图片文档倾斜矫正算法 附完整c代码
程序员文章站
2022-07-02 16:26:15
2年前在学习图像算法的时候看到一个文档倾斜矫正的算法。 也就是说能将一些文档图像进行旋转矫正， 当然这个算法一般用于一些文档扫描软件做后处理 或者用于ocr 文字识别做前处理。 相关的关键词: 抗倾斜 反倾斜 Deskew 等等。 最简单算法实现思路，采用 霍夫变换(Hough Transform) ......
2年前在学习图像算法的时候看到一个文档倾斜矫正的算法。
也就是说能将一些文档图像进行旋转矫正，
当然这个算法一般用于一些文档扫描软件做后处理
或者用于ocr 文字识别做前处理。
相关的关键词: 抗倾斜 反倾斜 Deskew 等等。
最简单算法实现思路，采用 霍夫变换(Hough Transform)进行直线检测，
当然也可以用霍夫变换检测圆。
在倾斜矫正算法中，自然就是检测直线。
通过对检测出来的直线进行角度判断，
一般取 认可度最高的几条直线进行计算，
最后求取均衡后的角度值。
进行图像角度的旋转即可。
大概算法步骤如下：
1.转换为灰度图
2.判断是否为文本图片，如果不是进行 进行 反相操作
3.检测直线，进行角度判断
4.通过角度进行图像旋转
这么一个基本思路，当然想要检测得更加精准，
可以做一些文本区域判断，图像修复增强之类的前处理操作。
最近有点强迫症犯了，开始回归本源，强迫自己用c语言来实现，
fastsin以及fastcos 来自 arm公司的开源项目。
霍夫变换相关算法原理，请移步 百度 google *。
或直接看代码实现，可了悟于心。
有事没事，多看看业内大公司的开源项目，
萝卜白菜都有，重点是学习其思路。
嗯，有些网友可能会说，opencv一两行代码就可以做到了。
对的，一些sdk,api,开源框架一两句代码是做到了，
知道，用到，与真正做到，这是两条路。
我只想说一句，愿世界和平。
附完整代码:
//如果是Windows的话，调用系统API ShellExecuteA打开图片
#if defined(_MSC_VER)
#define _CRT_SECURE_NO_WARNINGS
#include <windows.h>
#define USE_SHELL_OPEN
#endif
#define STB_IMAGE_STATIC
#define STB_IMAGE_IMPLEMENTATION
#include "stb_image.h"
//ref:https://github.com/nothings/stb/blob/master/stb_image.h
#define TJE_IMPLEMENTATION
#include "tiny_jpeg.h"
//ref:https://github.com/serge-rgb/TinyJPEG/blob/master/tiny_jpeg.h
#include <math.h>
#include <io.h>
#include <math.h>
#include <stdlib.h>
#include <stdbool.h>
//计时
#include <stdint.h>
#if defined(__APPLE__)
# include <mach/mach_time.h>
#elif defined(_WIN32)
# define WIN32_LEAN_AND_MEAN
# include <windows.h>
#else // __linux
# include <time.h>
# ifndef CLOCK_MONOTONIC //_RAW
# define CLOCK_MONOTONIC CLOCK_REALTIME
# endif
#endif
static
uint64_t nanotimer() {
static int ever = 0;
#if defined(__APPLE__)
static mach_timebase_info_data_t frequency;
if (!ever) {
if (mach_timebase_info(&frequency) != KERN_SUCCESS) {
return 0;
}
ever = 1;
}
return;
#elif defined(_WIN32)
static LARGE_INTEGER frequency;
if (!ever) {
QueryPerformanceFrequency(&frequency);
ever = 1;
}
LARGE_INTEGER t;
QueryPerformanceCounter(&t);
return (t.QuadPart * (uint64_t)1e9) / frequency.QuadPart;
#else // __linux
struct timespec t;
if (!ever) {
if (clock_gettime(CLOCK_MONOTONIC, &spec) != 0) {
return 0;
}
ever = 1;
}
clock_gettime(CLOCK_MONOTONIC, &spec);
return (t.tv_sec * (uint64_t)1e9) + t.tv_nsec;
#endif
}
static double now()
{
static uint64_t epoch = 0;
if (!epoch) {
epoch = nanotimer();
}
return (nanotimer() - epoch) / 1e9;
};
double calcElapsed(double start, double end)
{
double took = -start;
return took + end;
}
//存储当前传入文件位置的变量
char saveFile[1024];
//加载图片
unsigned char * loadImage(const char *filename, int *Width, int *Height, int *Channels)
{
return stbi_load(filename, Width, Height, Channels, 0);
}
//保存图片
void saveImage(const char *filename, int Width, int Height, int Channels, unsigned char *Output)
{
memcpy(saveFile + strlen(saveFile), filename, strlen(filename));
*(saveFile + strlen(saveFile) + 1) = 0;
//保存为jpg
if (!tje_encode_to_file(saveFile, Width, Height, Channels, true, Output))
{
fprintf(stderr, "写入 JPEG 文件失败.\n");
return;
}
#ifdef USE_SHELL_OPEN
ShellExecuteA(NULL, "open", saveFile, NULL, NULL, SW_SHOW);
#else
//其他平台暂不实现
#endif
}
#ifndef ClampToByte
#define ClampToByte( v ) ( ((unsigned)(int)(v)) <(255) ? (v) : ((int)(v) < 0) ? (0) : (255))
#endif
#define M_PI 3.14159265358979323846f
typedef struct cpu_HoughLine
{
float Theta;
int Radius;
int Intensity;
float RelativeIntensity;
} cpu_HoughLine;
typedef struct cpu_rect
{
int x;
int y;
int Width;
int Height;
