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histogram.go
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histogram.go
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// Copyright 2018 Fabian Wenzelmann
//
// 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 gomosaic
import (
"fmt"
"image"
"math"
"strings"
)
// Histogram describes a color histogram for an image.
// It counts the number of pixels with a certain color or the relative frequency
// of the color (normalized histogram).
//
// An entry for color r, g, b quantized to k sub-divisions is stored at position
// r + k * g + k * k * b.
//
// To compute the id of an r, g, b color use RGBID or ID on RGB objects.
type Histogram struct {
// Entries contains for each r, g, b color the frequency. The histogram does
// not save each possible r, g, b color but the quantizd version.
// That is it stores frequencies (r, g, b) where r, g, b < k.
Entries []float64
// K is the number of sub-divisions used to create the histogram.
// It must be a number between 1 and 256.
K uint
}
// NewHistogram creates a new histogram given the number of sub-divions in each
// direction. k must be a number between 1 and 256.
func NewHistogram(k uint) *Histogram {
return &Histogram{make([]float64, k*k*k), k}
}
// String returns a tuple representation of the histogram.
func (h *Histogram) String() string {
strs := make([]string, len(h.Entries))
for i, entry := range h.Entries {
strs[i] = fmt.Sprintf("%.2f", entry)
}
return "〈" + strings.Join(strs, ", ") + "〉"
}
// PrintInfo prints information about the histogram to the standard output.
// If verbose is true it prints a formatted table of all frequencies, otherwise
// it prints the shorter tuple representation.
func (h *Histogram) PrintInfo(verbose bool) {
numCategories := h.K * h.K * h.K
fmt.Printf("Histogram consisting of k = %d sub-divisions, leading to %d color categories\n", h.K, numCategories)
if verbose {
fmt.Printf("%-6s %6s %6s %10s\n", "red", "green", "blue", "value")
var r, g, b uint
for ; r < h.K; r++ {
g = 0
for ; g < h.K; g++ {
b = 0
for ; b < h.K; b++ {
fmt.Printf("%6d %6d %6d %10.2f\n", r, g, b, h.Entries[RGBID(r, g, b, h.K)])
}
}
}
} else {
fmt.Println(h)
}
}
// Equals checks if two histograms are equal. epsilon is the difference
// between that is allowed to still consider them equal.
func (h *Histogram) Equals(other *Histogram, epsilon float64) bool {
if h.K != other.K {
return false
}
for i, e1 := range h.Entries {
e2 := other.Entries[i]
if math.Abs(e1-e2) > epsilon {
return false
}
}
return true
}
// Add creates the histogram given an image, that is it counts how often
// a color appears in the image. k is the number of sub-divisions in each
// direction, it must be a number between 1 and 256.
// The histogram contains the freuqency of each color after quantiation in
// k sub-divisions.
//
// This method can be called multiple times to accumulate the histograms of
// multiple image,s it is however not save to concurrently call this method
// on the same histogram.
//
// To create a histogram for one image you can also use GenHistogram.
func (h *Histogram) Add(img image.Image, k uint) {
bounds := img.Bounds()
// don't do anything for empty images
if bounds.Empty() {
return
}
for y := bounds.Min.Y; y < bounds.Max.Y; y++ {
for x := bounds.Min.X; x < bounds.Max.X; x++ {
// get generic color
c := img.At(x, y)
// convert to internal rgb representation
rgb := ConvertRGB(c)
// quantize to k divisions
rgb = rgb.Quantize(k)
// update result entry
h.Entries[rgb.ID(k)]++
}
}
}
// GenHistogram creates a histogram given an image and the number of sub-divions
// in each direction (k), k must be a number between 1 and 256.
// The histogram contains the freuqency of each color after quantiation in
// k sub-divisions.
func GenHistogram(img image.Image, k uint, normalize bool) *Histogram {
res := NewHistogram(k)
res.Add(img, k)
bounds := img.Bounds()
if normalize && !bounds.Empty() {
return res.Normalize(bounds.Dx() * bounds.Dy())
}
return res
}
// GenHistogramFromList generates a histogram containing an entry for each image
// in the images list.
