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103 lines
2.7 KiB
Python
103 lines
2.7 KiB
Python
# -- coding: UTF-8
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import cv2
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import matplotlib.pyplot as plt
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from sklearn.cluster import KMeans
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import os
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boundaries = [
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([100,80,0],[240,220,110]), # yellow
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([0,40,50],[110,180,250]), # blue
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([0,60,0],[60,160,70]), # green
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]
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color_attr = ["黄牌","蓝牌",'绿牌','白牌','黑牌']
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threhold_green = 13
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threhold_blue = 13
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threhold_yellow1 = 50
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threhold_yellow2 = 70
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# plt.figure()
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# plt.axis("off")
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# plt.imshow(image)
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# plt.show()
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import numpy as np
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def centroid_histogram(clt):
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numLabels = np.arange(0, len(np.unique(clt.labels_)) + 1)
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(hist, _) = np.histogram(clt.labels_, bins=numLabels)
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# normalize the histogram, such that it sums to one
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hist = hist.astype("float")
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hist /= hist.sum()
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# return the histogram
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return hist
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def plot_colors(hist, centroids):
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bar = np.zeros((50, 300, 3), dtype="uint8")
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startX = 0
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for (percent, color) in zip(hist, centroids):
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endX = startX + (percent * 300)
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cv2.rectangle(bar, (int(startX), 0), (int(endX), 50),
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color.astype("uint8").tolist(), -1)
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startX = endX
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# return the bar chart
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return bar
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def search_boundaries(color):
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for i,color_bound in enumerate(boundaries):
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if np.all(color >= color_bound[0]) and np.all(color <= color_bound[1]):
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return i
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return -1
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def judge_color(color):
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r = color[0]
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g = color[1]
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b = color[2]
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if g - r >= threhold_green and g - b >= threhold_green:
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return 2
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if b - r >= threhold_blue and b - g >= threhold_blue:
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return 1
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if r- b > threhold_yellow2 and g - b > threhold_yellow2:
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return 0
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if r > 200 and b > 200 and g > 200:
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return 3
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if r < 50 and b < 50 and g < 50:
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return 4
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return -1
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def judge_plate_color(img):
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image = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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image = image.reshape((image.shape[0] * image.shape[1], 3))
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clt = KMeans(n_clusters=2)
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clt.fit(image)
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hist = centroid_histogram(clt)
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index = np.argmax(hist)
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#print clt.cluster_centers_[index]
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#color_index = search_boundaries(clt.cluster_centers_[index])
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color_index = judge_color(clt.cluster_centers_[index])
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if color_index == -1:
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if index == 0:
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secound_index = 1
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else:
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secound_index = 0
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color_index = judge_color(clt.cluster_centers_[secound_index])
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if color_index == -1:
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print clt.cluster_centers_
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bar = plot_colors(hist, clt.cluster_centers_)
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# show our color bart
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plt.figure()
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plt.axis("off")
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plt.imshow(bar)
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plt.show()
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if color_index != -1:
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return color_attr[color_index],clt.cluster_centers_[index]
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else:
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return None,clt.cluster_centers_[index] |