Matplotlib is an amazing visualization library in Python for 2D plots of arrays. Matplotlib is a multi-platform data visualization library built on NumPy arrays and designed to work with the broader SciPy stack.
matplotlib.colors.rgb_to_hsv()
The matplotlib.colors.rgb_to_hsv() function belongs to the matplotlib.colors module. The matplotlib.colors.rgb_to_hsv()
function is used to convert float rgb in the range of 0 to 1 into a numpy array of hsv values.
Syntax: matplotlib.colors.rgb_to_hsv(arr)
Parameters:
- arr: It is an array-like argument in the form of (…, 3) where all values must to be in the range of 0 to 1.
Returns:
- hsv: It returns an ndarray in the form of (…, 3) that comprises of colors converted to hsv values within the range of 0 to 1.
Example 1:
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
# helper function to plot a # color table def colortable(colors, title,
colors_sort = True ,
emptycols = 0 ):
# cell dimensions
width = 212
height = 22
swatch_width = 48
margin = 12
topmargin = 40
# Sorting colors based on hue,
# saturation, value and name.
if colors_sort is True :
to_hsv = sorted (( tuple (mcolors.rgb_to_hsv(mcolors.to_rgb(color))),
name)
for name, color in colors.items())
names = [name for hsv, name in to_hsv]
else :
names = list (colors)
length_of_names = len (names)
length_cols = 4 - emptycols
length_rows = length_of_names / / length_cols + int (length_of_names % length_cols > 0 )
width2 = width * 4 + 2 * margin
height2 = height * length_rows + margin + topmargin
dpi = 72
figure, axes = plt.subplots(figsize = (width2 / dpi, height2 / dpi),
dpi = dpi)
figure.subplots_adjust(margin / width2, margin / height2,
(width2 - margin) / width2,
(height2 - topmargin) / height2)
axes.set_xlim( 0 , width * 4 )
axes.set_ylim(height * (length_rows - 0.5 ), - height / 2. )
axes.yaxis.set_visible( False )
axes.xaxis.set_visible( False )
axes.set_axis_off()
axes.set_title(title, fontsize = 24 , loc = "left" , pad = 10 )
for i, name in enumerate (names):
rows = i % length_rows
cols = i / / length_rows
y = rows * height
swatch_start_x = width * cols
swatch_end_x = width * cols + swatch_width
text_pos_x = width * cols + swatch_width + 7
axes.text(text_pos_x, y, name, fontsize = 14 ,
horizontalalignment = 'left' ,
verticalalignment = 'center' )
axes.hlines(y, swatch_start_x, swatch_end_x,
color = colors[name], linewidth = 18 )
return figure
colortable(mcolors.BASE_COLORS, "Base Colors" ,
colors_sort = False , emptycols = 1 )
colortable(mcolors.TABLEAU_COLORS, "Tableau Palette" ,
colors_sort = False , emptycols = 2 )
colortable(mcolors.CSS4_COLORS, "CSS Colors" )
plt.show() |
Output:
Example 2:
Image Used:
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
image = mpimg.imread( 'food.jpeg' )
plt.title( "Output image" )
hsv = matplotlib.colors.rgb_to_hsv(image)
plt.imshow(hsv) |
Output: