Histograms – OpenCV 3.4 with python 3 Tutorial 11
Beginners Opencv, Tutorials
0
Source code:
import cv2 import numpy as np from matplotlib import pyplot as plt img = cv2.imread("sea_beach.jpg") b, g, r = cv2.split(img) cv2.imshow("img", img) cv2.imshow("b", b) cv2.imshow("g", g) cv2.imshow("r", r) plt.hist(b.ravel(), 256, [0, 256]) plt.hist(g.ravel(), 256, [0, 256]) plt.hist(r.ravel(), 256, [0, 256]) plt.show()
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Opencv Beginner Tutorial
- 1) Loading images
- 2) Loading Video and Webcam
- 3) Drawing and writing on images
- 4) Basic operations on images
- 5) Add images and Threshold
- 6) Blending images
- 7) Bitwise Operators
- 8) Trackbars
- 9) Object detection using HSV Color space
- 10) Basic Thresholding
- 11) Histograms
- 12) Basic geometric transformations
- 13) Perspective transformation
- 14) Affine transformation
- 15) Adaptive thresholding5
- 16) Smoothing images
- 17) Morphological transformation
- 18) Edge detection
- 19) Find and Draw Contours
- 20) Template matching
- 21) Lines detection with Hough Transform
- 22) Corners detection
- 23) Image Pyramids
- 24) Image Pyramids (Blending and reconstruction)
- 25) Feature detection (SIFT, SURF, ORB)
- 26) Feature Matching (Brute-Force)
- 27) Mouse Events
- 28) Histogram and Back Projection
- 29) Object tracking with Mean-shift
- 30) Object tracking with Camshift
- 31) Optical Flow with Lucas-Kanade method
- 32) Background Subtraction
- 33) k-Nearest Neighbour classification
- 34) Object tracking using Homography
- 35) Fourier Transform
- 36) Knn handwritten digits recognition
- 37) Face detection using Haar Cascades