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1.固定值二值化
2.自适应阈值二值化
3.Android JNI完整代码 1.固定值二值化
固定阈值二值化是OpenCV中一种简单而常用的图像处理技术#xff0c;用于将图像转换为二值图像。在固定阈值二值化中#xff0c;像素值根据一个预定义的阈值进行分类#xff0c;大于阈值的…目录
1.固定值二值化
2.自适应阈值二值化
3.Android JNI完整代码 1.固定值二值化
固定阈值二值化是OpenCV中一种简单而常用的图像处理技术用于将图像转换为二值图像。在固定阈值二值化中像素值根据一个预定义的阈值进行分类大于阈值的像素被设置为白色而小于或等于阈值的像素被设置为黑色。以下是关于OpenCV固定阈值二值化的一些重要知识点
1.阈值函数在OpenCV中固定阈值二值化可以使用 threshold() 函数来实现。该函数的原型如下 double threshold(InputArray src, OutputArray dst, double thresh, double maxValue, int thresholdType); src输入图像通常为灰度图像。dst输出二值图像与输入图像具有相同的大小和类型。thresh固定阈值用于将像素分类为黑色或白色。maxValue分配给大于阈值的像素的像素值。thresholdType阈值类型用于指定阈值化的方式如 THRESH_BINARY、THRESH_BINARY_INV 等。
2.阈值类型使用 thresholdType 参数可以选择不同的固定阈值化类型以满足特定需求。一些常用的阈值类型包括
THRESH_BINARY二进制阈值化大于阈值的像素值设为 maxValue小于等于阈值的像素值设为 0。THRESH_BINARY_INV反二进制阈值化大于阈值的像素值设为 0小于等于阈值的像素值设为 maxValue。THRESH_TRUNC截断阈值化大于阈值的像素值设为阈值小于等于阈值的保持不变。THRESH_TOZERO截断到零阈值化大于阈值的像素值保持不变小于等于阈值的像素值设为 0。THRESH_TOZERO_INV反截断到零阈值化大于阈值的像素值设为 0小于等于阈值的像素值保持不变。
3.例子 原图
1.将彩色图像image转换为灰度图像 Mat gray;cvtColor(image,gray,COLOR_BGR2GRAY); 灰度图像
2.彩色图像二值化 Mat img_B,img_B_V;threshold(image,img_B,125,255,THRESH_BINARY);imwrite(/sdcard/DCIM/img_B.png,img_B);3.灰度图BINARY二值化 Mat gray_B,gray_B_V;threshold(gray,gray_B,125,255,THRESH_BINARY);threshold(gray,gray_B_V,125,255,THRESH_BINARY_INV);imwrite(/sdcard/DCIM/gray_B.png,gray_B);imwrite(/sdcard/DCIM/gray_B_V.png,gray_B_V); gray_B gray_B_V
4.灰度图像TOZERO变换 Mat gray_T,gray_T_V;threshold(gray,gray_T,125,255,THRESH_TOZERO);threshold(gray,gray_T_V,125,255,THRESH_TOZERO_INV);imwrite(/sdcard/DCIM/gray_T.png,gray_T);imwrite(/sdcard/DCIM/gray_T_V.png,gray_T_V); gray_T gray_T_V
5.灰度图像TRUNC变换 Mat gray_TRUNC;threshold(gray,gray_TRUNC,125,255,THRESH_TRUNC);imwrite(/sdcard/DCIM/gray_TRUNC.png,gray_TRUNC); gray_TRUNC 6.灰度图像大津法和三角形法二值化 Mat img_Thr;cvtColor(image,img_Thr,COLOR_BGR2GRAY);Mat img_Thr_0,img_Thr_T;threshold(img_Thr,img_Thr_0,100,255,THRESH_BINARY|cv::THRESH_OTSU);threshold(img_Thr,img_Thr_T,125,255,THRESH_BINARY|cv::THRESH_TRIANGLE);imwrite(/sdcard/DCIM/img_Thr_0.png,img_Thr_0);imwrite(/sdcard/DCIM/img_Thr_T.png,img_Thr_T); img_Thr_T img_Thr_0
2.自适应阈值二值化
1.自适应阈值函数OpenCV中的自适应阈值二值化可以使用 adaptiveThreshold() 函数来实现。该函数的原型如下 void adaptiveThreshold(InputArray src, OutputArray dst, double maxValue, int adaptiveMethod, int thresholdType, int blockSize, double C); src输入图像通常为灰度图像。dst输出二值图像与输入图像具有相同的大小和类型。maxValue分配给超过阈值的像素的像素值。adaptiveMethod自适应阈值化方法用于指定计算局部阈值的方式可以是 ADAPTIVE_THRESH_MEAN_C 或 ADAPTIVE_THRESH_GAUSSIAN_C。thresholdType阈值化类型用于指定阈值化的方式如 THRESH_BINARY、THRESH_BINARY_INV 等。blockSize用于计算局部阈值的像素领域大小。必须是奇数且大于1。C从计算阈值中减去的常数。通常为正值。
2.自适应阈值化方法使用 adaptiveMethod 参数可以选择不同的自适应阈值化方法以满足特定需求。在OpenCV中常见的自适应阈值化方法有两种 ADAPTIVE_THRESH_MEAN_C根据领域中像素的均值计算局部阈值。 ADAPTIVE_THRESH_GAUSSIAN_C根据领域中像素的加权和权重为高斯窗口计算局部阈值。
