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hdrCore package

Submodules

hdrCore.image module

package hdrCoreconsists of the core classes for HDR imaging: (1) classes: class imageType

class channel class Image class ColorSpace class Histogram

  1. class: class metadata

  2. processing functions and classes:

    def XYZ_to_sRGB(XYZ,apply_cctf_encoding=True): def sRGB_to_XYZ(RGB,apply_cctf_decoding=True): def Lab_to_XYZ(Lab) def XYZ_to_Lab(XYZ) def Lab_to_sRGB(Lab, apply_cctf_encoding=True, clip = False) def sRGB_to_Lab(RGB, apply_cctf_decoding=True) def Lch_to_sRGB(Lch,apply_cctf_encoding=True, clip=False) class Processing(object) class tmo_cctf(Processing) class exposure(Processing) class contrast(Processing) class clip(Processing) class ColorSpaceTransform(Processing) class resize(Processing) class Ycurve(Processing) class saturation(Processing) class lightnessMask(Processing) class geometry(Processing)

  3. processing nodes that encapsulate processing objects in process-pipe:

    class ProcessNode(object)

  4. process-pipe that defines the pipeline of processing:

    class ProcessPipe(object)

  5. some utils functions:

    def filenamesplit(filename) def filterlistdir(path,extList) def ndarray2vector(nda) def NPlinearWeightMask(x,xMin,xMax,xTolerance) def croppRotated(h,w,alpha)

  6. HDRdisplay model

class hdrCore.image.ColorSpace

Bases: object

Mapping to colour.models.RGB_COLOURSPACES

static Lab()

TODO - Documentation de la méthode Lab

static Lch()

TODO - Documentation de la méthode Lch

static XYZ()

TODO - Documentation de la méthode XYZ

static build(name='sRGB')

TODO - Documentation de la méthode build

Args:
name=’sRGB’: str

TODO

static sRGB()

TODO - Documentation de la méthode sRGB

static scRGB()

TODO - Documentation de la méthode scRGB

class hdrCore.image.Histogram(histValue, edgeValue, name, channel, logSpace=False)

Bases: object

TODO - documentation de la classe image.Histogram

description of class

Attributes:
name: str

TODO

channel: TODO

TODO

histValue: TODO

TODO

edgeValue: TODO

TODO

logSpace: TODO

TODO

static build(img, channel, nbBins=100, range=None, logSpace=None)

Build an Histogram object from image.

Args:
img: Image

Required : input image from witch hsitogram will be build

channel: channel

Required : image channel used to build histogram

nbBins: int

Optional : histogram number of bins

range: (Float,Float)

Optional : range of histogram, if None min max of channel

logSpace: boolean

Optional : compute in log space if True, if None guess from image

Returns:
Histogram

Histogram of the image.

normalise(norm=None)

Normalise histogram according to norm=’probability’ | ‘dot’

Args:
norm: str

Norm of the normalization

Returns:
TODO

TODO

plot(ax, color='r', shortName=True, title=True)

Method to plot the histogram

Args:
ax: TODO

TODO

color: TODO

TODO

shortName: boolean

TODO

title: boolean

TODO

toNumpy()

TODO - Documentation de la méthode toNumpy

Returns:
TODO

TODO

class hdrCore.image.Image(path, name, colorData, type, linear, colorspace, scalingFactor=1.0)

Bases: object

class hdrCore.image.Image: the basic class that models HDR image and associated data

Attributes:

path (str): path to access image name (str): image filename colorData (numpy.ndarray): numpy array of pixels type (image.imageType): type of image linear (boolean): image encoding is linear colorSpace (colour.models.RGB_COLOURSPACES): colorspace scalingFactor (float): scaling factor to range[0..1] metadata (hdrCore.metadatametadata): metadata histogram(hdrCore.image.Histogram): image histogram

Methods:

isHDR: (boolean) returns True if image is HDR process: (hdrCore.image.Image) computes a processing and returns a new Image write: () write image and json metadata on disk (HDR image only) getChannel: () getDynamicRange: buildHistogram: plot: __repr__: (str) split: (list[[hdrCore.image.Image]]) split an image into sub-images

Static methods:

read: (hdrCore.image.Image) read an image from file toOne: /!not used in uHDR buildLchColorData:

buildHistogram(channel)

Build the histogram of the image

Args:
channel: TODO

TODO

static buildLchColorData(L, c, h, size, width, height)

