我在一个文件中有一个大的位图(比如3888x2592)。现在，我想将位图大小调整为800x533，并将其保存到另一个文件中。
我通常通过调用bitmap来缩放位图。createBitmap方法，但它需要一个源位图作为第一个参数，我不能提供，因为加载原始图像到位图对象当然会超过内存(例如，见这里)。
我也不能读取位图，例如，BitmapFactory.decodeFile(文件，选项)，提供BitmapFactory.Options。inSampleSize，因为我想把它调整到精确的宽度和高度。使用inSampleSize将位图大小调整为972x648(如果我使用inSampleSize=4)或778x518(如果我使用inSampleSize=5，这甚至不是2的幂)。
我还希望避免在第一步中使用inSampleSize读取图像，例如，在第一步中使用972x648，然后在第二步中将其大小调整为800x533，因为与直接调整原始图像的大小相比，质量会较差。
总结一下我的问题:
是否有一种方法来读取一个10MP或更多的大图像文件，并将其保存到一个新的图像文件，调整到一个特定的新宽度和高度，而不得到OutOfMemory异常?
我还尝试了BitmapFactory.decodeFile(文件，选项)和设置选项。out theight and Options。outidth值手动为800和533，但它不是这样工作的。
贾斯汀的答案翻译成代码(对我来说很完美):
private Bitmap getBitmap(String path) {
Uri uri = getImageUri(path);
InputStream in = null;
try {
final int IMAGE_MAX_SIZE = 1200000; // 1.2MP
in = mContentResolver.openInputStream(uri);
// Decode image size
BitmapFactory.Options options = new BitmapFactory.Options();
options.inJustDecodeBounds = true;
BitmapFactory.decodeStream(in, null, options);
in.close();
int scale = 1;
while ((options.outWidth * options.outHeight) * (1 / Math.pow(scale, 2)) >
IMAGE_MAX_SIZE) {
scale++;
}
Log.d(TAG, "scale = " + scale + ", orig-width: " + options.outWidth + ",
orig-height: " + options.outHeight);
Bitmap resultBitmap = null;
in = mContentResolver.openInputStream(uri);
if (scale > 1) {
scale--;
// scale to max possible inSampleSize that still yields an image
// larger than target
options = new BitmapFactory.Options();
options.inSampleSize = scale;
resultBitmap = BitmapFactory.decodeStream(in, null, options);
// resize to desired dimensions
int height = resultBitmap.getHeight();
int width = resultBitmap.getWidth();
Log.d(TAG, "1th scale operation dimenions - width: " + width + ",
height: " + height);
double y = Math.sqrt(IMAGE_MAX_SIZE
/ (((double) width) / height));
double x = (y / height) * width;
Bitmap scaledBitmap = Bitmap.createScaledBitmap(resultBitmap, (int) x,
(int) y, true);
resultBitmap.recycle();
resultBitmap = scaledBitmap;
System.gc();
} else {
resultBitmap = BitmapFactory.decodeStream(in);
}
in.close();
Log.d(TAG, "bitmap size - width: " +resultBitmap.getWidth() + ", height: " +
resultBitmap.getHeight());
return resultBitmap;
} catch (IOException e) {
Log.e(TAG, e.getMessage(),e);
return null;
}
我不知道我的解决方案是否是最佳实践，但我通过使用inDensity和inTargetDensity选项实现了加载我所需缩放的位图。当不加载可绘制资源时，inDensity初始值为0，因此此方法用于加载非资源图像。
变量imageUri, maxImageSideLength和context是我的方法的参数。为了清晰起见，我只发布了方法实现，没有包装AsyncTask。
ContentResolver resolver = context.getContentResolver();
InputStream is;
try {
is = resolver.openInputStream(imageUri);
} catch (FileNotFoundException e) {
Log.e(TAG, "Image not found.", e);
return null;
}
Options opts = new Options();
opts.inJustDecodeBounds = true;
BitmapFactory.decodeStream(is, null, opts);
// scale the image
float maxSideLength = maxImageSideLength;
float scaleFactor = Math.min(maxSideLength / opts.outWidth, maxSideLength / opts.outHeight);
// do not upscale!
