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| -rw-r--r-- | README.md | 12 | ||||
| -rw-r--r-- | updateColors.py | 70 |
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diff --git a/README.md b/README.md new file mode 100644 index 0000000..4625f81 --- /dev/null +++ b/README.md @@ -0,0 +1,12 @@ +# Dynamic Theme Switcher + +This program will take in an image and generate a color palette based on the image. This palette will be determined by first doing a KMeans analysis on the image to find the most prominent colors, and then generating a complimentary color for each of the most prominent colors. After determining the colors, the program will automatically update the colors for i3 and Polybar to the new color scheme. Next, the program will update the background displayed to the image passed in and prompt the user to re-load i3. + +## Usage + +`newTheme.py [image]` + +## Requirements + + - Need Python3 version `> 3.6` + - `numpy`, `cv2`, and `scipy_learn` module diff --git a/updateColors.py b/updateColors.py new file mode 100644 index 0000000..266dc5f --- /dev/null +++ b/updateColors.py @@ -0,0 +1,70 @@ +""" +Basically what I want this program to dynamically update the colors based on the desktop background +so it will grab colors from the desktop background by finding the most promident colors and then set +the system colors to these colors. I believe there are two major components involved in making this work + +i. The part of the program that analyzes an image and determines the most prominent colors +ii. The part of the program that goes into all the configuration files and updates their color schemes +""" +import sys +import numpy as np +import PIL +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 rgbToHex(input_values): + """ + 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 compColors(color_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] + compliments.append('#' + ''.join(comp)) + return compliments +def updatei3Colors(config_path: str): + pass + """ + Given a list of colors, update the color scheme using the most popular colors + + """ +popularColors = grabColors('/home/chandler/Pictures/papes/blueyellow.jpg', 5) +hex_colors = rgbToHex(popularColors) +hex_compliments = compColors(hex_colors) +print(hex_colors) +print(hex_compliments) + +def main(): + print(sys.argv[0]) + +main() |