├── QQ图片20200508231213.png ├── QQ图片20200508231234.png ├── QQ图片20200508231305.jpg ├── QQ图片20200508231318.jpg ├── accuracy ├── loss.py ├── miou-pa-cpa ├── miou.py └── train_loss.png ├── img_out ├── 0.png ├── 1.png ├── 10.png ├── 103.png ├── 104.png ├── 105.png ├── 109.png ├── 11.png ├── 111.png ├── 114.png ├── 116.png ├── 117.png ├── 118.png ├── 12.png ├── 120.png ├── 121.png ├── 122.png ├── 123.png ├── 124.png ├── 125.png ├── 127.png ├── 128.png ├── 130.png ├── 131.png ├── 132.png ├── 133.png ├── 134.png ├── 135.png ├── 136.png ├── 137.png ├── 138.png ├── 14.png ├── 142.png ├── 143.png ├── 144.png ├── 145.png ├── 146.png ├── 147.png ├── 148.png ├── 149.png ├── 152.png ├── 153.png ├── 154.png ├── 155.png ├── 16.png ├── 161.png ├── 162.png ├── 163.png ├── 164.png ├── 165.png ├── 166.png ├── 167.png ├── 168.png ├── 169.png ├── 170.png ├── 171.png ├── 174.png ├── 175.png ├── 178.png ├── 179.png ├── 18.png ├── 180.png ├── 182.png ├── 184.png ├── 186.png ├── 187.png ├── 189.png ├── 19.png ├── 190.png ├── 193.png ├── 194.png ├── 195.png ├── 198.png ├── 199.png ├── 202.png ├── 203.png ├── 206.png ├── 207.png ├── 208.png ├── 209.png ├── 21.png ├── 211.png ├── 212.png ├── 213.png ├── 214.png ├── 216.png ├── 22.png ├── 220.png ├── 223.png ├── 225.png ├── 229.png ├── 23.png ├── 230.png ├── 231.png ├── 233.png ├── 234.png ├── 235.png ├── 236.png ├── 238.png ├── 240.png ├── 241.png ├── 243.png ├── 248.png ├── 249.png ├── 250.png ├── 256.png ├── 259.png ├── 26.png ├── 262.png ├── 263.png ├── 266.png ├── 268.png ├── 269.png ├── 272.png ├── 274.png ├── 277.png ├── 279.png ├── 280.png ├── 285.png ├── 286.png ├── 288.png ├── 29.png ├── 291.png ├── 292.png ├── 297.png ├── 309.png ├── 31.png ├── 316.png ├── 318.png ├── 32.png ├── 323.png ├── 324.png ├── 33.png ├── 334.png ├── 335.png ├── 34.png ├── 344.png ├── 349.png ├── 35.png ├── 352.png ├── 356.png ├── 357.png ├── 36.png ├── 37.png ├── 38.png ├── 39.png ├── 40.png ├── 41.png ├── 42.png ├── 44.png ├── 45.png ├── 46.png ├── 47.png ├── 49.png ├── 5.png ├── 51.png ├── 52.png ├── 54.png ├── 57.png ├── 58.png ├── 59.png ├── 60.png ├── 61.png ├── 63.png ├── 64.png ├── 65.png ├── 66.png ├── 68.png ├── 69.png ├── 7.png ├── 70.png ├── 71.png ├── 73.png ├── 74.png ├── 75.png ├── 76.png ├── 77.png ├── 78.png ├── 79.png ├── 8.png ├── 81.png ├── 82.png ├── 83.png ├── 84.png ├── 85.png ├── 86.png ├── 87.png ├── 88.png ├── 89.png ├── 9.png ├── 90.png ├── 93.png ├── 94.png ├── 95.png ├── 96.png ├── 97.png ├── 98.png └── 99.png ├── img_out_bck ├── 0.png ├── 1.png ├── 10.png ├── 103.png ├── 104.png ├── 105.png ├── 109.png ├── 11.png ├── 111.png ├── 114.png ├── 116.png ├── 117.png ├── 118.png ├── 12.png ├── 120.png ├── 121.png ├── 122.png ├── 123.png ├── 124.png ├── 125.png ├── 127.png ├── 128.png ├── 130.png ├── 131.png ├── 132.png ├── 133.png ├── 134.png ├── 135.png ├── 136.png ├── 137.png ├── 138.png ├── 14.png ├── 142.png ├── 143.png ├── 144.png ├── 145.png ├── 146.png ├── 147.png ├── 148.png ├── 149.png ├── 152.png ├── 153.png ├── 154.png ├── 155.png ├── 16.png ├── 161.png ├── 162.png ├── 163.png ├── 164.png ├── 165.png ├── 166.png ├── 167.png ├── 168.png ├── 169.png ├── 170.png ├── 171.png ├── 174.png ├── 175.png ├── 178.png ├── 179.png ├── 18.png ├── 180.png ├── 182.png ├── 184.png ├── 186.png ├── 187.png ├── 189.png ├── 19.png ├── 190.png ├── 193.png ├── 194.png ├── 195.png ├── 198.png ├── 199.png ├── 202.png ├── 203.png ├── 206.png ├── 207.png ├── 208.png ├── 209.png ├── 21.png ├── 211.png ├── 212.png ├── 213.png ├── 214.png ├── 216.png ├── 22.png ├── 220.png ├── 223.png ├── 225.png ├── 229.png ├── 23.png ├── 230.png ├── 231.png ├── 233.png ├── 234.png ├── 235.png ├── 236.png ├── 238.png ├── 240.png ├── 241.png ├── 243.png ├── 248.png ├── 249.png ├── 250.png ├── 256.png ├── 259.png ├── 26.png ├── 262.png ├── 263.png ├── 266.png ├── 268.png ├── 269.png ├── 272.png ├── 274.png ├── 277.png ├── 279.png ├── 280.png ├── 