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12
vendor/github.com/disintegration/imaging/.travis.yml generated vendored Normal file
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language: go
go:
- "1.10.x"
- "1.11.x"
- "1.12.x"
before_install:
- go get github.com/mattn/goveralls
script:
- go test -v -race -cover
- $GOPATH/bin/goveralls -service=travis-ci

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vendor/github.com/disintegration/imaging/LICENSE generated vendored Normal file
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The MIT License (MIT)
Copyright (c) 2012 Grigory Dryapak
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

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# Imaging
[![GoDoc](https://godoc.org/github.com/disintegration/imaging?status.svg)](https://godoc.org/github.com/disintegration/imaging)
[![Build Status](https://travis-ci.org/disintegration/imaging.svg?branch=master)](https://travis-ci.org/disintegration/imaging)
[![Coverage Status](https://coveralls.io/repos/github/disintegration/imaging/badge.svg?branch=master&service=github)](https://coveralls.io/github/disintegration/imaging?branch=master)
[![Go Report Card](https://goreportcard.com/badge/github.com/disintegration/imaging)](https://goreportcard.com/report/github.com/disintegration/imaging)
Package imaging provides basic image processing functions (resize, rotate, crop, brightness/contrast adjustments, etc.).
All the image processing functions provided by the package accept any image type that implements `image.Image` interface
as an input, and return a new image of `*image.NRGBA` type (32bit RGBA colors, non-premultiplied alpha).
## Installation
go get -u github.com/disintegration/imaging
## Documentation
http://godoc.org/github.com/disintegration/imaging
## Usage examples
A few usage examples can be found below. See the documentation for the full list of supported functions.
### Image resizing
```go
// Resize srcImage to size = 128x128px using the Lanczos filter.
dstImage128 := imaging.Resize(srcImage, 128, 128, imaging.Lanczos)
// Resize srcImage to width = 800px preserving the aspect ratio.
dstImage800 := imaging.Resize(srcImage, 800, 0, imaging.Lanczos)
// Scale down srcImage to fit the 800x600px bounding box.
dstImageFit := imaging.Fit(srcImage, 800, 600, imaging.Lanczos)
// Resize and crop the srcImage to fill the 100x100px area.
dstImageFill := imaging.Fill(srcImage, 100, 100, imaging.Center, imaging.Lanczos)
```
Imaging supports image resizing using various resampling filters. The most notable ones:
- `Lanczos` - A high-quality resampling filter for photographic images yielding sharp results.
- `CatmullRom` - A sharp cubic filter that is faster than Lanczos filter while providing similar results.
- `MitchellNetravali` - A cubic filter that produces smoother results with less ringing artifacts than CatmullRom.
- `Linear` - Bilinear resampling filter, produces smooth output. Faster than cubic filters.
- `Box` - Simple and fast averaging filter appropriate for downscaling. When upscaling it's similar to NearestNeighbor.
- `NearestNeighbor` - Fastest resampling filter, no antialiasing.
The full list of supported filters: NearestNeighbor, Box, Linear, Hermite, MitchellNetravali, CatmullRom, BSpline, Gaussian, Lanczos, Hann, Hamming, Blackman, Bartlett, Welch, Cosine. Custom filters can be created using ResampleFilter struct.
**Resampling filters comparison**
Original image:
![srcImage](testdata/branches.png)
The same image resized from 600x400px to 150x100px using different resampling filters.
From faster (lower quality) to slower (higher quality):
Filter | Resize result
--------------------------|---------------------------------------------
`imaging.NearestNeighbor` | ![dstImage](testdata/out_resize_nearest.png)
`imaging.Linear` | ![dstImage](testdata/out_resize_linear.png)
`imaging.CatmullRom` | ![dstImage](testdata/out_resize_catrom.png)
`imaging.Lanczos` | ![dstImage](testdata/out_resize_lanczos.png)
### Gaussian Blur
```go
dstImage := imaging.Blur(srcImage, 0.5)
```
Sigma parameter allows to control the strength of the blurring effect.
Original image | Sigma = 0.5 | Sigma = 1.5
-----------------------------------|----------------------------------------|---------------------------------------
![srcImage](testdata/flowers_small.png) | ![dstImage](testdata/out_blur_0.5.png) | ![dstImage](testdata/out_blur_1.5.png)
### Sharpening
```go
dstImage := imaging.Sharpen(srcImage, 0.5)
```
`Sharpen` uses gaussian function internally. Sigma parameter allows to control the strength of the sharpening effect.
Original image | Sigma = 0.5 | Sigma = 1.5
-----------------------------------|-------------------------------------------|------------------------------------------
![srcImage](testdata/flowers_small.png) | ![dstImage](testdata/out_sharpen_0.5.png) | ![dstImage](testdata/out_sharpen_1.5.png)
### Gamma correction
```go
dstImage := imaging.AdjustGamma(srcImage, 0.75)
```
Original image | Gamma = 0.75 | Gamma = 1.25
-----------------------------------|------------------------------------------|-----------------------------------------
![srcImage](testdata/flowers_small.png) | ![dstImage](testdata/out_gamma_0.75.png) | ![dstImage](testdata/out_gamma_1.25.png)
### Contrast adjustment
```go
dstImage := imaging.AdjustContrast(srcImage, 20)
```
Original image | Contrast = 15 | Contrast = -15
-----------------------------------|--------------------------------------------|-------------------------------------------
![srcImage](testdata/flowers_small.png) | ![dstImage](testdata/out_contrast_p15.png) | ![dstImage](testdata/out_contrast_m15.png)
### Brightness adjustment
```go
dstImage := imaging.AdjustBrightness(srcImage, 20)
```
Original image | Brightness = 10 | Brightness = -10
-----------------------------------|----------------------------------------------|---------------------------------------------
![srcImage](testdata/flowers_small.png) | ![dstImage](testdata/out_brightness_p10.png) | ![dstImage](testdata/out_brightness_m10.png)
### Saturation adjustment
```go
dstImage := imaging.AdjustSaturation(srcImage, 20)
```
Original image | Saturation = 30 | Saturation = -30
-----------------------------------|----------------------------------------------|---------------------------------------------
![srcImage](testdata/flowers_small.png) | ![dstImage](testdata/out_saturation_p30.png) | ![dstImage](testdata/out_saturation_m30.png)
## FAQ
### Incorrect image orientation after processing (e.g. an image appears rotated after resizing)
Most probably, the given image contains the EXIF orientation tag.
