Блог

Я работаю над проектом глубокого обучения (обнаружение дефектов поверхности) для обнаружения трещин и исправлений дефектов на изображениях (с исходным размером (2160,3840,3))и с двумя метками(трещины и исправления). После того, как я обучил свою модель при тестировании модели на новых данных, она выдает следующую ошибку . Для справки я приведу свой код ниже.

 error: OpenCV(4.1.2) /io/opencv/modules/imgproc/src/resize.cpp:3720: error: (-215:Assertion failed) !ssize.empty() in function 'resize'   train_dir = '/content/drive/MyDrive/usetrain/train' test_dir = '/content/drive/MyDrive/usetrain/test'  from glob import glob  train_cracks = glob('/content/drive/MyDrive/usetrain/train/cracks/*')  train_cracks[:10]  '/content/drive/MyDrive/usetrain/train/cracks/F04486-L462-03-2021-C29-PILACAVA-CLX01_5.jpg',  '/content/drive/MyDrive/usetrain/train/cracks/F04701-L473-03-2021-C06-PILACAVA-CLX02_8.jpg',  '/content/drive/MyDrive/usetrain/train/cracks/F04486-L462-03-2021-C45-PILACAVA-CLX01_5.jpg',  '/content/drive/MyDrive/usetrain/train/cracks/F04704-L488-03-2021-C06-PILACAVA-CLX02_4.JPG',  '/content/drive/MyDrive/usetrain/train/cracks/F04486-L462-03-2021-C40-PILACAVA-CLX01_6.jpg',  '/content/drive/MyDrive/usetrain/train/cracks/F04701-L473-03-2021-C09-PILACAVA-CLX01_2.jpg',  '/content/drive/MyDrive/usetrain/train/cracks/F04486-L462-03-2021-C18-PILACAVA-CLX01_1.jpg',  '/content/drive/MyDrive/usetrain/train/cracks/F04486-L462-03-2021-C11-PILACAVA-CLX01_1.jpg',  '/content/drive/MyDrive/usetrain/train/cracks/F04704-L488-03-2021-C01-PILACAVA-CLX01_6.JPG',  '/content/drive/MyDrive/usetrain/train/cracks/F04486-L462-03-2021-C24-PILACAVA-CLX01_3.jpg']   img1 = cv2.imread('/content/drive/MyDrive/usetrain/test/cracks/F04478-L397-04-2021-C05-PILACAVA-CLX02_8.jpg') dimensions = img.shape print(dimensions)  (2160, 3840, 3)   train_patches = glob('/content/drive/MyDrive/usetrain/train/patches/*')   test_imgs = glob('/content/drive/MyDrive/usetrain/test/*')   train_imgs = train_cracks[:35]   train_patches[:35] # slice the dataset and use 2000 in each class random.shuffle(train_imgs) # shuffle it randomly    Function for labels and images   #A function to read and process the images to an acceptable format for our model def read_and_process_image(list_of_images):  '''  Returns two arravs  X is an array of resized images  y is an array of labels  '''   X=[] #Images  y = [] #labels   for image in list_of_images:  X.append(cv2.resize(cv2.imread(image, cv2. IMREAD_COLOR), (nrows, ncolumns), interpolation=cv2. INTER_CUBIC)) #Read the image  #get the labels  if'cracks' in image:  y. append(1)  elif 'patches'in image:  y. append(0)  return X,y   X, y = read_and_process_image(train_imgs)   X[0]  array([[[182, 200, 207],  [187, 199, 209],  [189, 199, 208],  ...,  [106, 121, 136],  [110, 124, 142],  [126, 134, 147]],   [[188, 201, 209],  [188, 199, 207],  [192, 203, 211],  ...,  [109, 124, 138],  [110, 127, 136],  [124, 136, 150]],   [[188, 201, 209],  [193, 205, 209],  [187, 201, 210],  ...,  [107, 124, 136],  [110, 126, 138],  [117, 129, 139]],   ...,   [[196, 199, 203],  [212, 215, 219],  [216, 220, 224],  ...,  [130, 144, 156],  [127, 144, 153],  [134, 152, 163]],   [[187, 190, 194],  [210, 213, 217],  [211, 215, 