doc_content stringlengths 1 386k | doc_id stringlengths 5 188 |
|---|---|
pandas.tseries.offsets.WeekOfMonth.rollback WeekOfMonth.rollback()
Roll provided date backward to next offset only if not on offset. Returns
TimeStamp
Rolled timestamp if not on offset, otherwise unchanged timestamp. | pandas.reference.api.pandas.tseries.offsets.weekofmonth.rollback |
pandas.tseries.offsets.WeekOfMonth.rollforward WeekOfMonth.rollforward()
Roll provided date forward to next offset only if not on offset. Returns
TimeStamp
Rolled timestamp if not on offset, otherwise unchanged timestamp. | pandas.reference.api.pandas.tseries.offsets.weekofmonth.rollforward |
pandas.tseries.offsets.WeekOfMonth.rule_code WeekOfMonth.rule_code | pandas.reference.api.pandas.tseries.offsets.weekofmonth.rule_code |
pandas.tseries.offsets.WeekOfMonth.week WeekOfMonth.week | pandas.reference.api.pandas.tseries.offsets.weekofmonth.week |
pandas.tseries.offsets.WeekOfMonth.weekday WeekOfMonth.weekday | pandas.reference.api.pandas.tseries.offsets.weekofmonth.weekday |
pandas.tseries.offsets.YearBegin classpandas.tseries.offsets.YearBegin
DateOffset increments between calendar year begin dates. Attributes
base Returns a copy of the calling offset object with n=1 and all other attributes equal.
freqstr
kwds
month
n
name
nanos
normalize
rule_co... | pandas.reference.api.pandas.tseries.offsets.yearbegin |
pandas.tseries.offsets.YearBegin.__call__ YearBegin.__call__(*args, **kwargs)
Call self as a function. | pandas.reference.api.pandas.tseries.offsets.yearbegin.__call__ |
pandas.tseries.offsets.YearBegin.apply YearBegin.apply() | pandas.reference.api.pandas.tseries.offsets.yearbegin.apply |
pandas.tseries.offsets.YearBegin.apply_index YearBegin.apply_index(other) | pandas.reference.api.pandas.tseries.offsets.yearbegin.apply_index |
pandas.tseries.offsets.YearBegin.base YearBegin.base
Returns a copy of the calling offset object with n=1 and all other attributes equal. | pandas.reference.api.pandas.tseries.offsets.yearbegin.base |
pandas.tseries.offsets.YearBegin.copy YearBegin.copy() | pandas.reference.api.pandas.tseries.offsets.yearbegin.copy |
pandas.tseries.offsets.YearBegin.freqstr YearBegin.freqstr | pandas.reference.api.pandas.tseries.offsets.yearbegin.freqstr |
pandas.tseries.offsets.YearBegin.is_anchored YearBegin.is_anchored() | pandas.reference.api.pandas.tseries.offsets.yearbegin.is_anchored |
pandas.tseries.offsets.YearBegin.is_month_end YearBegin.is_month_end() | pandas.reference.api.pandas.tseries.offsets.yearbegin.is_month_end |
pandas.tseries.offsets.YearBegin.is_month_start YearBegin.is_month_start() | pandas.reference.api.pandas.tseries.offsets.yearbegin.is_month_start |
pandas.tseries.offsets.YearBegin.is_on_offset YearBegin.is_on_offset() | pandas.reference.api.pandas.tseries.offsets.yearbegin.is_on_offset |
pandas.tseries.offsets.YearBegin.is_quarter_end YearBegin.is_quarter_end() | pandas.reference.api.pandas.tseries.offsets.yearbegin.is_quarter_end |
pandas.tseries.offsets.YearBegin.is_quarter_start YearBegin.is_quarter_start() | pandas.reference.api.pandas.tseries.offsets.yearbegin.is_quarter_start |
pandas.tseries.offsets.YearBegin.is_year_end YearBegin.is_year_end() | pandas.reference.api.pandas.tseries.offsets.yearbegin.is_year_end |