} cpu_rect;
#ifndef clamp
#define clamp(value,min,max) ((value) > (max )? (max ): (value) < (min) ? (min) : (value))
#endif
#define FAST_MATH_TABLE_SIZE 512
const float sinTable_f32[FAST_MATH_TABLE_SIZE + 1] = {
0.00000000f, 0.01227154f, 0.02454123f, 0.03680722f, 0.04906767f, 0.06132074f,
0.07356456f, 0.08579731f, 0.09801714f, 0.11022221f, 0.12241068f, 0.13458071f,
0.14673047f, 0.15885814f, 0.17096189f, 0.18303989f, 0.19509032f, 0.20711138f,
0.21910124f, 0.23105811f, 0.24298018f, 0.25486566f, 0.26671276f, 0.27851969f,
0.29028468f, 0.30200595f, 0.31368174f, 0.32531029f, 0.33688985f, 0.34841868f,
0.35989504f, 0.37131719f, 0.38268343f, 0.39399204f, 0.40524131f, 0.41642956f,
0.42755509f, 0.43861624f, 0.44961133f, 0.46053871f, 0.47139674f, 0.48218377f,
0.49289819f, 0.50353838f, 0.51410274f, 0.52458968f, 0.53499762f, 0.54532499f,
0.55557023f, 0.56573181f, 0.57580819f, 0.58579786f, 0.59569930f, 0.60551104f,
0.61523159f, 0.62485949f, 0.63439328f, 0.64383154f, 0.65317284f, 0.66241578f,
0.67155895f, 0.68060100f, 0.68954054f, 0.69837625f, 0.70710678f, 0.71573083f,
0.72424708f, 0.73265427f, 0.74095113f, 0.74913639f, 0.75720885f, 0.76516727f,
0.77301045f, 0.78073723f, 0.78834643f, 0.79583690f, 0.80320753f, 0.81045720f,
0.81758481f, 0.82458930f, 0.83146961f, 0.83822471f, 0.84485357f, 0.85135519f,
0.85772861f, 0.86397286f, 0.87008699f, 0.87607009f, 0.88192126f, 0.88763962f,
0.89322430f, 0.89867447f, 0.90398929f, 0.90916798f, 0.91420976f, 0.91911385f,
0.92387953f, 0.92850608f, 0.93299280f, 0.93733901f, 0.94154407f, 0.94560733f,
0.94952818f, 0.95330604f, 0.95694034f, 0.96043052f, 0.96377607f, 0.96697647f,
0.97003125f, 0.97293995f, 0.97570213f, 0.97831737f, 0.98078528f, 0.98310549f,
0.98527764f, 0.98730142f, 0.98917651f, 0.99090264f, 0.99247953f, 0.99390697f,
0.99518473f, 0.99631261f, 0.99729046f, 0.99811811f, 0.99879546f, 0.99932238f,
0.99969882f, 0.99992470f, 1.00000000f, 0.99992470f, 0.99969882f, 0.99932238f,
0.99879546f, 0.99811811f, 0.99729046f, 0.99631261f, 0.99518473f, 0.99390697f,
0.99247953f, 0.99090264f, 0.98917651f, 0.98730142f, 0.98527764f, 0.98310549f,
0.98078528f, 0.97831737f, 0.97570213f, 0.97293995f, 0.97003125f, 0.96697647f,
0.96377607f, 0.96043052f, 0.95694034f, 0.95330604f, 0.94952818f, 0.94560733f,
0.94154407f, 0.93733901f, 0.93299280f, 0.92850608f, 0.92387953f, 0.91911385f,
0.91420976f, 0.90916798f, 0.90398929f, 0.89867447f, 0.89322430f, 0.88763962f,
0.88192126f, 0.87607009f, 0.87008699f, 0.86397286f, 0.85772861f, 0.85135519f,
0.84485357f, 0.83822471f, 0.83146961f, 0.82458930f, 0.81758481f, 0.81045720f,
0.80320753f, 0.79583690f, 0.78834643f, 0.78073723f, 0.77301045f, 0.76516727f,
0.75720885f, 0.74913639f, 0.74095113f, 0.73265427f, 0.72424708f, 0.71573083f,
0.70710678f, 0.69837625f, 0.68954054f, 0.68060100f, 0.67155895f, 0.66241578f,
0.65317284f, 0.64383154f, 0.63439328f, 0.62485949f, 0.61523159f, 0.60551104f,
0.59569930f, 0.58579786f, 0.57580819f, 0.56573181f, 0.55557023f, 0.54532499f,
0.53499762f, 0.52458968f, 0.51410274f, 0.50353838f, 0.49289819f, 0.48218377f,
0.47139674f, 0.46053871f, 0.44961133f, 0.43861624f, 0.42755509f, 0.41642956f,
0.40524131f, 0.39399204f, 0.38268343f, 0.37131719f, 0.35989504f, 0.34841868f,
0.33688985f, 0.32531029f, 0.31368174f, 0.30200595f, 0.29028468f, 0.27851969f,
0.26671276f, 0.25486566f, 0.24298018f, 0.23105811f, 0.21910124f, 0.20711138f,
0.19509032f, 0.18303989f, 0.17096189f, 0.15885814f, 0.14673047f, 0.13458071f,
