Module `see.ColorSpace`

ColorSpace.py file.

Expand source code

"""ColorSpace.py file."""

from skimage import color
from see.base_classes import param_space, algorithm
import copy
import inspect
import random

import sys
import logging
import numpy as np
import skimage


# TODO My guess is this algorithm is very slow.  We need to use a cache to
# speed it up.


class color_params(param_space):
    """Class color_params."""
    
    descriptions = dict()
    ranges = dict()
    pkeys = []


color_params.add(
    'colorspace',
    [
        'RGB',
        'HSV',
        'RGB CIE',
        'XYZ',
        'YUV',
        'YIQ',
        'YPbPr',
        'YCbCr',
        'YDbDr'],
    "Pick a colorspace [‘RGB’, ‘HSV’, ‘RGB CIE’, ‘XYZ’, ‘YUV’, ‘YIQ’, ‘YPbPr’, ‘YCbCr’, ‘YDbDr’]")
color_params.add('multichannel',
                 [True, False],
                 "True/False parameter"
                 )
color_params.add('channel',
                 [0, 1, 2],
                 "A parameter for Picking the Channel 0,1,2"
                 )


class colorspace(algorithm):
    """colorspace."""
        
    def getchannel(img, colorspace, channel):
        """Function that returns a single channel from an image.
        ['RGB', ‘HSV’, ‘RGB CIE’, ‘XYZ’, ‘YUV’, ‘YIQ’, ‘YPbPr’, ‘YCbCr’, ‘YDbDr’]
        """

        if len(img.shape) == 2:
            c_img = img.copy()
            img = np.zeros([c_img.shape[0], c_img.shape[1], 3])
            img[:, :, 0] = c_img
            img[:, :, 1] = c_img
            img[:, :, 2] = c_img
            return [img, c_img, 1]

        if colorspace == 'RGB':
            return [img, img[:, :, channel], 3]
        space = color.convert_colorspace(img, 'RGB', colorspace)
        return [space, space[:, :, channel], 3]

    # TODO Update to allow paramlist to be either a list or the parameters
    # class
    def __init__(self, paramlist=None):
        """Generate algorithm params from parameter list."""
        # init_params()
        self.params = color_params()
        self.params['colorspace'] = 'RGB'
        self.params['multichannel'] = True
        self.params['channel'] = 2

        self.chache = dict()
        if paramlist:
            if isinstance(paramlist, list):
                self.params.fromlist(paramlist)
            else:
                self.params = paramlist
        else:
            self.params["multichannel"] = True
            self.params["colorspace"] = "RGB"
            self.params["channel"] = 2
        self.paramindexes = ["colorspace", "multichannel", "channel"]
        self.checkparamindex()

    # TODO use name to build a dictionary to use as a chache
    def evaluate(self, img, name=None):
        """Run segmentation algorithm to get inferred mask."""
        multichannel = self.params['multichannel']

        if len(img.shape) > 2:
            multichannel = False

        [img, channel, _] = colorspace.getchannel(
            img, self.params['colorspace'], self.params['channel'])

        return img if multichannel else channel

    def pipe(self, data):
        """Set inputimage and img to evaluated data images."""
        data.inputimage = data.img
        data.img = self.evaluate(data.img)
        return data

Classes

class color_params (*args, **kwargs)

Class color_params.

Expand source code

class color_params(param_space):
    """Class color_params."""
    
    descriptions = dict()
    ranges = dict()
    pkeys = []

Ancestors

param_space
builtins.dict

Class variables

var descriptions
var pkeys
var ranges

Inherited members

param_space:
- add
- addall
- fromlist
- printparam
- tolist

class colorspace (paramlist=None)

colorspace.

Generate algorithm params from parameter list.

Expand source code

class colorspace(algorithm):
    """colorspace."""
        
    def getchannel(img, colorspace, channel):
        """Function that returns a single channel from an image.
        ['RGB', ‘HSV’, ‘RGB CIE’, ‘XYZ’, ‘YUV’, ‘YIQ’, ‘YPbPr’, ‘YCbCr’, ‘YDbDr’]
        """

        if len(img.shape) == 2:
            c_img = img.copy()
            img = np.zeros([c_img.shape[0], c_img.shape[1], 3])
            img[:, :, 0] = c_img
            img[:, :, 1] = c_img
            img[:, :, 2] = c_img
            return [img, c_img, 1]

        if colorspace == 'RGB':
            return [img, img[:, :, channel], 3]
        space = color.convert_colorspace(img, 'RGB', colorspace)
        return [space, space[:, :, channel], 3]

    # TODO Update to allow paramlist to be either a list or the parameters
    # class
    def __init__(self, paramlist=None):
        """Generate algorithm params from parameter list."""
        # init_params()
        self.params = color_params()
        self.params['colorspace'] = 'RGB'
        self.params['multichannel'] = True
        self.params['channel'] = 2

        self.chache = dict()
        if paramlist:
            if isinstance(paramlist, list):
                self.params.fromlist(paramlist)
            else:
                self.params = paramlist
        else:
            self.params["multichannel"] = True
            self.params["colorspace"] = "RGB"
            self.params["channel"] = 2
        self.paramindexes = ["colorspace", "multichannel", "channel"]
        self.checkparamindex()

    # TODO use name to build a dictionary to use as a chache
    def evaluate(self, img, name=None):
        """Run segmentation algorithm to get inferred mask."""
        multichannel = self.params['multichannel']

        if len(img.shape) > 2:
            multichannel = False

        [img, channel, _] = colorspace.getchannel(
            img, self.params['colorspace'], self.params['channel'])

        return img if multichannel else channel

    def pipe(self, data):
        """Set inputimage and img to evaluated data images."""
        data.inputimage = data.img
        data.img = self.evaluate(data.img)
        return data

Ancestors

algorithm

Methods

def evaluate(self, img, name=None)

Run segmentation algorithm to get inferred mask.

Expand source code

def evaluate(self, img, name=None):
    """Run segmentation algorithm to get inferred mask."""
    multichannel = self.params['multichannel']

    if len(img.shape) > 2:
        multichannel = False

    [img, channel, _] = colorspace.getchannel(
        img, self.params['colorspace'], self.params['channel'])

    return img if multichannel else channel

def getchannel(img, colorspace, channel)

Function that returns a single channel from an image. ['RGB', ‘HSV’, ‘RGB CIE’, ‘XYZ’, ‘YUV’, ‘YIQ’, ‘YPbPr’, ‘YCbCr’, ‘YDbDr’]

Expand source code

def getchannel(img, colorspace, channel):
    """Function that returns a single channel from an image.
    ['RGB', ‘HSV’, ‘RGB CIE’, ‘XYZ’, ‘YUV’, ‘YIQ’, ‘YPbPr’, ‘YCbCr’, ‘YDbDr’]
    """

    if len(img.shape) == 2:
        c_img = img.copy()
        img = np.zeros([c_img.shape[0], c_img.shape[1], 3])
        img[:, :, 0] = c_img
        img[:, :, 1] = c_img
        img[:, :, 2] = c_img
        return [img, c_img, 1]

    if colorspace == 'RGB':
        return [img, img[:, :, channel], 3]
    space = color.convert_colorspace(img, 'RGB', colorspace)
    return [space, space[:, :, channel], 3]

def pipe(self, data)

Set inputimage and img to evaluated data images.

Expand source code

def pipe(self, data):
    """Set inputimage and img to evaluated data images."""
    data.inputimage = data.img
    data.img = self.evaluate(data.img)
    return data

Inherited members

algorithm:
- algorithm_code
- checkparamindex
- mutate_self
- mutateself
- runAlgo
- set_params