ValueError: Error when checking input: expected input_1 to have 4 dimensions, but got array with shape (2048, 256, 3)

I got error when running my U-net architecture network and I'm not sure where is the error and to fix that. Here's my codes input image codes: and I have tried so many times to fix this problem, but at the end still error. I am using U-Net architecture network to segment my image data. Since my input image data is M x N x 3, but the dimension is not the same as 256 x 256 x 3. My dimension input image is 251 x 257 x 3 uint8.

I have tried to change the codes into:

imgs = np.asarray((total, image_rows, image_cols, 3), dtype=np.uint8)
imgs_mask = np.asarray((total, image_rows, image_cols), dtype=np.uint8)

from this:

imgs = np.ndarray((total, image_rows, image_cols, 3), dtype=np.uint8)
imgs_mask = np.ndarray((total, image_rows, image_cols), dtype=np.uint8)

Here the full codes for my testing input data:

def create_test_data():
for i in range(len(image)):
    print('-'*9)
    print("Creating testing images...")
    print('-'*9)
    print(image[i])
    
    
    test_data_path = '/home/motmot/Documents/unet/data/test/img/'
    test_mask_path = '/home/motmot/Documents/unet/data/test/msk/'
    image = os.listdir(test_data_path)
    mask = os.listdir(test_mask_path)
    total = len(image)
    imgs = np.ndarray((total, image_rows, image_cols, 3), dtype=np.uint8)
    #imgs = np.asarray((total, image_rows, image_cols, 3), dtype=np.uint8)
    imgs_mask = np.ndarray((total, image_rows, image_cols), dtype=np.uint8)
    #imgs_mask = np.asarray((total, image_rows, image_cols), dtype=np.uint8)
    
    create_test_data()
    

And here my U-net architecture network codes:

        def get_unet():
    inputs = Input((img_rows, img_cols, 3))
    conv1 = Conv2D(32, (3, 3), activation='relu', padding='same')(inputs)
    conv1 = BatchNormalization()(conv1)
    conv1 = Conv2D(32, (3, 3), activation='relu', padding='same')(conv1)
    conv1 = BatchNormalization()(conv1)
    pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
    
    conv2 = Conv2D(64, (3, 3), activation='relu', padding='same')(pool1)
    conv2 = BatchNormalization()(conv2)
    conv2 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv2)
    conv2 = BatchNormalization()(conv2)
    pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
    
    conv3 = Conv2D(128, (3, 3), activation='relu', padding='same')(pool2)
    conv3 = BatchNormalization()(conv3)
    conv3 = Conv2D(128, (3, 3), activation='relu', padding='same')(conv3)
    conv3 = BatchNormalization()(conv3)
    pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
    
    conv4 = Conv2D(256, (3, 3), activation='relu', padding='same')(pool3)
    conv4 = BatchNormalization()(conv4)
    conv4 = Conv2D(256, (3, 3), activation='relu', padding='same')(conv4)
    conv4 = BatchNormalization()(conv4)
    pool4 = MaxPooling2D(pool_size=(2, 2))(conv4)
    
    conv5 = Conv2D(512, (3, 3), activation='relu', padding='same')(pool4)
    conv5 = BatchNormalization()(conv5)
    conv5 = Conv2D(512, (3, 3), activation='relu', padding='same')(conv5)
    conv5 = BatchNormalization()(conv5)
    
    up6 = concatenate([Conv2DTranspose(256, (2, 2), strides=(2, 2), padding='same')(conv5), conv4], axis=3)
    conv6 = Conv2D(256, (3, 3), activation='relu', padding='same')(up6)
    conv6 = BatchNormalization()(conv6)
    conv6 = Conv2D(256, (3, 3), activation='relu', padding='same')(conv6)
    conv6 = BatchNormalization()(conv6)
    
    up7 = concatenate([Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same')(conv6), conv3], axis=3)
    conv7 = Conv2D(128, (3, 3), activation='relu', padding='same')(up7)
    conv7 = BatchNormalization()(conv7)
    conv7 = Conv2D(128, (3, 3), activation='relu', padding='same')(conv7)
    conv7 = BatchNormalization()(conv7)

    up8 = concatenate([Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same')(conv7), conv2], axis=3)
    conv8 = Conv2D(64, (3, 3), activation='relu', padding='same')(up8)
    conv8 = BatchNormalization()(conv8)
    conv8 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv8)
    conv8 = BatchNormalization()(conv8)
    
    up9 = concatenate([Conv2DTranspose(32, (2, 2), strides=(2, 2), padding='same')(conv8), conv1], axis=3)
    conv9 = Conv2D(32, (3, 3), activation='relu', padding='same')(up9)
    conv9 = BatchNormalization()(conv9)
    conv9 = Conv2D(32, (3, 3), activation='relu', padding='same')(conv9)
    conv9 = BatchNormalization()(conv9)
        
    conv10 = Conv2D(1, (1, 1), activation='sigmoid')(conv9)

    model = Model(inputs=[inputs], outputs=[conv10])
    model.compile(optimizer=Adam(), loss=[loss], metrics=[dice_coef, iou_coef])
    return model
    

And the error occured :

    ValueError: Error when checking input: expected input_1 to have 4 dimensions, but got array with shape (2048, 256, 3)

    

Is the ValueError said the error is in this codes?

imgs = np.ndarray((total, image_rows, image_cols, 3), dtype=np.uint8)
imgs_mask = np.ndarray((total, image_rows, image_cols), dtype=np.uint8)

Because I have mentions the imgs:

((total, image_rows, image_cols, 3), dtype=np.uint8)

in 4_dimension, and the ground truth imgs_mask:

((total, image_rows, image_cols), dtype=np.uint8)

which in 3_dimension? Am I correct? How should I fix this?

[Walter Roberson]

Duplicates https://www.mathworks.com/matlabcentral/answers/1982039-how-to-change-257x251x1-uint8-into-257x251x3-uint8?s_tid=srchtitle