Python keras_preprocessing.image.img_to_array() Examples

The following are 4 code examples of keras_preprocessing.image.img_to_array(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module keras_preprocessing.image , or try the search function .
Example #1
Source File: run.py    From Generative-Adversarial-Networks-Projects with MIT License 6 votes vote down vote up 
def load_images(data_dir, image_paths, image_shape):
    images = None

    for i, image_path in enumerate(image_paths):
        print()
        try:
            # Load image
            loaded_image = image.load_img(os.path.join(data_dir, image_path), target_size=image_shape)

            # Convert PIL image to numpy ndarray
            loaded_image = image.img_to_array(loaded_image)

            # Add another dimension (Add batch dimension)
            loaded_image = np.expand_dims(loaded_image, axis=0)

            # Concatenate all images into one tensor
            if images is None:
                images = loaded_image
            else:
                images = np.concatenate([images, loaded_image], axis=0)
        except Exception as e:
            print("Error:", i, e)

    return images
Example #2
Source File: test_build.py    From keras-efficientnets with MIT License 5 votes vote down vote up 
def get_preds(model):
    size = model.input_shape[1]

    filename = os.path.join(os.path.dirname(__file__),
                            'data', '565727409_61693c5e14.jpg')

    batch = KE.preprocess_input(img_to_array(load_img(
                                filename, target_size=(size, size))))

    batch = np.expand_dims(batch, 0)

    pred = decode_predictions(model.predict(batch),
                              backend=K, utils=utils)

    return pred
Example #3
Source File: image.py    From GraphicDesignPatternByPython with MIT License 5 votes vote down vote up 
def img_to_array(img, data_format=None, dtype=None):
    if data_format is None:
        data_format = backend.image_data_format()
    if 'dtype' in inspect.getargspec(image.img_to_array).args:
        if dtype is None:
            dtype = backend.floatx()
        return image.img_to_array(img, data_format=data_format, dtype=dtype)
    return image.img_to_array(img, data_format=data_format)
Example #4
Source File: utils.py    From deepxplore with MIT License 5 votes vote down vote up 
def preprocess_image(img_path):
    img = image.load_img(img_path, target_size=(224, 224))
    input_img_data = image.img_to_array(img)
    input_img_data = np.expand_dims(input_img_data, axis=0)
    input_img_data = preprocess_input(input_img_data)  # final input shape = (1,224,224,3)
    return input_img_data