pandas.tseries.offsets.YearBegin.is_year_start YearBegin.is_year_start() | pandas.reference.api.pandas.tseries.offsets.yearbegin.is_year_start |
pandas.tseries.offsets.YearBegin.isAnchored YearBegin.isAnchored() | pandas.reference.api.pandas.tseries.offsets.yearbegin.isanchored |
pandas.tseries.offsets.YearBegin.kwds YearBegin.kwds | pandas.reference.api.pandas.tseries.offsets.yearbegin.kwds |
pandas.tseries.offsets.YearBegin.month YearBegin.month | pandas.reference.api.pandas.tseries.offsets.yearbegin.month |
pandas.tseries.offsets.YearBegin.n YearBegin.n | pandas.reference.api.pandas.tseries.offsets.yearbegin.n |
pandas.tseries.offsets.YearBegin.name YearBegin.name | pandas.reference.api.pandas.tseries.offsets.yearbegin.name |
pandas.tseries.offsets.YearBegin.nanos YearBegin.nanos | pandas.reference.api.pandas.tseries.offsets.yearbegin.nanos |
pandas.tseries.offsets.YearBegin.normalize YearBegin.normalize | pandas.reference.api.pandas.tseries.offsets.yearbegin.normalize |
pandas.tseries.offsets.YearBegin.onOffset YearBegin.onOffset() | pandas.reference.api.pandas.tseries.offsets.yearbegin.onoffset |
pandas.tseries.offsets.YearBegin.rollback YearBegin.rollback()
Roll provided date backward to next offset only if not on offset. Returns
TimeStamp
Rolled timestamp if not on offset, otherwise unchanged timestamp. | pandas.reference.api.pandas.tseries.offsets.yearbegin.rollback |
pandas.tseries.offsets.YearBegin.rollforward YearBegin.rollforward()
Roll provided date forward to next offset only if not on offset. Returns
TimeStamp
Rolled timestamp if not on offset, otherwise unchanged timestamp. | pandas.reference.api.pandas.tseries.offsets.yearbegin.rollforward |
pandas.tseries.offsets.YearBegin.rule_code YearBegin.rule_code | pandas.reference.api.pandas.tseries.offsets.yearbegin.rule_code |
pandas.tseries.offsets.YearEnd classpandas.tseries.offsets.YearEnd
DateOffset increments between calendar year ends. Attributes
base Returns a copy of the calling offset object with n=1 and all other attributes equal.
freqstr
kwds
month
n
name
nanos
normalize
rule_code Meth... | pandas.reference.api.pandas.tseries.offsets.yearend |
pandas.tseries.offsets.YearEnd.__call__ YearEnd.__call__(*args, **kwargs)
Call self as a function. | pandas.reference.api.pandas.tseries.offsets.yearend.__call__ |
pandas.tseries.offsets.YearEnd.apply YearEnd.apply() | pandas.reference.api.pandas.tseries.offsets.yearend.apply |
pandas.tseries.offsets.YearEnd.apply_index YearEnd.apply_index(other) | pandas.reference.api.pandas.tseries.offsets.yearend.apply_index |
pandas.tseries.offsets.YearEnd.base YearEnd.base
Returns a copy of the calling offset object with n=1 and all other attributes equal. | pandas.reference.api.pandas.tseries.offsets.yearend.base |
pandas.tseries.offsets.YearEnd.copy YearEnd.copy() | pandas.reference.api.pandas.tseries.offsets.yearend.copy |
pandas.tseries.offsets.YearEnd.freqstr YearEnd.freqstr | pandas.reference.api.pandas.tseries.offsets.yearend.freqstr |
pandas.tseries.offsets.YearEnd.is_anchored YearEnd.is_anchored() | pandas.reference.api.pandas.tseries.offsets.yearend.is_anchored |
pandas.tseries.offsets.YearEnd.is_month_end YearEnd.is_month_end() | pandas.reference.api.pandas.tseries.offsets.yearend.is_month_end |