The stadard `image/*` packages do not support loading and saving
this kind of information. To fix the issue, try opening images with
the `AutoOrientation` decode option. If this option is set to `true`,
the image orientation is changed after decoding, according to the
orientation tag (if present). Here's the example:
```go
img, err := imaging.Open("test.jpg", imaging.AutoOrientation(true))
```
### What's the difference between `imaging` and `gift` packages?
[imaging](https://github.com/disintegration/imaging)
is designed to be a lightweight and simple image manipulation package.
It provides basic image processing functions and a few helper functions
such as `Open` and `Save`. It consistently returns *image.NRGBA image
type (8 bits per channel, RGBA).
[gift](https://github.com/disintegration/gift)
supports more advanced image processing, for example, sRGB/Linear color
space conversions. It also supports different output image types
(e.g. 16 bits per channel) and provides easy-to-use API for chaining
multiple processing steps together.
## Example code
```go
package main
import (
"image"
"image/color"
"log"
"github.com/disintegration/imaging"
)
func main() {
// Open a test image.
src, err := imaging.Open("testdata/flowers.png")
if err != nil {
log.Fatalf("failed to open image: %v", err)
}
// Crop the original image to 300x300px size using the center anchor.
src = imaging.CropAnchor(src, 300, 300, imaging.Center)
// Resize the cropped image to width = 200px preserving the aspect ratio.
src = imaging.Resize(src, 200, 0, imaging.Lanczos)
// Create a blurred version of the image.
img1 := imaging.Blur(src, 5)
// Create a grayscale version of the image with higher contrast and sharpness.
img2 := imaging.Grayscale(src)
img2 = imaging.AdjustContrast(img2, 20)
img2 = imaging.Sharpen(img2, 2)
// Create an inverted version of the image.
img3 := imaging.Invert(src)
// Create an embossed version of the image using a convolution filter.
img4 := imaging.Convolve3x3(
src,
[9]float64{
-1, -1, 0,
-1, 1, 1,
0, 1, 1,
},
nil,
)
// Create a new image and paste the four produced images into it.
dst := imaging.New(400, 400, color.NRGBA{0, 0, 0, 0})
dst = imaging.Paste(dst, img1, image.Pt(0, 0))
dst = imaging.Paste(dst, img2, image.Pt(0, 200))
dst = imaging.Paste(dst, img3, image.Pt(200, 0))
dst = imaging.Paste(dst, img4, image.Pt(200, 200))
// Save the resulting image as JPEG.
err = imaging.Save(dst, "testdata/out_example.jpg")
if err != nil {
log.Fatalf("failed to save image: %v", err)
}
}
```
Output:
![dstImage](testdata/out_example.jpg)

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package imaging
import (
"image"
"image/color"
"math"
)
// Grayscale produces a grayscale version of the image.
func Grayscale(img image.Image) *image.NRGBA {
src := newScanner(img)
dst := image.NewNRGBA(image.Rect(0, 0, src.w, src.h))
parallel(0, src.h, func(ys <-chan int) {
for y := range ys {
i := y * dst.Stride
src.scan(0, y, src.w, y+1, dst.Pix[i:i+src.w*4])
for x := 0; x < src.w; x++ {
d := dst.Pix[i : i+3 : i+3]
r := d[0]
g := d[1]
b := d[2]
f := 0.299*float64(r) + 0.587*float64(g) + 0.114*float64(b)
y := uint8(f + 0.5)
d[0] = y
d[1] = y
d[2] = y
i += 4
}
}
})
return dst
}
// Invert produces an inverted (negated) version of the image.
func Invert(img image.Image) *image.NRGBA {
src := newScanner(img)
dst := image.NewNRGBA(image.Rect(0, 0, src.w, src.h))
parallel(0, src.h, func(ys <-chan int) {
for y := range ys {
i := y * dst.Stride
src.scan(0, y, src.w, y+1, dst.Pix[i:i+src.w*4])
for x := 0; x < src.w; x++ {
d := dst.Pix[i : i+3 : i+3]
d[0] = 255 - d[0]
d[1] = 255 - d[1]
d[2] = 255 - d[2]
i += 4
}
}
})
return dst
}
// AdjustSaturation changes the saturation of the image using the percentage parameter and returns the adjusted image.
// The percentage must be in the range (-100, 100).
// The percentage = 0 gives the original image.
// The percentage = 100 gives the image with the saturation value doubled for each pixel.
// The percentage = -100 gives the image with the saturation value zeroed for each pixel (grayscale).
//
// Examples:
// dstImage = imaging.AdjustSaturation(srcImage, 25) // Increase image saturation by 25%.
// dstImage = imaging.AdjustSaturation(srcImage, -10) // Decrease image saturation by 10%.
