每天进步一点点------如何实现Sobel Edge Detector? (Image Processing) (C/C++)
使用C與C++/CLI實現Sobel Edge Detector。
http://www.cnblogs.com/oomusou/archive/2008/07/23/sobel_edge_detector.html
/*
(C) OOMusou 2007 http://oomusou.cnblogs.com Filename : sobel_edge.c
Compiler : Visual C++ 8.0
Description : Demo the how to use sobel detector on gray level image
Release : 07/23/2008 1.0
*/
#include <stdio.h>
#include <stdlib.h>
#include <math.h> #define MASK_N 2
#define MASK_X 3
#define MASK_Y 3
#define WHITE 255
#define BLACK 0 unsigned char *image_s = NULL; // source image array
unsigned char *image_t = NULL; // target image array
FILE *fp_s = NULL; // source file handler
FILE *fp_t = NULL; // target file handler unsigned int width, height; // image width, image height
unsigned int rgb_raw_data_offset;// RGB raw data offset
unsigned char bit_per_pixel; // bit per pixel
unsigned short byte_per_pixel; // byte per pixel // bitmap header
unsigned char header[] = {
0x42, // identity : B
0x4d, // identity : M
, , , , // file size
, , // reserved1
, , // reserved2
, , , , // RGB data offset
, , , , // struct BITMAPINFOHEADER size
, , , , // bmp width
, , , , // bmp height
, , // planes
, , // bit per pixel
, , , , // compression
, , , , // data size
, , , , // h resolution
, , , , // v resolution
, , , , // used colors
, , , // important colors
}; // sobel mask
int mask[MASK_N][MASK_X][MASK_Y] = {
{{-,-,-},
{ , , },
{ , , }}, {{-, , },
{-, , },
{-, , }}
}; int read_bmp(const char *fname_s) {
fp_s = fopen(fname_s, "rb");
if (fp_s == NULL) {
printf("fopen fp_s error\n");
return -;
} // move offset to 10 to find rgb raw data offset
fseek(fp_s, , SEEK_SET);
fread(&rgb_raw_data_offset, sizeof(unsigned int), , fp_s); // move offset to 18 to get width & height;
fseek(fp_s, , SEEK_SET);
fread(&width, sizeof(unsigned int), , fp_s);
fread(&height, sizeof(unsigned int), , fp_s); // get bit per pixel
fseek(fp_s, , SEEK_SET);
fread(&bit_per_pixel, sizeof(unsigned short), , fp_s);
byte_per_pixel = bit_per_pixel / ; // move offset to rgb_raw_data_offset to get RGB raw data
fseek(fp_s, rgb_raw_data_offset, SEEK_SET); image_s = (unsigned char *)malloc((size_t)width * height * byte_per_pixel);
if (image_s == NULL) {
printf("malloc images_s error\n");
return -;
} image_t = (unsigned char *)malloc((size_t)width * height * byte_per_pixel);
if (image_t == NULL) {
printf("malloc image_t error\n");
return -;
} fread(image_s, sizeof(unsigned char), (size_t)(long)width * height * byte_per_pixel, fp_s); return ;
} // convert RGB to gray level int
int color_to_int(int r, int g, int b) {
return (r + g + b) / ;
} int sobel(double threshold) {
unsigned int x, y, i, v, u; // for loop counter
unsigned char R, G, B; // color of R, G, B
double val[MASK_N] = {0.0};
int adjustX, adjustY, xBound, yBound;
double total; for(y = ; y != height; ++y) {
for(x = ; x != width; ++x) {
for(i = ; i != MASK_N; ++i) {
adjustX = (MASK_X % ) ? : ;
adjustY = (MASK_Y % ) ? : ;
xBound = MASK_X / ;
yBound = MASK_Y / ; val[i] = 0.0;
for(v = -yBound; v != yBound + adjustY; ++v) {
for (u = -xBound; u != xBound + adjustX; ++u) {
if (x + u >= && x + u < width && y + v >= && y + v < height) {
R = *(image_s + byte_per_pixel * (width * (y+v) + (x+u)) + );
G = *(image_s + byte_per_pixel * (width * (y+v) + (x+u)) + );
B = *(image_s + byte_per_pixel * (width * (y+v) + (x+u)) + ); val[i] += color_to_int(R, G, B) * mask[i][u + xBound][v + yBound];
}
}
}
} total = 0.0;
for (i = ; i != MASK_N; ++i) {
total += val[i] * val[i];
