!apt update && apt install -y python3-opencv
!pip install opencv-python
!pip install scikit-image
image_to_read = '/home/jovyan/work/5klogos/002192.png'
image_to_read_2 = 'https://i.stack.imgur.com/DNM65.png'
import cv2
import numpy as np
from skimage import io
from matplotlib import pyplot as plt
img = io.imread(image_to_read)[:, :]
plt.imshow(img, interpolation='nearest')
plt.show()
def getDominantColors(img):
average = img.mean(axis=0).mean(axis=0)
pixels = np.float32(img.reshape(-1, 3))
n_colors = 5
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 200, .1)
flags = cv2.KMEANS_RANDOM_CENTERS
_, labels, palette = cv2.kmeans(pixels, n_colors, None, criteria, 10, flags)
_, counts = np.unique(labels, return_counts=True)
return (palette, counts)
def pairCountsAndPalette(palette, counts):
countPaletteMap = {}
for count in counts:
if count not in countPaletteMap:
countPaletteMap[count] = palette[np.where(counts==count)]
return countPaletteMap
palette, counts = getDominantColors(img)
countPalette = sorted(pairCountsAndPalette(palette, counts).items(), reverse=True)
dominantNonGrayColor = None
for count, color in countPalette:
meanColor = sum(color[0]) / len(color[0])
if meanColor > 5 and meanColor < 250:
dominantNonGrayColor = color[0]
break
from PIL import Image, ImageDraw
im = Image.new('RGB', (100, 100), color=(dominantNonGrayColor[0], dominantNonGrayColor[1], dominantNonGrayColor[2]))
im