// k is the number of sub-divisons. If normalize is true the normalized
// histogram will be computed instead of the frequency histogram.
func GenHistogramFromList(k uint, normalize bool, images ...image.Image) *Histogram {
res := NewHistogram(k)
// we add the size of all images from the list to improve normalization
size := 0
for _, img := range images {
bounds := img.Bounds()
if bounds.Empty() {
continue
}
res.Add(img, k)
size += (bounds.Dx() * bounds.Dy())
}
if normalize {
res = res.Normalize(size)
}
return res
}
// EntrySum returns the sum of all entries in the histogram.
func (h *Histogram) EntrySum() float64 {
var res float64
for _, entry := range h.Entries {
res += entry
}
return res
}
// Normalize computes the normalized histogram of h if h contains the number
// of occurrences in the image.
// pixels is the total number of pixels in the original image the historam was
// created for. If pixels is a negative number or 0 the number of pixels will be
// computed as the sum of all entries in the original histogram.
// If no pixels exist in the image all result entries are set to 0.
func (h *Histogram) Normalize(pixels int) *Histogram {
var size float64
if pixels > 0 {
size = float64(pixels)
} else {
// sum all entries
size = h.EntrySum()
}
res := NewHistogram(h.K)
// testing 0.0 should be okay.
if size == 0.0 {
return res
}
for i, entry := range h.Entries {
res.Entries[i] = entry / size
}
return res
}
// CreateHistograms creates histograms for all images in the ids list and loads
// the images through the given storage.
// If you want to create all histograms for a given storage you can use
// CreateAllHistograms as a shortcut.
// It runs the creation of histograms concurrently (how many go routines run
// concurrently can be controlled by numRoutines).
// k is the number of sub-divisons as described in the histogram type,
// If normalized is true the normalized histograms are computed.
// progress is a function that is called to inform about the progress,
// see doucmentation for ProgressFunc.
func CreateHistograms(ids []ImageID, storage ImageStorage, normalize bool, k uint, numRoutines int, progress ProgressFunc) ([]*Histogram, error) {
if numRoutines <= 0 {
numRoutines = 1
}
numImages := len(ids)
// any error that occurs sets this variable (first error)
// this is done later
var err error
// struct that we use for the channel
type job struct {
pos int
id ImageID
}
res := make([]*Histogram, numImages)
jobs := make(chan job, BufferSize)
errorChan := make(chan error, BufferSize)
for w := 0; w < numRoutines; w++ {
go func() {
for next := range jobs {
image, imageErr := storage.LoadImage(next.id)
if imageErr != nil {
errorChan <- imageErr
continue
}
hist := GenHistogram(image, k, normalize)
res[next.pos] = hist
errorChan <- nil
}
}()
}
go func() {
for i, id := range ids {
jobs <- job{pos: i, id: id}
}
close(jobs)
}()
for i := 0; i < numImages; i++ {
nextErr := <-errorChan
if nextErr != nil && err == nil {
err = nextErr
}
if progress != nil {
progress(i)
}
}
if err != nil {
return nil, err
}
return res, nil
}
// CreateAllHistograms creates all histograms for images in the storage.
// It is a shortcut using CreateHistograms, see this documentation for details.
func CreateAllHistograms(storage ImageStorage, normalize bool, k uint, numRoutines int, progress ProgressFunc) ([]*Histogram, error) {
return CreateHistograms(IDList(storage), storage, normalize, k, numRoutines, progress)
}
// CreateHistogramsSequential works as CreateAllHistograms but does not use
// concurrency.
func CreateHistogramsSequential(storage ImageStorage, normalize bool, k uint, progress ProgressFunc) ([]*Histogram, error) {
numImages := storage.NumImages()
res := make([]*Histogram, numImages)
var i ImageID
for ; i < numImages; i++ {
image, imageErr := storage.LoadImage(i)
if imageErr != nil {
return nil, imageErr
}
hist := GenHistogram(image, k, normalize)
res[i] = hist
if progress != nil {
progress(int(i))
}
}
return res, nil
}