TODO - Documentation de la méthode buildLchColorData

Args:
L: TODO

TODO

c: TODO

TODO

h: TODO

TODO

size: TODO

TODO

width: TODO

TODO

height: TODO

TODO

Returns:
TODO

TODO

getChannel(channel)

To get channel : works only for sR|sG|sB, X|Y|Z and L|a|b

Args:
channel: TODO

TODO

getDynamicRange(percentile=None)

Retrieve the dynamic range of image

Args:
percentile: float

Optional : percentile if None: just remove zero values

Returns:
float

The dynamic range of the image

isHDR()

isHDR: return True is image is HDR

Args:

Returns:
(boolean)

True if the image is an HDR image, False otherwise

static merge(imgList)

merge: merge 2D list of images into a single image (same size, same characteristics)

Args:

imgList (list[[hdrCore.image.Image]], Required): 2D list of images

Returns:

(hdrCore.image.Image)

plot(ax, displayTitle=False, title=None, forceToneMapping=True, TMO=None)

Method to plot the image.

Args:
ax: TODO

TODO

displayTitle: boolean

Indicates if the name and colorSpace must be displayed above the image.

title: str

Title for the image. If None, the title will be the name of the image and its colorspace.

forceToneMapping: boolean

TODO

TMO: TODO

TODO

process(process, **kwargs)

compute a process according to the Processing object parameter.

process, instance of Processing class, has a compute(image, **kwargs) method

Args:

process: Processing object (see processing.Processing)

kwargs: optionnal parameters packed as dict

parameters of the compute method of the Processing object

static read(filename, thumb=False)

Method to read image from its filename. blablabla TODO à compléter.

Args:
filename: str

Filename of the file to read.

thumb: boolean

This flag indicates us if soft have to read the thumbnail or the original file for what the thumbnail will be created.

Returns:
image.Image

the image object build from file.

Example:

>>> import uhdCore
>>> img = Image.read(filename = "test.hdr", thumb = True)

TODO - Exemple à modifier

split(widthSegment, heightSegment)

split: split an image widthSegment x heightSegment sub-images.

Args:

widthSegment (int, Required): number of horizonatal segments heightSegment (int required): number of vertical segments

Returns

list[[hdrCore.image.Image]] : list of sub-images

static toOne(colorData)

This method scale the image colorData in [0, 1] space

Args:
colorData: TODO

TODO

Returns:
TODO

TODO

write(filename)

Method to write the image in a file.

Args:
filename: str

Filename of the file to read.

class hdrCore.image.channel(value)

Bases: enum.Enum

This class groups constant values and methods relative to a channel of an image together. This class is an easy definition of channel.

Attributes :
value: int

Store the type of channel.

colorSpace()

Retrieve the channel as a string.

Returns:
str

Colorspace type of the channel : ‘sRGB’, ‘XYZ’ or ‘Lab’.

getValue()

Retrieve the channel index.

Returns:
int

Index of the channel.

static toChannel(s)

Convert a channel name as a constant value defined in this class.

Args:
s: str

Name of the desired channel. This argument have to be in the list : ‘sR’, ‘sG’, ‘sB’, ‘X’, ‘Y’, ‘Z’, ‘L’, ‘a’ or ‘b’.

Returns:
int

Index of the channel.

L = 8
Lab = 11
X = 4
XYZ = 7
Y = 5
Z = 6
a = 9
b = 10
sB = 2
sG = 1
sR = 0
sRGB = 3
class hdrCore.image.imageType(value)

Bases: enum.Enum

This class contains a list of constant values for different type of images.

The constants are : SDR, RAW, HDR.

ARW = 1
HDR = 2
SDR = 0

hdrCore.metadata module

package hdrCore consists of the core classes for HDR imaging: (1) classes: class imageType

class channel class Image class ColorSpace class Histogram

  1. class: class metadata

  2. processing functions and classes:

    def XYZ_to_sRGB(XYZ,apply_cctf_encoding=True): def sRGB_to_XYZ(RGB,apply_cctf_decoding=True): def Lab_to_XYZ(Lab) def XYZ_to_Lab(XYZ) def Lab_to_sRGB(Lab, apply_cctf_encoding=True, clip = False) def sRGB_to_Lab(RGB, apply_cctf_decoding=True) def Lch_to_sRGB(Lch,apply_cctf_encoding=True, clip=False) class Processing(object) class tmo_cctf(Processing) class exposure(Processing) class contrast(Processing) class clip(Processing) class ColorSpaceTransform(Processing) class resize(Processing) class Ycurve(Processing) class saturation(Processing) class lightnessMask(Processing) class geometry(Processing)