285.png ├── 286.png ├── 288.png ├── 29.png ├── 291.png ├── 292.png ├── 297.png ├── 309.png ├── 31.png ├── 316.png ├── 318.png ├── 32.png ├── 323.png ├── 324.png ├── 33.png ├── 334.png ├── 335.png ├── 34.png ├── 344.png ├── 349.png ├── 35.png ├── 352.png ├── 356.png ├── 357.png ├── 36.png ├── 37.png ├── 38.png ├── 39.png ├── 40.png ├── 41.png ├── 42.png ├── 44.png ├── 45.png ├── 46.png ├── 47.png ├── 49.png ├── 5.png ├── 51.png ├── 52.png ├── 54.png ├── 57.png ├── 58.png ├── 59.png ├── 60.png ├── 61.png ├── 63.png ├── 64.png ├── 65.png ├── 66.png ├── 68.png ├── 69.png ├── 7.png ├── 70.png ├── 71.png ├── 73.png ├── 74.png ├── 75.png ├── 76.png ├── 77.png ├── 78.png ├── 79.png ├── 8.png ├── 81.png ├── 82.png ├── 83.png ├── 84.png ├── 85.png ├── 86.png ├── 87.png ├── 88.png ├── 89.png ├── 9.png ├── 90.png ├── 93.png ├── 94.png ├── 95.png ├── 96.png ├── 97.png ├── 98.png └── 99.png ├── img_real ├── 0.png ├── 1.png ├── 10.png ├── 103.png ├── 104.png ├── 105.png ├── 109.png ├── 11.png ├── 111.png ├── 114.png ├── 116.png ├── 117.png ├── 118.png ├── 12.png ├── 120.png ├── 121.png ├── 122.png ├── 123.png ├── 124.png ├── 125.png ├── 127.png ├── 128.png ├── 130.png ├── 131.png ├── 132.png ├── 133.png ├── 134.png ├── 135.png ├── 136.png ├── 137.png ├── 138.png ├── 14.png ├── 142.png ├── 143.png ├── 144.png ├── 145.png ├── 146.png ├── 147.png ├── 148.png ├── 149.png ├── 152.png ├── 153.png ├── 154.png ├── 155.png ├── 16.png ├── 161.png ├── 162.png ├── 163.png ├── 164.png ├── 165.png ├── 166.png ├── 167.png ├── 168.png ├── 169.png ├── 170.png ├── 171.png ├── 174.png ├── 175.png ├── 178.png ├── 179.png ├── 18.png ├── 180.png ├── 182.png ├── 184.png ├── 186.png ├── 187.png ├── 189.png ├── 19.png ├── 190.png ├── 193.png ├── 194.png ├── 195.png ├── 198.png ├── 199.png ├── 202.png ├── 203.png ├── 206.png ├── 207.png ├── 208.png ├── 209.png ├── 21.png ├── 211.png ├── 212.png ├── 213.png ├── 214.png ├── 216.png ├── 22.png ├── 220.png ├── 223.png ├── 225.png ├── 229.png ├── 23.png ├── 230.png ├── 231.png ├── 233.png ├── 234.png ├── 235.png ├── 236.png ├── 238.png ├── 240.png ├── 241.png ├── 243.png ├── 248.png ├── 249.png ├── 250.png ├── 256.png ├── 259.png ├── 26.png ├── 262.png ├── 263.png ├── 266.png ├── 268.png ├── 269.png ├── 272.png ├── 274.png ├── 277.png ├── 279.png ├── 280.png ├── 285.png ├── 286.png ├── 288.png ├── 29.png ├── 291.png ├── 292.png ├── 297.png ├── 309.png ├── 31.png ├── 316.png ├── 318.png ├── 32.png ├── 323.png ├── 324.png ├── 33.png ├── 334.png ├── 335.png ├── 34.png ├── 344.png ├── 349.png ├── 35.png ├── 352.png ├── 356.png ├── 357.png ├── 36.png ├── 37.png ├── 38.png ├── 39.png ├── 40.png ├── 41.png ├── 42.png ├── 44.png ├── 45.png ├── 46.png ├── 47.png ├── 49.png ├── 5.png ├── 51.png ├── 52.png ├── 54.png ├── 57.png ├── 58.png ├── 59.png ├── 60.png ├── 61.png ├── 63.png ├── 64.png ├── 65.png ├── 66.png ├── 68.png ├── 69.png ├── 7.png ├── 70.png ├── 71.png ├── 73.png ├── 74.png ├── 75.png ├── 76.png ├── 77.png ├── 78.png ├── 79.png ├── 8.png ├── 81.png ├── 82.png ├── 83.png ├── 84.png ├── 85.png ├── 86.png ├── 87.png ├── 88.png ├── 89.png ├── 9.png ├── 90.png ├── 93.png ├── 94.png ├── 95.png ├── 96.png ├── 97.png ├── 98.png └── 99.png ├── logs └── ep056-loss0.242-val_loss0.215.h5 ├── model └── mobilenet_1_0_224_tf_no_top.h5 ├── nets ├── mobilenet.py └── unet.py ├── predict.py ├── readme.md ├── show_summary.py ├── train.py ├── train_dataset ├── copytest_files.py ├── data_config.py ├── get_train_txt.py ├── process_label.py ├── show_data.py ├── test_data.txt ├── tmp.txt └── train_data.txt └── train_log ├── train_log0-3.txt └── train_log3-96.txt /QQ图片20200508231213.