# 애들 교육용으로 만든 자료 아래 링크로 COLAB 접속하셔요~~~~~~~~~~~~ Dataset download: https://drive.google.com/file/d/1mfPYpM7MkwpWN92oK_BKn5lm4OVvNGYN/view?usp=sharing Colab computing environment: https://colab.research.google.com/drive/1UWqUtZdTbFKBWgcSD9x-LhjfTF9nUOW-?usp=sharing 　 [![강의 동영상](readme_figures/capture.png)](https://youtu.be/LpWHXQ51Nh0) 　 # Jeelab mouse body tracking example using U-Net written by 한효빈, HioBeen Han - hiobeen.han@kaist.ac.kr JEELAB, KIST version 2021-04-03a Visit here: https://colab.research.google.com/drive/1UWqUtZdTbFKBWgcSD9x-LhjfTF9nUOW-?usp=sharing # Part 1. 환경 설정 & 데이터 불러오기 ```python """ SET ENVIRONMENT """ # Drive mount from google.colab import auth,drive,files drive.mount('/content/gdrive') """ Load modules """ import os, cv2 import numpy as np from glob import glob from matplotlib import pyplot as plt; from matplotlib import cm; plt.style.use('ggplot') # Download U-net !pip install git+https://github.com/karolzak/keras-unet from keras_unet.models import custom_unet ``` Mounted at /content/gdrive Collecting git+https://github.com/karolzak/keras-unet Cloning https://github.com/karolzak/keras-unet to /tmp/pip-req-build-f1bk3r61 Running command git clone -q https://github.com/karolzak/keras-unet /tmp/pip-req-build-f1bk3r61 Building wheels for collected packages: keras-unet Building wheel for keras-unet (setup.py) ... [?25l[?25hdone Created wheel for keras-unet: filename=keras_unet-0.1.2-cp37-none-any.whl size=16992 sha256=5fababfae4ceea970f38f08de8905ecaa2a730a742f19c8c2d23559848a6bb2c Stored in directory: /tmp/pip-ephem-wheel-cache-6fwpnftv/wheels/b3/3a/85/c3df1c96b5d83dcd2c09b634e72a98cafcf12a52501ac5cd77 Successfully built keras-unet Installing collected packages: keras-unet Successfully installed keras-unet-0.1.2 ----------------------------------------- keras-unet init: TF version is >= 2.0.0 - using `tf.keras` instead of `Keras` ----------------------------------------- ```python """ Google Drive 경로 파일에 되었는지 확인하기 """ # 시스템(리눅스) 명령어 사용은 커맨드 앞에 느낌표(!)를 붙이고 작성 # ls -> listdir의 약자로, directory 내 파일들을 listing 하는 기능을 수행함 # !ls gdrive/MyDrive/'Colab Notebooks'/tutorial_data/ ``` frames.zip trainset_added.zip trainset.zip ```python """ 구글드라이브에 올라간 데이터를 Colab 가상환경으로 복사하기 """ # cp 명령어는 copy의 약자로, cp A B라고 쓰면 A를 B 경로로 복사함. # cp -r A B 라고 쓰면, A라는 디렉토리 내 파일들을 통째로 B에 복사함. # 이 예시에서는 tutorial_data/ 폴더 내의 파일들을 content/ 안으로 옮길 것. # !cp -r gdrive/MyDrive/'Colab Notebooks'/tutorial_data/ content/ ``` ```python """ Zip 파일들 압축 해제하기 """ !unzip content/trainset.zip -d content/ !unzip content/frames.zip -d content/ ``` Archive: content/trainset.zip inflating: content/data/trainset/.DS_Store inflating: content/data/trainset/frame-000007-sample_04-from1024x1024-to448x448.jpg inflating: content/data/trainset/frame-000024-sample_02-from1024x1024-to448x448.jpg inflating: content/data/frames/sample_02/frame-000223-sample_02-from1024x1024-to448x448.jpg inflating: content/data/frames/sample_02/frame-000224-sample_02-from1024x1024-to448x448.jpg inflating: content/data/frames/sample_02/frame-000225-sample_02-from1024x1024-to448x448.jpg ```python """ 이미지 데이터를 Numpy array로 읽어오기 """ data_dir = 'content/data/trainset/' filename_x = glob( data_dir+'frame*.jpg' ); filename_x.sort() filename_y = glob( data_dir+'mask*.jpg' ); filename_y.sort() n_files = len( filename_x ) shape = (448, 448) x_raw = np.zeros(shape=(n_files,shape[0],shape[1]), dtype='float32') y_raw = np.zeros(shape=(n_files,shape[0],shape[1]), dtype='bool') for fileIdx in range(n_files): x_raw[fileIdx,] = cv2.imread( filename_x[fileIdx] )[:,:,0] y_raw[fileIdx,] = cv2.imread( filename_y[fileIdx] )[:,:,0]>0 ``` ```python """ Dataset augmentation """ # # 이미지를 가로/세로/가로세로 반전시켜서 4배르 뻥튀기하기 # x_aug = np.zeros( (x_raw.shape[0]*4, x_raw.shape[1], x_raw.shape[2] ), dtype='float32') y_aug = np.zeros( (y_raw.shape[0]*4, y_raw.shape[1], y_raw.shape[2] ), dtype='bool') imgIdx = 0 for fileIdx in range(x_raw.shape[0]): frame, mask = x_raw[fileIdx,], y_raw[fileIdx,] frame = frame/255 # Appending original data x_aug[imgIdx,:,:] = frame y_aug[imgIdx,:,:] = mask imgIdx+=1 # Flip x-axis x_aug[imgIdx,:,:] = frame[::-1,:] y_aug[imgIdx,:,:] = mask[::-1,:] imgIdx+=1 # Flip y-axis x_aug[imgIdx,:,:] = frame[:,::-1] y_aug[imgIdx,:,:] = mask[:,::-1] imgIdx+=1 # Flip x & y-axis x_aug[imgIdx,:,:] = frame[::-1,::-1] y_aug[imgIdx,:,:] = mask[::-1,::-1] imgIdx+=1 print(x_aug.shape) ``` (400, 448, 448) ```python """ 잘 import 되었는지 확인하기 위한 visualization """ plt.style.use('ggplot') np.random.seed(20210404) for fIdx in np.random.randint(0,x_aug.shape[0],3): print('Image number %s'%fIdx ) plt.figure(figsize=(8,4)) plt.subplot(121); plt.imshow(x_aug[fIdx,:,:], cmap=cm.gray); plt.axis('off') plt.title('Input (X)') plt.subplot(122); plt.imshow(y_aug[fIdx,:,:], cmap=cm.gray); plt.axis('off') plt.title('Mask (Y)') plt.show() ``` img idx = 182 ![png](readme_figures/output_8_1.png) img idx = 239 ![png](readme_figures/output_8_3.png) img idx = 306 ![png](readme_figures/output_8_5.png) # Part 2. CNN 모델 빌드 & 훈련 진행하기 세부적인 파라미터 튜닝 필요 1. use_batch_norm => 각 레이어의 입/출력을 0-1 사이로 표준화. 2. filters => 필터 사이즈 (클수록 sparse하게 봄). 3. dropout => 랜덤하게 끊을 커넥션의 비율 (0-1). 4. output_activation => 활성 함수의 종류 (*relu*, *sigmoid*, *tanh*, etc.). 5. optimizer => *adam*으로 learning_rate = 1e-04 정도면 적당히 빨리 배움 ```python """ Train/Test 세트 나누기 """ randseed = 20210404 from sklearn.model_selection import train_test_split x_train, x_test, y_train, y_test = train_test_split( np.expand_dims( x_aug, 3 ), np.expand_dims( y_aug, 3 ), test_size=0.1, random_state=np.random.seed(randseed)) print('Training set size => (%d,%d,%d,%d)'%x_train.shape) print(' Test set size => (%d,%d,%d,%d)'%x_test.shape) ``` Training set size => (360,448,448,1) Test set size => (40,448,448,1) ```python """ CNN 네트워크 빌드하기 """ from keras.callbacks import History; history = History(); histories = [] from keras import backend as K; import gc gc.collect(); K.clear_session() from keras import optimizers model = custom_unet(input_shape=(shape[0],shape[1],1), use_batch_norm=1, num_classes=y_train.shape[-1], filters=16, dropout=0.20, output_activation='sigmoid') metric = 'binary_crossentropy' model.compile(optimizer = 'Adam', loss = metric, metrics = [metric]) #model.summary() ``` ```python """ Training """ fitIdx, nFit = 1, 100 while fitIdx < nFit: history = model.fit(x_train,y_train,epochs=1,verbose=2,batch_size=32, validation_data=(x_test,y_test) ) # Sample visualization if np.mod(fitIdx,10)==1: print('Epoch => [~%3d] / %3d'%((fitIdx), nFit)) pred = model.predict(x_test) for predIdx in range(3): plt.figure(figsize=(6,3)) plt.subplot(1,3,1); plt.imshow(x_test[predIdx,:,:,0], cmap=cm.gray); plt.axis('off'); plt.title('input X') plt.subplot(1,3,2); plt.imshow(y_test[predIdx,:,:,0], cmap=cm.gray); plt.axis('off'); plt.title('actual Y') plt.subplot(1,3,3); plt.imshow(pred[predIdx,:,:,0], cmap=cm.gray); plt.axis('off'); plt.title('predicted Y') plt.clim(0,1); plt.show() fitIdx += 1 ``` 12/12 - 43s - loss: 0.7374 - binary_crossentropy: 0.7374 - val_loss: 0.6141 - val_binary_crossentropy: 0.6141 Epoch => [~ 1] / 100 ![png](readme_figures/output_12_1.png) ![png](readme_figures/output_12_2.png) ![png](readme_figures/output_12_3.png) 12/12 - 6s - loss: 0.6564 - binary_crossentropy: 0.6564 - val_loss: 0.3968 - val_binary_crossentropy: 0.3968 12/12 - 6s - loss: 0.6179 - binary_crossentropy: 0.6179 - val_loss: 0.4370 - val_binary_crossentropy: 0.4370 12/12 - 6s - loss: 0.5834 - binary_crossentropy: 0.5834 - val_loss: 0.4926 - val_binary_crossentropy: 0.4926 12/12 - 6s - loss: 0.5477 - binary_crossentropy: 0.5477 - val_loss: 0.4957 - val_binary_crossentropy: 0.4957 12/12 - 6s - loss: 0.5095 - binary_crossentropy: 0.5095 - val_loss: 1.3305 - val_binary_crossentropy: 1.3305 12/12 - 6s - loss: 0.4682 - binary_crossentropy: 0.4682 - val_loss: 0.3847 - val_binary_crossentropy: 0.3847 12/12 - 6s - loss: 0.4213 - binary_crossentropy: 0.4213 - val_loss: 0.7654 - val_binary_crossentropy: 0.7654 12/12 - 6s - loss: 0.3729 - binary_crossentropy: 0.3729 - val_loss: 0.2787 - val_binary_crossentropy: 0.2787 12/12 - 6s - loss: 0.3347 - binary_crossentropy: 0.3347 - val_loss: 0.7169 - val_binary_crossentropy: 0.7169 12/12 - 6s - loss: 0.3018 - binary_crossentropy: 0.3018 - val_loss: 0.3398 - val_binary_crossentropy: 0.3398 Epoch => [~ 11] / 100 ![png](readme_figures/output_12_5.png) ![png](readme_figures/output_12_6.png) ![png](readme_figures/output_12_7.png) 12/12 - 6s - loss: 0.2703 - binary_crossentropy: 0.2703 - val_loss: 0.1813 - val_binary_crossentropy: 0.1813 12/12 - 6s - loss: 0.2422 - binary_crossentropy: 0.2422 - val_loss: 0.1555 - val_binary_crossentropy: 0.1555 12/12 - 6s - loss: 0.2183 - binary_crossentropy: 0.2183 - val_loss: 0.1343 - val_binary_crossentropy: 0.1343 12/12 - 6s - loss: 0.1976 - binary_crossentropy: 0.1976 - val_loss: 0.1250 - val_binary_crossentropy: 0.1250 12/12 - 6s - loss: 0.1786 - binary_crossentropy: 0.1786 - val_loss: 0.1164 - val_binary_crossentropy: 0.1164 12/12 - 6s - loss: 0.1608 - binary_crossentropy: 0.1608 - val_loss: 0.1065 - val_binary_crossentropy: 0.1065 12/12 - 6s - loss: 