100644 CTAI_flask/core/get_feature.py
 create mode 100644 CTAI_flask/core/main.py

diff --git a/CTAI_flask/core/get_feature.py b/CTAI_flask/core/get_feature.py
new file mode 100644
index 0000000..c08f4f1
--- /dev/null
+++ b/CTAI_flask/core/get_feature.py
@@ -0,0 +1,200 @@
+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()
diff --git a/CTAI_flask/core/main.py b/CTAI_flask/core/main.py
new file mode 100644
index 0000000..5fd9240
--- /dev/null
+++ b/CTAI_flask/core/main.py
@@ -0,0 +1,15 @@
+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

From 9d942ddab7a3f79b1ed0419b77da8a0e0addde89 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E5=B0=8F=E9=93=AD?= <1223398803@qq.com>
Date: Mon, 17 Feb 2020 16:07:57 +0800
Subject: [PATCH 2/2] Add files via upload

---
 CTAI_flask/core/net/unet.py | 68 +++++++++++++++++++++++++++++++++++++
 1 file changed, 68 insertions(+)
 create mode 100644 CTAI_flask/core/net/unet.py

diff --git a/CTAI_flask/core/net/unet.py b/CTAI_flask/core/net/unet.py
new file mode 100644
index 0000000..0a2bca6
--- /dev/null
+++ b/CTAI_flask/core/net/unet.py
@@ -0,0 +1,68 @@
+import torch.nn as nn
+import torch
+from torch import autograd
+
+class DoubleConv(nn.Module):
+    def __init__(self, in_ch, out_ch):
+        super(DoubleConv, self).__init__()
+        self.conv = nn.Sequential(
+            nn.Conv2d(in_ch, out_ch, 3, padding=1),
+            nn.BatchNorm2d(out_ch),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(out_ch, out_ch, 3, padding=1),
+            nn.BatchNorm2d(out_ch),
+            nn.ReLU(inplace=True)
+        )
+
+    def forward(self, input):
+        return self.conv(input)
+
+
+class Unet(nn.Module):
+    def __init__(self,in_ch,out_ch):
+        super(Unet, self).__init__()
+
+        self.conv1 = DoubleConv(in_ch, 64)
+        self.pool1 = nn.MaxPool2d(2)
+        self.conv2 = DoubleConv(64, 128)
+        self.pool2 = nn.MaxPool2d(2)
+        self.conv3 = DoubleConv(128, 256)
+        self.pool3 = nn.MaxPool2d(2)
+        self.conv4 = DoubleConv(256, 512)
+        self.pool4 = nn.MaxPool2d(2)
+        self.conv5 = DoubleConv(512, 1024)
+        self.up6 = nn.ConvTranspose2d(1024, 512, 2, stride=2)
+        self.conv6 = DoubleConv(1024, 512)
+        self.up7 = nn.ConvTranspose2d(512, 256, 2, stride=2)
+        self.conv7 = DoubleConv(512, 256)
+        self.up8 = nn.ConvTranspose2d(256, 128, 2, stride=2)
+        self.conv8 = DoubleConv(256, 128)
+        self.up9 = nn.ConvTranspose2d(128, 64, 2, stride=2)
+        self.conv9 = DoubleConv(128, 64)
+        self.conv10 = nn.Conv2d(64,out_ch, 1)
+
+    def forward(self,x):
+        c1=self.conv1(x)
+        p1=self.pool1(c1)
+        c2=self.conv2(p1)
+        p2=self.pool2(c2)
+        c3=self.conv3(p2)
+        p3=self.pool3(c3)
+        c4=self.conv4(p3)
+        p4=self.pool4(c4)
+        c5=self.conv5(p4)
+        up_6= self.up6(c5)
+        merge6 = torch.cat([up_6, c4], dim=1)
+        c6=self.conv6(merge6)
+        up_7=self.up7(c6)
+        merge7 = torch.cat([up_7, c3], dim=1)
+        c7=self.conv7(merge7)
+        up_8=self.up8(c7)
+        merge8 = torch.cat([up_8, c2], dim=1)
+        c8=self.conv8(merge8)
+        up_9=self.up9(c8)
+        merge9=torch.cat([up_9,c1],dim=1)
+        c9=self.conv9(merge9)
+        c10=self.conv10(c9)
+        out = nn.Sigmoid()(c10)
+        return out