OpenCV 2D Histogram

2D Histogram

One dimensional histogram only needs to consider gray value, but 2D histogram needs to consider hue and saturation.

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2D histogram in OpenCV
The parameters of cv2.calcHist() method are different between one-dimensional histogram and 2D histogram,parameters should be modified as follows:

• channels = [0, 1] :We need to deal with both H and S channels at the same time
• bins = [180, 256]: 180 for H channel and 256 for S channel
• range = [0, 180, 0, 256]: The value range of H is 0 to 180, and the value range of S is 0 to 256

import cv2
import numpy as np
img = cv2.imread('picture.jpeg')
hsv = cv2.cvtColor(img,cv2.COLOR_BGR2HSV)  # Convert picture to BGR
hist = cv2.calcHist([hsv], [0, 1], None, [180, 256], [0, 180, 0, 256]) # draw 2d histogram

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2D histogram in Numpy
Numpy also provides a function to draw a 2D histogram: np.histogram2d()

import cv2
import numpy as np
from matplotlib import pyplot as plt
img = cv2.imread('picture.jpeg')
hsv = cv2.cvtColor(img,cv2.COLOR_BGR2HSV)
h, s = np.indices(hsv.shape[:2])  # return a new array built from an array index
hist, xbins, ybins = np.histogram2d(h.ravel(),s.ravel(),[180,256],[[0,180],[0,256]])

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OpenCV Draw 2D histogram

Use the function matplotlib.pyplot.imshow() to draw a 2D histogram, and then match it with a different color map. In this way, we will have a more intuitive understanding of the numerical value represented by each point.

Note: when using this function, remember to set the interpolation parameter to nearest.

import cv2
import numpy as np
from matplotlib import pyplot as plt
img = cv2.imread('picture.jpeg')
hsv = cv2.cvtColor(img,cv2.COLOR_BGR2HSV)
hist = cv2.calcHist( [hsv], [0, 1], None, [180, 256], [0, 180, 0, 256] )
plt.imshow(hist,interpolation = 'nearest')
plt.show()

x-axis shows s value, y-axis shows H value