# sample code from https://stackoverflow.com/questions/43111029/how-to-find-the-average-colour-of-an-image-in-python-with-opencv

import matplotlib
matplotlib.use('TkAgg')

import matplotlib.pyplot as plt
import cv2
import numpy as np
from skimage import io
import colorsys

img_path = 'input.jpg'

from picamera import PiCamera
camera = PiCamera()
time.sleep(2) # wait for the camera to initialize
camera.capture(img_path)


# Read the result
img = io.imread(img_path)[:, :, :]

# Average color
average = img.mean(axis=0).mean(axis=0)

# Dominant color
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)

dominant = palette[np.argmax(counts)]

# Graph visualization
avg_patch = np.ones(shape=img.shape, dtype=np.uint8)*np.uint8(average)

indices = np.argsort(counts)[::-1]
freqs = np.cumsum(np.hstack([[0], counts[indices]/float(counts.sum())]))
rows = np.int_(img.shape[0]*freqs)

dom_patch = np.zeros(shape=img.shape, dtype=np.uint8)
for i in range(len(rows) - 1):
    dom_patch[rows[i]:rows[i + 1], :, :] += np.uint8(palette[indices[i]])

palette /= 255.0

def get_saturation(rgb_color):
   return colorsys.rgb_to_hsv(*rgb_color)[1]

# Lid color is likely the color with the highest saturation
lid_color = max(palette, key=get_saturation)

fig, (ax0, ax1, ax2) = plt.subplots(1, 3, figsize=(12,4))
ax0.imshow(img)
ax0.set_title('Input')
ax0.axis('off')
ax1.imshow(dom_patch)
ax1.set_title('Dominant colors')
ax1.axis('off')
ax2.add_patch(matplotlib.patches.Rectangle((0, 0), 200, 200, color=lid_color))
ax2.set_title('Probable Lid Color')
ax2.axis('off')

plt.savefig(img_path + "_result.png")