0.12241068f, 0.11022221f, 0.09801714f, 0.08579731f, 0.07356456f, 0.06132074f,
0.04906767f, 0.03680722f, 0.02454123f, 0.01227154f, 0.00000000f, -0.01227154f,
-0.02454123f, -0.03680722f, -0.04906767f, -0.06132074f, -0.07356456f,
-0.08579731f, -0.09801714f, -0.11022221f, -0.12241068f, -0.13458071f,
-0.14673047f, -0.15885814f, -0.17096189f, -0.18303989f, -0.19509032f,
-0.20711138f, -0.21910124f, -0.23105811f, -0.24298018f, -0.25486566f,
-0.26671276f, -0.27851969f, -0.29028468f, -0.30200595f, -0.31368174f,
-0.32531029f, -0.33688985f, -0.34841868f, -0.35989504f, -0.37131719f,
-0.38268343f, -0.39399204f, -0.40524131f, -0.41642956f, -0.42755509f,
-0.43861624f, -0.44961133f, -0.46053871f, -0.47139674f, -0.48218377f,
-0.49289819f, -0.50353838f, -0.51410274f, -0.52458968f, -0.53499762f,
-0.54532499f, -0.55557023f, -0.56573181f, -0.57580819f, -0.58579786f,
-0.59569930f, -0.60551104f, -0.61523159f, -0.62485949f, -0.63439328f,
-0.64383154f, -0.65317284f, -0.66241578f, -0.67155895f, -0.68060100f,
-0.68954054f, -0.69837625f, -0.70710678f, -0.71573083f, -0.72424708f,
-0.73265427f, -0.74095113f, -0.74913639f, -0.75720885f, -0.76516727f,
-0.77301045f, -0.78073723f, -0.78834643f, -0.79583690f, -0.80320753f,
-0.81045720f, -0.81758481f, -0.82458930f, -0.83146961f, -0.83822471f,
-0.84485357f, -0.85135519f, -0.85772861f, -0.86397286f, -0.87008699f,
-0.87607009f, -0.88192126f, -0.88763962f, -0.89322430f, -0.89867447f,
-0.90398929f, -0.90916798f, -0.91420976f, -0.91911385f, -0.92387953f,
-0.92850608f, -0.93299280f, -0.93733901f, -0.94154407f, -0.94560733f,
-0.94952818f, -0.95330604f, -0.95694034f, -0.96043052f, -0.96377607f,
-0.96697647f, -0.97003125f, -0.97293995f, -0.97570213f, -0.97831737f,
-0.98078528f, -0.98310549f, -0.98527764f, -0.98730142f, -0.98917651f,
-0.99090264f, -0.99247953f, -0.99390697f, -0.99518473f, -0.99631261f,
-0.99729046f, -0.99811811f, -0.99879546f, -0.99932238f, -0.99969882f,
-0.99992470f, -1.00000000f, -0.99992470f, -0.99969882f, -0.99932238f,
-0.99879546f, -0.99811811f, -0.99729046f, -0.99631261f, -0.99518473f,
-0.99390697f, -0.99247953f, -0.99090264f, -0.98917651f, -0.98730142f,
-0.98527764f, -0.98310549f, -0.98078528f, -0.97831737f, -0.97570213f,
-0.97293995f, -0.97003125f, -0.96697647f, -0.96377607f, -0.96043052f,
-0.95694034f, -0.95330604f, -0.94952818f, -0.94560733f, -0.94154407f,
-0.93733901f, -0.93299280f, -0.92850608f, -0.92387953f, -0.91911385f,
-0.91420976f, -0.90916798f, -0.90398929f, -0.89867447f, -0.89322430f,
-0.88763962f, -0.88192126f, -0.87607009f, -0.87008699f, -0.86397286f,
-0.85772861f, -0.85135519f, -0.84485357f, -0.83822471f, -0.83146961f,
-0.82458930f, -0.81758481f, -0.81045720f, -0.80320753f, -0.79583690f,
-0.78834643f, -0.78073723f, -0.77301045f, -0.76516727f, -0.75720885f,
-0.74913639f, -0.74095113f, -0.73265427f, -0.72424708f, -0.71573083f,
-0.70710678f, -0.69837625f, -0.68954054f, -0.68060100f, -0.67155895f,
-0.66241578f, -0.65317284f, -0.64383154f, -0.63439328f, -0.62485949f,
-0.61523159f, -0.60551104f, -0.59569930f, -0.58579786f, -0.57580819f,
-0.56573181f, -0.55557023f, -0.54532499f, -0.53499762f, -0.52458968f,
-0.51410274f, -0.50353838f, -0.49289819f, -0.48218377f, -0.47139674f,
-0.46053871f, -0.44961133f, -0.43861624f, -0.42755509f, -0.41642956f,
-0.40524131f, -0.39399204f, -0.38268343f, -0.37131719f, -0.35989504f,
-0.34841868f, -0.33688985f, -0.32531029f, -0.31368174f, -0.30200595f,
-0.29028468f, -0.27851969f, -0.26671276f, -0.25486566f, -0.24298018f,