//
func AdjustSaturation(img image.Image, percentage float64) *image.NRGBA {
percentage = math.Min(math.Max(percentage, -100), 100)
multiplier := 1 + percentage/100
return AdjustFunc(img, func(c color.NRGBA) color.NRGBA {
h, s, l := rgbToHSL(c.R, c.G, c.B)
s *= multiplier
if s > 1 {
s = 1
}
r, g, b := hslToRGB(h, s, l)
return color.NRGBA{r, g, b, c.A}
})
}
// AdjustContrast changes the contrast of the image using the percentage parameter and returns the adjusted image.
// The percentage must be in range (-100, 100). The percentage = 0 gives the original image.
// The percentage = -100 gives solid gray image.
//
// Examples:
//
// dstImage = imaging.AdjustContrast(srcImage, -10) // Decrease image contrast by 10%.
// dstImage = imaging.AdjustContrast(srcImage, 20) // Increase image contrast by 20%.
//
func AdjustContrast(img image.Image, percentage float64) *image.NRGBA {
percentage = math.Min(math.Max(percentage, -100.0), 100.0)
lut := make([]uint8, 256)
v := (100.0 + percentage) / 100.0
for i := 0; i < 256; i++ {
switch {
case 0 <= v && v <= 1:
lut[i] = clamp((0.5 + (float64(i)/255.0-0.5)*v) * 255.0)
case 1 < v && v < 2:
lut[i] = clamp((0.5 + (float64(i)/255.0-0.5)*(1/(2.0-v))) * 255.0)
default:
lut[i] = uint8(float64(i)/255.0+0.5) * 255
}
}
return adjustLUT(img, lut)
}
// AdjustBrightness changes the brightness of the image using the percentage parameter and returns the adjusted image.
// The percentage must be in range (-100, 100). The percentage = 0 gives the original image.
// The percentage = -100 gives solid black image. The percentage = 100 gives solid white image.
//
// Examples:
//
// dstImage = imaging.AdjustBrightness(srcImage, -15) // Decrease image brightness by 15%.
// dstImage = imaging.AdjustBrightness(srcImage, 10) // Increase image brightness by 10%.
//
func AdjustBrightness(img image.Image, percentage float64) *image.NRGBA {
percentage = math.Min(math.Max(percentage, -100.0), 100.0)
lut := make([]uint8, 256)
shift := 255.0 * percentage / 100.0
for i := 0; i < 256; i++ {
lut[i] = clamp(float64(i) + shift)
}
return adjustLUT(img, lut)
}
// AdjustGamma performs a gamma correction on the image and returns the adjusted image.
// Gamma parameter must be positive. Gamma = 1.0 gives the original image.
// Gamma less than 1.0 darkens the image and gamma greater than 1.0 lightens it.
//
// Example:
//
// dstImage = imaging.AdjustGamma(srcImage, 0.7)
//
func AdjustGamma(img image.Image, gamma float64) *image.NRGBA {
e := 1.0 / math.Max(gamma, 0.0001)
lut := make([]uint8, 256)
for i := 0; i < 256; i++ {
lut[i] = clamp(math.Pow(float64(i)/255.0, e) * 255.0)
}
return adjustLUT(img, lut)
}
// AdjustSigmoid changes the contrast of the image using a sigmoidal function and returns the adjusted image.
// It's a non-linear contrast change useful for photo adjustments as it preserves highlight and shadow detail.
// The midpoint parameter is the midpoint of contrast that must be between 0 and 1, typically 0.5.
// The factor parameter indicates how much to increase or decrease the contrast, typically in range (-10, 10).
// If the factor parameter is positive the image contrast is increased otherwise the contrast is decreased.
//
// Examples:
//
// dstImage = imaging.AdjustSigmoid(srcImage, 0.5, 3.0) // Increase the contrast.
// dstImage = imaging.AdjustSigmoid(srcImage, 0.5, -3.0) // Decrease the contrast.
//
func AdjustSigmoid(img image.Image, midpoint, factor float64) *image.NRGBA {
if factor == 0 {
return Clone(img)
}
lut := make([]uint8, 256)
a := math.Min(math.Max(midpoint, 0.0), 1.0)
b := math.Abs(factor)
sig0 := sigmoid(a, b, 0)
sig1 := sigmoid(a, b, 1)
e := 1.0e-6
if factor > 0 {
for i := 0; i < 256; i++ {
x := float64(i) / 255.0
sigX := sigmoid(a, b, x)
f := (sigX - sig0) / (sig1 - sig0)
lut[i] = clamp(f * 255.0)
}
} else {
for i := 0; i < 256; i++ {
x := float64(i) / 255.0
arg := math.Min(math.Max((sig1-sig0)*x+sig0, e), 1.0-e)
f := a - math.Log(1.0/arg-1.0)/b
lut[i] = clamp(f * 255.0)
}
}
return adjustLUT(img, lut)
}
func sigmoid(a, b, x float64) float64 {
return 1 / (1 + math.Exp(b*(a-x)))
}
// adjustLUT applies the given lookup table to the colors of the image.
func adjustLUT(img image.Image, lut []uint8) *image.NRGBA {
src := newScanner(img)
dst := image.NewNRGBA(image.Rect(0, 0, src.w, src.h))
lut = lut[0:256]
parallel(0, src.h, func(ys <-chan int) {
for y := range ys {
i := y * dst.Stride
src.scan(0, y, src.w, y+1, dst.Pix[i:i+src.w*4])
for x := 0; x < src.w; x++ {
d := dst.Pix[i : i+3 : i+3]
d[0] = lut[d[0]]
d[1] = lut[d[1]]
d[2] = lut[d[2]]
i += 4
}
}
})
return dst
}
// AdjustFunc applies the fn function to each pixel of the img image and returns the adjusted image.
//
// Example:
//
// dstImage = imaging.AdjustFunc(
// srcImage,
// func(c color.NRGBA) color.NRGBA {
// // Shift the red channel by 16.
