Matplotlib.colors.rgb_to_hsv() in Python

07 Oct, 2021
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.
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 pltimport matplotlib.colors as mcolors     # helper function to plot a # color tabledef 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 matplotlibimport matplotlib.pyplot as pltimport matplotlib.image as mpimg    image = mpimg.imread('food.jpeg')plt.title("Output image")    hsv = matplotlib.colors.rgb_to_hsv(image)plt.imshow(hsv)
Output:
adnanirshad158
Python-matplotlib
Python
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