  3. processing nodes that encapsulate processing objects in process-pipe:

    class ProcessNode(object)

  4. process-pipe that defines the pipeline of processing:

    class ProcessPipe(object)

  5. some utils functions:

    def filenamesplit(filename) def filterlistdir(path,extList) def ndarray2vector(nda) def NPlinearWeightMask(x,xMin,xMax,xTolerance) def croppRotated(h,w,alpha)

  6. HDRdisplay model

class hdrCore.metadata.metadata(_image)

Bases: object

The metadatas of the image can be store in an object of this class. The informations which can be stored are : filename, path, a description, the exif data, the category, the processes to apply on the image.

Atributes:
metadata: TODO

TODO

image: TODO

TODO

static build(_image)

Build metadata object from an image.

Args:
_image: TODO

TODO

Returns:
TODO

TODO

static readExif(filename)

Return exif (Dict) from image file

Args:
filename: str

Name of the image file.

Returns:
TODO

TODO

recoverData(exif)

TODO - Documentation de la méthode recoverData

Args:
exif: TODO

TODO

save()

Save the metadata in a json file. The extension .json is added to the filename of the image.

defaultColorSpaceName = 'sRGB'

hdrCore.processing module

package hdrCore consists of the core classes for HDR imaging: (1) classes: class imageType

class channel class Image class ColorSpace class Histogram

  1. class: class metadata

  2. processing functions and classes:

    def XYZ_to_sRGB(XYZ,apply_cctf_encoding=True): def sRGB_to_XYZ(RGB,apply_cctf_decoding=True): def Lab_to_XYZ(Lab) def XYZ_to_Lab(XYZ) def Lab_to_sRGB(Lab, apply_cctf_encoding=True, clip = False) def sRGB_to_Lab(RGB, apply_cctf_decoding=True) def Lch_to_sRGB(Lch,apply_cctf_encoding=True, clip=False) class Processing(object) class tmo_cctf(Processing) class exposure(Processing) class contrast(Processing) class clip(Processing) class ColorSpaceTransform(Processing) class resize(Processing) class Ycurve(Processing) class saturation(Processing) class lightnessMask(Processing) class geometry(Processing)

  3. processing nodes that encapsulate processing objects in process-pipe:

    class ProcessNode(object)

  4. process-pipe that defines the pipeline of processing:

    class ProcessPipe(object)

  5. some utils functions:

    def filenamesplit(filename) def filterlistdir(path,extList) def ndarray2vector(nda) def NPlinearWeightMask(x,xMin,xMax,xTolerance) def croppRotated(h,w,alpha)

  6. HDRdisplay model

class hdrCore.processing.ColorSpaceTransform

Bases: hdrCore.processing.Processing

TODO - Documentation de la colorspace transorm processing

compute(img, **kwargs)

Color space transform operator

Args:
img: hdrCore.image.Image

Required : hdr image

kwargs: dict

Optionnal : parameters TODO

Returns:
TODO

TODO

class hdrCore.processing.ProcessPipe

Bases: object

TODO - Documentation de la classe ProcessPipe

Attributes:
originalImage: TODO

TODO

__inputImage: TODO

TODO

__outputImage: TODO

TODO

processNodes: TODO

TODO

previewHDR: boolean

TODO

previewHDR_process: TODO

TODO

class ProcessNode(process, paramDict=None, name=None)

Bases: object

compute(img)
condCompute(img)
getParameters()
setParameters(paramDict)
toDict()
id = 0
append(process, paramDict=None, name=None)

TODO - Documentation de la méthode append

Args:
process: TODO

TODO

paramDict: TODO

TODO

name: TODO

TODO

Returns:
TODO

TODO

compute(progress=None)

TODO - Documentation de la méthode compute

Args:
progress: TODO

TODO

export(dirName, size=None, to=None, progress=None)

TODO - Documentation de la méthode export

Args:

dirName: TODO size: TODO to: TODO progress: TODO

Returns:

(hdrCore.image.Image)

getImage(toneMap=True)

TODO - Documentation de la méthode getImage

Args:
toneMap: TODO

TODO

Returns:
TODO

TODO

getInputImage()

TODO - Documentation de la méthode getInputImage

Returns:
TODO

TODO

getName()

TODO - Documentation de la méthode getName

Returns:
TODO

TODO

getParameters(id)