-0.23105811f, -0.21910124f, -0.20711138f, -0.19509032f, -0.18303989f,
-0.17096189f, -0.15885814f, -0.14673047f, -0.13458071f, -0.12241068f,
-0.11022221f, -0.09801714f, -0.08579731f, -0.07356456f, -0.06132074f,
-0.04906767f, -0.03680722f, -0.02454123f, -0.01227154f, -0.00000000f
};
inline float fastSin(
float x)
{
float sinVal, fract, in;
unsigned short index;
float a, b;
int n;
float findex;
in = x * 0.159154943092f;
n = (int)in;
if (x < 0.0f)
{
n--;
}
in = in - (float)n;
findex = (float)FAST_MATH_TABLE_SIZE * in;
if (findex >= 512.0f) {
findex -= 512.0f;
}
index = ((unsigned short)findex) & 0x1ff;
fract = findex - (float)index;
a = sinTable_f32[index];
b = sinTable_f32[index + 1];
sinVal = (1.0f - fract)*a + fract*b;
return (sinVal);
}
inline float fastCos(
float x)
{
float cosVal, fract, in;
unsigned short index;
float a, b;
int n;
float findex;
in = x * 0.159154943092f + 0.25f;
n = (int)in;
if (in < 0.0f)
{
n--;
}
in = in - (float)n;
findex = (float)FAST_MATH_TABLE_SIZE * in;
index = ((unsigned short)findex) & 0x1ff;
fract = findex - (float)index;
a = sinTable_f32[index];
b = sinTable_f32[index + 1];
cosVal = (1.0f - fract)*a + fract*b;
return (cosVal);
}
void CPUImageGrayscaleFilter(unsigned char* Input, unsigned char* Output, int Width, int Height, int Stride)
{
int Channels = Stride / Width;
const int B_WT = (int)(0.114 * 256 + 0.5);
const int G_WT = (int)(0.587 * 256 + 0.5);
const int R_WT = 256 - B_WT - G_WT; // int(0.299 * 256 + 0.5);
int Channel = Stride / Width;
if (Channel == 3)
{
for (int Y = 0; Y < Height; Y++)
{
unsigned char *LinePS = Input + Y * Stride;
unsigned char *LinePD = Output + Y * Width;
int X = 0;
for (; X < Width - 4; X += 4, LinePS += Channel * 4)
{
LinePD[X + 0] = (B_WT * LinePS[0] + G_WT * LinePS[1] + R_WT * LinePS[2]) >> 8;
LinePD[X + 1] = (B_WT * LinePS[3] + G_WT * LinePS[4] + R_WT * LinePS[5]) >> 8;
LinePD[X + 2] = (B_WT * LinePS[6] + G_WT * LinePS[7] + R_WT * LinePS[8]) >> 8;
LinePD[X + 3] = (B_WT * LinePS[9] + G_WT * LinePS[10] + R_WT * LinePS[11]) >> 8;
}
for (; X < Width; X++, LinePS += Channel)
{
LinePD[X] = (B_WT * LinePS[0] + G_WT * LinePS[1] + R_WT * LinePS[2]) >> 8;
}
}
}
else if (Channel == 4)
{
for (int Y = 0; Y < Height; Y++)
{
unsigned char *LinePS = Input + Y * Stride;
unsigned char *LinePD = Output + Y * Width;
int X = 0;
for (; X < Width - 4; X += 4, LinePS += Channel * 4)
{
LinePD[X + 0] = (B_WT * LinePS[0] + G_WT * LinePS[1] + R_WT * LinePS[2]) >> 8;
LinePD[X + 1] = (B_WT * LinePS[4] + G_WT * LinePS[5] + R_WT * LinePS[6]) >> 8;
LinePD[X + 2] = (B_WT * LinePS[8] + G_WT * LinePS[9] + R_WT * LinePS[10]) >> 8;
LinePD[X + 3] = (B_WT * LinePS[12] + G_WT * LinePS[13] + R_WT * LinePS[14]) >> 8;
}
for (; X < Width; X++, LinePS += Channel)
{
LinePD[X] = (B_WT * LinePS[0] + G_WT * LinePS[1] + R_WT * LinePS[2]) >> 8;
}
}
}
else if (Channel == 1)
{
if (Output != Input)
{
memcpy(Output, Input, Height*Stride);
}
}
}
void CPUImageColorInvertFilter(unsigned char* Input, unsigned char* Output, int Width, int Height, int Stride)
{
int Channels = Stride / Width; unsigned char invertMap[256] = { 0 };
for (int pixel = 0; pixel < 256; pixel++)
{
invertMap[pixel] = (255 - pixel);
}
if (Channels == 1) {
for (int Y = 0; Y < Height; Y++)
{
unsigned char* pOutput = Output + (Y * Stride);
unsigned char* pInput = Input + (Y * Stride);
for (int X = 0; X < Width; X++)
{
pOutput[X] = invertMap[pInput[X]];
}
}
}
else
{
for (int Y = 0; Y < Height; Y++)
{
unsigned char* pOutput = Output + (Y * Stride);