if (scaleFactor < 1) {
opts.inDensity = 10000;
opts.inTargetDensity = (int) ((float) opts.inDensity * scaleFactor);
}
opts.inJustDecodeBounds = false;
try {
is.close();
} catch (IOException e) {
// ignore
}
try {
is = resolver.openInputStream(imageUri);
} catch (FileNotFoundException e) {
Log.e(TAG, "Image not found.", e);
return null;
}
Bitmap bitmap = BitmapFactory.decodeStream(is, null, opts);
try {
is.close();
} catch (IOException e) {
// ignore
}
return bitmap;
这是“Mojo Risin”和“Ofir”解决方案的“结合”。这将给你一个按比例调整图像的最大宽度和最大高度的边界。
它只读取元数据以获得原始大小(选项。
它使用粗略的调整大小来节省内存(itmap.createScaledBitmap)
它使用了基于之前创建的粗糙Bitamp精确调整大小的图像。
对我来说，它在500万像素以下的图像上表现良好。
try
{
int inWidth = 0;
int inHeight = 0;
InputStream in = new FileInputStream(pathOfInputImage);
// decode image size (decode metadata only, not the whole image)
BitmapFactory.Options options = new BitmapFactory.Options();
options.inJustDecodeBounds = true;
BitmapFactory.decodeStream(in, null, options);
in.close();
in = null;
// save width and height
inWidth = options.outWidth;
inHeight = options.outHeight;
// decode full image pre-resized
in = new FileInputStream(pathOfInputImage);
options = new BitmapFactory.Options();
// calc rought re-size (this is no exact resize)
options.inSampleSize = Math.max(inWidth/dstWidth, inHeight/dstHeight);
// decode full image
Bitmap roughBitmap = BitmapFactory.decodeStream(in, null, options);
// calc exact destination size
Matrix m = new Matrix();
RectF inRect = new RectF(0, 0, roughBitmap.getWidth(), roughBitmap.getHeight());
RectF outRect = new RectF(0, 0, dstWidth, dstHeight);
m.setRectToRect(inRect, outRect, Matrix.ScaleToFit.CENTER);
float[] values = new float[9];
m.getValues(values);
// resize bitmap
Bitmap resizedBitmap = Bitmap.createScaledBitmap(roughBitmap, (int) (roughBitmap.getWidth() * values[0]), (int) (roughBitmap.getHeight() * values[4]), true);
// save image
try
{
FileOutputStream out = new FileOutputStream(pathOfOutputImage);
resizedBitmap.compress(Bitmap.CompressFormat.JPEG, 80, out);
}
catch (Exception e)
{
Log.e("Image", e.getMessage(), e);
}
}
catch (IOException e)
{
Log.e("Image", e.getMessage(), e);
}
贾斯汀的答案翻译成代码(对我来说很完美):
private Bitmap getBitmap(String path) {
Uri uri = getImageUri(path);
InputStream in = null;
try {
final int IMAGE_MAX_SIZE = 1200000; // 1.2MP
in = mContentResolver.openInputStream(uri);
// Decode image size
BitmapFactory.Options options = new BitmapFactory.Options();
options.inJustDecodeBounds = true;
BitmapFactory.decodeStream(in, null, options);
in.close();
int scale = 1;
while ((options.outWidth * options.outHeight) * (1 / Math.pow(scale, 2)) >
IMAGE_MAX_SIZE) {
scale++;
}
Log.d(TAG, "scale = " + scale + ", orig-width: " + options.outWidth + ",
orig-height: " + options.outHeight);
Bitmap resultBitmap = null;
in = mContentResolver.openInputStream(uri);
if (scale > 1) {
scale--;
// scale to max possible inSampleSize that still yields an image
// larger than target
options = new BitmapFactory.Options();
options.inSampleSize = scale;
resultBitmap = BitmapFactory.decodeStream(in, null, options);
// resize to desired dimensions
int height = resultBitmap.getHeight();
int width = resultBitmap.getWidth();
Log.d(TAG, "1th scale operation dimenions - width: " + width + ",
height: " + height);
double y = Math.sqrt(IMAGE_MAX_SIZE
/ (((double) width) / height));
double x = (y / height) * width;
Bitmap scaledBitmap = Bitmap.createScaledBitmap(resultBitmap, (int) x,
(int) y, true);
resultBitmap.recycle();
resultBitmap = scaledBitmap;
System.gc();
} else {
resultBitmap = BitmapFactory.decodeStream(in);
}
in.close();
Log.d(TAG, "bitmap size - width: " +resultBitmap.getWidth() + ", height: " +
resultBitmap.getHeight());
return resultBitmap;
} catch (IOException e) {
Log.e(TAG, e.getMessage(),e);
return null;
}
我使用Integer。numberOfLeadingZeros计算最佳样本量，性能更好。
kotlin完整代码:
@Throws(IOException::class)
fun File.decodeBitmap(options: BitmapFactory.Options): Bitmap? {
return inputStream().use {
BitmapFactory.decodeStream(it, null, options)
}
}
@Throws(IOException::class)
fun File.decodeBitmapAtLeast(
@androidx.annotation.IntRange(from = 1) width: Int,
@androidx.annotation.IntRange(from = 1) height: Int
): Bitmap? {
val options = BitmapFactory.Options()
options.inJustDecodeBounds = true
decodeBitmap(options)
val ow = options.outWidth
val oh = options.outHeight
if (ow == -1 || oh == -1) return null
val w = ow / width
val h = oh / height
if (w > 1 && h > 1) {
val p = 31 - maxOf(Integer.numberOfLeadingZeros(w), Integer.numberOfLeadingZeros(h))
options.inSampleSize = 1 shl maxOf(0, p)
}
options.inJustDecodeBounds = false
return decodeBitmap(options)
}
Bitmap yourBitmap;
Bitmap resized = Bitmap.createScaledBitmap(yourBitmap, newWidth, newHeight, true);
or:
resized = Bitmap.createScaledBitmap(yourBitmap,(int)(yourBitmap.getWidth()*0.8), (int)(yourBitmap.getHeight()*0.8), true);