// r := int(c.R) + 16
// if r > 255 {
// r = 255
// }
// return color.NRGBA{uint8(r), c.G, c.B, c.A}
// }
// )
//
func AdjustFunc(img image.Image, fn func(c color.NRGBA) color.NRGBA) *image.NRGBA {
src := newScanner(img)
dst := image.NewNRGBA(image.Rect(0, 0, src.w, src.h))
parallel(0, src.h, func(ys <-chan int) {
for y := range ys {
i := y * dst.Stride
src.scan(0, y, src.w, y+1, dst.Pix[i:i+src.w*4])
for x := 0; x < src.w; x++ {
d := dst.Pix[i : i+4 : i+4]
r := d[0]
g := d[1]
b := d[2]
a := d[3]
c := fn(color.NRGBA{r, g, b, a})
d[0] = c.R
d[1] = c.G
d[2] = c.B
d[3] = c.A
i += 4
}
}
})
return dst
}

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package imaging
import (
"image"
)
// ConvolveOptions are convolution parameters.
type ConvolveOptions struct {
// If Normalize is true the kernel is normalized before convolution.
Normalize bool
// If Abs is true the absolute value of each color channel is taken after convolution.
Abs bool
// Bias is added to each color channel value after convolution.
Bias int
}
// Convolve3x3 convolves the image with the specified 3x3 convolution kernel.
// Default parameters are used if a nil *ConvolveOptions is passed.
func Convolve3x3(img image.Image, kernel [9]float64, options *ConvolveOptions) *image.NRGBA {
return convolve(img, kernel[:], options)
}
// Convolve5x5 convolves the image with the specified 5x5 convolution kernel.
// Default parameters are used if a nil *ConvolveOptions is passed.
func Convolve5x5(img image.Image, kernel [25]float64, options *ConvolveOptions) *image.NRGBA {
return convolve(img, kernel[:], options)
}
func convolve(img image.Image, kernel []float64, options *ConvolveOptions) *image.NRGBA {
src := toNRGBA(img)
w := src.Bounds().Max.X
h := src.Bounds().Max.Y
dst := image.NewNRGBA(image.Rect(0, 0, w, h))
if w < 1 || h < 1 {
return dst
}
if options == nil {
options = &ConvolveOptions{}
}
if options.Normalize {
normalizeKernel(kernel)
}
type coef struct {
x, y int
k float64
}
var coefs []coef
var m int
switch len(kernel) {
case 9:
m = 1
case 25:
m = 2
}
i := 0
for y := -m; y <= m; y++ {
for x := -m; x <= m; x++ {
if kernel[i] != 0 {
coefs = append(coefs, coef{x: x, y: y, k: kernel[i]})
}
i++
}
}
parallel(0, h, func(ys <-chan int) {
for y := range ys {
for x := 0; x < w; x++ {
var r, g, b float64
for _, c := range coefs {
ix := x + c.x
if ix < 0 {
ix = 0
} else if ix >= w {
ix = w - 1
}
iy := y + c.y
if iy < 0 {
iy = 0
} else if iy >= h {
iy = h - 1
}
off := iy*src.Stride + ix*4
s := src.Pix[off : off+3 : off+3]
r += float64(s[0]) * c.k
g += float64(s[1]) * c.k
b += float64(s[2]) * c.k
}
if options.Abs {
if r < 0 {
r = -r
}
if g < 0 {
g = -g
}
if b < 0 {
b = -b
}
}
if options.Bias != 0 {
r += float64(options.Bias)
g += float64(options.Bias)
b += float64(options.Bias)
}
srcOff := y*src.Stride + x*4
dstOff := y*dst.Stride + x*4
d := dst.Pix[dstOff : dstOff+4 : dstOff+4]
d[0] = clamp(r)
d[1] = clamp(g)
d[2] = clamp(b)
d[3] = src.Pix[srcOff+3]
}
}
})
return dst
}
func normalizeKernel(kernel []float64) {
var sum, sumpos float64
for i := range kernel {
sum += kernel[i]
if kernel[i] > 0 {
sumpos += kernel[i]
}
}
if sum != 0 {
for i := range kernel {
kernel[i] /= sum
}
} else if sumpos != 0 {
for i := range kernel {
kernel[i] /= sumpos
}
}
}

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vendor/github.com/disintegration/imaging/doc.go generated vendored Normal file
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/*
Package imaging provides basic image processing functions (resize, rotate, crop, brightness/contrast adjustments, etc.).
All the image processing functions provided by the package accept any image type that implements image.Image interface
as an input, and return a new image of *image.NRGBA type (32bit RGBA colors, non-premultiplied alpha).
*/
package imaging

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vendor/github.com/disintegration/imaging/effects.go generated vendored Normal file
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package imaging
import (
"image"
"math"
)
func gaussianBlurKernel(x, sigma float64) float64 {
return math.Exp(-(x*x)/(2*sigma*sigma)) / (sigma * math.Sqrt(2*math.Pi))
}
// Blur produces a blurred version of the image using a Gaussian function.
// Sigma parameter must be positive and indicates how much the image will be blurred.