} total = sqrt(total); if (total - threshold >= ) {
// black
*(image_t + byte_per_pixel * (width * y + x) + ) = BLACK;
*(image_t + byte_per_pixel * (width * y + x) + ) = BLACK;
*(image_t + byte_per_pixel * (width * y + x) + ) = BLACK;
}
else {
// white
*(image_t + byte_per_pixel * (width * y + x) + ) = WHITE;
*(image_t + byte_per_pixel * (width * y + x) + ) = WHITE;
*(image_t + byte_per_pixel * (width * y + x) + ) = WHITE;
}
}
} return ;
} int write_bmp(const char *fname_t) {
unsigned int file_size; // file size fp_t = fopen(fname_t, "wb");
if (fp_t == NULL) {
printf("fopen fname_t error\n");
return -;
} // file size
file_size = width * height * byte_per_pixel + rgb_raw_data_offset;
header[] = (unsigned char)(file_size & 0x000000ff);
header[] = (file_size >> ) & 0x000000ff;
header[] = (file_size >> ) & 0x000000ff;
header[] = (file_size >> ) & 0x000000ff; // width
header[] = width & 0x000000ff;
header[] = (width >> ) & 0x000000ff;
header[] = (width >> ) & 0x000000ff;
header[] = (width >> ) & 0x000000ff; // height
header[] = height &0x000000ff;
header[] = (height >> ) & 0x000000ff;
header[] = (height >> ) & 0x000000ff;
header[] = (height >> ) & 0x000000ff; // bit per pixel
header[] = bit_per_pixel; // write header
fwrite(header, sizeof(unsigned char), rgb_raw_data_offset, fp_t); // write image
fwrite(image_t, sizeof(unsigned char), (size_t)(long)width * height * byte_per_pixel, fp_t); fclose(fp_s);
fclose(fp_t); return ;
} int main() {
read_bmp("lena.bmp"); // 24 bit gray level image
sobel(90.0);
write_bmp("lena_sobel.bmp");
}
/*
(C) OOMusou 2007 http://oomusou.cnblogs.com Filename : sobel_edge.cpp
Compiler : C++/CLI / Visual C++ 8.0
Description : Demo the how to use sobel detector on gray level image
Release : 07/23/2008 1.0
*/ #include "stdafx.h"
#include <cmath> using namespace System::Drawing;
using namespace System::Drawing::Imaging; const int MASK_N = ;
const int MASK_X = ;
const int MASK_Y = ; // Convert RGB to gray level int
int colorToInt(Color %color) {
return (color.R + color.G + color.B) / ;
} void edgeDetector(Bitmap^ oriImg, Bitmap^ resImg, const int mask[MASK_N][MASK_X][MASK_Y], const double &threshold) {
double val[MASK_N] = {0.0}; for(int y = ; y != oriImg->Height; ++y) {
for(int x = ; x != oriImg->Width; ++x) {
for(int i = ; i != MASK_N; ++i) {
int adjustX = (MASK_X % ) ? : ;
int adjustY = (MASK_Y % ) ? : ;
int xBound = MASK_X / ;
int yBound = MASK_Y / ; val[i] = 0.0;
for(int v = -yBound; v != yBound + adjustY; ++v) {
for (int u = -xBound; u != xBound + adjustX; ++u) {
if (x + u >= && x + u < oriImg->Width && y + v >= && y + v < oriImg->Height) {
val[i] += colorToInt(oriImg->GetPixel(x + u, y + v)) * mask[i][u + xBound][v + yBound];
}
}
}
} double total = 0.0;
for (int i = ; i != MASK_N; ++i) {
total += val[i] * val[i];
} total = sqrt(total); if (total - threshold >= )
resImg->SetPixel(x , y, Color::Black);
else
resImg->SetPixel(x, y, Color::White);
}
}
} int main() {
const int mask[MASK_N][MASK_X][MASK_Y] = {
{{-,-,-},
{ , , },
{ , , }}, {{-, , },
{-, , },
{-, , }}
}; Bitmap^ oriImg = gcnew Bitmap("lena.bmp");
Bitmap^ resImg = gcnew Bitmap(oriImg->Width, oriImg->Height); const double threshold = 90.0;
edgeDetector(oriImg, resImg, mask, threshold); resImg->Save("lena_sobel.bmp"); return ;
}
每天进步一点点------如何实现Sobel Edge Detector? (Image Processing) (C/C++)的更多相关文章
- Canny Edge Detector
Canny边缘检测算法有自己的理论和经验性的推导, 没仔细看/没看明白. 它的步骤如下: 对原图的灰度图进行高斯滤波 求一阶导数, 得到每个像素点的梯度强度和方向. 非最大抑制. 对每个edge ca ...