pandas.tseries.offsets.YearEnd.is_month_start YearEnd.is_month_start() | pandas.reference.api.pandas.tseries.offsets.yearend.is_month_start |
pandas.tseries.offsets.YearEnd.is_on_offset YearEnd.is_on_offset() | pandas.reference.api.pandas.tseries.offsets.yearend.is_on_offset |
pandas.tseries.offsets.YearEnd.is_quarter_end YearEnd.is_quarter_end() | pandas.reference.api.pandas.tseries.offsets.yearend.is_quarter_end |
pandas.tseries.offsets.YearEnd.is_quarter_start YearEnd.is_quarter_start() | pandas.reference.api.pandas.tseries.offsets.yearend.is_quarter_start |
pandas.tseries.offsets.YearEnd.is_year_end YearEnd.is_year_end() | pandas.reference.api.pandas.tseries.offsets.yearend.is_year_end |
pandas.tseries.offsets.YearEnd.is_year_start YearEnd.is_year_start() | pandas.reference.api.pandas.tseries.offsets.yearend.is_year_start |
pandas.tseries.offsets.YearEnd.isAnchored YearEnd.isAnchored() | pandas.reference.api.pandas.tseries.offsets.yearend.isanchored |
pandas.tseries.offsets.YearEnd.kwds YearEnd.kwds | pandas.reference.api.pandas.tseries.offsets.yearend.kwds |
pandas.tseries.offsets.YearEnd.month YearEnd.month | pandas.reference.api.pandas.tseries.offsets.yearend.month |
pandas.tseries.offsets.YearEnd.n YearEnd.n | pandas.reference.api.pandas.tseries.offsets.yearend.n |
pandas.tseries.offsets.YearEnd.name YearEnd.name | pandas.reference.api.pandas.tseries.offsets.yearend.name |
pandas.tseries.offsets.YearEnd.nanos YearEnd.nanos | pandas.reference.api.pandas.tseries.offsets.yearend.nanos |
pandas.tseries.offsets.YearEnd.normalize YearEnd.normalize | pandas.reference.api.pandas.tseries.offsets.yearend.normalize |
pandas.tseries.offsets.YearEnd.onOffset YearEnd.onOffset() | pandas.reference.api.pandas.tseries.offsets.yearend.onoffset |
pandas.tseries.offsets.YearEnd.rollback YearEnd.rollback()
Roll provided date backward to next offset only if not on offset. Returns
TimeStamp
Rolled timestamp if not on offset, otherwise unchanged timestamp. | pandas.reference.api.pandas.tseries.offsets.yearend.rollback |
pandas.tseries.offsets.YearEnd.rollforward YearEnd.rollforward()
Roll provided date forward to next offset only if not on offset. Returns
TimeStamp
Rolled timestamp if not on offset, otherwise unchanged timestamp. | pandas.reference.api.pandas.tseries.offsets.yearend.rollforward |
pandas.tseries.offsets.YearEnd.rule_code YearEnd.rule_code | pandas.reference.api.pandas.tseries.offsets.yearend.rule_code |
pandas.UInt16Dtype classpandas.UInt16Dtype[source]
An ExtensionDtype for uint16 integer data. Changed in version 1.0.0: Now uses pandas.NA as its missing value, rather than numpy.nan. Attributes
None Methods
None | pandas.reference.api.pandas.uint16dtype |
pandas.UInt32Dtype classpandas.UInt32Dtype[source]
An ExtensionDtype for uint32 integer data. Changed in version 1.0.0: Now uses pandas.NA as its missing value, rather than numpy.nan. Attributes
None Methods
None | pandas.reference.api.pandas.uint32dtype |
pandas.UInt64Dtype classpandas.UInt64Dtype[source]
An ExtensionDtype for uint64 integer data. Changed in version 1.0.0: Now uses pandas.NA as its missing value, rather than numpy.nan. Attributes
None Methods
None | pandas.reference.api.pandas.uint64dtype |
