Use Python,Opencv Calculate and draw the two-dimensional histogram

This blog will show you how to use Python,Opencv Calculate and draw the two-dimensional histogram （ Use them separately Opencv and Numpy Calculation ）, Two dimensional histogram can give us a better understanding of different pixel densities .

1. design sketch

The original figure is as follows ： 1 The dimensional histogram is as follows ： 2 The dimensional histogram is as follows ： X Axis display S value ,Y Axis display tone . hsvmap The renderings are as follows ：

2. Source code

# OpenCV 2-D histogram in : Use the same function cv2.calcHist（） Calculation .
#  about 1D Histogram , We from BGR Convert to grayscale 
#  about 2D Histogram , You need to remove the image from BGR Convert to HSV

import cv2
import numpy as np
from matplotlib import pyplot as plt

img = cv2.imread('ym.jpg')
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)

# 1 Dimensional histogram 
hist = cv2.calcHist([gray], [0], None, [256], [0, 256])
hist, bins = np.histogram(img.ravel(), 256, [0, 256])
plt.hist(img.ravel(), 256, [0, 256])
plt.show()

#  Two dimensional histogram can give us a better understanding of different pixel densities 
# OpenCV Calculation 2D Histogram 
# HSV Images  [0,1] Express H、S passageway ,[180,256] Express H、S Of bins Respectively 180、256
# [0,180,0,256] Represents the range of values 
hist = cv2.calcHist([hsv], [0, 1], None, [180, 256], [0, 180, 0, 256])

hist = np.clip(hist * 0.005, 0, 1)
cv2.imshow('hist', hist)
cv2.waitKey(0)

plt.imshow(hist, interpolation='nearest')
plt.show()

# Numpy Calculation 1D Histogram ：np.histogram（）;
# Numpy Calculation 2D Histogram ：np.historogram2d（）
h, s, v = cv2.split(hsv)
hist, xbins, ybins = np.histogram2d(h.ravel(), s.ravel(), [180, 256], [[0, 180], [0, 256]])
plt.imshow(hist, interpolation='nearest')
plt.show()
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Reference resources

• docs.opencv.org/3.0-beta/do…
• github.com/seminar2012…