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import cv2 as cv
from sklearn.cluster import KMeans
def grabColors(img_path: str, num_colors: int) -> list:
"""
Takes in an image, and Number of colors, then returns a list of those colors.
The list of colors will contain the most prominent colors present in the image.
img_path - the path where your image lives (IE, /home/chandler/Pictures/moss.png)
num_colors - the number of colors you need back from the image
"""
img = cv.imread(img_path)
img = cv.cvtColor(img, cv.COLOR_BGR2RGB)
# scale image down by factor of 10 to decrease computation time
dim = (int(len(img[0])/10), int(len(img)/10))
img = cv.resize(img, dim, interpolation= cv.INTER_AREA)
clt = KMeans(n_clusters=num_colors, n_init='auto')
clt.fit(img.reshape(-1, 3))
return clt.cluster_centers_
def compColors(color_list: list) -> list:
"""
given a list of colors, generate complimentary colors to contrast the prominent colors.
return a list of these colors.
"""
compliments = []
for color in color_list:
curr_hex = color[1:]
rgb = (curr_hex[0:2], curr_hex[2:4], curr_hex[4:6])
comp = ['%02X' % (255 - int(a, 16)) for a in rgb]
compliments.append('#' + ''.join(comp))
return compliments
def checkContrast(hex_color_list: list, hex_compliment_list: list) -> list:
"""
Given the list of colors and their compliments, reutrn a list of the contrast values
between the colors
"""
color_list = hexToRGB_list(hex_color_list)
compliment_list = hexToRGB_list(hex_compliment_list)
contrast_values = []
for i, color in enumerate(color_list):
compliment = compliment_list[i]
# determine relative luminance of each color
color_luminence = relativeLuminance(color)
compliment_luminence = relativeLuminance(compliment)
value = (max(color_luminence, compliment_luminence) + 0.05) / (min(color_luminence, compliment_luminence) + 0.05)
contrast_values.append(value)
return contrast_values
def relativeLuminance(color: list) -> float:
"""
Determines the luminance of color. The luminance allows us to
compute the contrast between two colors by comparing their
relative luminance.
The magic constants in this function are all taken from the following
description of relative luminance:
https://www.w3.org/TR/WCAG20/#relativeluminancedef
"""
threshold = 0.03928
channels = []
for channel in color:
channel_norm = channel / 255
if channel_norm <= threshold:
channels.append(channel_norm / 12.92)
else:
channel_val = ((channel_norm + 0.055) / 1.055)**(2.4)
channels.append(channel_val)
red, green, blue = channels
luminance = (0.2126 * red) + (0.7152 * green) + (0.0722 * blue)
return luminance
def rgbToHex(input_values: list) -> list:
"""
Takes in a list of RBG color values and returns a list of those same colors as hex values
"""
hex_list=[]
for color in input_values:
red = int(color[0])
green = int(color[1])
blue = int(color[2])
hex_list.append('#{:02x}{:02x}{:02x}'.format(red, green, blue))
return hex_list
def hexToRGB(hex_value: str) -> tuple:
"""
Takes in a list of Hex values and returns a tuple of those colors as rgb values
"""
hex_value = hex_value.lstrip('#')
return tuple(int(hex_value[i:i+2], 16) for i in (0, 2, 4))
def hexToRGB_list(hex_list: list) -> list:
colors = []
for color in hex_list:
hex_value = color.lstrip('#')
colors.append(tuple(int(hex_value[i:i+2], 16) for i in (0, 2, 4)))
return colors
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