unsigned char* pInput = Input + (Y * Stride);
for (int X = 0; X < Width; X++)
{
pOutput[0] = invertMap[pInput[0]];
pOutput[1] = invertMap[pInput[1]];
pOutput[2] = invertMap[pInput[2]];
pInput += Channels;
pOutput += Channels;
}
}
}
}
float CPUImageCalcSkewAngle(unsigned char* Input, int Width, int Height, cpu_rect *CheckRectPtr, int maxSkewToDetect, int stepsPerDegree, int localPeakRadius, int nLineCount)
{
cpu_rect CheckRect = *CheckRectPtr;
//确定指定的区域在原图片范围内
CheckRect.x = clamp(CheckRect.x, 0, Width - 1);
CheckRect.y = clamp(CheckRect.y, 0, Height - 1);
CheckRect.Width = clamp(CheckRect.Width, 1, Width - 1);
CheckRect.Height = clamp(CheckRect.Height, 1, Height - 1);
// 处理参数
maxSkewToDetect = clamp(maxSkewToDetect, 0, 91);
localPeakRadius = clamp(localPeakRadius, 1, 10);
stepsPerDegree = clamp(stepsPerDegree, 1, 10);
int houghHeight = (2 * maxSkewToDetect * stepsPerDegree);
float thetaStep = (2 * maxSkewToDetect * M_PI / 180) / houghHeight;
int halfWidth = Width >> 1;
int halfHeight = Height >> 1;
// 计算 Hough 映射宽度
int halfHoughWidth = (int)sqrtf((float)(halfWidth * halfWidth + halfHeight * halfHeight));
int houghWidth = (halfHoughWidth * 2);
float minTheta = 90.0f - maxSkewToDetect;
unsigned short * houghMap = (unsigned short *)calloc(houghHeight*houghWidth, sizeof(unsigned short));
float* sinMap = (float*)malloc(houghHeight * sizeof(float));
float* cosMap = (float*)malloc(houghHeight * sizeof(float));
cpu_HoughLine* HoughLines = (cpu_HoughLine*)calloc(houghHeight*houghWidth, sizeof(cpu_HoughLine));
if (houghMap == NULL || sinMap == NULL || cosMap == NULL || HoughLines == NULL)
{
if (houghMap)
{
free(houghMap);
houghMap = NULL;
}
if (sinMap)
{
free(sinMap);
sinMap = NULL;
}
if (cosMap)
{
free(cosMap);
cosMap = NULL;
}
if (HoughLines)
{
free(HoughLines);
HoughLines = NULL;
}
return 0.0f;
}
else
{
// 预计算 Sin 与 Cos表
float mt = (minTheta * M_PI / 180.0f);
for (int i = 0; i < houghHeight; i++)
{
float cur_weight = mt + (i * thetaStep);
sinMap[i] = fastSin(cur_weight);
cosMap[i] = fastCos(cur_weight);
}
}
int startX = -halfWidth + CheckRect.x;
int startY = -halfHeight + CheckRect.y;
int stopX = Width - halfWidth - (Width - CheckRect.Width);
int stopY = Height - halfHeight - (Height - CheckRect.Height) - 1;
int offset = Width - CheckRect.Width;
unsigned char* src = Input + CheckRect.y * Width + CheckRect.x;
unsigned char* srcBelow = src + Width;
for (int Y = startY; Y < stopY; Y++)
{
for (int X = startX; X < stopX; X++, src++, srcBelow++)
{
if ((*src < 128) && (*srcBelow >= 128))
{
for (int theta = 0; theta < houghHeight; theta++)
{
int radius = (int)(cosMap[theta] * X - sinMap[theta] * Y) + halfHoughWidth;
if ((radius < 0) || (radius >= houghWidth))
{
continue;
}
houghMap[theta*houghWidth + radius]++;
}
}
}
src += offset;
srcBelow += offset;
}
// 找到 Hough映射的最大值
float maxMapIntensity = 0.0000000001f;
for (int theta = 0; theta < houghHeight; theta++)
{
unsigned short * houghMapLine = houghMap + theta*houghWidth;
for (int radius = 0; radius < houghWidth; radius++)
{
maxMapIntensity = max(maxMapIntensity, houghMapLine[radius]);
}
}
int minLineIntensity = Width / 10;
// 收集大于或等于指定强度的直线
int lineIntensity = 0;
bool foundGreater = false;
int lineSize = 0;
for (int theta = 0; theta < houghHeight; theta++)
{
unsigned short * houghMapLine = houghMap + theta*houghWidth;
for (int radius = 0; radius < houghWidth; radius++)
{
// 取当前强度
lineIntensity = houghMapLine[radius];