3.灰度图像转换在进行自适应阈值二值化之前要将彩色图像转换为灰度图像。可以使用 cvtColor() 函数将彩色图像转换为灰度图像。
cvtColor(image,img_Thr,COLOR_BGR2GRAY); 4.例子 Mat adaptive_mean,adaptive_gauss;
adaptiveThreshold(img_Thr,adaptive_mean,255,ADAPTIVE_THRESH_MEAN_C,THRESH_BINARY,55,0);
adaptiveThreshold(img_Thr,adaptive_gauss,255,ADAPTIVE_THRESH_GAUSSIAN_C,THRESH_BINARY,55,0);
imwrite(/sdcard/DCIM/adaptive_mean.png,adaptive_mean);
imwrite(/sdcard/DCIM/adaptive_gauss.png,adaptive_gauss); adaptive_gauss adaptive_mean
3.Android JNI完整代码
#include jni.h
#include string
#include android/bitmap.h
#include opencv2/opencv.hpp
#include iostream
#include android/log.h#define LOG_TAG xxx
#define LOGD(...) __android_log_print(ANDROID_LOG_DEBUG, LOG_TAG, __VA_ARGS__)using namespace cv;
using namespace std;
extern C
JNIEXPORT void JNICALL
Java_com_example_myapplication_MainActivity_opencv_1test(JNIEnv *env, jclass clazz,jobject bitmap) {AndroidBitmapInfo info;void *pixels;CV_Assert(AndroidBitmap_getInfo(env, bitmap, info) 0);//判断图片是位图格式有RGB_565 、RGBA_8888CV_Assert(info.format ANDROID_BITMAP_FORMAT_RGBA_8888 ||info.format ANDROID_BITMAP_FORMAT_RGB_565);CV_Assert(AndroidBitmap_lockPixels(env, bitmap, pixels) 0);CV_Assert(pixels);//将bitmap转化为Mat类Mat image(info.height, info.width, CV_8UC4, pixels);Mat gray;cvtColor(image,gray,COLOR_BGR2GRAY);imwrite(/sdcard/DCIM/gray23.png,gray);Mat img_B,img_B_V,gray_B,gray_B_V,gray_T,gray_T_V,gray_TRUNC;//彩色图像二值化threshold(image,img_B,125,255,THRESH_BINARY);imwrite(/sdcard/DCIM/img_B.png,img_B);//灰度图BINARY二值化threshold(gray,gray_B,125,255,THRESH_BINARY);threshold(gray,gray_B_V,125,255,THRESH_BINARY_INV);imwrite(/sdcard/DCIM/gray_B.png,gray_B);imwrite(/sdcard/DCIM/gray_B_V.png,gray_B_V);//灰度图像TOZERO变换threshold(gray,gray_T,125,255,THRESH_TOZERO);threshold(gray,gray_T_V,125,255,THRESH_TOZERO_INV);imwrite(/sdcard/DCIM/gray_T.png,gray_T);imwrite(/sdcard/DCIM/gray_T_V.png,gray_T_V);//灰度图像TRUNC变换threshold(gray,gray_TRUNC,125,255,THRESH_TRUNC);imwrite(/sdcard/DCIM/gray_TRUNC.png,gray_TRUNC);//灰度图像大津法和三角形法二值化Mat img_Thr;cvtColor(image,img_Thr,COLOR_BGR2GRAY);Mat img_Thr_0,img_Thr_T;threshold(img_Thr,img_Thr_0,100,255,THRESH_BINARY|cv::THRESH_OTSU);threshold(img_Thr,img_Thr_T,125,255,THRESH_BINARY|cv::THRESH_TRIANGLE);imwrite(/sdcard/DCIM/img_Thr_0.png,img_Thr_0);imwrite(/sdcard/DCIM/img_Thr_T.png,img_Thr_T);Mat adaptive_mean,adaptive_gauss;adaptiveThreshold(img_Thr,adaptive_mean,255,ADAPTIVE_THRESH_MEAN_C,THRESH_BINARY,55,0);adaptiveThreshold(img_Thr,adaptive_gauss,255,ADAPTIVE_THRESH_GAUSSIAN_C,THRESH_BINARY,55,0);imwrite(/sdcard/DCIM/adaptive_mean.png,adaptive_mean);imwrite(/sdcard/DCIM/adaptive_gauss.png,adaptive_gauss);
}
