Module `brevettiai.data.image.image_processor`

Expand source code

import numpy as np

from pydantic import BaseModel
from abc import ABC, abstractmethod


class ImageProcessor(BaseModel, ABC):
    """
    Baseclass for implementing interface for image proccessors
    """
    type: str

    def output_size(self, input_height, input_width):
        """Calculated output size of output after postprocessing, given input image sizes"""
        return input_height, input_width

    def output_channels(self, input_channels):
        """Calculated output channel number"""
        return input_channels

    @abstractmethod
    def process(self, image):
        """Process image according to processor"""
        raise NotImplementedError("process(image)-> image should be implemented")
        # noinspection PyUnreachableCode
        return image

    @staticmethod
    def affine_transform(input_height, input_width):
        return np.eye(3)

Classes

class ImageProcessor (**data: Any)

Baseclass for implementing interface for image proccessors

Create a new model by parsing and validating input data from keyword arguments.

Raises ValidationError if the input data cannot be parsed to form a valid model.

Expand source code

class ImageProcessor(BaseModel, ABC):
    """
    Baseclass for implementing interface for image proccessors
    """
    type: str

    def output_size(self, input_height, input_width):
        """Calculated output size of output after postprocessing, given input image sizes"""
        return input_height, input_width

    def output_channels(self, input_channels):
        """Calculated output channel number"""
        return input_channels

    @abstractmethod
    def process(self, image):
        """Process image according to processor"""
        raise NotImplementedError("process(image)-> image should be implemented")
        # noinspection PyUnreachableCode
        return image

    @staticmethod
    def affine_transform(input_height, input_width):
        return np.eye(3)

Ancestors

pydantic.main.BaseModel
pydantic.utils.Representation
abc.ABC

Subclasses

Class variables

var type : str

Static methods

def affine_transform(input_height, input_width)

Expand source code

@staticmethod
def affine_transform(input_height, input_width):
    return np.eye(3)

Methods

def output_channels(self, input_channels)

Calculated output channel number

Expand source code

def output_channels(self, input_channels):
    """Calculated output channel number"""
    return input_channels

def output_size(self, input_height, input_width)

Calculated output size of output after postprocessing, given input image sizes

Expand source code

def output_size(self, input_height, input_width):
    """Calculated output size of output after postprocessing, given input image sizes"""
    return input_height, input_width

def process(self, image)

Process image according to processor

Expand source code

@abstractmethod
def process(self, image):
    """Process image according to processor"""
    raise NotImplementedError("process(image)-> image should be implemented")
    # noinspection PyUnreachableCode
    return image