GlobalHistogramBinarizer.java revision 7e3fa36d69ffee874dd364b8e3d9aa3cab9a273b
/*
* 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
*
*
* 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.
*/
/**
* 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 byte[] EMPTY = new byte[0];
private byte[] luminances;
private final int[] buckets;
super(source);
luminances = EMPTY;
buckets = new int[LUMINANCE_BUCKETS];
}
// Applies simple sharpening to the row data to improve performance of the 1D Readers.
} else {
}
int[] localBuckets = buckets;
for (int x = 0; x < width; x++) {
}
// A simple -1 4 -1 box filter with a weight of 2.
if (luminance < blackPoint) {
}
}
return row;
}
// Does not sharpen the data, as this call is intended to only be used by 2D Readers.
// 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.
int[] localBuckets = buckets;
for (int y = 1; y < 5; y++) {
}
}
// 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.
for (int y = 0; y < height; y++) {
for (int x = 0; x< width; x++) {
if (pixel < blackPoint) {
}
}
}
return matrix;
}
return new GlobalHistogramBinarizer(source);
}
private void initArrays(int luminanceSize) {
luminances = new byte[luminanceSize];
}
for (int x = 0; x < LUMINANCE_BUCKETS; x++) {
buckets[x] = 0;
}
}
// Find the tallest peak in the histogram.
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.
if (score > secondPeakScore) {
secondPeak = x;
}
}
// Make sure firstPeak corresponds to the black peak.
if (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.
throw NotFoundException.getNotFoundInstance();
}
// Find a valley between them that is low and closer to the white peak.
int bestValleyScore = -1;
if (score > bestValleyScore) {
bestValley = x;
}
}
return bestValley << LUMINANCE_SHIFT;
}
}