//
// Example:
//
// dstImage := imaging.Blur(srcImage, 3.5)
//
func Blur(img image.Image, sigma float64) *image.NRGBA {
if sigma <= 0 {
return Clone(img)
}
radius := int(math.Ceil(sigma * 3.0))
kernel := make([]float64, radius+1)
for i := 0; i <= radius; i++ {
kernel[i] = gaussianBlurKernel(float64(i), sigma)
}
return blurVertical(blurHorizontal(img, kernel), kernel)
}
func blurHorizontal(img image.Image, kernel []float64) *image.NRGBA {
src := newScanner(img)
dst := image.NewNRGBA(image.Rect(0, 0, src.w, src.h))
radius := len(kernel) - 1
parallel(0, src.h, func(ys <-chan int) {
scanLine := make([]uint8, src.w*4)
scanLineF := make([]float64, len(scanLine))
for y := range ys {
src.scan(0, y, src.w, y+1, scanLine)
for i, v := range scanLine {
scanLineF[i] = float64(v)
}
for x := 0; x < src.w; x++ {
min := x - radius
if min < 0 {
min = 0
}
max := x + radius
if max > src.w-1 {
max = src.w - 1
}
var r, g, b, a, wsum float64
for ix := min; ix <= max; ix++ {
i := ix * 4
weight := kernel[absint(x-ix)]
wsum += weight
s := scanLineF[i : i+4 : i+4]
wa := s[3] * weight
r += s[0] * wa
g += s[1] * wa
b += s[2] * wa
a += wa
}
if a != 0 {
aInv := 1 / a
j := y*dst.Stride + x*4
d := dst.Pix[j : j+4 : j+4]
d[0] = clamp(r * aInv)
d[1] = clamp(g * aInv)
d[2] = clamp(b * aInv)
d[3] = clamp(a / wsum)
}
}
}
})
return dst
}
func blurVertical(img image.Image, kernel []float64) *image.NRGBA {
src := newScanner(img)
dst := image.NewNRGBA(image.Rect(0, 0, src.w, src.h))
radius := len(kernel) - 1
parallel(0, src.w, func(xs <-chan int) {
scanLine := make([]uint8, src.h*4)
scanLineF := make([]float64, len(scanLine))
for x := range xs {
src.scan(x, 0, x+1, src.h, scanLine)
for i, v := range scanLine {
scanLineF[i] = float64(v)
}
for y := 0; y < src.h; y++ {
min := y - radius
if min < 0 {
min = 0
}
max := y + radius
if max > src.h-1 {
max = src.h - 1
}
var r, g, b, a, wsum float64
for iy := min; iy <= max; iy++ {
i := iy * 4
weight := kernel[absint(y-iy)]
wsum += weight
s := scanLineF[i : i+4 : i+4]
wa := s[3] * weight
r += s[0] * wa
g += s[1] * wa
b += s[2] * wa
a += wa
}
if a != 0 {
aInv := 1 / a
j := y*dst.Stride + x*4
d := dst.Pix[j : j+4 : j+4]
d[0] = clamp(r * aInv)
d[1] = clamp(g * aInv)
d[2] = clamp(b * aInv)
d[3] = clamp(a / wsum)
}
}
}
})
return dst
}
// Sharpen produces a sharpened version of the image.
// Sigma parameter must be positive and indicates how much the image will be sharpened.
//
// Example:
//
// dstImage := imaging.Sharpen(srcImage, 3.5)
//
func Sharpen(img image.Image, sigma float64) *image.NRGBA {
if sigma <= 0 {
return Clone(img)
}
src := newScanner(img)
dst := image.NewNRGBA(image.Rect(0, 0, src.w, src.h))
blurred := Blur(img, sigma)
parallel(0, src.h, func(ys <-chan int) {
scanLine := make([]uint8, src.w*4)
for y := range ys {
src.scan(0, y, src.w, y+1, scanLine)
j := y * dst.Stride
for i := 0; i < src.w*4; i++ {
val := int(scanLine[i])<<1 - int(blurred.Pix[j])
if val < 0 {
val = 0
} else if val > 0xff {
val = 0xff
}
dst.Pix[j] = uint8(val)
j++
}
}
})
return dst
}

3
vendor/github.com/disintegration/imaging/go.mod generated vendored Normal file
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@ -0,0 +1,3 @@
module github.com/disintegration/imaging
require golang.org/x/image v0.0.0-20191009234506-e7c1f5e7dbb8

3
vendor/github.com/disintegration/imaging/go.sum generated vendored Normal file
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@ -0,0 +1,3 @@
golang.org/x/image v0.0.0-20191009234506-e7c1f5e7dbb8 h1:hVwzHzIUGRjiF7EcUjqNxk3NCfkPxbDKRdnNE1Rpg0U=
golang.org/x/image v0.0.0-20191009234506-e7c1f5e7dbb8/go.mod h1:FeLwcggjj3mMvU+oOTbSwawSJRM1uh48EjtB4UJZlP0=
golang.org/x/text v0.3.0/go.mod h1:NqM8EUOU14njkJ3fqMW+pc6Ldnwhi/IjpwHt7yyuwOQ=

52
vendor/github.com/disintegration/imaging/histogram.go generated vendored Normal file
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@ -0,0 +1,52 @@
package imaging
import (
"image"
"sync"
)
// Histogram returns a normalized histogram of an image.