0.1459 - binary_crossentropy: 0.1459 - val_loss: 0.1003 - val_binary_crossentropy: 0.1003 12/12 - 6s - loss: 0.1328 - binary_crossentropy: 0.1328 - val_loss: 0.0941 - val_binary_crossentropy: 0.0941 12/12 - 6s - loss: 0.1207 - binary_crossentropy: 0.1207 - val_loss: 0.0890 - val_binary_crossentropy: 0.0890 12/12 - 6s - loss: 0.1116 - binary_crossentropy: 0.1116 - val_loss: 0.0861 - val_binary_crossentropy: 0.0861 Epoch => [~ 21] / 100 ![png](readme_figures/output_12_9.png) ![png](readme_figures/output_12_10.png) ![png](readme_figures/output_12_11.png) 12/12 - 6s - loss: 0.1023 - binary_crossentropy: 0.1023 - val_loss: 0.0797 - val_binary_crossentropy: 0.0797 12/12 - 6s - loss: 0.0933 - binary_crossentropy: 0.0933 - val_loss: 0.0759 - val_binary_crossentropy: 0.0759 12/12 - 6s - loss: 0.0845 - binary_crossentropy: 0.0845 - val_loss: 0.0719 - val_binary_crossentropy: 0.0719 12/12 - 6s - loss: 0.0775 - binary_crossentropy: 0.0775 - val_loss: 0.0654 - val_binary_crossentropy: 0.0654 12/12 - 6s - loss: 0.0715 - binary_crossentropy: 0.0715 - val_loss: 0.0627 - val_binary_crossentropy: 0.0627 12/12 - 6s - loss: 0.0663 - binary_crossentropy: 0.0663 - val_loss: 0.0583 - val_binary_crossentropy: 0.0583 12/12 - 6s - loss: 0.0616 - binary_crossentropy: 0.0616 - val_loss: 0.0623 - val_binary_crossentropy: 0.0623 12/12 - 6s - loss: 0.0571 - binary_crossentropy: 0.0571 - val_loss: 0.0571 - val_binary_crossentropy: 0.0571 12/12 - 6s - loss: 0.0531 - binary_crossentropy: 0.0531 - val_loss: 0.0475 - val_binary_crossentropy: 0.0475 12/12 - 6s - loss: 0.0503 - binary_crossentropy: 0.0503 - val_loss: 0.0443 - val_binary_crossentropy: 0.0443 Epoch => [~ 31] / 100 ![png](readme_figures/output_12_13.png) ![png](readme_figures/utput_12_14.png) ![png](readme_figures/output_12_15.png) 12/12 - 6s - loss: 0.0469 - binary_crossentropy: 0.0469 - val_loss: 0.0424 - val_binary_crossentropy: 0.0424 12/12 - 6s - loss: 0.0438 - binary_crossentropy: 0.0438 - val_loss: 0.0411 - val_binary_crossentropy: 0.0411 12/12 - 6s - loss: 0.0411 - binary_crossentropy: 0.0411 - val_loss: 0.0386 - val_binary_crossentropy: 0.0386 12/12 - 6s - loss: 0.0388 - binary_crossentropy: 0.0388 - val_loss: 0.0358 - val_binary_crossentropy: 0.0358 12/12 - 6s - loss: 0.0368 - binary_crossentropy: 0.0368 - val_loss: 0.0337 - val_binary_crossentropy: 0.0337 12/12 - 6s - loss: 0.0348 - binary_crossentropy: 0.0348 - val_loss: 0.0328 - val_binary_crossentropy: 0.0328 12/12 - 6s - loss: 0.0330 - binary_crossentropy: 0.0330 - val_loss: 0.0318 - val_binary_crossentropy: 0.0318 12/12 - 6s - loss: 0.0312 - binary_crossentropy: 0.0312 - val_loss: 0.0310 - val_binary_crossentropy: 0.0310 12/12 - 6s - loss: 0.0297 - binary_crossentropy: 0.0297 - val_loss: 0.0295 - val_binary_crossentropy: 0.0295 12/12 - 6s - loss: 0.0283 - binary_crossentropy: 0.0283 - val_loss: 0.0289 - val_binary_crossentropy: 0.0289 Epoch => [~ 41] / 100 ![png](readme_figures/output_12_17.png) ![png](readme_figures/output_12_18.png) ![png](readme_figures/output_12_19.png) 