TODO - Documentation de la méthode getParameters

Args:
id: TODO

TODO

Returns:
TODO

TODO

getProcessNodeByName(name)

TODO - Documentation de la méthode getProcessNodeByName

Args:
name: TODO

TODO

Returns:
TODO

TODO

setImage(img)
set the input image to the process-pipeline:
  1. a copy of the image is set to ‘originalImage’

  2. if ProcessPipe.autoResize: the image is resized

  3. the (resize) is set to ‘__inputImage’

  4. a copy of the resized image is set to ‘__outputImage’ (for display)

  5. initialize processpipe using ‘img.metadata’

  6. for all processes in the pipe ‘requireUpdate’ is set to True

Args:

img (hdrCore.image.Image, Required) : input image

Returns:

setParameters(id, paramDicts)

TODO - Documentation de la méthode setParameters

Args:
id: TODO

TODO

paramDicts: TODO

TODO

toDict()

TODO - Documentation de la méthode toDict

Returns:
TODO

TODO

updateHDRuseCase(hdrmeta)

TODO - Documentation de la méthode updateHDRuseCase

Args:
hdrmeta: TODO

TODO

updateProcessPipeMetadata()

TODO - Documentation de la méthode updateProcessPipeMetadata

autoResize = True
maxSize = 1200
maxWorking = 1200
class hdrCore.processing.Processing

Bases: object

TODO - Documentation de la classe Processing

compute(image, **kwargs)

TODO - Documentation de la méthode compute

Args:
image: TODO

TODO

kwargs: TODO

TODO

Returns:
TODO

TODO

class hdrCore.processing.Ycurve

Bases: hdrCore.processing.Processing

TODO - Documentation de la classe Ycurve

compute(img, **kwargs)

compute: compute Ycurve according to parameters.

Args:

img (hdrCore.image.Image, Required): image kwargs (dict,Optionnal): parameters gieven as dict

Returns:
(hdrCore.image.Image, Required): image

result of Ycurve processing

class hdrCore.processing.clip

Bases: hdrCore.processing.Processing

TODO - Documentation de la classe clip

compute(img, **kwargs)

clip image color data

Args:

img (hdrCore.image.Image, Required): input image image kwargs(dict, Optionnal) : parameters

‘min’, ‘max’ : float, float

Returns:

(hdrCore.image.Image): output image

class hdrCore.processing.colorEditor

Bases: hdrCore.processing.Processing

TODO - Documentation de la classe colorEditor

compute(img, **kwargs)

color editor operator

Args:

img (hdrCore.image.Image, Required): input image kwargs (dict, Optionnal): parameters

default value= { ‘selection’: {‘lightness’: (0,100),’chroma’: (0,100),’hue’:(0,360)},

‘tolerance’: 0.1, ‘edit’: {‘hue’:0.0,’exposure’:0.0,’contrast’:0.0,’saturation’:0.0}, ‘mask’: False}

Returns:

(hdrCore.image.Image): output image

class hdrCore.processing.contrast

Bases: hdrCore.processing.Processing

TODO - Documentation de la classe contrast

compute(img, **kwargs)

contrast operator.

Args:

img (hdrCore.image.Image, Required): input image kwargs (dict, Optionnal) : parameters

“contrast”: float

default value: { “contrast”: 0 }

Returns:

(hdrCore.image.Image, Required): output image

class hdrCore.processing.exposure

Bases: hdrCore.processing.Processing

TODO - Documentation de la classe exposure

auto(img)

TODO - Documentation de la méthode auto

Args:
img: TODO

TODO

Returns:
TODO

TODO

compute(img, **kwargs)

exposure operator.

Args:

img (hdrCore.image.Image, Required) : input image kwargs( dict, Optionnal) : parameters

‘EV’: float

default value: {‘EV’: 0.0}

Returns:

(hdrCore.image.Image): output image

class hdrCore.processing.geometry

Bases: hdrCore.processing.Processing

TODO - Documentation de la classe geometry

compute(img, **kwargs)

geometry operator.

Args:

img (hdrCore.image.Image,Required): input image kwargs (dict,Optionnal) : parameters

‘ratio’: (int,int) ‘up’: int ‘rotation’: float

default value = { ‘ratio’: (16,9), ‘up’: 0,’rotation’: 0.0}

Returns:

(hdrCore.image.Image,Required): input image

class hdrCore.processing.lightnessMask

Bases: hdrCore.processing.Processing

TODO - Documentation de la classe lightnessMask

compute(img, **kwargs)

Lightness Mask operator.

