我正在创建一个在图片上运行一些图像处理的Android应用程序，处理的第一阶段是运行Canny Edge Detector。虽然它适用于较小的图片，但只要图片中的像素阈值超过一定阈值，应用就会因为过多的内存使用而关闭。
我发现应用程序在computeGradients（）方法中被杀死了，因为创建了太多（非常大）的数组，但我看不出有什么方法可以解决这个问题！在此实现中，每个数组都是完全必要的（适用于来自http://www.tomgibara.com/computer-vision/CannyEdgeDetector.java的Android）。我能否就如何防止这种情况发生任何想法？
CannyEdgeDetector代码：
package com.example.myfirstapp;
import android.app.Activity;
import android.graphics.Bitmap;
import android.util.Log;
import java.util.Arrays;
/**
* Created with IntelliJ IDEA.
* User: Wesley
* Date: 7/29/13
* Time: 5:26 PM
*/
public class CannyEdgeDetector extends Activity {
private final static float GAUSSIAN_CUT_OFF = 0.005f;
private final static float MAGNITUDE_SCALE = 10F;
private final static float MAGNITUDE_LIMIT = 100F;
private final static int MAGNITUDE_MAX = (int) (MAGNITUDE_SCALE * MAGNITUDE_LIMIT);
private int height;
private int width;
private int picSize;
private int[] data;
private int[] magnitude;
private Bitmap sourceImg;
private Bitmap edgesImg;
private float gaussianKernelRadius;
private float lowThreshold;
private float highThreshold;
private int gaussianKernelWidth;
private boolean contrastNormalised;
private int mFollowStackDepth = 100;
public CannyEdgeDetector () {
lowThreshold = 2.5f;
highThreshold = 7.5f;
gaussianKernelRadius = 3f;
gaussianKernelWidth = 18;
contrastNormalised = false;
}
/**
* Accessor method which simply returns the bitmap image that is
* being used as the source.
* @return the source image, or null
*/
public Bitmap getSourceImg () {
return sourceImg;
}
/**
* Accessor method which simply returns the bitmap image with edge
* detection.
* @return the source image, or null
*/
public Bitmap getEdgesImg () {
return edgesImg;
}
/**
* Mutator method to set the source image
* @param sourceImg_ the image to set the source image variable to
*/
public void setSourceImg (Bitmap sourceImg_) {
sourceImg = sourceImg_.copy(Bitmap.Config.ARGB_8888, true);
}
/**
* Mutator method to set the source image
* @param edgesImg_ the image to set the source image variable to
*/
public void setEdgesImg (Bitmap edgesImg_) {
edgesImg = edgesImg_;
}
/**
* Sets whether the contrast is normalized
* @param contrastNormalised true if the contrast should be normalized,
* false otherwise
*/
public void setContrastNormalised(boolean contrastNormalised) {
this.contrastNormalised = contrastNormalised;
}
/**
* Whether the luminance data extracted from the source image is normalized
* by linearizing its histogram prior to edge extraction. The default value
* is false.
*
* @return whether the contrast is normalized
*/
public boolean isContrastNormalized() {
return contrastNormalised;
}
public void process () {
long start = System.nanoTime();
height = sourceImg.getHeight();
width = sourceImg.getWidth();
picSize = height*width;
initArrs();
readLuminance();
if (contrastNormalised) {
normalizeContrast();
}
computeGradients(gaussianKernelRadius, gaussianKernelWidth);
int low = Math.round (lowThreshold * MAGNITUDE_SCALE);
int high = Math.round (highThreshold * MAGNITUDE_SCALE);
performHysteresis(low, high);
thresholdEdges();
writeEdges(data);
Log.i("Processing", "Processing terminated, time required: " + (System.nanoTime() - start));
}
private void initArrs() {
if (data == null || picSize != data.length) {
data = new int[picSize];
magnitude = new int[picSize];
}
}
private void readLuminance() {
int [] pixels = new int[picSize];
sourceImg.getPixels(pixels, 0, width, 0, 0, width, height);
for (int i = 0 ; i < picSize ; i++) {
int p = pixels[i];
int r = (p & 0xff0000) >> 16;
int g = (p & 0xff00) >> 8;
int b = p & 0xff;
data[i] = luminance(r, g, b);
}
}
private int luminance (int R, int G, int B) {
return Math.round (0.299f * R + 0.587f * G + 0.114f * B);
}
private void normalizeContrast() {
int[] histogram = new int[256];
for (int i = 0 ; i < data.length ; i++) {
histogram[data[i]]++;
}
// for (int i = 0 ; i < data.length ; i++) {
// Log.i("BEFORE contrast data", "Pixel number " + i + " = " + data[i]);
// }
int[] remap = new int[256];
int sum = 0;
int j = 0;
for (int i = 0; i < histogram.length; i++) {
sum += histogram[i];
int target = sum*255/picSize;
for (int k = j+1; k <=target; k++) {
remap[k] = i;
}
j = target;
}
for (int i = 0; i < data.length; i++) {
data[i] = remap[data[i]];
}