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jiye-ML/heart_segmentation_CT/b45c4845d571d3073be1bc0b80e2c5d847e7d6e0/QQ图片20200508231213.png -------------------------------------------------------------------------------- /QQ图片20200508231234.png: -------------------------------------------------------------------------------- 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all_line = [] 5 | with open("../train_log/train_log3-96.txt", "r", encoding="utf-8") as f: 6 | lline = f.readlines() 7 | for ll in lline: 8 | all_line.append(ll) 9 | dict = {} 10 | index = 0 11 | for line in all_line: 12 | if line.find("s/step - loss: ") > 0 and line.find(" - accuracy: ") > 0: 13 | value = line.split("s/step - loss: ")[-1].split(" - accuracy: ")[0].strip() 14 | print(value) 15 | dict[int(index)] = float(value) 16 | index += 1 17 | 18 | test_data_1 = sorted(dict.items(), key=lambda x: x[0]) 19 | print(test_data_1) 20 | x_axis = [] 21 | y_axis = [] 22 | for item in test_data_1: 23 | x_axis.append(item[0]) 24 | y_axis.append(item[1]) 25 | plt.title('train loss') 26 | plt.plot(x_axis[3:], y_axis[3:], color='green', label='training loss') 27 | plt.grid() 28 | plt.xlabel('step') 29 | plt.ylabel('loss') 30 | plt.show() 31 | -------------------------------------------------------------------------------- /accuracy/miou-pa-cpa: -------------------------------------------------------------------------------- 1 | pa is : 0.967733 2 | cpa is : 3 | [0.98448304 0.84279415] 4 | mpa is : 0.913639 5 | mIoU is : 0.859765 -------------------------------------------------------------------------------- /accuracy/miou.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | __all__ = ['SegmentationMetric'] 4 | 5 | """ 6 | confusionMetric # 注意：此处横着代表预测值，竖着代表真实值，与之前介绍的相反 7 | P\L P N 8 | P TP FP 9 | N FN TN 10 | """ 11 | 12 | 13 | class SegmentationMetric(object): 14 | def __init__(self, numClass): 15 | self.numClass = numClass 16 | self.confusionMatrix = np.zeros((self.numClass,) * 2) 17 | 18 | def pixelAccuracy(self): 19 | # return all class overall pixel accuracy 20 | # PA = acc = (TP + TN) / (TP + TN + FP + TN) 21 | acc = np.diag(self.confusionMatrix).sum() / self.confusionMatrix.sum() 22 | return acc 23 | 24 | def classPixelAccuracy(self): 25 | # return each category pixel accuracy(A more accurate way to call it precision) 26 | # acc = (TP) / TP + FP 27 | classAcc = np.diag(self.confusionMatrix) / self.confusionMatrix.sum(axis=1) 28 | return classAcc # 返回的是一个列表值，如：[0.90, 0.80, 0.96]，表示类别1 2 3各类别的预测准确率 29 | 30 | def meanPixelAccuracy(self): 31 | classAcc = self.classPixelAccuracy() 32 | meanAcc = np.nanmean(classAcc) # np.nanmean 求平均值，nan表示遇到Nan类型，其值取为0 33 | return meanAcc # 返回单个值，如：np.nanmean([0.90, 0.80, 0.96, nan, nan]) = (0.90 + 0.80 + 0.96） / 3 = 0.89 34 | 35 | def meanIntersectionOverUnion(self): 36 | # Intersection = TP Union = TP + FP + FN 37 | # IoU = TP / (TP + FP + FN) 38 | intersection = np.diag(self.confusionMatrix) # 取对角元素的值，返回列表 39 | # axis = 1表示混淆矩阵行的值，返回列表； axis = 0表示取混淆矩阵列的值，返回列表 40 | union = ( 41 | np.sum(self.confusionMatrix, axis=1) + 42 | np.sum(self.confusionMatrix, axis=0) - 43 | np.diag(self.confusionMatrix) 44 | ) 45 | IoU = intersection / union # 返回列表，其值为各个类别的IoU 46 | mIoU = np.nanmean(IoU) # 求各类别IoU的平均 47 | return mIoU 48 | 49 | def genConfusionMatrix(self, imgPredict, imgLabel): # 同FCN中score.py的fast_hist()函数 50 | # remove classes from unlabeled pixels in gt image and predict 51 | imgLabel[imgLabel != 0] = 1 52 | imgPredict[imgPredict != 0] = 1 53 | mask = (imgLabel >= 0) & (imgLabel < self.numClass) 54 | 55 | # 混淆均镇的某一行 + 预测到的某一列 56 | label = self.numClass * imgLabel[mask] + imgPredict[mask] 57 | 58 | # 判断每种label出现此处， 比如 0.1,2,3 ， 如果是二分类，则 59 | # 0,1为label=0 的预测为[0, 1] 的数目 60 | # 2,3为label=1的预测为[0, 1] 的数目 61 | count = np.bincount(label, minlength=self.numClass ** 2) 62 | confusionMatrix = count.reshape(self.numClass, self.numClass) 63 | return confusionMatrix 64 | 65 | def Frequency_Weighted_Intersection_over_Union(self): 66 | # FWIOU = [(TP+FN)/(TP+FP+TN+FN)] *[TP / (TP + FP + FN)] 67 | freq = np.sum(self.confusionMatrix, axis=1) / np.sum(self.confusionMatrix) 68 | iu = np.diag(self.confusionMatrix) / ( 