pandas.UInt64Index classpandas.UInt64Index(data=None, dtype=None, copy=False, name=None)[source]
Immutable sequence used for indexing and alignment. The basic object storing axis labels for all pandas objects. UInt64Index is a special case of Index with purely unsigned integer labels. . Deprecated since version 1.... | pandas.reference.api.pandas.uint64index |
pandas.UInt8Dtype classpandas.UInt8Dtype[source]
An ExtensionDtype for uint8 integer data. Changed in version 1.0.0: Now uses pandas.NA as its missing value, rather than numpy.nan. Attributes
None Methods
None | pandas.reference.api.pandas.uint8dtype |
pandas.unique pandas.unique(values)[source]
Return unique values based on a hash table. Uniques are returned in order of appearance. This does NOT sort. Significantly faster than numpy.unique for long enough sequences. Includes NA values. Parameters
values:1d array-like
Returns
numpy.ndarray or ExtensionAr... | pandas.reference.api.pandas.unique |
pandas.util.hash_array pandas.util.hash_array(vals, encoding='utf8', hash_key='0123456789123456', categorize=True)[source]
Given a 1d array, return an array of deterministic integers. Parameters
vals:ndarray or ExtensionArray
encoding:str, default ‘utf8’
Encoding for data & key when strings.
hash_key:str,... | pandas.reference.api.pandas.util.hash_array |
pandas.util.hash_pandas_object pandas.util.hash_pandas_object(obj, index=True, encoding='utf8', hash_key='0123456789123456', categorize=True)[source]
Return a data hash of the Index/Series/DataFrame. Parameters
obj:Index, Series, or DataFrame
index:bool, default True
Include the index in the hash (if Series... | pandas.reference.api.pandas.util.hash_pandas_object |
pandas.wide_to_long pandas.wide_to_long(df, stubnames, i, j, sep='', suffix='\\d+')[source]
Unpivot a DataFrame from wide to long format. Less flexible but more user-friendly than melt. With stubnames [‘A’, ‘B’], this function expects to find one or more group of columns with format A-suffix1, A-suffix2,…, B-suffix... | pandas.reference.api.pandas.wide_to_long |
Plotting The following functions are contained in the pandas.plotting module.
andrews_curves(frame, class_column[, ax, ...]) Generate a matplotlib plot of Andrews curves, for visualising clusters of multivariate data.
autocorrelation_plot(series[, ax]) Autocorrelation plot for time series.
bootstrap_plot(ser... | pandas.reference.plotting |
Resampling Resampler objects are returned by resample calls: pandas.DataFrame.resample(), pandas.Series.resample(). Indexing, iteration
Resampler.__iter__() Groupby iterator.
Resampler.groups Dict {group name -> group labels}.
Resampler.indices Dict {group name -> group indices}.
Resampler.get_group(name[... | pandas.reference.resampling |
Series Constructor
Series([data, index, dtype, name, copy, ...]) One-dimensional ndarray with axis labels (including time series). Attributes Axes
Series.index The index (axis labels) of the Series.
Series.array The ExtensionArray of the data backing this Series or Index.
Series.values Return Se... | pandas.reference.series |
Style Styler objects are returned by pandas.DataFrame.style. Styler constructor
Styler(data[, precision, table_styles, ...]) Helps style a DataFrame or Series according to the data with HTML and CSS.
