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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)
# TODO: implement KMeans clustering from scratch to
# improve program modularity
clt = KMeans(n_clusters=num_colors, n_init='auto')
clt.fit(img.reshape(-1, 3))
return clt.cluster_centers_
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:
hex_value = hex_value.lstrip('#')
return tuple(int(hex_value[i:i+2], 16) for i in (0, 2, 4))
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:] # slice off the # from the hex code
rgb = (curr_hex[0:2], curr_hex[2:4], curr_hex[4:6])
comp = ['%02X' % (255 - int(a, 16)) for a in rgb] # magic :D
compliments.append('#' + ''.join(comp))
return compliments
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