69 lines
1.8 KiB
Python
Executable File
69 lines
1.8 KiB
Python
Executable File
# sample code from https://stackoverflow.com/questions/43111029/how-to-find-the-average-colour-of-an-image-in-python-with-opencv
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import matplotlib
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matplotlib.use('TkAgg')
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import matplotlib.pyplot as plt
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import cv2
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import numpy as np
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from skimage import io
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import colorsys
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img_path = 'input.jpg'
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from picamera import PiCamera
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camera = PiCamera()
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time.sleep(2) # wait for the camera to initialize
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camera.capture(img_path)
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# Read the result
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img = io.imread(img_path)[:, :, :]
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# Average color
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average = img.mean(axis=0).mean(axis=0)
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# Dominant color
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pixels = np.float32(img.reshape(-1, 3))
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n_colors = 5
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criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 200, .1)
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flags = cv2.KMEANS_RANDOM_CENTERS
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_, labels, palette = cv2.kmeans(pixels, n_colors, None, criteria, 10, flags)
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_, counts = np.unique(labels, return_counts=True)
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dominant = palette[np.argmax(counts)]
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# Graph visualization
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avg_patch = np.ones(shape=img.shape, dtype=np.uint8)*np.uint8(average)
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indices = np.argsort(counts)[::-1]
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freqs = np.cumsum(np.hstack([[0], counts[indices]/float(counts.sum())]))
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rows = np.int_(img.shape[0]*freqs)
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dom_patch = np.zeros(shape=img.shape, dtype=np.uint8)
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for i in range(len(rows) - 1):
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dom_patch[rows[i]:rows[i + 1], :, :] += np.uint8(palette[indices[i]])
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palette /= 255.0
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def get_saturation(rgb_color):
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return colorsys.rgb_to_hsv(*rgb_color)[1]
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# Lid color is likely the color with the highest saturation
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lid_color = max(palette, key=get_saturation)
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fig, (ax0, ax1, ax2) = plt.subplots(1, 3, figsize=(12,4))
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ax0.imshow(img)
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ax0.set_title('Input')
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ax0.axis('off')
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ax1.imshow(dom_patch)
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ax1.set_title('Dominant colors')
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ax1.axis('off')
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ax2.add_patch(matplotlib.patches.Rectangle((0, 0), 200, 200, color=lid_color))
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ax2.set_title('Probable Lid Color')
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ax2.axis('off')
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plt.savefig(img_path + "_result.png")
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