if (lineIntensity < minLineIntensity)
{
continue;
}
foundGreater = false;
// 检查邻边
for (int t = theta - localPeakRadius, ttMax = theta + localPeakRadius; t < ttMax; t++)
{
//跳过map值
if (t < 0)
{
continue;
}
if (t >= houghHeight)
{
break;
}
//如果不是局部最大则跳出
if (foundGreater == true)
{
break;
}
for (int r = radius - localPeakRadius, trMax = radius + localPeakRadius; r < trMax; r++)
{
//跳过map值
if (r < 0)
{
continue;
}
if (r >= houghWidth)
{
break;
}
// 当前值与邻边对比
if (houghMap[t*houghWidth + r] > lineIntensity)
{
foundGreater = true;
break;
}
}
}
// 可能是局部最大值,记录下来
if (!foundGreater)
{
cpu_HoughLine tempVar;
tempVar.Theta = 90.0f - maxSkewToDetect + (theta) / stepsPerDegree;
tempVar.Radius = (radius - halfHoughWidth);
tempVar.Intensity = lineIntensity;
tempVar.RelativeIntensity = lineIntensity / maxMapIntensity;
HoughLines[lineSize] = tempVar;
lineSize++;
}
}
}
float skewAngle = 0;
if (lineSize > 0)
{
//排序，从大到小
cpu_HoughLine temp;
for (int i = 0; i < lineSize; i++)
{
for (int j = 0; j < lineSize - 1; j++)
{
if (HoughLines[j].Intensity < HoughLines[j + 1].Intensity)
{
temp = HoughLines[j + 1];
HoughLines[j + 1] = HoughLines[j];
HoughLines[j] = temp;
}
}
}
int n = min(nLineCount, lineSize);
float sumIntensity = 0;
for (int i = 0; i < n; i++)
{
if (HoughLines[i].RelativeIntensity > 0.5f)
{
skewAngle += (HoughLines[i].Theta * HoughLines[i].RelativeIntensity);
sumIntensity += HoughLines[i].RelativeIntensity;
}
}
skewAngle = skewAngle / sumIntensity;
}
if (houghMap)
{
free(houghMap);
houghMap = NULL;
}
if (sinMap)
{
free(sinMap);
sinMap = NULL;
}
if (cosMap)
{
free(cosMap);
cosMap = NULL;
}
if (HoughLines)
{
free(HoughLines);
HoughLines = NULL;
}
if (skewAngle != 0)
{
return skewAngle - 90.0f;
}
return skewAngle;
}
void CPUImageRotateBilinear(unsigned char * Input, int Width, int Height, int Stride, unsigned char * Output, int outWidth, int outHeight, float angle, bool keepSize, int fillColorR, int fillColorG, int fillColorB)
{
if (Input == NULL || Output == NULL) return;
float oldXradius = (float)(Width - 1) / 2;
float oldYradius = (float)(Height - 1) / 2;
// 输出图像的半径大小
float newXradius = (float)(outWidth - 1) / 2;
float newYradius = (float)(outHeight - 1) / 2;
// 角度的正弦和余弦
float angleRad = -angle * M_PI / 180.0f;
float angleCos = fastCos(angleRad);
float angleSin = fastSin(angleRad);
int Channels = Stride / Width;
int dstOffset = outWidth*Channels - ((Channels == 1) ? outWidth : outWidth * Channels);
// 背景色
unsigned char fillR = fillColorR;
unsigned char fillG = fillColorG;
unsigned char fillB = fillColorB;
// 临界点
int lastHeight = Height - 1;
int lastWidth = Width - 1;
// 四点指针
unsigned char* src = (unsigned char*)Input;
unsigned char* dst = (unsigned char*)Output;
// cx, cy 目标像素的相对于图像中心的坐标
if (Channels == 1)
{
float cy = -newYradius;
for (int y = 0; y < outHeight; y++)
{
const float tx = angleSin * cy + oldXradius;
const float ty = angleCos * cy + oldYradius;
float cx = -newXradius;
for (int x = 0; x < outWidth; x++, dst++)
{
// 初始起点位置
const float ox = tx + angleCos * cx;
const float oy = ty - angleSin * cx;
const int ox1 = (int)ox;
const int oy1 = (int)oy;
// 判断是否为有效区域
if ((ox1 < 0) || (oy1 < 0) || (ox1 >= Width) || (oy1 >= Height))
{
// 无效区域填充背景
*dst = fillG;
}
else
{
// 边界点处理
const int ox2 = (ox1 == lastWidth) ? ox1 : ox1 + 1;
const int oy2 = (oy1 == lastHeight) ? oy1 : oy1 + 1;
float dx1 = ox - (float)ox1;
if (dx1 < 0)
dx1 = 0;
const float dx2 = 1.0f - dx1;
float dy1 = oy - (float)oy1;
if (dy1 < 0)