Styler.from_custom_template(searchpath[, ...]) Factory function for creating a subclass of Styler. Styler... | pandas.reference.style |
Window Rolling objects are returned by .rolling calls: pandas.DataFrame.rolling(), pandas.Series.rolling(), etc. Expanding objects are returned by .expanding calls: pandas.DataFrame.expanding(), pandas.Series.expanding(), etc. ExponentialMovingWindow objects are returned by .ewm calls: pandas.DataFrame.ewm(), pandas.Se... | pandas.reference.window |
Module: color
skimage.color.combine_stains(stains, conv_matrix) Stain to RGB color space conversion.
skimage.color.convert_colorspace(arr, …) Convert an image array to a new color space.
skimage.color.deltaE_cie76(lab1, lab2) Euclidean distance between two points in Lab color space
skimage.color.deltaE_ciede200... | skimage.api.skimage.color |
skimage.color.combine_stains(stains, conv_matrix) [source]
Stain to RGB color space conversion. Parameters
stains(…, 3) array_like
The image in stain color space. Final dimension denotes channels. conv_matrix: ndarray
The stain separation matrix as described by G. Landini [1]. Returns
out(…, 3) ndarray ... | skimage.api.skimage.color#skimage.color.combine_stains |
skimage.color.convert_colorspace(arr, fromspace, tospace) [source]
Convert an image array to a new color space. Valid color spaces are:
‘RGB’, ‘HSV’, ‘RGB CIE’, ‘XYZ’, ‘YUV’, ‘YIQ’, ‘YPbPr’, ‘YCbCr’, ‘YDbDr’ Parameters
arr(…, 3) array_like
The image to convert. Final dimension denotes channels.
fromspaces... | skimage.api.skimage.color#skimage.color.convert_colorspace |
skimage.color.deltaE_cie76(lab1, lab2) [source]
Euclidean distance between two points in Lab color space Parameters
lab1array_like
reference color (Lab colorspace)
lab2array_like
comparison color (Lab colorspace) Returns
dEarray_like
distance between colors lab1 and lab2 References
1
https:/... | skimage.api.skimage.color#skimage.color.deltaE_cie76 |
skimage.color.deltaE_ciede2000(lab1, lab2, kL=1, kC=1, kH=1) [source]
Color difference as given by the CIEDE 2000 standard. CIEDE 2000 is a major revision of CIDE94. The perceptual calibration is largely based on experience with automotive paint on smooth surfaces. Parameters
lab1array_like
reference color (Lab... | skimage.api.skimage.color#skimage.color.deltaE_ciede2000 |
skimage.color.deltaE_ciede94(lab1, lab2, kH=1, kC=1, kL=1, k1=0.045, k2=0.015) [source]
Color difference according to CIEDE 94 standard Accommodates perceptual non-uniformities through the use of application specific scale factors (kH, kC, kL, k1, and k2). Parameters
lab1array_like
reference color (Lab colorspa... | skimage.api.skimage.color#skimage.color.deltaE_ciede94 |
skimage.color.deltaE_cmc(lab1, lab2, kL=1, kC=1) [source]
Color difference from the CMC l:c standard. This color difference was developed by the Colour Measurement Committee (CMC) of the Society of Dyers and Colourists (United Kingdom). It is intended for use in the textile industry. The scale factors kL, kC set the ... | skimage.api.skimage.color#skimage.color.deltaE_cmc |
skimage.color.gray2rgb(image, alpha=None) [source]
Create an RGB representation of a gray-level image. Parameters
imagearray_like
Input image.
alphabool, optional
Ensure that the output image has an alpha layer. If None, alpha layers are passed through but not created. Returns
rgb(…, 3) ndarray
RGB ... | skimage.api.skimage.color#skimage.color.gray2rgb |
skimage.color.gray2rgba(image, alpha=None) [source]
Create a RGBA representation of a gray-level image. Parameters
imagearray_like
Input image.
alphaarray_like, optional
Alpha channel of the output image. It may be a scalar or an array that can be broadcast to image. If not specified it is set to the maximu... | skimage.api.skimage.color#skimage.color.gray2rgba |
skimage.color.grey2rgb(image, alpha=None) [source]
Create an RGB representation of a gray-level image. Parameters
imagearray_like
Input image.
