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/*
* Copyright 2009 ZXing authors
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package com.google.zxing.common;
import com.google.zxing.Binarizer;
import com.google.zxing.LuminanceSource;
import com.google.zxing.NotFoundException;
/**
* This Binarizer implementation uses the old ZXing global histogram approach. It is suitable
* for low-end mobile devices which don't have enough CPU or memory to use a local thresholding
* algorithm. However, because it picks a global black point, it cannot handle difficult shadows
* and gradients.
*
* Faster mobile devices and all desktop applications should probably use HybridBinarizer instead.
*
* @author dswitkin@google.com (Daniel Switkin)
* @author Sean Owen
*/
public class GlobalHistogramBinarizer extends Binarizer {
private static final int LUMINANCE_BITS = 5;
private static final int LUMINANCE_SHIFT = 8 - LUMINANCE_BITS;
private static final int LUMINANCE_BUCKETS = 1 << LUMINANCE_BITS;
private byte[] luminances = null;
private int[] buckets = null;
public GlobalHistogramBinarizer(LuminanceSource source) {
super(source);
}
// Applies simple sharpening to the row data to improve performance of the 1D Readers.
public BitArray getBlackRow(int y, BitArray row) throws NotFoundException {
LuminanceSource source = getLuminanceSource();
int width = source.getWidth();
if (row == null || row.getSize() < width) {
row = new BitArray(width);
} else {
row.clear();
}
initArrays(width);
byte[] localLuminances = source.getRow(y, luminances);
int[] localBuckets = buckets;
for (int x = 0; x < width; x++) {
int pixel = localLuminances[x] & 0xff;
localBuckets[pixel >> LUMINANCE_SHIFT]++;
}
int blackPoint = estimateBlackPoint(localBuckets);
int left = localLuminances[0] & 0xff;
int center = localLuminances[1] & 0xff;
for (int x = 1; x < width - 1; x++) {
int right = localLuminances[x + 1] & 0xff;
// A simple -1 4 -1 box filter with a weight of 2.
int luminance = ((center << 2) - left - right) >> 1;
if (luminance < blackPoint) {
row.set(x);
}
left = center;
center = right;
}
return row;
}
// Does not sharpen the data, as this call is intended to only be used by 2D Readers.
public BitMatrix getBlackMatrix() throws NotFoundException {
LuminanceSource source = getLuminanceSource();
int width = source.getWidth();
int height = source.getHeight();
BitMatrix matrix = new BitMatrix(width, height);
// Quickly calculates the histogram by sampling four rows from the image. This proved to be
// more robust on the blackbox tests than sampling a diagonal as we used to do.
initArrays(width);
int[] localBuckets = buckets;
for (int y = 1; y < 5; y++) {
int row = height * y / 5;
byte[] localLuminances = source.getRow(row, luminances);
int right = (width << 2) / 5;
for (int x = width / 5; x < right; x++) {
int pixel = localLuminances[x] & 0xff;
localBuckets[pixel >> LUMINANCE_SHIFT]++;
}
}
int blackPoint = estimateBlackPoint(localBuckets);
// We delay reading the entire image luminance until the black point estimation succeeds.
// Although we end up reading four rows twice, it is consistent with our motto of
// "fail quickly" which is necessary for continuous scanning.
byte[] localLuminances = source.getMatrix();
for (int y = 0; y < height; y++) {
int offset = y * width;
for (int x = 0; x< width; x++) {
int pixel = localLuminances[offset + x] & 0xff;
if (pixel < blackPoint) {
matrix.set(x, y);
}
}
}
return matrix;
}
public Binarizer createBinarizer(LuminanceSource source) {
return new GlobalHistogramBinarizer(source);
}
private void initArrays(int luminanceSize) {
if (luminances == null || luminances.length < luminanceSize) {
luminances = new byte[luminanceSize];
}
if (buckets == null) {
buckets = new int[LUMINANCE_BUCKETS];
} else {
for (int x = 0; x < LUMINANCE_BUCKETS; x++) {
buckets[x] = 0;
}
}
}
private static int estimateBlackPoint(int[] buckets) throws NotFoundException {
// Find the tallest peak in the histogram.
int numBuckets = buckets.length;
int maxBucketCount = 0;
int firstPeak = 0;
int firstPeakSize = 0;
for (int x = 0; x < numBuckets; x++) {
if (buckets[x] > firstPeakSize) {
firstPeak = x;
firstPeakSize = buckets[x];
}
if (buckets[x] > maxBucketCount) {
maxBucketCount = buckets[x];
}
}
// Find the second-tallest peak which is somewhat far from the tallest peak.
int secondPeak = 0;
int secondPeakScore = 0;
for (int x = 0; x < numBuckets; x++) {
int distanceToBiggest = x - firstPeak;
// Encourage more distant second peaks by multiplying by square of distance.
int score = buckets[x] * distanceToBiggest * distanceToBiggest;
if (score > secondPeakScore) {
secondPeak = x;
secondPeakScore = score;
}
}
// Make sure firstPeak corresponds to the black peak.
if (firstPeak > secondPeak) {
int temp = firstPeak;
firstPeak = secondPeak;
secondPeak = temp;
}
// If there is too little contrast in the image to pick a meaningful black point, throw rather
// than waste time trying to decode the image, and risk false positives.
if (secondPeak - firstPeak <= numBuckets >> 4) {
throw NotFoundException.getNotFoundInstance();
}
// Find a valley between them that is low and closer to the white peak.
int bestValley = secondPeak - 1;
int bestValleyScore = -1;
for (int x = secondPeak - 1; x > firstPeak; x--) {
int fromFirst = x - firstPeak;
int score = fromFirst * fromFirst * (secondPeak - x) * (maxBucketCount - buckets[x]);
if (score > bestValleyScore) {
bestValley = x;
bestValleyScore = score;
}
}
return bestValley << LUMINANCE_SHIFT;
}
}
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