Args:
img: hdrCore.image.Image

Required : hdr image

kwargs: dict

Optionnal : parameters TODO

Returns:
TODO

TODO

class hdrCore.processing.resize

Bases: hdrCore.processing.Processing

TODO - Documentation de la classe Resize image processing

compute(img, size=(None, None), anti_aliasing=False)

Resize operator.

Args:
img: hdrCore.image.Image

Required : hdr image

size: TODO

TODO

anti_aliasing: boolean

TODO

Returns:
TODO

TODO

class hdrCore.processing.saturation

Bases: hdrCore.processing.Processing

TODO - Documentation de la classe saturation

compute(img, **kwargs)

saturation operator

Args:

img (hdrCore.image.Image,Required): input image kwargs (dict, Optionnal) : parameters

‘saturation’: float ‘method’: str

‘method’ parameter must be gamma

default value: {‘saturation’: 0.0, ‘method’: ‘gamma’}

Returns:

(hdrCore.image.Image): output image

class hdrCore.processing.tmo_cctf

Bases: hdrCore.processing.Processing

TODO - Documentation de la classe tmo_cctf

compute(img, **kwargs)

CCTF tone mapping operator

Args:
img: hdrCore.image.Image

Required : hdr image

kwargs: dict

Optionnal : parameters ‘function’ : ‘sRGB’ str

Returns:
TODO

TODO

hdrCore.processing.Lab_to_XYZ(Lab)

convert pixel array from Lab to XYZ colorspace.

Args:

Lab (numpy.ndarray, Required): array of pixels in Lab colorspace.

Returns:

(numpy.ndarray): array of pixels in XYZ colorspace.

hdrCore.processing.Lab_to_sRGB(Lab, apply_cctf_encoding=True, clip=False)

convert pixel array from Lab to sRGB colorspace.

Args:

Lab (numpy.ndarray, Required): array of pixels in Lab colorspace. apply_cctf_encoding (boolean, Optionnal): True to encode with sRGB cctf encoding function. clip (boolean, Optionnal): False do not clip values beyond colorspace limits (RGB values < 0 or RGB values > 1).

Returns:

(numpy.ndarray): array of pixels in sRGB colorspace.

hdrCore.processing.Lch_to_sRGB(Lch, apply_cctf_encoding=True, clip=False)

convert pixel array from Lch to sRGB colorspace.

Args:

Lch (numpy.ndarray, Required): array of pixels in Lab colorspace. apply_cctf_encoding (boolean, Optionnal): True to encode with sRGB cctf encoding function. clip (boolean, Optionnal): False do not clip values beyond colorspace limits (RGB values < 0 or RGB values > 1).

Returns:

(numpy.ndarray): array of pixels in sRGB colorspace.

hdrCore.processing.XYZ_to_Lab(XYZ)

convert pixel array from Lab to Lab colorspace.

Args:

XYZ (numpy.ndarray, Required): array of pixels in Lab colorspace.

Returns:

(numpy.ndarray): array of pixels in Lab colorspace.

hdrCore.processing.XYZ_to_sRGB(XYZ, apply_cctf_encoding=True)

convert pixel array from XYZ to sRGB colorspace.

Args:

XYZ (numpy.ndarray, Required): array of pixels in XYZ colorspace. apply_cctf_encoding (boolean, Optionnal): True to encode with sRGB cctf encoding function.

Returns:

(numpy.ndarray): array of pixels in sRGB colorspace.

hdrCore.processing.sRGB_to_Lab(RGB, apply_cctf_decoding=True)

convert pixel array from sRGB to Lab colorspace.

Args:

RGB (numpy.ndarray, Required): array of pixels in RGB colorspace. apply_cctf_decoding (boolean, Optionnal): True to encode with sRGB cctf decoding function.

Returns:

(numpy.ndarray): array of pixels in sRGB colorspace.

hdrCore.processing.sRGB_to_XYZ(RGB, apply_cctf_decoding=True)

convert pixel array from sRGB to XYZ colorspace.

Args:

RGB (numpy.ndarray, Required): array of pixels in sRGB colorspace. apply_cctf_decoding (boolean, Optionnal): True to encode with sRGB cctf decoding function.