//
// Resulting histogram is represented as an array of 256 floats, where
// histogram[i] is a probability of a pixel being of a particular luminance i.
func Histogram(img image.Image) [256]float64 {
var mu sync.Mutex
var histogram [256]float64
var total float64
src := newScanner(img)
if src.w == 0 || src.h == 0 {
return histogram
}
parallel(0, src.h, func(ys <-chan int) {
var tmpHistogram [256]float64
var tmpTotal float64
scanLine := make([]uint8, src.w*4)
for y := range ys {
src.scan(0, y, src.w, y+1, scanLine)
i := 0
for x := 0; x < src.w; x++ {
s := scanLine[i : i+3 : i+3]
r := s[0]
g := s[1]
b := s[2]
y := 0.299*float32(r) + 0.587*float32(g) + 0.114*float32(b)
tmpHistogram[int(y+0.5)]++
tmpTotal++
i += 4
}
}
mu.Lock()
for i := 0; i < 256; i++ {
histogram[i] += tmpHistogram[i]
}
total += tmpTotal
mu.Unlock()
})
for i := 0; i < 256; i++ {
histogram[i] = histogram[i] / total
}
return histogram
}

444
vendor/github.com/disintegration/imaging/io.go generated vendored Normal file
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@ -0,0 +1,444 @@
package imaging
import (
"encoding/binary"
"errors"
"image"
"image/draw"
"image/gif"
"image/jpeg"
"image/png"
"io"
"io/ioutil"
"os"
"path/filepath"
"strings"
"golang.org/x/image/bmp"
"golang.org/x/image/tiff"
)
type fileSystem interface {
Create(string) (io.WriteCloser, error)
Open(string) (io.ReadCloser, error)
}
type localFS struct{}
func (localFS) Create(name string) (io.WriteCloser, error) { return os.Create(name) }
func (localFS) Open(name string) (io.ReadCloser, error) { return os.Open(name) }
var fs fileSystem = localFS{}
type decodeConfig struct {
autoOrientation bool
}
var defaultDecodeConfig = decodeConfig{
autoOrientation: false,
}
// DecodeOption sets an optional parameter for the Decode and Open functions.
type DecodeOption func(*decodeConfig)
// AutoOrientation returns a DecodeOption that sets the auto-orientation mode.
// If auto-orientation is enabled, the image will be transformed after decoding
// according to the EXIF orientation tag (if present). By default it's disabled.
func AutoOrientation(enabled bool) DecodeOption {
return func(c *decodeConfig) {
c.autoOrientation = enabled
}
}
// Decode reads an image from r.
func Decode(r io.Reader, opts ...DecodeOption) (image.Image, error) {
cfg := defaultDecodeConfig
for _, option := range opts {
option(&cfg)
}
if !cfg.autoOrientation {
img, _, err := image.Decode(r)
return img, err
}
var orient orientation
pr, pw := io.Pipe()
r = io.TeeReader(r, pw)
done := make(chan struct{})
go func() {
defer close(done)
orient = readOrientation(pr)
io.Copy(ioutil.Discard, pr)
}()
img, _, err := image.Decode(r)
pw.Close()
<-done
if err != nil {
return nil, err
}
return fixOrientation(img, orient), nil
}
// Open loads an image from file.
//
// Examples:
//
// // Load an image from file.
// img, err := imaging.Open("test.jpg")
//
// // Load an image and transform it depending on the EXIF orientation tag (if present).
// img, err := imaging.Open("test.jpg", imaging.AutoOrientation(true))
//
func Open(filename string, opts ...DecodeOption) (image.Image, error) {
file, err := fs.Open(filename)
if err != nil {
return nil, err
}
defer file.Close()
return Decode(file, opts...)
}
// Format is an image file format.
type Format int
// Image file formats.
const (
JPEG Format = iota
PNG
GIF
TIFF
BMP
)
var formatExts = map[string]Format{
"jpg": JPEG,
"jpeg": JPEG,
"png": PNG,
"gif": GIF,
"tif": TIFF,
"tiff": TIFF,
"bmp": BMP,
}
var formatNames = map[Format]string{
JPEG: "JPEG",
PNG: "PNG",
GIF: "GIF",
TIFF: "TIFF",
BMP: "BMP",
}
func (f Format) String() string {
return formatNames[f]
}
// ErrUnsupportedFormat means the given image format is not supported.
var ErrUnsupportedFormat = errors.New("imaging: unsupported image format")
// FormatFromExtension parses image format from filename extension:
// "jpg" (or "jpeg"), "png", "gif", "tif" (or "tiff") and "bmp" are supported.
func FormatFromExtension(ext string) (Format, error) {
if f, ok := formatExts[strings.ToLower(strings.TrimPrefix(ext, "."))]; ok {
return f, nil
}
return -1, ErrUnsupportedFormat
}
// FormatFromFilename parses image format from filename:
// "jpg" (or "jpeg"), "png", "gif", "tif" (or "tiff") and "bmp" are supported.
func FormatFromFilename(filename string) (Format, error) {
ext := filepath.Ext(filename)
return FormatFromExtension(ext)
}
type encodeConfig struct {
jpegQuality int
gifNumColors int
gifQuantizer draw.Quantizer
gifDrawer draw.Drawer
pngCompressionLevel png.CompressionLevel
}
var defaultEncodeConfig = encodeConfig{
jpegQuality: 95,
gifNumColors: 256,
gifQuantizer: nil,
gifDrawer: nil,
pngCompressionLevel: png.DefaultCompression,
}
// EncodeOption sets an optional parameter for the Encode and Save functions.
type EncodeOption func(*encodeConfig)
// JPEGQuality returns an EncodeOption that sets the output JPEG quality.