dy1 = 0;
const float dy2 = 1.0f - dy1;
unsigned char*p1 = src + oy1 * Stride;
unsigned char* p2 = src + oy2 * Stride;
// 进行四点插值
*dst = (unsigned char)(
dy2 * (dx2 * p1[ox1] + dx1 * p1[ox2]) +
dy1 * (dx2 * p2[ox1] + dx1 * p2[ox2]));
}
cx++;
}
cy++;
dst += dstOffset;
}
}
else
{
float cy = -newYradius;
for (int y = 0; y < outHeight; y++)
{
const float tx = angleSin * cy + oldXradius;
const float ty = angleCos * cy + oldYradius;
float cx = -newXradius;
for (int x = 0; x < outWidth; x++, dst += Channels)
{
// 初始起点位置
const float ox = tx + angleCos * cx;
const float oy = ty - angleSin * cx;
const int ox1 = (int)ox;
const int oy1 = (int)oy;
// 判断是否为有效区域
if ((ox1 < 0) || (oy1 < 0) || (ox1 >= Width) || (oy1 >= Height))
{
// 无效区域填充背景
dst[0] = fillR;
dst[1] = fillG;
dst[2] = fillB;
}
else
{
// 边界点处理
const int ox2 = (ox1 == lastWidth) ? ox1 : ox1 + 1;
const int oy2 = (oy1 == lastHeight) ? oy1 : oy1 + 1;
float dx1 = ox - (float)ox1;
if (dx1 < 0)
dx1 = 0;
const float dx2 = 1.0f - dx1;
float dy1 = oy - (float)oy1;
if (dy1 < 0)
dy1 = 0;
const float dy2 = 1.0f - dy1;
// 计算四点的坐标
unsigned char* p1 = src + oy1 * Stride;
unsigned char* p2 = p1;
p1 += ox1 * Channels;
p2 += ox2 * Channels;
unsigned char* p3 = src + oy2 * Stride;
unsigned char* p4 = p3;
p3 += ox1 * Channels;
p4 += ox2 * Channels;
// 进行四点插值
dst[0] = (unsigned char)(
dy2 * (dx2 * p1[0] + dx1 * p2[0]) +
dy1 * (dx2 * p3[0] + dx1 * p4[0]));
dst[1] = (unsigned char)(
dy2 * (dx2 * p1[1] + dx1 * p2[1]) +
dy1 * (dx2 * p3[1] + dx1 * p4[1]));
dst[2] = (unsigned char)(
dy2 * (dx2 * p1[2] + dx1 * p2[2]) +
dy1 * (dx2 * p3[2] + dx1 * p4[2]));
}
cx++;
}
cy++;
dst += dstOffset;
}
}
}
bool CPUImageIsTextImage(unsigned char * Input, int Width, int Height)
{
const int blacklimit = 20;
const int greylimit = 140;
const int contrast_offset = 80;
int prev_color[256];
int cur_color[256];
for (int i = 0; i < 256; i++)
{
cur_color[i] = 0;
prev_color[i] = 0;
}
for (int i = 0; i <= blacklimit; i++)
{
//黑色
cur_color[i] = 100;
prev_color[i] = 100000;
}
for (int i = blacklimit + 1 + contrast_offset; i <= greylimit; i++)
{
//灰色
cur_color[i] = 10;
prev_color[i] = 10000;
}
for (int i = greylimit + 1 + contrast_offset; i <= 255; i++)
{
//白色
cur_color[i] = 1;
prev_color[i] = 1000;
}
int line_count = 0;
int n = -1;
for (int y = 0; y < Height; y += 10)
{
n++;
int white_amt = 0;
unsigned char * buffer = Input + y*Width;
int x = 0;
for (x = 1; x < Width; x++)
{
const unsigned char prev_pixel = buffer[(x - 1)];
const unsigned char cur_pixel = buffer[x];
if ((prev_color[prev_pixel]) && (cur_color[cur_pixel]))
{
//是否是白色
if ((prev_color[prev_pixel] + cur_color[cur_pixel]) == 1001)
{
white_amt++;
}
}
}
//白色的一行
if (((float)white_amt / (float)x) > 0.85f)
{
line_count++;
}
}
float line_count_ratio = (n != 0.f) ? (float)line_count / (float)n : 0.0f;
if (line_count_ratio < 0.4f || line_count_ratio > 1.0f)
{
return false;
}
return true;
}
bool CPUImageDocumentDeskew(unsigned char * Input, unsigned char *Output, int Width, int Height, int Stride)
{
if (Input == NULL || Output == NULL || Input == Output)
return false;
int Channels = Stride / Width;
//最大倾斜角度
int maxSkewToDetect = 89;
cpu_rect rect = { 0 };
rect.Width = Width;
rect.Height = Height;
// 以最大权重的2条直线为基准计算倾斜角度
int nLineCount = 2;
//角度步进数
int stepsPerDegree = 1;
//局部临界半径
int localPeakRadius = 10;
CPUImageGrayscaleFilter(Input, Output, Width, Height, Stride);
if (!CPUImageIsTextImage(Output, Width, Height))
{