Returns:

(numpy.ndarray): array of pixels in XYZ colorspace.

hdrCore.quality module

package hdrCore consists of the core classes for HDR imaging: (1) classes: class imageType

class channel class Image class ColorSpace class Histogram

  1. class: class metadata

  2. processing functions and classes:

    def XYZ_to_sRGB(XYZ,apply_cctf_encoding=True): def sRGB_to_XYZ(RGB,apply_cctf_decoding=True): def Lab_to_XYZ(Lab) def XYZ_to_Lab(XYZ) def Lab_to_sRGB(Lab, apply_cctf_encoding=True, clip = False) def sRGB_to_Lab(RGB, apply_cctf_decoding=True) def Lch_to_sRGB(Lch,apply_cctf_encoding=True, clip=False) class Processing(object) class tmo_cctf(Processing) class exposure(Processing) class contrast(Processing) class clip(Processing) class ColorSpaceTransform(Processing) class resize(Processing) class Ycurve(Processing) class saturation(Processing) class lightnessMask(Processing) class geometry(Processing)

  3. processing nodes that encapsulate processing objects in process-pipe:

    class ProcessNode(object)

  4. process-pipe that defines the pipeline of processing:

    class ProcessPipe(object)

  5. some utils functions:

    def filenamesplit(filename) def filterlistdir(path,extList) def ndarray2vector(nda) def NPlinearWeightMask(x,xMin,xMax,xTolerance) def croppRotated(h,w,alpha)

  6. HDRdisplay model

class hdrCore.quality.quality

Bases: object

TODO - Documentation de la classe quality

Attributes:
_image: TODO

TODO

imageNpath: TODO

TODO

user: TODO

TODO

score: TODO

TODO

artifact: TODO

TODO

toDict()

Convert the object into a dict.

Returns:
TODO

TODO

hdrCore.utils module

package hdrCore consists of the core classes for HDR imaging: (1) classes: class imageType

class channel class Image class ColorSpace class Histogram

  1. class: class metadata

  2. processing functions and classes:

    def XYZ_to_sRGB(XYZ,apply_cctf_encoding=True): def sRGB_to_XYZ(RGB,apply_cctf_decoding=True): def Lab_to_XYZ(Lab) def XYZ_to_Lab(XYZ) def Lab_to_sRGB(Lab, apply_cctf_encoding=True, clip = False) def sRGB_to_Lab(RGB, apply_cctf_decoding=True) def Lch_to_sRGB(Lch,apply_cctf_encoding=True, clip=False) class Processing(object) class tmo_cctf(Processing) class exposure(Processing) class contrast(Processing) class clip(Processing) class ColorSpaceTransform(Processing) class resize(Processing) class Ycurve(Processing) class saturation(Processing) class lightnessMask(Processing) class geometry(Processing)

  3. processing nodes that encapsulate processing objects in process-pipe:

    class ProcessNode(object)

  4. process-pipe that defines the pipeline of processing:

    class ProcessPipe(object)

  5. some utils functions:

    def filenamesplit(filename) def filterlistdir(path,extList) def ndarray2vector(nda) def NPlinearWeightMask(x,xMin,xMax,xTolerance) def croppRotated(h,w,alpha)

  6. HDRdisplay model

hdrCore.utils.NPlinearWeightMask(x, xMin, xMax, xTolerance)

TODO - Documentation de la méthode NPlinearWeightMask

Args:
x: TODO

TODO

xMin: TODO

TODO

xMax: TODO

TODO

xTolerance: TODO

TODO

Returns:
TODO

TODO

hdrCore.utils.croppRotated(h, w, alpha)

TODO - Documentation de la méthode croppRotated

Args:
h: TODO

TODO

w: TODO

TODO

alpha: TODO

TODO

Returns:
TODO

TODO

hdrCore.utils.filenamesplit(filename)

retrieve path, name and extension from a filename.

Args:

filename (str,Required): filename

Returns:

(str,str,str): (path,name,ext)

Example:

filenamesplit(“./dir0/dir1/name.ext”) returns (“./dir0/dir1/”,”name”,”ext”)

hdrCore.utils.filterlistdir(path, extList)

return the files in path that end by one of the string in extList

Args:

path (str, Required): path to directory extList (tuple of str, Required)

Returns:

([str])or (tuple of str) or (str): list, tuple or single str

Example:

filterlistdir(‘./images/’, (‘.jpg’, ‘.JPG’, ‘.png’))

hdrCore.utils.ndarray2vector(nda)

transform 2D array of color data to vector

Args:

nda (numpy.ndarray, Required): numpy array (2D of scalar or vector)

Returns:

(numpy.ndarray, Required): numpy array (1D of scalar or vector)

Module contents