69 | np.sum(self.confusionMatrix, axis=1) + np.sum(self.confusionMatrix, axis=0) - 70 | np.diag(self.confusionMatrix)) 71 | FWIoU = (freq[freq > 0] * iu[freq > 0]).sum() 72 | return FWIoU 73 | 74 | def addBatch(self, imgPredict, imgLabel): 75 | assert imgPredict.shape == imgLabel.shape 76 | self.confusionMatrix += self.genConfusionMatrix(imgPredict, imgLabel) 77 | 78 | def reset(self): 79 | self.confusionMatrix = np.zeros((self.numClass, self.numClass)) 80 | 81 | 82 | import glob 83 | from PIL import Image 84 | 85 | if __name__ == '__main__': 86 | 87 | all_file = glob.glob("../img_real/*.png") 88 | 89 | metric = SegmentationMetric(2) # 14+1 90 | for file in all_file: 91 | print(file) 92 | if file.endswith("59.png"): 93 | print("sdfsfd") 94 | img = Image.open(file) 95 | predictImg = file.replace("img_real", "img_out") 96 | predictImg = Image.open(predictImg) 97 | imgPredict = np.array(predictImg)[:, :, 0] # 可直接换成预测图片 98 | orininal_h1 = np.array(imgPredict).shape[0] 99 | orininal_w1 = np.array(imgPredict).shape[1] 100 | img = img.resize((orininal_w1, orininal_h1)) 101 | imgLabel = np.array(img) # 可直接换成标注图片 102 | metric.addBatch(imgPredict, imgLabel) 103 | pa = metric.pixelAccuracy() 104 | cpa = metric.classPixelAccuracy() 105 | mpa = metric.meanPixelAccuracy() 106 | mIoU = metric.meanIntersectionOverUnion() 107 | print('pa is : %f' % pa) 108 | print('cpa is :') # 列表 109 | print(cpa) 110 | print('mpa is : %f' % mpa) 111 | print('mIoU is : %f' % mIoU) 112 | -------------------------------------------------------------------------------- /accuracy/train_loss.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jiye-ML/heart_segmentation_CT/b45c4845d571d3073be1bc0b80e2c5d847e7d6e0/accuracy/train_loss.png -------------------------------------------------------------------------------- /img_out/0.png: -------------------------------------------------------------------------------- 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strides=(1, 1)): 14 | channel_axis = 1 if IMAGE_ORDERING == 'channels_first' else -1 15 | filters = int(filters * alpha) 16 | x = ZeroPadding2D(padding=(1, 1), name='conv1_pad', data_format=IMAGE_ORDERING)(inputs) 17 | x = Conv2D(filters, kernel, data_format=IMAGE_ORDERING, 18 | padding='valid', 19 | use_bias=False, 20 | strides=strides, 21 | name='conv1')(x) 22 | x = BatchNormalization(axis=channel_axis, name='conv1_bn')(x) 23 | return Activation(relu6, name='conv1_relu')(x) 24 | 25 | 26 | def _depthwise_conv_block(inputs, pointwise_conv_filters, alpha, 27 | depth_multiplier=1, strides=(1, 1), block_id=1): 28 | channel_axis = 1 if IMAGE_ORDERING == 'channels_first' else -1 29 | pointwise_conv_filters = int(pointwise_conv_filters * alpha) 30 | 31 | x = ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING, name='conv_pad_%d' % block_id)(inputs) 32 | x = DepthwiseConv2D((3, 3), data_format=IMAGE_ORDERING, 33 | padding='valid', 34 | depth_multiplier=depth_multiplier, 35 | strides=strides, 36 | use_bias=False, 37 | name='conv_dw_%d' % block_id)(x) 38 | x = BatchNormalization( 39 | axis=channel_axis, name='conv_dw_%d_bn' % block_id)(x) 40 | x = Activation(relu6, name='conv_dw_%d_relu' % block_id)(x) 41 | 42 | x = Conv2D(pointwise_conv_filters, (1, 1), data_format=IMAGE_ORDERING, 43 | padding='same', 44 | use_bias=False, 45 | strides=(1, 1), 46 | name='conv_pw_%d' % block_id)(x) 47 | x = BatchNormalization(axis=channel_axis, 48 | name='conv_pw_%d_bn' % block_id)(x) 49 | return Activation(relu6, name='conv_pw_%d_relu' % block_id)(x) 50 | 51 | 52 | def get_mobilenet_encoder(input_height=224, input_width=224, pretrained='imagenet'): 53 | alpha = 1.0 54 | depth_multiplier = 1 55 | dropout = 1e-3 56 | 57 | img_input = Input(shape=(input_height, input_width, 3)) # [?, 416, 416, 3] 58 | 59 | x = _conv_block(img_input, 32, alpha, strides=(2, 2)) # [?, 208, 208, 32] 60 | x = _depthwise_conv_block(x, 64, alpha, depth_multiplier, block_id=1) 