alphabool, optional
Ensure that the output image has an alpha layer. If None, alpha layers are passed through but not created. Returns
rgb(…, 3) ndarray
RGB ... | skimage.api.skimage.color#skimage.color.grey2rgb |
skimage.color.hed2rgb(hed) [source]
Haematoxylin-Eosin-DAB (HED) to RGB color space conversion. Parameters
hed(…, 3) array_like
The image in the HED color space. Final dimension denotes channels. Returns
out(…, 3) ndarray
The image in RGB. Same dimensions as input. Raises
ValueError
If hed is not ... | skimage.api.skimage.color#skimage.color.hed2rgb |
skimage.color.hsv2rgb(hsv) [source]
HSV to RGB color space conversion. Parameters
hsv(…, 3) array_like
The image in HSV format. Final dimension denotes channels. Returns
out(…, 3) ndarray
The image in RGB format. Same dimensions as input. Raises
ValueError
If hsv is not at least 2-D with shape (…,... | skimage.api.skimage.color#skimage.color.hsv2rgb |
skimage.color.lab2lch(lab) [source]
CIE-LAB to CIE-LCH color space conversion. LCH is the cylindrical representation of the LAB (Cartesian) colorspace Parameters
lab(…, 3) array_like
The N-D image in CIE-LAB format. The last (N+1-th) dimension must have at least 3 elements, corresponding to the L, a, and b colo... | skimage.api.skimage.color#skimage.color.lab2lch |
skimage.color.lab2rgb(lab, illuminant='D65', observer='2') [source]
Lab to RGB color space conversion. Parameters
lab(…, 3) array_like
The image in Lab format. Final dimension denotes channels.
illuminant{“A”, “D50”, “D55”, “D65”, “D75”, “E”}, optional
The name of the illuminant (the function is NOT case se... | skimage.api.skimage.color#skimage.color.lab2rgb |
skimage.color.lab2xyz(lab, illuminant='D65', observer='2') [source]
CIE-LAB to XYZcolor space conversion. Parameters
lab(…, 3) array_like
The image in Lab format. Final dimension denotes channels.
illuminant{“A”, “D50”, “D55”, “D65”, “D75”, “E”}, optional
The name of the illuminant (the function is NOT case... | skimage.api.skimage.color#skimage.color.lab2xyz |
skimage.color.label2rgb(label, image=None, colors=None, alpha=0.3, bg_label=-1, bg_color=(0, 0, 0), image_alpha=1, kind='overlay') [source]
Return an RGB image where color-coded labels are painted over the image. Parameters
labelarray, shape (M, N)
Integer array of labels with the same shape as image.
imagear... | skimage.api.skimage.color#skimage.color.label2rgb |
skimage.color.lch2lab(lch) [source]
CIE-LCH to CIE-LAB color space conversion. LCH is the cylindrical representation of the LAB (Cartesian) colorspace Parameters
lch(…, 3) array_like
The N-D image in CIE-LCH format. The last (N+1-th) dimension must have at least 3 elements, corresponding to the L, a, and b colo... | skimage.api.skimage.color#skimage.color.lch2lab |
skimage.color.rgb2gray(rgb) [source]
Compute luminance of an RGB image. Parameters
rgb(…, 3) array_like
The image in RGB format. Final dimension denotes channels. Returns
outndarray
The luminance image - an array which is the same size as the input array, but with the channel dimension removed. Raise... | skimage.api.skimage.color#skimage.color.rgb2gray |
skimage.color.rgb2grey(rgb) [source]
Compute luminance of an RGB image. Parameters
rgb(…, 3) array_like
The image in RGB format. Final dimension denotes channels. Returns
outndarray
The luminance image - an array which is the same size as the input array, but with the channel dimension removed. Raise... | skimage.api.skimage.color#skimage.color.rgb2grey |
skimage.color.rgb2hed(rgb) [source]
RGB to Haematoxylin-Eosin-DAB (HED) color space conversion. Parameters
rgb(…, 3) array_like
The image in RGB format. Final dimension denotes channels. Returns
out(…, 3) ndarray
The image in HED format. Same dimensions as input. Raises
ValueError
If rgb is not at... | skimage.api.skimage.color#skimage.color.rgb2hed |
skimage.color.rgb2hsv(rgb) [source]
RGB to HSV color space conversion. Parameters
rgb(…, 3) array_like
The image in RGB format. Final dimension denotes channels. Returns
out(…, 3) ndarray
The image in HSV format. Same dimensions as input. Raises
ValueError
If rgb is not at least 2-D with shape (…,... | skimage.api.skimage.color#skimage.color.rgb2hsv |
skimage.color.rgb2lab(rgb, illuminant='D65', observer='2') [source]
Conversion from the sRGB color space (IEC 61966-2-1:1999) to the CIE Lab colorspace under the given illuminant and observer. Parameters
rgb(…, 3) array_like
The image in RGB format. Final dimension denotes channels.