// Quality ranges from 1 to 100 inclusive, higher is better. Default is 95.
func JPEGQuality(quality int) EncodeOption {
return func(c *encodeConfig) {
c.jpegQuality = quality
}
}
// GIFNumColors returns an EncodeOption that sets the maximum number of colors
// used in the GIF-encoded image. It ranges from 1 to 256. Default is 256.
func GIFNumColors(numColors int) EncodeOption {
return func(c *encodeConfig) {
c.gifNumColors = numColors
}
}
// GIFQuantizer returns an EncodeOption that sets the quantizer that is used to produce
// a palette of the GIF-encoded image.
func GIFQuantizer(quantizer draw.Quantizer) EncodeOption {
return func(c *encodeConfig) {
c.gifQuantizer = quantizer
}
}
// GIFDrawer returns an EncodeOption that sets the drawer that is used to convert
// the source image to the desired palette of the GIF-encoded image.
func GIFDrawer(drawer draw.Drawer) EncodeOption {
return func(c *encodeConfig) {
c.gifDrawer = drawer
}
}
// PNGCompressionLevel returns an EncodeOption that sets the compression level
// of the PNG-encoded image. Default is png.DefaultCompression.
func PNGCompressionLevel(level png.CompressionLevel) EncodeOption {
return func(c *encodeConfig) {
c.pngCompressionLevel = level
}
}
// Encode writes the image img to w in the specified format (JPEG, PNG, GIF, TIFF or BMP).
func Encode(w io.Writer, img image.Image, format Format, opts ...EncodeOption) error {
cfg := defaultEncodeConfig
for _, option := range opts {
option(&cfg)
}
switch format {
case JPEG:
if nrgba, ok := img.(*image.NRGBA); ok && nrgba.Opaque() {
rgba := &image.RGBA{
Pix: nrgba.Pix,
Stride: nrgba.Stride,
Rect: nrgba.Rect,
}
return jpeg.Encode(w, rgba, &jpeg.Options{Quality: cfg.jpegQuality})
}
return jpeg.Encode(w, img, &jpeg.Options{Quality: cfg.jpegQuality})
case PNG:
encoder := png.Encoder{CompressionLevel: cfg.pngCompressionLevel}
return encoder.Encode(w, img)
case GIF:
return gif.Encode(w, img, &gif.Options{
NumColors: cfg.gifNumColors,
Quantizer: cfg.gifQuantizer,
Drawer: cfg.gifDrawer,
})
case TIFF:
return tiff.Encode(w, img, &tiff.Options{Compression: tiff.Deflate, Predictor: true})
case BMP:
return bmp.Encode(w, img)
}
return ErrUnsupportedFormat
}
// Save saves the image to file with the specified filename.
// The format is determined from the filename extension:
// "jpg" (or "jpeg"), "png", "gif", "tif" (or "tiff") and "bmp" are supported.
//
// Examples:
//
// // Save the image as PNG.
// err := imaging.Save(img, "out.png")
//
// // Save the image as JPEG with optional quality parameter set to 80.
// err := imaging.Save(img, "out.jpg", imaging.JPEGQuality(80))
//
func Save(img image.Image, filename string, opts ...EncodeOption) (err error) {
f, err := FormatFromFilename(filename)
if err != nil {
return err
}
file, err := fs.Create(filename)
if err != nil {
return err
}
err = Encode(file, img, f, opts...)
errc := file.Close()
if err == nil {
err = errc
}
return err
}
// orientation is an EXIF flag that specifies the transformation
// that should be applied to image to display it correctly.
type orientation int
const (
orientationUnspecified = 0
orientationNormal = 1
orientationFlipH = 2
orientationRotate180 = 3
orientationFlipV = 4
orientationTranspose = 5
orientationRotate270 = 6
orientationTransverse = 7
orientationRotate90 = 8
)
// readOrientation tries to read the orientation EXIF flag from image data in r.
// If the EXIF data block is not found or the orientation flag is not found
// or any other error occures while reading the data, it returns the
// orientationUnspecified (0) value.
func readOrientation(r io.Reader) orientation {
const (
markerSOI = 0xffd8
markerAPP1 = 0xffe1
exifHeader = 0x45786966
byteOrderBE = 0x4d4d
byteOrderLE = 0x4949
orientationTag = 0x0112
)
// Check if JPEG SOI marker is present.
var soi uint16
if err := binary.Read(r, binary.BigEndian, &soi); err != nil {
return orientationUnspecified
}
if soi != markerSOI {
return orientationUnspecified // Missing JPEG SOI marker.
}
// Find JPEG APP1 marker.
for {
var marker, size uint16
if err := binary.Read(r, binary.BigEndian, &marker); err != nil {
return orientationUnspecified
}
if err := binary.Read(r, binary.BigEndian, &size); err != nil {
return orientationUnspecified
}
if marker>>8 != 0xff {
return orientationUnspecified // Invalid JPEG marker.
}
if marker == markerAPP1 {
break
}
if size < 2 {
return orientationUnspecified // Invalid block size.
}
if _, err := io.CopyN(ioutil.Discard, r, int64(size-2)); err != nil {
return orientationUnspecified
}
}
// Check if EXIF header is present.
var header uint32
if err := binary.Read(r, binary.BigEndian, &header); err != nil {
return orientationUnspecified
}
if header != exifHeader {
return orientationUnspecified
}
if _, err := io.CopyN(ioutil.Discard, r, 2); err != nil {
return orientationUnspecified
}
// Read byte order information.
var (
byteOrderTag uint16
byteOrder binary.ByteOrder
)
if err := binary.Read(r, binary.BigEndian, &byteOrderTag); err != nil {
return orientationUnspecified
}
switch byteOrderTag {
case byteOrderBE:
byteOrder = binary.BigEndian
case byteOrderLE:
byteOrder = binary.LittleEndian
default:
return orientationUnspecified // Invalid byte order flag.
}
if _, err := io.CopyN(ioutil.Discard, r, 2); err != nil {
return orientationUnspecified
}
// Skip the EXIF offset.
var offset uint32
if err := binary.Read(r, byteOrder, &offset); err != nil {
return orientationUnspecified
}
if offset < 8 {
return orientationUnspecified // Invalid offset value.