12/12 - 6s - loss: 0.0271 - binary_crossentropy: 0.0271 - val_loss: 0.0281 - val_binary_crossentropy: 0.0281 12/12 - 6s - loss: 0.0259 - binary_crossentropy: 0.0259 - val_loss: 0.0264 - val_binary_crossentropy: 0.0264 12/12 - 6s - loss: 0.0249 - binary_crossentropy: 0.0249 - val_loss: 0.0270 - val_binary_crossentropy: 0.0270 12/12 - 6s - loss: 0.0238 - binary_crossentropy: 0.0238 - val_loss: 0.0248 - val_binary_crossentropy: 0.0248 12/12 - 6s - loss: 0.0229 - binary_crossentropy: 0.0229 - val_loss: 0.0238 - val_binary_crossentropy: 0.0238 12/12 - 6s - loss: 0.0219 - binary_crossentropy: 0.0219 - val_loss: 0.0233 - val_binary_crossentropy: 0.0233 12/12 - 6s - loss: 0.0212 - binary_crossentropy: 0.0212 - val_loss: 0.0233 - val_binary_crossentropy: 0.0233 12/12 - 6s - loss: 0.0205 - binary_crossentropy: 0.0205 - val_loss: 0.0219 - val_binary_crossentropy: 0.0219 12/12 - 6s - loss: 0.0197 - binary_crossentropy: 0.0197 - val_loss: 0.0202 - val_binary_crossentropy: 0.0202 12/12 - 6s - loss: 0.0190 - binary_crossentropy: 0.0190 - val_loss: 0.0209 - val_binary_crossentropy: 0.0209 Epoch => [~ 51] / 100 ![png](readme_figures/output_12_21.png) ![png](readme_figures/output_12_22.png) ![png](readme_figures/output_12_23.png) 12/12 - 6s - loss: 0.0182 - binary_crossentropy: 0.0182 - val_loss: 0.0207 - val_binary_crossentropy: 0.0207 12/12 - 6s - loss: 0.0178 - binary_crossentropy: 0.0178 - val_loss: 0.0209 - val_binary_crossentropy: 0.0209 12/12 - 6s - loss: 0.0173 - binary_crossentropy: 0.0173 - val_loss: 0.0194 - val_binary_crossentropy: 0.0194 12/12 - 6s - loss: 0.0166 - binary_crossentropy: 0.0166 - val_loss: 0.0189 - val_binary_crossentropy: 0.0189 12/12 - 6s - loss: 0.0161 - binary_crossentropy: 0.0161 - val_loss: 0.0195 - val_binary_crossentropy: 0.0195 12/12 - 6s - loss: 0.0156 - binary_crossentropy: 0.0156 - val_loss: 0.0186 - val_binary_crossentropy: 0.0186 12/12 - 6s - loss: 0.0153 - binary_crossentropy: 0.0153 - val_loss: 0.0175 - val_binary_crossentropy: 0.0175 12/12 - 6s - loss: 0.0149 - binary_crossentropy: 0.0149 - val_loss: 0.0162 - val_binary_crossentropy: 0.0162 12/12 - 6s - loss: 0.0149 - binary_crossentropy: 0.0149 - val_loss: 0.0157 - val_binary_crossentropy: 0.0157 12/12 - 6s - loss: 0.0149 - binary_crossentropy: 0.0149 - val_loss: 0.0153 - val_binary_crossentropy: 0.0153 Epoch => [~ 61] / 100 ![png](readme_figures/output_12_25.png) ![png](readme_figures/output_12_26.png) ![png](readme_figures/output_12_27.png) 12/12 - 6s - loss: 0.0144 - binary_crossentropy: 0.0144 - val_loss: 0.0177 - val_binary_crossentropy: 0.0177 12/12 - 6s - loss: 0.0140 - binary_crossentropy: 0.0140 - val_loss: 0.0153 - val_binary_crossentropy: 0.0153 12/12 - 6s - loss: 0.0135 - binary_crossentropy: 0.0135 - val_loss: 0.0150 - val_binary_crossentropy: 0.0150 12/12 - 6s - loss: 0.0129 - binary_crossentropy: 0.0129 - val_loss: 0.0162 - val_binary_crossentropy: 0.0162 12/12 - 6s - loss: 0.0126 - binary_crossentropy: 0.0126 - val_loss: 0.0155 - val_binary_crossentropy: 0.0155 12/12 - 6s - loss: 