61 | f1 = x # # [?, 208, 208, 64] 62 | 63 | x = _depthwise_conv_block(x, 128, alpha, depth_multiplier, 64 | strides=(2, 2), block_id=2) 65 | x = _depthwise_conv_block(x, 128, alpha, depth_multiplier, block_id=3) 66 | f2 = x # # [?, 104, 104, 128] 67 | 68 | x = _depthwise_conv_block(x, 256, alpha, depth_multiplier, 69 | strides=(2, 2), block_id=4) 70 | x = _depthwise_conv_block(x, 256, alpha, depth_multiplier, block_id=5) 71 | f3 = x # [?, 52, 52, 256] 72 | 73 | x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, 74 | strides=(2, 2), block_id=6) 75 | x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=7) 76 | x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=8) 77 | x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=9) 78 | x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=10) 79 | x = _depthwise_conv_block(x, 512, alpha, depth_multiplier, block_id=11) 80 | f4 = x # [?, 26, 26, 512] 81 | 82 | x = _depthwise_conv_block(x, 1024, alpha, depth_multiplier, 83 | strides=(2, 2), block_id=12) 84 | x = _depthwise_conv_block(x, 1024, alpha, depth_multiplier, block_id=13) 85 | f5 = x # [?, 13, 13, 1024] 86 | 87 | return img_input, [f1, f2, f3, f4, f5] 88 | -------------------------------------------------------------------------------- /nets/unet.py: -------------------------------------------------------------------------------- 1 | from tensorflow.keras.models import * 2 | from tensorflow.keras.layers import * 3 | from nets.mobilenet import get_mobilenet_encoder 4 | 5 | IMAGE_ORDERING = 'channels_last' 6 | MERGE_AXIS = -1 7 | 8 | 9 | def _unet(n_classes, encoder, l1_skip_conn=True, input_height=416, input_width=608): 10 | img_input, levels = encoder(input_height=input_height, input_width=input_width) 11 | [f1, f2, f3, f4, f5] = levels 12 | 13 | o = f4 # [?, 26, 26, 512] 14 | # 26,26,512 15 | o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o) 16 | o = (Conv2D(512, (3, 3), padding='valid', data_format=IMAGE_ORDERING))(o) 17 | o = BatchNormalization()(o) 18 | 19 | # 52,52,512 20 | o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o) 21 | # 52,52,768 22 | o = (concatenate([o, f3], axis=MERGE_AXIS)) 23 | o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o) 24 | # 52,52,256 25 | o = (Conv2D(256, (3, 3), padding='valid', data_format=IMAGE_ORDERING))(o) 26 | o = (BatchNormalization())(o) 27 | 28 | # 104,104,256 29 | o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o) 30 | # 104,104,384 31 | o = (concatenate([o, f2], axis=MERGE_AXIS)) 32 | o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o) 33 | # 104,104,128 34 | o = (Conv2D(128, (3, 3), padding='valid', data_format=IMAGE_ORDERING))(o) 35 | o = (BatchNormalization())(o) 36 | # 208,208,128 37 | o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o) 38 | 39 | if l1_skip_conn: 40 | o = (concatenate([o, f1], axis=MERGE_AXIS)) 41 | 42 | o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o) 43 | o = (Conv2D(64, (3, 3), padding='valid', data_format=IMAGE_ORDERING))(o) 44 | o = (BatchNormalization())(o) 45 | 46 | o = Conv2D(n_classes, (3, 3), padding='same', data_format=IMAGE_ORDERING)(o) 47 | 48 | # 将结果进行reshape 49 | o = Reshape((int(input_height / 2) * int(input_width / 2), -1))(o) 50 | o = Softmax()(o) 51 | model = Model(img_input, o) 52 | 53 | return model 54 | 55 | 56 | def mobilenet_unet(n_classes, input_height=224, input_width=224, encoder_level=3): 57 | model = _unet(n_classes, get_mobilenet_encoder, input_height=input_height, input_width=input_width) 58 | model.model_name = "mobilenet_unet" 59 | return model 60 | -------------------------------------------------------------------------------- /predict.py: -------------------------------------------------------------------------------- 1 | from nets.unet import mobilenet_unet 2 | from PIL import Image 3 | import numpy as np 4 | import random 5 | import copy 6 | import os 7 | import glob 8 | 9 | random.seed(0) 10 | NCLASSES = 15 11 | class_colors = [[random.randint(200, 255), random.randint(200, 255), random.randint(0, 255)] for _ in range(NCLASSES)] 12 | HEIGHT = 416 13 | WIDTH = 416 14 | 15 | model = mobilenet_unet(n_classes=NCLASSES, input_height=HEIGHT, input_width=WIDTH) 16 | model.load_weights("logs/ep056-loss0.242-val_loss0.215.h5") 17 | 18 | # imgs = [] 19 | # with open("train_dataset/test_data.txt", "r") as f: 20 | # fff = f.readlines() 21 | # for img in fff: 22 | # imgs.append(img.split(";")[0].strip()) 23 | # np.random.shuffle(imgs) 24 | 25 | test_img = './img_real' 26 | imgs = glob.glob(os.path.join(test_img, "*.png")) 27 | LABEL = [0, 255, 165, 246, 126, 180, 150, 61, 195, 131, 171, 178, 87, 212, 105] 28 | for jpg in imgs: 29 | 30 | img = Image.open(jpg) 31 | # old_img = copy.deepcopy(img) 32 | # old_img = np.array(old_img) 33 | # old_img = np.expand_dims(old_img, axis=-1) 34 | # old_img = np.repeat(old_img, repeats=3, axis=-1) 35 | # old_img = Image.fromarray(old_img) 36 | orininal_h = np.array(img).shape[0] 37 | orininal_w = np.array(img).shape[1] 38 | print(jpg, orininal_h, orininal_w) 39 | img = img.resize((WIDTH, HEIGHT)) 40 | img = np.array(img) 41 | img = img / 255 42 | img = np.expand_dims(img, axis=-1) 43 | img = np.repeat(img, repeats=3, axis=-1) 44 | img = img.reshape(-1, HEIGHT, WIDTH, 3) 45 | pr = model.predict(img)[0] 46 | # 47 | pr = pr.reshape((int(HEIGHT / 2), int(WIDTH / 2), NCLASSES)).argmax(axis=-1) 48 | # for im in np.array(pr): 49 | # for mm in im: 50 | # if mm != 0: 51 | # print(mm) 52 | seg_img = np.zeros((int(HEIGHT / 2), int(WIDTH / 2))) 53 | colors = class_colors 54 | seg_img[:, :] = pr[:, :] 55 | for c in range(NCLASSES): 56 | seg_img[:, :] += ((pr[:, :] == c) * (LABEL[c])).astype('uint8') 57 | 58 | seg_img = Image.fromarray(np.uint8(seg_img)).resize((orininal_w, orininal_h)).convert('RGB') 59 | 60 | jpg_name = os.path.basename(jpg) 61 | seg_img.save("./img_out/" + jpg_name) 62 | # image = Image.blend(old_img,seg_img,0.3) 63 | # jpg_name = jpg.split("/")[-1] 64 | # image.save("./img_out/"+jpg_name) 65 | -------------------------------------------------------------------------------- /readme.md: -------------------------------------------------------------------------------- 1 | * 完成心脏分割项目的论文 -------------------------------------------------------------------------------- /show_summary.py: -------------------------------------------------------------------------------- 1 | from nets.unet import mobilenet_unet 2 | model = mobilenet_unet(2,416,416) 3 | model.summary() -------------------------------------------------------------------------------- /train.py: -------------------------------------------------------------------------------- 1 | from nets.unet import mobilenet_unet 2 | from tensorflow.keras.optimizers import Adam 3 | from tensorflow.keras.callbacks import TensorBoard, ModelCheckpoint, ReduceLROnPlateau, EarlyStopping 4 | from PIL import Image 5 | import tensorflow.keras 6 | 7 | from tensorflow.keras.utils import get_file 8 | from tensorflow.keras import backend as K 9 | import numpy as np 10 | import os 11 | 12 | NCLASSES = 15 # n+1 13 | HEIGHT = 416 14 | WIDTH = 416 15 | 16 | 17 | def generate_arrays_from_file(lines, batch_size): 18 | # 获取总长度 19 | n = len(lines) 20 | i = 0 21 | while 1: 22 | X_train = [] 23 | Y_train = [] 24 | # 获取一个batch_size大小的数据 25 | for _ in range(batch_size): 26 | if i == 0: 27 | np.random.shuffle(lines) 28 | name = lines[i].split(';')[0] # jpg 29 | # 从文件中读取图像 30 | img = Image.open(name) 31 | img = img.resize((WIDTH, HEIGHT)) 32 | img = np.array(img) 33 | img = img / 255 34 | img = np.expand_dims(img, axis=-1) 35 | img = np.repeat(img, repeats=3, axis=-1) 36 | X_train.append(img) 37 | 38 | name = (lines[i].split(';')[1]).replace("\n", "") # png 39 | # 从文件中读取图像 40 | img = Image.open(name) 41 | img = img.resize((int(WIDTH / 2), int(HEIGHT / 2))) 42 | img = np.array(img) 43 | seg_labels = np.zeros((int(HEIGHT / 2), int(WIDTH / 2), NCLASSES)) 44 | for c in range(NCLASSES): 45 | seg_labels[:, :, c] = (img[:, :] == c).astype(int) 46 | seg_labels = np.reshape(seg_labels, (-1, NCLASSES)) 47 | Y_train.append(seg_labels) 48 | 49 | # 读完一个周期后重新开始 50 | i = (i + 1) % n 51 | yield (np.array(X_train), np.array(Y_train)) 52 | 53 | 54 | def loss(y_true, y_pred): 55 | crossloss = K.binary_crossentropy(y_true, y_pred) 56 | loss = 4 * K.sum(crossloss) / HEIGHT / WIDTH 57 | return loss 58 | 59 | 60 | if __name__ == "__main__": 61 | log_dir = "logs/" 62 | # 获取model 63 | model = mobilenet_unet(n_classes=NCLASSES, input_height=HEIGHT, input_width=WIDTH) 64 | # model.summary() 65 | weights_path = "./logs/ep056-loss0.242-val_loss0.215.h5" 66 | model.load_weights(weights_path, by_name=True) 67 | 68 | # model.summary() 69 | # 打开数据集的txt 70 | with open(r".\train_dataset\train_data.txt", "r") as f: 71 | lines = f.readlines() 72 | 73 | # 打乱行，这个txt主要用于帮助读取数据来训练 74 | # 打乱的数据更有利于训练 75 | np.random.seed(10101) 76 | np.random.shuffle(lines) 77 | np.random.seed(None) 78 | 79 | # 90%用于训练，10%用于估计。 80 | num_val = int(len(lines) * 0.1) 81 | num_train = len(lines) - num_val 82 | 83 | # 保存的方式，1世代保存一次 84 | checkpoint_period = ModelCheckpoint( 85 | log_dir + 'ep{epoch:03d}-loss{loss:.3f}-val_loss{val_loss:.3f}.h5', 86 | monitor='val_loss', 87 | save_weights_only=True, 88 | save_best_only=True, 89 | period=1 90 | ) 91 | # 学习率下降的方式，val_loss三次不下降就下降学习率继续训练 92 | reduce_lr = ReduceLROnPlateau( 93 | monitor='val_loss', 94 | factor=0.5, 95 | patience=3, 96 | verbose=1 97 | ) 98 | # 是否需要早停，当val_loss一直不下降的时候意味着模型基本训练完毕，可以停止 99 | early_stopping = EarlyStopping( 100 | monitor='val_loss', 101 | min_delta=0, 102 | patience=10, 103 | verbose=1 104 | ) 105 | 106 | # 交叉熵 107 | model.compile(loss=loss, optimizer=Adam(lr=1e-3), metrics=['accuracy']) 108 | batch_size = 2 109 | print('Train on {} samples, val on {} samples, with batch size {}.'.format(num_train, num_val, batch_size)) 110 | 111 | # 开始训练 112 | model.fit_generator( 113 | generate_arrays_from_file(lines[:num_train], batch_size), 114 | steps_per_epoch=max(1, num_train // batch_size), 115 | validation_data=generate_arrays_from_file(lines[num_train:], batch_size), 116 | validation_steps=max(1, num_val // batch_size), 117 | epochs=150, 118 | initial_epoch=3, 119 | callbacks=[checkpoint_period, reduce_lr] 120 | ) 121 | 122 | model.save_weights(log_dir + 'last1.h5') 123 | -------------------------------------------------------------------------------- /train_dataset/copytest_files.py: -------------------------------------------------------------------------------- 1 | import shutil 2 | import os 3 | from train_dataset.data_config import get_dat_parent 4 | rootpath = get_dat_parent() 5 | test_data ="../img_real" #rootpath+"test_dataset" 6 | if not os.path.exists(test_data): 7 | os.mkdir(test_data) 8 | 9 | with open("test_data.txt","r") as f: 10 | for file in f.readlines(): 11 | origin_path = file.split(";")[-1].strip() 12 | origin_name = origin_path.split("/")[-1] 13 | shutil.copy(origin_path, test_data + "/" + origin_name) 14 | -------------------------------------------------------------------------------- /train_dataset/data_config.py: -------------------------------------------------------------------------------- 1 | 2 | 3 | def get_dat_parent(): 4 | return "F://train_data2/MM-WHS/train1-img/" -------------------------------------------------------------------------------- /train_dataset/get_train_txt.py: -------------------------------------------------------------------------------- 1 | import os 2 | from train_dataset.data_config import get_dat_parent 3 | import numpy as np 4 | import glob 5 | 6 | allimgs =[] 7 | allabel = [] 8 | with open("tmp.txt","w") as f: 9 | for fold in range(1,21): 10 | if fold<10: 11 | ind = "0" 12 | else: 13 | ind ="" 14 | allabel = glob.glob(get_dat_parent() + "ct_train_10"+ind+str(fold)+"_label" + "/*.png") 15 | print(len(allabel)) 16 | for index in range(0,len(allabel)): 17 | f.write(get_dat_parent() + "ct_train_10"+ind+str(fold)+"_image" + "/"+str(index)+".png"+";"+ 18 | get_dat_parent() + "ct_train_10"+ind+str(fold)+"_label" + "/"+str(index)+".png") 19 | f.write("\n") 20 | 21 | k = 0 22 | train = open("train_data.txt", "w") 23 | test = open("test_data.txt", "w") 24 | with open("tmp.txt","r") as f: 25 | allfile = f.readlines() 26 | np.random.shuffle(allfile) 27 | for ii in allfile: 28 | if k<300: 29 | test.write(ii) 30 | else: 31 | train.write(ii) 32 | k+=1 33 | 34 | train.close() 35 | test.close() -------------------------------------------------------------------------------- /train_dataset/process_label.py: -------------------------------------------------------------------------------- 1 | import os 2 | import glob 3 | from PIL import Image 4 | import cv2 5 | import numpy as np 6 | from train_dataset.data_config import get_dat_parent 7 | filenames = os.listdir(get_dat_parent()) 8 | 9 | # 255,165,246,126,180,150,61,195,131,171,178,87,212,105 10 | aallsit = [] 11 | for fold in filenames: 12 | if fold.endswith("_label"): 13 | train_origin_label = os.path.join(get_dat_parent()+fold) 14 | if not os.path.exists(get_dat_parent()+fold+"_new"): 15 | os.mkdir(get_dat_parent()+fold+"_new") 16 | train_origin_label_file = os.path.join(train_origin_label) 17 | all_mask= glob.glob(os.path.join(train_origin_label_file,"*.png")) 18 | for file in all_mask: 19 | img = Image.open(file) 20 | file_name = file.split("\\")[-1] 21 | #print(np.array((img))) 22 | for nn in np.array((img)): 23 | for n in nn: 24 | if n!=0 and n not in aallsit: 25 | aallsit.append(n) 26 | print(n) 27 | #break 28 | # print(file_name) 29 | # new = Image.new("RGB", [np.shape(img)[1], np.shape(img)[0]]) 30 | # new = new + np.expand_dims(0 * (np.array(img) == 0), -1) 31 | # new = new + np.expand_dims(1 * (np.array(img) == 255), -1) 32 | # new = new + np.expand_dims(2 * (np.array(img) == 246), -1) 33 | # new = new + np.expand_dims(3 * (np.array(img) == 180), -1) 34 | # new = new + np.expand_dims(4 * (np.array(img) == 165), -1) 35 | # new = new + np.expand_dims(5 * (np.array(img) == 150), -1) 36 | # new = new + np.expand_dims(6 * (np.array(img) == 61), -1) 37 | # new = new + np.expand_dims(7 * (np.array(img) == 195), -1) 38 | # new = Image.fromarray(np.uint8(new)) 39 | # #print(np.max(new),np.min(new)) 40 | # #new.show() 41 | # new.save(os.path.join(get_dat_parent()+fold+"_new",file_name)) -------------------------------------------------------------------------------- /train_dataset/show_data.py: -------------------------------------------------------------------------------- 1 | # encoding=utf8 2 | ''' 3 | 查看和显示nii文件 4 | ''' 5 | 6 | import matplotlib 7 | 8 | matplotlib.use('TkAgg') 9 | example_filename = r'F:\train_data2\MM-WHS\ct_train2' 10 | import numpy as np 11 | import os # 遍历文件夹 12 | import nibabel as nib 13 | import imageio # 转换成图像 14 | 15 | 16 | def nii_to_image(niifile): 17 | filenames = os.listdir(filepath) # 读取nii文件夹 18 | slice_trans = [] 19 | 20 | for f in filenames: 21 | # 开始读取nii文件 22 | img_path = os.path.join(filepath, f) 23 | img = nib.load(img_path) # 读取nii 24 | img_fdata = img.get_fdata() 25 | fname = f.replace('.nii', '') # 去掉nii的后缀名 26 | img_f_path = os.path.join(imgfile, fname) 27 | # 创建nii对应的图像的文件夹 28 | if not os.path.exists(img_f_path): 29 | os.mkdir(img_f_path) # 新建文件夹 30 | 31 | # 开始转换为图像 32 | (x, y, z) = img.shape 33 | for i in range(z): # z是图像的序列 34 | silce = img_fdata[i, :, :] # 选择哪个方向的切片都可以 35 | imageio.imwrite(os.path.join(img_f_path, '{}.png'.format(i)), silce) 36 | # 保存图像 37 | 38 | 39 | if __name__ == '__main__': 40 | filepath = example_filename 41 | imgfile = r"F:\train_data2\MM-WHS\train1-img" 42 | nii_to_image(filepath) 43 | --------------------------------------------------------------------------------