图像处理常用经典算法资源整理

最新推荐文章于 2024-01-27 19:58:59 发布

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最新推荐文章于 2024-01-27 19:58:59 发布 · 1.5k 阅读

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#计算机视觉 #opencv

本文深入探讨了多种图像处理技术，包括二值化、图像增强、边缘提取、直线检测、图像矩阵运算、对极几何及空间中相机标定、TPS 2D插值、车牌生成、图像去模糊、图像合成、图像调试、颜色直方图、方向投影、有限对比适应性直方图均衡化等。同时提供了丰富的资源链接，帮助读者深入理解并应用这些技术。

二值化方法介绍

https://www.cnblogs.com/Imageshop/p/3307308.html

https://github.com/jingweizhanghuai/image

图像增强方法介绍

https://www.cnblogs.com/molakejin/p/5766127.html

https://www.cnblogs.com/sleepwalker/p/3676600.html

图像暗角去除

https://www.cnblogs.com/Imageshop/p/6166394.html

边缘提取方法介绍

https://blog.youkuaiyun.com/weixin_38907330/article/details/80874031

直线检测方法介绍

https://blog.youkuaiyun.com/weixin_42647783/article/details/81200534

https://blog.youkuaiyun.com/leonardohaig/article/details/87907462

图像矩阵运算方法介绍

rotate

https://www.cnblogs.com/meteoric_cry/p/7987548.html

对极几何及空间中相机标定

https://blog.youkuaiyun.com/qq_36622009/article/details/104919996

https://blog.youkuaiyun.com/Ketal_N/article/details/83744626

# -*- coding: utf-8 -*-
"""
Created on Tue Jan 28 14:20:43 2014
@author: duan
"""
import cv2
import numpy as np
from matplotlib import pyplot as plt

img1 = cv2.imread('myleft.jpg',0) #queryimage # left image
img2 = cv2.imread('myright.jpg',0) #trainimage # right image
sift = cv2.SIFT()
# find the keypoints and descriptors with SIFT
kp1, des1 = sift.detectAndCompute(img1,None)
kp2, des2 = sift.detectAndCompute(img2,None)
# FLANN parameters
FLANN_INDEX_KDTREE = 0
index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)
search_params = dict(checks=50)
flann = cv2.FlannBasedMatcher(index_params,search_params)
matches = flann.knnMatch(des1,des2,k=2)
good = []
pts1 = []
pts2 = []
# ratio test as per Lowe's paper
for i,(m,n) in enumerate(matches):
    if m.distance < 0.8*n.distance:
        good.append(m)
        pts2.append(kp2[m.trainIdx].pt)
        pts1.append(kp1[m.queryIdx].pt)

pts