Module ktrain.vision.preprocessor

Classes

class ImagePreprocessor (datagen, classes, target_size=(224, 224), color_mode='rgb')

Image preprocessing

Expand source code

class ImagePreprocessor(Preprocessor):
    """
    ```
    Image preprocessing
    ```
    """

    def __init__(self, datagen, classes, target_size=(224, 224), color_mode="rgb"):
        if not isinstance(datagen, keras.preprocessing.image.ImageDataGenerator):
            raise ValueError("datagen must be instance of ImageDataGenerator")
        self.datagen = datagen
        self.c = classes
        self.target_size = target_size
        self.color_mode = color_mode

    def get_preprocessor(self):
        return self.datagen

    def get_classes(self):
        return self.c

    def preprocess_test(self, data, batch_size=U.DEFAULT_BS):
        """
        ```
        Alias for preprocess
        ```
        """
        return self.preprocess(data, batch_size=batch_size)

    def preprocess(self, data, batch_size=U.DEFAULT_BS):
        """
        ```
        Receives raw data and returns
        tuple containing the generator and steps
        argument for model.predict.
        ```
        """
        # input is an array of pixel values
        if isinstance(data, np.ndarray):
            generator = self.datagen.flow(data, shuffle=False)
            generator.batch_size = batch_size
            nsamples = len(data)
            steps = math.ceil(nsamples / batch_size)
            return (generator, steps)

        # input is a folder of images
        elif os.path.isdir(data):
            folder = data
            if folder[-1] != os.sep:
                folder += os.sep
            parent = os.path.dirname(os.path.dirname(folder))
            folder_name = os.path.basename(os.path.dirname(folder))
            if self.target_size is None or self.color_mode is None:
                raise Exception(
                    "To use predict_folder, you must load the data using either "
                    + "the images_from_folder function or the images_from_csv function."
                )
            generator = self.datagen.flow_from_directory(
                parent,
                classes=[folder_name],
                target_size=self.target_size,
                class_mode="categorical",
                shuffle=False,
                interpolation="bicubic",
                color_mode=self.color_mode,
            )
            generator.batch_size = batch_size
            nsamples = generator.samples
            steps = math.ceil(nsamples / batch_size)
            return (generator, steps)
        # input is the path to an image file
        elif os.path.isfile(data):
            if self.target_size is None or self.color_mode is None:
                raise Exception(
                    "To use predict_filename, you must load the data using either "
                    + "the ktrain.vision.images_from_folder function or the "
                    + "ktrain.vision.images_from_csv function."
                )
            img = keras.preprocessing.image.load_img(
                data, target_size=self.target_size, color_mode=self.color_mode
            )
            x = keras.preprocessing.image.img_to_array(img)
            x = np.expand_dims(x, axis=0)
            generator = self.datagen.flow(np.array(x), shuffle=False)
            generator.batch_size = batch_size
            nsamples = 1
            steps = math.ceil(nsamples / batch_size)
            return (generator, steps)
        else:
            raise ValueError(
                "data argument is not valid file, folder, or numpy.ndarray"
            )

Ancestors

• Preprocessor
• abc.ABC

Methods

def get_classes(self)

Expand source code

def get_classes(self):
    return self.c

def get_preprocessor(self)

Expand source code

def get_preprocessor(self):
    return self.datagen

def preprocess(self, data, batch_size=32)

Receives raw data and returns
tuple containing the generator and steps
argument for model.predict.

Expand source code

def preprocess(self, data, batch_size=U.DEFAULT_BS):
    """
    ```
    Receives raw data and returns
    tuple containing the generator and steps
    argument for model.predict.
    ```
    """
    # input is an array of pixel values
    if isinstance(data, np.ndarray):
        generator = self.datagen.flow(data, shuffle=False)
        generator.batch_size = batch_size
        nsamples = len(data)
        steps = math.ceil(nsamples / batch_size)
        return (generator, steps)

    # input is a folder of images
    elif os.path.isdir(data):
        folder = data
        if folder[-1] != os.sep:
            folder += os.sep
        parent = os.path.dirname(os.path.dirname(folder))
        folder_name = os.path.basename(os.path.dirname(folder))
        if self.target_size is None or self.color_mode is None:
            raise Exception(
                "To use predict_folder, you must load the data using either "
                + "the images_from_folder function or the images_from_csv function."
            )
        generator = self.datagen.flow_from_directory(
            parent,
            classes=[folder_name],
            target_size=self.target_size,
            class_mode="categorical",
            shuffle=False,
            interpolation="bicubic",
            color_mode=self.color_mode,
        )
        generator.batch_size = batch_size
        nsamples = generator.samples
        steps = math.ceil(nsamples / batch_size)
        return (generator, steps)
    # input is the path to an image file
    elif os.path.isfile(data):
        if self.target_size is None or self.color_mode is None:
            raise Exception(
                "To use predict_filename, you must load the data using either "
                + "the ktrain.vision.images_from_folder function or the "
                + "ktrain.vision.images_from_csv function."
            )
        img = keras.preprocessing.image.load_img(
            data, target_size=self.target_size, color_mode=self.color_mode
        )
        x = keras.preprocessing.image.img_to_array(img)
        x = np.expand_dims(x, axis=0)
        generator = self.datagen.flow(np.array(x), shuffle=False)
        generator.batch_size = batch_size
        nsamples = 1
        steps = math.ceil(nsamples / batch_size)
        return (generator, steps)
    else:
        raise ValueError(
            "data argument is not valid file, folder, or numpy.ndarray"
        )

def preprocess_test(self, data, batch_size=32)

Alias for preprocess

Expand source code

def preprocess_test(self, data, batch_size=U.DEFAULT_BS):
    """
    ```
    Alias for preprocess
    ```
    """
    return self.preprocess(data, batch_size=batch_size)