// for (int i = 0 ; i < data.length ; i++) {
// Log.i("AFTER contrast data", "Pixel number " + i + " = " + data[i]);
// }
}
private void computeGradients(float kernelRadius, int kernelWidth) {
float[] xConv = new float[picSize];
float[] yConv = new float[picSize];
//generate the gaussian convolution masks
float kernel[] = new float[kernelWidth];
float diffKernel[] = new float[kernelWidth];
int kwidth;
for (kwidth = 0 ; kwidth < kernelWidth ; kwidth++) {
float g1 = gaussian(kwidth, kernelRadius);
if (g1 <= GAUSSIAN_CUT_OFF && kwidth >= 2) break;
float g2 = gaussian(kwidth - 0.5f, kernelRadius);
float g3 = gaussian(kwidth + 0.5f, kernelRadius);
kernel[kwidth] = (g1 + g2 + g3) / 3f / (2f * (float) Math.PI * kernelRadius * kernelRadius);
diffKernel[kwidth] = g3 - g2;
}
//kwidth now == kernelWidth-1
int initX = kwidth - 1; //So that it does not go too close to edges
int maxX = width - (kwidth - 1); //Same same
int initY = width * (kwidth - 1); //Same same
int maxY = width * (height - (kwidth - 1)); //Same same
//perform convolution (Gaussian blurring) in x and y directions
for (int x = initX; x < maxX; x++) {
for (int y = initY; y < maxY; y += width) {
int index = x + y;
float sumX = data[index] * kernel[0];
float sumY = sumX;
int xOffset = 1; //Initial values
int yOffset = width;
for( ; xOffset < kwidth ; ) {
sumY += kernel[xOffset] * (data[index - yOffset] + data[index + yOffset]);
sumX += kernel[xOffset] * (data[index - xOffset] + data[index + xOffset]);
yOffset += width;
xOffset++;
}
yConv[index] = sumY;
xConv[index] = sumX;
}
}
float[] xGradient = new float[picSize];
float[] yGradient = new float[picSize];
for (int x = initX; x < maxX; x++) {
for (int y = initY; y < maxY; y += width) {
float sum = 0f;
int index = x + y;
for (int i = 1; i < kwidth; i++)
sum += diffKernel[i] * (yConv[index - i] - yConv[index + i]);
xGradient[index] = sum;
}
}
for (int x = kwidth; x < width - kwidth; x++) {
for (int y = initY; y < maxY; y += width) {
float sum = 0.0f;
int index = x + y;
int yOffset = width;
for (int i = 1; i < kwidth; i++) {
sum += diffKernel[i] * (xConv[index - yOffset] - xConv[index + yOffset]);
yOffset += width;
}
yGradient[index] = sum;
}
}
initX = kwidth;
maxX = width - kwidth;
initY = width * kwidth;
maxY = width * (height - kwidth);
for (int x = initX; x < maxX; x++) {
for (int y = initY; y < maxY; y += width) {
int index = x + y;
int indexN = index - width;
int indexS = index + width;
int indexW = index - 1;
int indexE = index + 1;
int indexNW = indexN - 1;
int indexNE = indexN + 1;
int indexSW = indexS - 1;
int indexSE = indexS + 1;
float xGrad = xGradient[index];
float yGrad = yGradient[index];
float gradMag = hypot(xGrad, yGrad);
//perform non-maximal supression
float nMag = hypot(xGradient[indexN], yGradient[indexN]);
float sMag = hypot(xGradient[indexS], yGradient[indexS]);
float wMag = hypot(xGradient[indexW], yGradient[indexW]);
float eMag = hypot(xGradient[indexE], yGradient[indexE]);
float neMag = hypot(xGradient[indexNE], yGradient[indexNE]);
float seMag = hypot(xGradient[indexSE], yGradient[indexSE]);
float swMag = hypot(xGradient[indexSW], yGradient[indexSW]);
float nwMag = hypot(xGradient[indexNW], yGradient[indexNW]);
float tmp;
/*
* An explanation of what's happening here, for those who want
* to understand the source: This performs the "non-maximal
* supression" phase of the Canny edge detection in which we
* need to compare the gradient magnitude to that in the
* direction of the gradient; only if the value is a local
* maximum do we consider the point as an edge candidate.
*
* We need to break the comparison into a number of different
* cases depending on the gradient direction so that the
* appropriate values can be used. To avoid computing the
* gradient direction, we use two simple comparisons: first we
* check that the partial derivatives have the same sign (1)
* and then we check which is larger (2). As a consequence, we
* have reduced the problem to one of four identical cases that
* each test the central gradient magnitude against the values at
* two points with 'identical support'; what this means is that
* the geometry required to accurately interpolate the magnitude
* of gradient function at those points has an identical
* geometry (upto right-angled-rotation/reflection).