CPUImageColorInvertFilter(Output, Output, Width, Height, Width);
}
float skewAngle = CPUImageCalcSkewAngle(Output, Width, Height, &rect, maxSkewToDetect, stepsPerDegree, localPeakRadius, nLineCount);
if ((skewAngle == 0) || (skewAngle < -maxSkewToDetect || skewAngle > maxSkewToDetect))
{
memcpy(Output, Input, Height* Stride * sizeof(unsigned char));
return false;
}
else
{
CPUImageRotateBilinear(Input, Width, Height, Stride, Output, Width, Height, -skewAngle, true, 255, 255, 255);
}
return true;
}
//分割路径函数
void splitpath(const char* path, char* drv, char* dir, char* name, char* ext)
{
const char* end;
const char* p;
const char* s;
if (path[0] && path[1] == ':') {
if (drv) {
*drv++ = *path++;
*drv++ = *path++;
*drv = '\0';
}
}
else if (drv)
*drv = '\0';
for (end = path; *end && *end != ':';)
end++;
for (p = end; p > path && *--p != '\\' && *p != '/';)
if (*p == '.') {
end = p;
break;
}
if (ext)
for (s = end; (*ext = *s++);)
ext++;
for (p = end; p > path;)
if (*--p == '\\' || *p == '/') {
p++;
break;
}
if (name) {
for (s = p; s < end;)
*name++ = *s++;
*name = '\0';
}
if (dir) {
for (s = path; s < p;)
*dir++ = *s++;
*dir = '\0';
}
}
//取当前传入的文件位置
void getCurrentFilePath(const char *filePath, char *saveFile)
{
char drive[_MAX_DRIVE];
char dir[_MAX_DIR];
char fname[_MAX_FNAME];
char ext[_MAX_EXT];
splitpath(filePath, drive, dir, fname, ext);
int n = strlen(filePath);
memcpy(saveFile, filePath, n);
char * cur_saveFile = saveFile + (n - strlen(ext));
cur_saveFile[0] = '_';
cur_saveFile[1] = 0;
}
int main(int argc, char **argv)
{
printf("Image Processing \n ");
printf("博客:http://tntmonks.cnblogs.com/ \n ");
printf("支持解析如下图片格式: \n ");
printf("JPG, PNG, TGA, BMP, PSD, GIF, HDR, PIC \n ");
//检查参数是否正确
if (argc < 2)
{
printf("参数错误。 \n ");
printf("请拖放文件到可执行文件上，或使用命令行：imageProc.exe 图片 \n ");
printf("请拖放文件例如: imageProc.exe d:\\image.jpg \n ");
return 0;
}
char*szfile = argv[1];
//检查输入的文件是否存在
if (_access(szfile, 0) == -1)
{
printf("输入的文件不存在，参数错误！ \n ");
}
getCurrentFilePath(szfile, saveFile);
int Width = 0; //图片宽度
int Height = 0; //图片高度
int Channels = 0; //图片通道数
unsigned char *inputImage = NULL; //输入图片指针
double startTime = now();
//加载图片
inputImage = loadImage(szfile, &Width, &Height, &Channels);
double nLoadTime = calcElapsed(startTime, now());
printf("加载耗时: %d 毫秒!\n ", (int)(nLoadTime * 1000));
if ((Channels != 0) && (Width != 0) && (Height != 0))
{
//分配与载入同等内存用于处理后输出结果
unsigned char *outputImg = (unsigned char *)stbi__malloc(Width * Channels * Height * sizeof(unsigned char));
if (inputImage)
{
//如果图片加载成功，则将内容复制给输出内存，方便处理
memcpy(outputImg, inputImage, Width * Channels * Height);
}
else
{
printf("加载文件: %s 失败!\n ", szfile);
}
startTime = now();
//处理算法
CPUImageDocumentDeskew(inputImage, outputImg, Width, Height, Width*Channels);
double nProcessTime = calcElapsed(startTime, now());
printf("处理耗时: %d 毫秒!\n ", (int)(nProcessTime * 1000));
//保存处理后的图片
startTime = now();
saveImage("_done.jpg", Width, Height, Channels, outputImg);
double nSaveTime = calcElapsed(startTime, now());
printf("保存耗时: %d 毫秒!\n ", (int)(nSaveTime * 1000));
//释放占用的内存
if (outputImg)
{
stbi_image_free(outputImg);
outputImg = NULL;
}
if (inputImage)
{
stbi_image_free(inputImage);
inputImage = NULL;
}
}
else
{
printf("加载文件: %s 失败!\n", szfile);
}
getchar();
printf("按任意键退出程序 \n");
return EXIT_SUCCESS;
}
项目地址:https://github.com/cpuimage/deskew
贴上几张效果图.
以上，权当抛砖引玉。
若有其他相关问题或者需求也可以邮件联系俺探讨。
邮箱地址是:
gaozhihan@vip.qq.com
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