0.0122 - binary_crossentropy: 0.0122 - val_loss: 0.0156 - val_binary_crossentropy: 0.0156 12/12 - 6s - loss: 0.0120 - binary_crossentropy: 0.0120 - val_loss: 0.0159 - val_binary_crossentropy: 0.0159 12/12 - 6s - loss: 0.0118 - binary_crossentropy: 0.0118 - val_loss: 0.0149 - val_binary_crossentropy: 0.0149 12/12 - 6s - loss: 0.0114 - binary_crossentropy: 0.0114 - val_loss: 0.0148 - val_binary_crossentropy: 0.0148 12/12 - 6s - loss: 0.0111 - binary_crossentropy: 0.0111 - val_loss: 0.0150 - val_binary_crossentropy: 0.0150 Epoch => [~ 71] / 100 ![png](readme_figures/readme_figures/output_12_29.png) ![png](readme_figures/output_12_30.png) ![png](readme_figures/output_12_31.png) 12/12 - 6s - loss: 0.0109 - binary_crossentropy: 0.0109 - val_loss: 0.0155 - val_binary_crossentropy: 0.0155 12/12 - 6s - loss: 0.0107 - binary_crossentropy: 0.0107 - val_loss: 0.0145 - val_binary_crossentropy: 0.0145 12/12 - 6s - loss: 0.0105 - binary_crossentropy: 0.0105 - val_loss: 0.0149 - val_binary_crossentropy: 0.0149 12/12 - 6s - loss: 0.0103 - binary_crossentropy: 0.0103 - val_loss: 0.0151 - val_binary_crossentropy: 0.0151 12/12 - 6s - loss: 0.0101 - binary_crossentropy: 0.0101 - val_loss: 0.0138 - val_binary_crossentropy: 0.0138 12/12 - 6s - loss: 0.0099 - binary_crossentropy: 0.0099 - val_loss: 0.0143 - val_binary_crossentropy: 0.0143 12/12 - 6s - loss: 0.0098 - binary_crossentropy: 0.0098 - val_loss: 0.0157 - val_binary_crossentropy: 0.0157 12/12 - 6s - loss: 0.0097 - binary_crossentropy: 0.0097 - val_loss: 0.0145 - val_binary_crossentropy: 0.0145 12/12 - 6s - loss: 0.0094 - binary_crossentropy: 0.0094 - val_loss: 0.0157 - val_binary_crossentropy: 0.0157 12/12 - 6s - loss: 0.0093 - binary_crossentropy: 0.0093 - val_loss: 0.0153 - val_binary_crossentropy: 0.0153 Epoch => [~ 81] / 100 ![png](readme_figures/output_12_33.png) ![png](readme_figures/output_12_34.png) ![png](readme_figures/output_12_35.png) 12/12 - 6s - loss: 0.0091 - binary_crossentropy: 0.0091 - val_loss: 0.0143 - val_binary_crossentropy: 0.0143 12/12 - 6s - loss: 0.0089 - binary_crossentropy: 0.0089 - val_loss: 0.0159 - val_binary_crossentropy: 0.0159 12/12 - 6s - loss: 0.0088 - binary_crossentropy: 0.0088 - val_loss: 0.0148 - val_binary_crossentropy: 0.0148 12/12 - 6s - loss: 0.0086 - binary_crossentropy: 0.0086 - val_loss: 0.0140 - val_binary_crossentropy: 0.0140 12/12 - 6s - loss: 0.0088 - binary_crossentropy: 0.0088 - val_loss: 0.0136 - val_binary_crossentropy: 0.0136 12/12 - 6s - loss: 0.0087 - binary_crossentropy: 0.0087 - val_loss: 0.0116 - val_binary_crossentropy: 0.0116 12/12 - 6s - loss: 0.0084 - binary_crossentropy: 0.0084 - val_loss: 0.0133 - val_binary_crossentropy: 0.0133 12/12 - 6s - loss: 0.0083 - binary_crossentropy: 0.0083 - val_loss: 0.0134 - val_binary_crossentropy: 0.0134 12/12 - 6s - loss: 0.0081 - binary_crossentropy: 0.0081 - val_loss: 0.0150 - val_binary_crossentropy: 0.0150 12/12 - 6s - loss: 0.0080 - binary_crossentropy: 0.0080 - val_loss: 0.0138 - val_binary_crossentropy: 0.0138 Epoch => [~ 91] / 100 ![png](readme_figures/output_12_37.png) ![png](readme_figures/output_12_38.png) ![png](readme_figures/output_12_39.png) 12/12 - 6s - loss: 0.0079 - binary_crossentropy: 0.0079 - val_loss: 0.0145 - val_binary_crossentropy: 0.0145 12/12 - 6s - loss: 0.0078 - binary_crossentropy: 0.0078 - val_loss: 0.0132 - val_binary_crossentropy: 0.0132 12/12 - 6s - loss: 0.0077 - binary_crossentropy: 0.0077 - val_loss: 0.0131 - val_binary_crossentropy: 0.0131 12/12 - 6s - loss: 0.0075 - binary_crossentropy: 0.0075 - val_loss: 0.0150 - val_binary_crossentropy: 0.0150 12/12 - 6s - loss: 0.0074 - binary_crossentropy: 0.0074 - val_loss: 0.0134 - val_binary_crossentropy: 0.0134 12/12 - 6s - loss: 0.0074 - binary_crossentropy: 0.0074 - val_loss: 0.0133 - val_binary_crossentropy: 0.0133 12/12 - 6s - loss: 0.0074 - binary_crossentropy: 0.0074 - val_loss: 0.0118 - val_binary_crossentropy: 0.0118 12/12 - 6s - loss: 0.0072 - binary_crossentropy: 0.0072 - val_loss: 0.0141 - val_binary_crossentropy: 0.0141 # Part 3. Non-training frames (Full video) 에 대해 모델 prediction 적용하기 ```python """ Video frame 읽어온 후 predict 하기 + 그림 파일로 save 하기 """ from datetime import datetime, timedelta datestr = (datetime.now()+timedelta(hours=9)).strftime("%Y%m%d-%H%M%S") dir_import = 'content/data/frames/' dirList = glob(dir_import+'*sample*') dirList.sort() writedir = 'content/data/predicts/' #!rm -r content/data/predicts/ os.system('mkdir %s'%writedir); for dirName in dirList: print('Now processing [%s]..'%dirName) predicts, frames = [], [] # set directory dir_export = writedir+dirName.split('/')[-1]+'/' if not os.path.isdir(dir_export): os.mkdir(dir_export) # Get files fileList = glob(dirName+'/frame*.jpg'); fileList.sort() for fileName_load in fileList: fileName_save = dir_export+'mask'+fileName_load.split('/')[-1][5:] exist = len( glob(fileName_save) )>0 if True:#if not exist: # Load raw frame frame = cv2.imread(fileName_load)[:,:,0]/255 frame = np.swapaxes(np.expand_dims(np.array(frame),2),0,2) # Predict pred = np.squeeze(model.predict(frame)) #plt.imshow(255*pred); plt.show() # Save mask fileName_save = dir_export+'pred'+fileName_load.split('/')[-1][5:] cv2.imwrite(fileName_save,pred*255) os.system('zip -r content/predicts-%s.zip content/data/predicts/'%datestr) files.download('content/predicts-%s.zip'%datestr) ``` Now processing [content/data/frames/sample_01].. Now processing [content/data/frames/sample_02].. Now processing [content/data/frames/sample_03].. Now processing [content/data/frames/sample_04].. ```python """ 저장하기 """ # 모델 파일 저장하기 model_fname = 'content/model-%s.h5'%datestr model.save_weights(model_fname) # 구글드라이브에 백업하기 os.system('zip -r content/predicts-%s.zip content/data/predicts/'%datestr) os.system("cp content/predicts-%s.zip content/gdrive/MyDrive/'Colab Notebooks'/tutorial_data/"%datestr) os.system("cp content/model-%s.h5 content/gdrive/MyDrive/'Colab Notebooks'/tutorial_data/"%datestr) # 로컬 컴퓨터에 다운로드 받기 files.download('content/predicts-%s.zip'%datestr) files.download('content/model-%s.h5'%datestr) ```