*
* When comparing the central gradient to the two interpolated
* values, we avoid performing any divisions by multiplying both
* sides of each inequality by the greater of the two partial
* derivatives. The common comparand is stored in a temporary
* variable (3) and reused in the mirror case (4).
*
*/
if (xGrad * yGrad <= (float) 0 /*(1)*/
? Math.abs(xGrad) >= Math.abs(yGrad) /*(2)*/
? (tmp = Math.abs(xGrad * gradMag)) >= Math.abs(yGrad * neMag - (xGrad + yGrad) * eMag) /*(3)*/
&& tmp > Math.abs(yGrad * swMag - (xGrad + yGrad) * wMag) /*(4)*/
: (tmp = Math.abs(yGrad * gradMag)) >= Math.abs(xGrad * neMag - (yGrad + xGrad) * nMag) /*(3)*/
&& tmp > Math.abs(xGrad * swMag - (yGrad + xGrad) * sMag) /*(4)*/
: Math.abs(xGrad) >= Math.abs(yGrad) /*(2)*/
? (tmp = Math.abs(xGrad * gradMag)) >= Math.abs(yGrad * seMag + (xGrad - yGrad) * eMag) /*(3)*/
&& tmp > Math.abs(yGrad * nwMag + (xGrad - yGrad) * wMag) /*(4)*/
: (tmp = Math.abs(yGrad * gradMag)) >= Math.abs(xGrad * seMag + (yGrad - xGrad) * sMag) /*(3)*/
&& tmp > Math.abs(xGrad * nwMag + (yGrad - xGrad) * nMag) /*(4)*/
) {
magnitude[index] = gradMag >= MAGNITUDE_LIMIT ? MAGNITUDE_MAX : (int) (MAGNITUDE_SCALE * gradMag);
//NOTE: The orientation of the edge is not employed by this
//implementation. It is a simple matter to compute it at
//this point as: Math.atan2(yGrad, xGrad);
} else {
magnitude[index] = 0;
}
}
}
}
private float hypot(float x, float y) {
return (float) Math.hypot(x, y);
}
private float gaussian (float x, float sigma) {
return (float) Math.exp(-(x * x) / (2f * sigma * sigma));
} //I guess the box blur basically doesn't use this shit
private void performHysteresis(int low, int high) {
//NOTE: this implementation reuses the data array to store both
//luminance data from the image, and edge intensity from the processing.
//This is done for memory efficiency, other implementations may wish
//to separate these functions.
Arrays.fill(data, 0);
int offset = 0;
for (int x = 0; x < width; x++) {
for (int y = 0; y < height; y++) {
if (data[offset] == 0 && magnitude[offset] >= high) {
follow(x, y, offset, low, 0);
}
offset++;
}
}
}
private void follow(int x1, int y1, int i1, int threshold, int depth) {
if( depth > mFollowStackDepth) // don't run out of stack!
return;
int x0 = x1 == 0 ? x1 : x1 - 1;
int x2 = x1 == width - 1 ? x1 : x1 + 1;
int y0 = y1 == 0 ? y1 : y1 - 1;
int y2 = y1 == height -1 ? y1 : y1 + 1;
data[i1] = magnitude[i1];
for (int x = x0; x <= x2; x++) {
for (int y = y0; y <= y2; y++) {
int i2 = x + y * width;
if ((y != y1 || x != x1)
&& data[i2] == 0
&& magnitude[i2] >= threshold) {
follow(x, y, i2, threshold, depth+1);
return;
}
}
}
}
private void thresholdEdges() {
for (int i = 0 ; i < picSize ; i++) {
data[i] = data[i] > 0 ? -1 : 0xff000000;
}
}
private void writeEdges (int pixels[]) {
if (edgesImg == null) {
edgesImg = Bitmap.createBitmap(width, height, Bitmap.Config.ARGB_8888);
}
edgesImg.setPixels(pixels, 0, width, 0, 0, width, height);
}
}
答案 0 :(得分：2)
您在Android上进行图像处理的一些建议：
不要在Java中对大型数组实现每元素操作 码。这是非常低效的。而是在RenderScript中或在 使用JNI的C / C ++。这个建议与你没有直接关系 问题，但它会对你的程序性能产生巨大影响。
为避免内存问题，请勿直接在大图像上操作。
通常，Android设备的内存（RAM）远远少于传统设备
计算设备。相反，将图像分成小块和
一个接一个地操作它们。这项技术应该适用于所有
上面程序中的操作。当这是不可能的
（例如，某些算法的输入不是所有附近的像素），你
可缩小图像并对缩放图像进行操作，然后
将结果缩放回原始图像。这影响了
结果的准确性有点，但比接收要好
OutOfMemoryError。
希望这些建议有所帮助。