219],  ...,  [128, 144, 152],  [131, 149, 158],  [132, 150, 161]],   [[180, 183, 187],  [204, 209, 212],  [210, 217, 220],  ...,  [123, 140, 148],  [133, 152, 159],  [135, 154, 162]]], dtype=uint8)   y  [1,  1,  0,  1,  1,  0,  1,  1,  1,  0,  1,  1,  1,  0,  0,  0,  1,  0,  1,  0,  0,  1,  0,  0,  1,  1,  1,  0,  0,  1,  0,  1,  0,  0,  0,  1,  1,  0,  1,  1,  0,  1,  0,  1,  1,  1,  0,  1,  1,  1,  0,  0,  0,  0,  0,  0,  1,  0,  1,  0,  1,  1,  0,  0,  0,  0,  1,  0,  1,  0]   plt. figure(figsize=(20,10)) columns = 5 for i in range (columns) :  plt. subplot (5 / columns   1, columns, i   1)  plt.imshow(X[i])     import seaborn as sns  #Convert list to numpy array X = np.array(X) y = np.array(y)  #Lets plot the label to be sure we just have two class sns.countplot(y)  plt.title('Labels for Cracks and Patches')     print( "Shape of train images is:", X. shape) print ("Shape of labels is:", y. shape)  Shape of train images is: (70, 150, 150, 3) Shape of labels is: (70,)   #Lets split the data into train and test set from sklearn.model_selection import train_test_split X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.20, random_state=2)  print("Shape of train images is:", X_train.shape) print("Shape of validation images is:", X_val.shape) print("Shape of labels is:", y_train. shape) print("Shape of labels is:", y_val.shape)  Shape of train images is: (56, 150, 150, 3) Shape of validation images is: (14, 150, 150, 3) Shape of labels is: (56,) Shape of labels is: (14,)   #get the length of the train and validation data ntrain = len(X_train) nval = len(X_val)   batch_size = 4   #Keras Model   from keras import layers from keras import models from keras import optimizers from keras.preprocessing.image import ImageDataGenerator from keras.preprocessing.image import img_to_array, load_img   model = models.Sequential() model.add(layers.Conv2D(32, (3, 3), activation='relu',input_shape=(150, 150, 3))) model.add(layers.MaxPooling2D((2, 2))) model.add(layers.Conv2D(64, (3, 3), activation='relu')) model.add(layers.MaxPooling2D((2, 2))) model.add(layers.Conv2D(128, (3, 3), activation='relu')) model.add(layers.MaxPooling2D((2, 2))) model.add(layers.Conv2D(128, (3, 3), activation='relu')) model.add(layers.MaxPooling2D((2, 2))) model.add(layers.Flatten()) model.add(layers.Dropout(0.5)) #Dropout for regularization model.add(layers.Dense (512, activation='relu')) model.add(layers.Dense(1, activation='sigmoid'))   model.summary()  Model: "sequential" _________________________________________________________________ Layer (type) Output Shape Param #  ================================================================= conv2d (Conv2D) (None, 148, 148, 32) 896  _________________________________________________________________ max_pooling2d (MaxPooling2D) (None, 74, 74, 32) 0  _________________________________________________________________ conv2d_1 (Conv2D) (None, 72, 72, 64) 18496  _________________________________________________________________ max_pooling2d_1 (MaxPooling2 (None, 36, 36, 64) 0  _________________________________________________________________ conv2d_2 (Conv2D) (None, 34, 34, 128) 73856  _________________________________________________________________ max_pooling2d_2 (MaxPooling2 (None, 17, 17, 128) 0  _________________________________________________________________ conv2d_3 (Conv2D) (None, 15, 15, 128) 147584  _________________________________________________________________ max_pooling2d_3 (MaxPooling2 (None, 7, 7, 128) 0  _________________________________________________________________ flatten (Flatten) (None, 6272) 0  _________________________________________________________________ dropout (Dropout) (None, 6272) 0  _________________________________________________________________ dense (Dense) (None, 512) 3211776  _________________________________________________________________ dense_1 (Dense) (None, 1) 513  ================================================================= Total params: 3,453,121 Trainable params: 3,453,121 Non-trainable params: 0 _________________________________________________________________   from tensorflow.keras import optimizers   #We'Il use the RMSprop bptimizer with a learning rate of .0001  #We'1l use binary_crossenropy loss because its a binary classification model.compile(loss='binary_crossentropy', optimizer=optimizers.RMSprop(learning_rate=1e-4), metrics=['acc'])   #Lets create the augmentation configuration  train_datagen = ImageDataGenerator (rescale=1./255,  rotation_range=40,  width_shift_range=0.2,  height_shift_range=0.2,  shear_range=0.2,  zoom_range=0.2,horizontal_flip=True,)  val_datagen = ImageDataGenerator(rescale=1./255)   #Create the image generators train_generator = train_datagen.flow(X_train, y_train, batch_size=batch_size) val_generator = val_datagen.flow(X_val, y_val, batch_size=batch_size)   #The training part  history = model.fit(train_generator, steps_per_epoch= ntrain // batch_size, epochs=100, validation_data=val_generator,validation_steps= nval//batch_size)  Epoch 1/100 14/14 [==============================] - 3s 52ms/step - loss: 0.7155 - acc: 0.4464 - val_loss: 0.7217 - val_acc: 0.4167 Epoch 2/100 14/14 [==============================] - 1s 35ms/step - loss: 0.7124 - acc: 0.4107 - val_loss: 0.6927 - val_acc: 0.5000 Epoch 3/100 14/14 [==============================] - 1s 37ms/step - loss: 0.6994 - acc: 0.5000 - val_loss: 0.7094 - val_acc: 0.4167 Epoch 4/100 14/14 [==============================] - 1s 38ms/step - loss: 0.7052 - acc: 0.4821 - val_loss: 0.6899 - val_acc: 0.7500 Epoch 5/100 14/14 [==============================] - 1s 38ms/step - loss: 0.6988 - acc: 0.4643 - val_loss: 0.6958 - val_acc: 0.4167 Epoch 6/100 14/14 [==============================] - 1s 39ms/step - loss: 0.6984 - acc: 0.5000 - val_loss: 0.6925 - val_acc: 0.5833 Epoch 7/100 14/14 [==============================] - 1s 35ms/step - loss: 0.7043 - acc: 0.4286 - val_loss: 0.6923 - val_acc: 0.5833 Epoch 8/100 14/14 [==============================] - 1s 40ms/step - loss: 0.6980 - acc: 0.5000 - val_loss: 0.6939 - val_acc: 0.5000 Epoch 9/100 14/14 [==============================] - 1s 36ms/step - loss: 0.7056 - acc: 0.3750 - val_loss: 0.6931 - val_acc: 0.5000 Epoch 10/100 14/14 [==============================] - 1s 37ms/step - loss: 0.6975 - acc: 0.5536 - val_loss: 0.6957 - val_acc: 0.4167 Epoch 11/100 14/14 [==============================] - 1s 39ms/step - loss: 0.6974 - acc: 0.4286 - val_loss: 0.6956 - val_acc: 0.4167 Epoch 12/100 