illuminant{“A”, “D50”, “D5... | skimage.api.skimage.color#skimage.color.rgb2lab |
skimage.color.rgb2rgbcie(rgb) [source]
RGB to RGB CIE color space conversion. Parameters
rgb(…, 3) array_like
The image in RGB format. Final dimension denotes channels. Returns
out(…, 3) ndarray
The image in RGB CIE format. Same dimensions as input. Raises
ValueError
If rgb is not at least 2-D wit... | skimage.api.skimage.color#skimage.color.rgb2rgbcie |
skimage.color.rgb2xyz(rgb) [source]
RGB to XYZ color space conversion. Parameters
rgb(…, 3) array_like
The image in RGB format. Final dimension denotes channels. Returns
out(…, 3) ndarray
The image in XYZ format. Same dimensions as input. Raises
ValueError
If rgb is not at least 2-D with shape (…,... | skimage.api.skimage.color#skimage.color.rgb2xyz |
skimage.color.rgb2ycbcr(rgb) [source]
RGB to YCbCr color space conversion. Parameters
rgb(…, 3) array_like
The image in RGB format. Final dimension denotes channels. Returns
out(…, 3) ndarray
The image in YCbCr format. Same dimensions as input. Raises
ValueError
If rgb is not at least 2-D with sha... | skimage.api.skimage.color#skimage.color.rgb2ycbcr |
skimage.color.rgb2ydbdr(rgb) [source]
RGB to YDbDr color space conversion. Parameters
rgb(…, 3) array_like
The image in RGB format. Final dimension denotes channels. Returns
out(…, 3) ndarray
The image in YDbDr format. Same dimensions as input. Raises
ValueError
If rgb is not at least 2-D with sha... | skimage.api.skimage.color#skimage.color.rgb2ydbdr |
skimage.color.rgb2yiq(rgb) [source]
RGB to YIQ color space conversion. Parameters
rgb(…, 3) array_like
The image in RGB format. Final dimension denotes channels. Returns
out(…, 3) ndarray
The image in YIQ format. Same dimensions as input. Raises
ValueError
If rgb is not at least 2-D with shape (…,... | skimage.api.skimage.color#skimage.color.rgb2yiq |
skimage.color.rgb2ypbpr(rgb) [source]
RGB to YPbPr color space conversion. Parameters
rgb(…, 3) array_like
The image in RGB format. Final dimension denotes channels. Returns
out(…, 3) ndarray
The image in YPbPr format. Same dimensions as input. Raises
ValueError
If rgb is not at least 2-D with sha... | skimage.api.skimage.color#skimage.color.rgb2ypbpr |
skimage.color.rgb2yuv(rgb) [source]
RGB to YUV color space conversion. Parameters
rgb(…, 3) array_like
The image in RGB format. Final dimension denotes channels. Returns
out(…, 3) ndarray
The image in YUV format. Same dimensions as input. Raises
ValueError
If rgb is not at least 2-D with shape (…,... | skimage.api.skimage.color#skimage.color.rgb2yuv |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.