- Understanding Convolution in Deep Learning
Understanding Convolution in Deep Learning Convolution is probably the most important concept in dee ...
- 32个最热CPLD-FPGA论坛
1. OPENCORES.ORG这里提供非常多,非常好的PLD了内核,8051内核就可以在里面找到.进入后,选择project或者由http//www.opencores.org/browse.cgi ...
- [转]FPGA网站推荐
1. OPENCORES.ORG这里提供非常多,非常好的PLD了内核,8051内核就可以在里面找到.进入后,选择project或者由http//www.opencores.org/browse.cgi ...
- Computer Vision_33_SIFT:Remote Sensing Image Registration With Modified SIFT and Enhanced Feature Matching——2017
此部分是计算机视觉部分,主要侧重在底层特征提取,视频分析,跟踪,目标检测和识别方面等方面.对于自己不太熟悉的领域比如摄像机标定和立体视觉,仅仅列出上google上引用次数比较多的文献.有一些刚刚出版的 ...
- OpenCV2马拉松第14圈——边缘检測(Sobel,prewitt,roberts)
收入囊中 差分在边缘检測的角色 Sobel算子 OpenCV sobel函数 OpenCV Scharr函数 prewitt算子 Roberts算子 葵花宝典 差分在边缘检測究竟有什么用呢?先看以下的 ...
- Sobel Derivatives
https://docs.opencv.org/2.4/doc/tutorials/imgproc/imgtrans/sobel_derivatives/sobel_derivatives.html ...
- 线特征---Edge Drawing(七)
http://ceng.anadolu.edu.tr/cv/edgedrawing/ References C. Topal, C. Akinlar, Edge Drawing: A Combined ...
- 2.7 Sobel导数
OpenCV函数 Sobel(src_gray,grad_x/grad_y,ddepth,x_order,y_order,scale,delta,BORDER_DEFAULT ) Scharr( ) ...
随机推荐
- [HNOI2017] 大佬 - DP,BFS,Hash,单调性
这真的是一道综合题.然而感觉A得莫名其妙,交上去的时候其实非常虚,然后就莫名其妙地AC了? 首先我们考虑到,所有和怼有关的操作都是时刻无关的.也就是说,我们把这些操作拆散放到任何时候都对结果不会有影响 ...
- PP: A multi-horizon quantile recurrent forecaster
2017 NIPS, time series workshop traditional methods: ARIMA. Seq2Seq quantile forecast; RELATED WORK ...
- <软件工程基础>个人项目——数独
参见GitHub:https://github.com/1773262526/Software-Foundation Personal Software Process Stages ...
- (四)tensorflow-基础(数据类型,张量操作,数学运算)
摘要: 1.数据类型:标量.向量.矩阵.张量 :数值精度:变量(张量) 2.张量操作:索引.切片.维度操作 3.数学运算:加减乘除(整除和余除):乘方(平方.开方.指数):自然底对数(任意底对数需要 ...
- 使用wsgiref手撸web框架
模板 前言 要说到应用程序,就不得不提的就是cs架构和BS架构 所谓的cs架构就是client端和server端,就像我们的电脑上的qq,微信等应用程序 bs架构就是浏览器端和server端,我们不需 ...
- TCP的粘包和拆包问题及解决
前言 TCP属于传输层的协议,传输层除了有TCP协议外还有UDP协议.那么UDP是否会发生粘包或拆包的现象呢?答案是不会.UDP是基于报文发送的,从UDP的帧结构可以看出,在UDP首部采用了16bit ...
- 【16】LRUChache
题目 LRU 思路 LRU 大家都不陌生,操作系统的作业做过,思路就是一旦添加或者访问某个元素,则将其的"访问属性"置零,而其他元素的访问属性统统减一,这样一来,访问属性最小的元素 ...
- scp知识点
小伙伴的博客(详细): https://www.cnblogs.com/ppp204-is-a-VC/p/11673567.html
- [BZOJ4310] 跳蚤 - 后缀数组,二分,ST表
[BZOJ4310] 跳蚤 Description 首先,他会把串分成不超过 \(k\) 个子串,然后对于每个子串 \(S\) ,他会从 \(S\) 的所有子串中选择字典序最大的那一个,并在选出来的 ...
- [CF705B] Spider Man - 博弈论
[CF705B] Description ICG 游戏有若干个环,每次操作将一个环断成非空的两部分,节点数总和不变.集合初态为空,每次向集合中添加一个环,询问当前集合用于游戏的胜负. \(n \le ...