14/14 [==============================] - 1s 37ms/step - loss: 0.6965 - acc: 0.5000 - val_loss: 0.6902 - val_acc: 0.5000 Epoch 13/100 14/14 [==============================] - 1s 40ms/step - loss: 0.6944 - acc: 0.5179 - val_loss: 0.6920 - val_acc: 0.5000 Epoch 14/100 14/14 [==============================] - 1s 39ms/step - loss: 0.6997 - acc: 0.4821 - val_loss: 0.6920 - val_acc: 0.5000 Epoch 15/100 14/14 [==============================] - 1s 42ms/step - loss: 0.6962 - acc: 0.4464 - val_loss: 0.6938 - val_acc: 0.5000 Epoch 16/100 14/14 [==============================] - 1s 39ms/step - loss: 0.6940 - acc: 0.5714 - val_loss: 0.6995 - val_acc: 0.4167 Epoch 17/100 14/14 [==============================] - 1s 36ms/step - loss: 0.6906 - acc: 0.5179 - val_loss: 0.6956 - val_acc: 0.5000 Epoch 18/100 14/14 [==============================] - 1s 36ms/step - loss: 0.6944 - acc: 0.5179 - val_loss: 0.6836 - val_acc: 0.5833 Epoch 19/100 14/14 [==============================] - 1s 38ms/step - loss: 0.6989 - acc: 0.4821 - val_loss: 0.6953 - val_acc: 0.4167 Epoch 20/100 14/14 [==============================] - 1s 37ms/step - loss: 0.6960 - acc: 0.4464 - val_loss: 0.6905 - val_acc: 0.6667 Epoch 21/100 14/14 [==============================] - 1s 36ms/step - loss: 0.6952 - acc: 0.4821 - val_loss: 0.6903 - val_acc: 0.5000 Epoch 22/100 14/14 [==============================] - 1s 37ms/step - loss: 0.6951 - acc: 0.5357 - val_loss: 0.6928 - val_acc: 0.5000 Epoch 23/100 14/14 [==============================] - 1s 36ms/step - loss: 0.6979 - acc: 0.4107 - val_loss: 0.6907 - val_acc: 0.5833 Epoch 24/100 14/14 [==============================] - 1s 37ms/step - loss: 0.6949 - acc: 0.5179 - val_loss: 0.6912 - val_acc: 0.5000 Epoch 25/100 14/14 [==============================] - 1s 36ms/step - loss: 0.6932 - acc: 0.5357 - val_loss: 0.6882 - val_acc: 0.5833 Epoch 26/100 14/14 [==============================] - 1s 40ms/step - loss: 0.6989 - acc: 0.4286 - val_loss: 0.6910 - val_acc: 0.5000 Epoch 27/100 14/14 [==============================] - 1s 38ms/step - loss: 0.6940 - acc: 0.5179 - val_loss: 0.6904 - val_acc: 0.4167 Epoch 28/100 14/14 [==============================] - 1s 38ms/step - loss: 0.6881 - acc: 0.5000 - val_loss: 0.6898 - val_acc: 0.5000 Epoch 29/100 14/14 [==============================] - 1s 36ms/step - loss: 0.6898 - acc: 0.5357 - val_loss: 0.6899 - val_acc: 0.5833 Epoch 30/100 14/14 [==============================] - 1s 37ms/step - loss: 0.6878 - acc: 0.5714 - val_loss: 0.6921 - val_acc: 0.5833 Epoch 31/100 14/14 [==============================] - 1s 36ms/step - loss: 0.6911 - acc: 0.6071 - val_loss: 0.6911 - val_acc: 0.5000 Epoch 32/100 14/14 [==============================] - 1s 39ms/step - loss: 0.6831 - acc: 0.6071 - val_loss: 0.6873 - val_acc: 0.5000 Epoch 33/100 14/14 [==============================] - 1s 37ms/step - loss: 0.7060 - acc: 0.5536 - val_loss: 0.6945 - val_acc: 0.5833 Epoch 34/100 14/14 [==============================] - 1s 38ms/step - loss: 0.6813 - acc: 0.5893 - val_loss: 0.7053 - val_acc: 0.5000 Epoch 35/100 14/14 [==============================] - 1s 38ms/step - loss: 0.6911 - acc: 0.5536 - val_loss: 0.6920 - val_acc: 0.5000 Epoch 36/100 14/14 [==============================] - 1s 39ms/step - loss: 0.6879 - acc: 0.5893 - val_loss: 0.6875 - val_acc: 0.5833 Epoch 37/100 14/14 [==============================] - 1s 36ms/step - loss: 0.6726 - acc: 0.5714 - val_loss: 0.6945 - val_acc: 0.5000 Epoch 38/100 14/14 [==============================] - 1s 36ms/step - loss: 0.6677 - acc: 0.6250 - val_loss: 0.6694 - val_acc: 0.5000 Epoch 39/100 14/14 [==============================] - 1s 37ms/step - loss: 0.6877 - acc: 0.5893 - val_loss: 0.6891 - val_acc: 0.5000 Epoch 40/100 14/14 [==============================] - 1s 37ms/step - loss: 0.6633 - acc: 0.5714 - val_loss: 0.7008 - val_acc: 0.5000 Epoch 41/100 14/14 [==============================] - 1s 38ms/step - loss: 0.6667 - acc: 0.5893 - val_loss: 0.6740 - val_acc: 0.6667 Epoch 42/100 14/14 [==============================] - 1s 39ms/step - loss: 0.6540 - acc: 0.6607 - val_loss: 0.7073 - val_acc: 0.4167 Epoch 43/100 14/14 [==============================] - 1s 41ms/step - loss: 0.6649 - acc: 0.5893 - val_loss: 0.7120 - val_acc: 0.5000 Epoch 44/100 14/14 [==============================] - 1s 39ms/step - loss: 0.6808 - acc: 0.5000 - val_loss: 0.6928 - val_acc: 0.5833 Epoch 45/100 14/14 [==============================] - 1s 37ms/step - loss: 0.6486 - acc: 0.6250 - val_loss: 0.7054 - val_acc: 0.4167 Epoch 46/100 14/14 [==============================] - 1s 41ms/step - loss: 0.6472 - acc: 0.5714 - val_loss: 0.6984 - val_acc: 0.5833 Epoch 47/100 14/14 [==============================] - 1s 37ms/step - loss: 0.6636 - acc: 0.5714 - val_loss: 0.8272 - val_acc: 0.5000 Epoch 48/100 14/14 [==============================] - 1s 39ms/step - loss: 0.6599 - acc: 0.5179 - val_loss: 0.6857 - val_acc: 0.5833 Epoch 49/100 14/14 [==============================] - 1s 40ms/step - loss: 0.6593 - acc: 0.6786 - val_loss: 0.7024 - val_acc: 0.5833 Epoch 50/100 14/14 [==============================] - 1s 37ms/step - loss: 0.6481 - acc: 0.6071 - val_loss: 0.6696 - val_acc: 0.5833 Epoch 51/100 14/14 [==============================] - 1s 38ms/step - loss: 0.6189 - acc: 0.5893 - val_loss: 0.7829 - val_acc: 0.5000 Epoch 52/100 14/14 [==============================] - 1s 39ms/step - loss: 0.6671 - acc: 0.5357 - val_loss: 0.6825 - val_acc: 0.3333 Epoch 53/100 14/14 [==============================] - 1s 37ms/step - loss: 0.6594 - acc: 0.6071 - val_loss: 0.6937 - val_acc: 0.5000 Epoch 54/100 14/14 [==============================] - 1s 37ms/step - loss: 0.6382 - acc: 0.5000 - val_loss: 0.6992 - val_acc: 0.5000 Epoch 55/100 14/14 [==============================] - 1s 37ms/step - loss: 0.6456 - acc: 0.6071 - val_loss: 0.7051 - val_acc: 0.5000 Epoch 56/100 14/14 [==============================] - 1s 37ms/step - loss: 0.5973 - acc: 0.6071 - val_loss: 0.7803 - val_acc: 0.5000 Epoch 57/100 14/14 [==============================] - 1s 38ms/step - loss: 0.6598 - acc: 0.6429 - val_loss: 0.6868 - val_acc: 0.4167 Epoch 58/100 14/14 [==============================] - 1s 38ms/step - loss: 0.6090 - acc: 0.6786 - val_loss: 0.6781 - val_acc: 0.5833 Epoch 59/100 14/14 [==============================] - 1s 39ms/step - loss: 0.5979 - acc: 0.6964 - val_loss: 0.6984 - val_acc: 0.5833 Epoch 60/100 14/14 [==============================] - 1s 39ms/step - loss: 0.6809 - acc: 0.5893 - val_loss: 0.6575 - val_acc: 0.5000 Epoch 61/100 14/14 [==============================] - 1s 38ms/step - loss: 0.6201 - acc: 0.7321 - val_loss: 0.7751 - val_acc: 0.5000 Epoch 62/100 14/14 [==============================] - 1s 37ms/step - loss: 0.6777 - acc: 0.6786 - val_loss: 0.6880 - val_acc: 0.4167 Epoch 63/100 14/14 [==============================] - 1s 39ms/step - loss: 0.6538 - acc: 0.6607 - val_loss: 0.7105 - val_acc: 0.4167 Epoch 64/100 14/14 [==============================] - 1s 38ms/step - loss: 0.6501 - acc: 0.6071 - val_loss: 0.6990 - val_acc: 0.5833 Epoch 65/100 14/14 [==============================] - 1s 37ms/step - loss: 0.6747 - acc: 0.6250 - val_loss: 0.6909 - val_acc: 0.5000 Epoch 66/100 14/14 [==============================] - 1s 38ms/step - loss: 0.6176 - acc: 0.6607 - val_loss: 0.7157 - val_acc: 0.5833 Epoch 67/100 14/14 [==============================] - 1s 38ms/step - loss: 0.6256 - acc: 0.6607 - val_loss: 0.7766 - val_acc: 0.4167 Epoch 68/100 14/14 [==============================] - 1s 37ms/step - loss: 0.5733 - acc: 0.6786 - val_loss: 0.7187 - val_acc: 0.4167 Epoch 69/100 14/14 [==============================] - 1s 38ms/step - loss: 0.6577 - acc: 0.6607 - val_loss: 0.6613 - val_acc: 0.5833 Epoch 70/100 14/14 [==============================] - 1s 39ms/step - loss: 0.6062 - acc: 0.6429 - val_loss: 0.9178 - val_acc: 0.4167 Epoch 71/100 14/14 [==============================] - 1s 37ms/step - loss: 0.6403 - acc: 0.6250 - val_loss: 0.8255 - val_acc: 0.5833 Epoch 72/100 14/14 [==============================] - 1s 38ms/step - loss: 0.6436 - acc: 0.6429 - val_loss: 0.7849 - val_acc: 0.4167 Epoch 73/100 14/14 [==============================] - 1s 38ms/step - loss: 0.5983 - acc: 0.6250 - val_loss: 0.8304 - val_acc: 0.5000 Epoch 74/100 14/14 [==============================] - 1s 41ms/step - loss: 0.6738 - acc: 0.5714 - val_loss: 0.7302 - val_acc: 0.4167 Epoch 75/100 14/14 [==============================] - 1s 36ms/step - loss: 0.5941 - acc: 0.6607 - val_loss: 0.7187 - val_acc: 0.5000 Epoch 76/100 14/14 [==============================] - 1s 37ms/step - loss: 0.6028 - acc: 0.6786 - val_loss: 0.7375 - val_acc: 0.4167 Epoch 77/100 14/14 [==============================] - 1s 37ms/step - loss: 0.6261 - acc: 0.6429 - val_loss: 0.7407 - val_acc: 0.4167 Epoch 78/100 14/14 [==============================] - 1s 38ms/step - loss: 0.5387 - acc: 0.6964 - val_loss: 0.7237 - val_acc: 0.5000 Epoch 79/100 14/14 [==============================] - 1s 38ms/step - loss: 0.5672 - acc: 0.6607 - val_loss: 0.9385 - val_acc: 0.5000 Epoch 80/100 14/14 [==============================] - 1s 39ms/step - loss: 0.6389 - acc: 0.6964 - val_loss: 0.8926 - val_acc: 0.5000 Epoch 81/100 14/14 [==============================] - 1s 41ms/step - loss: 0.5603 - acc: 0.7321 - val_loss: 0.9412 - val_acc: 0.5000 Epoch 82/100 14/14 [==============================] - 1s 39ms/step - loss: 0.6297 - acc: 0.6964 - val_loss: 0.7572 - val_acc: 0.5833 Epoch 83/100 14/14 [==============================] - 1s 40ms/step - loss: 0.5175 - acc: 0.6964 - val_loss: 0.7978 - val_acc: 0.4167 Epoch 84/100 14/14 [==============================] - 1s 37ms/step - loss: 0.5738 - acc: 0.6964 - val_loss: 0.7796 - val_acc: 0.5833 Epoch 85/100 14/14 [==============================] - 1s 40ms/step - loss: 0.5711 - acc: 0.6429 - val_loss: 1.0373 - val_acc: 0.5000 Epoch 86/100 14/14 [==============================] - 1s 39ms/step - loss: 0.6091 - acc: 0.6786 - val_loss: 0.7931 - val_acc: 0.4167 Epoch 87/100 14/14 [==============================] - 1s 42ms/step - loss: 0.6229 - acc: 0.6964 - val_loss: 0.7175 - val_acc: 0.5000 Epoch 88/100 14/14 [==============================] - 1s 40ms/step - loss: 0.5781 - acc: 0.6607 - val_loss: 0.7392 - val_acc: 0.6667 Epoch 89/100 14/14 [==============================] - 1s 38ms/step - loss: 0.6144 - acc: 0.6250 - val_loss: 0.7039 - val_acc: 0.6667 Epoch 90/100 14/14 [==============================] - 1s 39ms/step - loss: 0.5126 - acc: 0.7143 - val_loss: 0.7843 - val_acc: 0.5000 Epoch 91/100 14/14 [==============================] - 1s 38ms/step - loss: 0.6374 - acc: 0.6607 - val_loss: 0.7806 - val_acc: 0.5000 Epoch 92/100 14/14 [==============================] - 1s 37ms/step - loss: 0.7390 - acc: 0.5714 - val_loss: 0.7436 - val_acc: 0.5000 Epoch 93/100 14/14 [==============================] - 1s 39ms/step - loss: 0.6417 - acc: 0.6250 - val_loss: 0.7791 - val_acc: 0.4167 Epoch 94/100 14/14 [==============================] - 1s 38ms/step - loss: 0.6310 - acc: 0.6786 - val_loss: 0.6743 - val_acc: 0.5000 Epoch 95/100 14/14 [==============================] - 1s 37ms/step - loss: 0.5615 - acc: 0.6429 - val_loss: 0.8794 - val_acc: 0.5833 Epoch 96/100 14/14 [==============================] - 1s 38ms/step - loss: 0.5540 - acc: 0.7321 - val_loss: 0.9387 - val_acc: 0.6667 Epoch 97/100 14/14 [==============================] - 1s 38ms/step - loss: 0.5913 - acc: 0.7321 - val_loss: 0.8006 - val_acc: 0.4167 Epoch 98/100 14/14 [==============================] - 1s 38ms/step - loss: 0.5682 - acc: 0.7679 - val_loss: 0.7195 - val_acc: 0.4167 Epoch 99/100 14/14 [==============================] - 1s 41ms/step - loss: 0.6450 - acc: 0.7143 - val_loss: 0.7179 - val_acc: 0.5000 Epoch 100/100 14/14 [==============================] - 1s 42ms/step - loss: 0.5651 - acc: 0.6786 - val_loss: 0.7568 - val_acc: 0.4167   #Now lets predict on the first 10 Images of the test set X_test, y_test = read_and_process_image(test_imgs[:3]) x = np.array(X_test) test_datagen = ImageDataGenerator (rescale=1./255)  error Traceback (most recent call last) lt;ipython-input-35-922ff9d9ecb5gt; in lt;modulegt;()  1 #Now lets predict on the first 10 Images of the test set ----gt; 2 X_test, y_test = read_and_process_image(test_imgs[:3])  3 x = np.array(X_test)  4 test_datagen = ImageDataGenerator (rescale=1./255)  lt;ipython-input-11-e3fe44f59019gt; in read_and_process_image(list_of_images)  11   12 for image in list_of_images: ---gt; 13 X.append(cv2.resize(cv2.imread(image, cv2. IMREAD_COLOR), (nrows, ncolumns), interpolation=cv2. INTER_CUBIC)) #Read the image  14 #get the labels  15 if'cracks' in image:  error: OpenCV(4.1.2) /io/opencv/modules/imgproc/src/resize.cpp:3720: error: (-215:Assertion failed) !ssize.empty() in function 'resize'     

Путь к вашим тестовым изображениям нарушен. Ошибка возникает в результате cv2 попытки изменить размер пустого numpy массива. Вы должны проверить путь к тестовому набору данных (убедитесь image_paths , что они доступны).