}
if _, err := io.CopyN(ioutil.Discard, r, int64(offset-8)); err != nil {
return orientationUnspecified
}
// Read the number of tags.
var numTags uint16
if err := binary.Read(r, byteOrder, &numTags); err != nil {
return orientationUnspecified
}
// Find the orientation tag.
for i := 0; i < int(numTags); i++ {
var tag uint16
if err := binary.Read(r, byteOrder, &tag); err != nil {
return orientationUnspecified
}
if tag != orientationTag {
if _, err := io.CopyN(ioutil.Discard, r, 10); err != nil {
return orientationUnspecified
}
continue
}
if _, err := io.CopyN(ioutil.Discard, r, 6); err != nil {
return orientationUnspecified
}
var val uint16
if err := binary.Read(r, byteOrder, &val); err != nil {
return orientationUnspecified
}
if val < 1 || val > 8 {
return orientationUnspecified // Invalid tag value.
}
return orientation(val)
}
return orientationUnspecified // Missing orientation tag.
}
// fixOrientation applies a transform to img corresponding to the given orientation flag.
func fixOrientation(img image.Image, o orientation) image.Image {
switch o {
case orientationNormal:
case orientationFlipH:
img = FlipH(img)
case orientationFlipV:
img = FlipV(img)
case orientationRotate90:
img = Rotate90(img)
case orientationRotate180:
img = Rotate180(img)
case orientationRotate270:
img = Rotate270(img)
case orientationTranspose:
img = Transpose(img)
case orientationTransverse:
img = Transverse(img)
}
return img
}

595
vendor/github.com/disintegration/imaging/resize.go generated vendored Normal file
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package imaging
import (
"image"
"math"
)
type indexWeight struct {
index int
weight float64
}
func precomputeWeights(dstSize, srcSize int, filter ResampleFilter) [][]indexWeight {
du := float64(srcSize) / float64(dstSize)
scale := du
if scale < 1.0 {
scale = 1.0
}
ru := math.Ceil(scale * filter.Support)
out := make([][]indexWeight, dstSize)
tmp := make([]indexWeight, 0, dstSize*int(ru+2)*2)
for v := 0; v < dstSize; v++ {
fu := (float64(v)+0.5)*du - 0.5
begin := int(math.Ceil(fu - ru))
if begin < 0 {
begin = 0
}
end := int(math.Floor(fu + ru))
if end > srcSize-1 {
end = srcSize - 1
}
var sum float64
for u := begin; u <= end; u++ {
w := filter.Kernel((float64(u) - fu) / scale)
if w != 0 {
sum += w
tmp = append(tmp, indexWeight{index: u, weight: w})
}
}
if sum != 0 {
for i := range tmp {
tmp[i].weight /= sum
}
}
out[v] = tmp
tmp = tmp[len(tmp):]
}
return out
}
// Resize resizes the image to the specified width and height using the specified resampling
// filter and returns the transformed image. If one of width or height is 0, the image aspect
// ratio is preserved.
//
// Example:
//
// dstImage := imaging.Resize(srcImage, 800, 600, imaging.Lanczos)
//
func Resize(img image.Image, width, height int, filter ResampleFilter) *image.NRGBA {
dstW, dstH := width, height
if dstW < 0 || dstH < 0 {
return &image.NRGBA{}
}
if dstW == 0 && dstH == 0 {
return &image.NRGBA{}
}
srcW := img.Bounds().Dx()
srcH := img.Bounds().Dy()
if srcW <= 0 || srcH <= 0 {
return &image.NRGBA{}
}
// If new width or height is 0 then preserve aspect ratio, minimum 1px.
if dstW == 0 {
tmpW := float64(dstH) * float64(srcW) / float64(srcH)
dstW = int(math.Max(1.0, math.Floor(tmpW+0.5)))
}
if dstH == 0 {
tmpH := float64(dstW) * float64(srcH) / float64(srcW)
dstH = int(math.Max(1.0, math.Floor(tmpH+0.5)))
}
if filter.Support <= 0 {
// Nearest-neighbor special case.
return resizeNearest(img, dstW, dstH)
}
if srcW != dstW && srcH != dstH {
return resizeVertical(resizeHorizontal(img, dstW, filter), dstH, filter)
}
if srcW != dstW {
return resizeHorizontal(img, dstW, filter)
}
if srcH != dstH {
return resizeVertical(img, dstH, filter)
}
return Clone(img)
}
func resizeHorizontal(img image.Image, width int, filter ResampleFilter) *image.NRGBA {
src := newScanner(img)
dst := image.NewNRGBA(image.Rect(0, 0, width, src.h))
weights := precomputeWeights(width, src.w, filter)
parallel(0, src.h, func(ys <-chan int) {
scanLine := make([]uint8, src.w*4)
for y := range ys {
src.scan(0, y, src.w, y+1, scanLine)
j0 := y * dst.Stride
for x := range weights {
var r, g, b, a float64
for _, w := range weights[x] {
i := w.index * 4
s := scanLine[i : i+4 : i+4]
aw := float64(s[3]) * w.weight
r += float64(s[0]) * aw
g += float64(s[1]) * aw
b += float64(s[2]) * aw
a += aw
}
if a != 0 {
aInv := 1 / a
j := j0 + x*4
d := dst.Pix[j : j+4 : j+4]
d[0] = clamp(r * aInv)
d[1] = clamp(g * aInv)
d[2] = clamp(b * aInv)
d[3] = clamp(a)
}
}
}
})
return dst
}
func resizeVertical(img image.Image, height int, filter ResampleFilter) *image.NRGBA {
src := newScanner(img)
dst := image.NewNRGBA(image.Rect(0, 0, src.w, height))
weights := precomputeWeights(height, src.h, filter)
parallel(0, src.w, func(xs <-chan int) {
scanLine := make([]uint8, src.h*4)
for x := range xs {
src.scan(x, 0, x+1, src.h, scanLine)
for y := range weights {
var r, g, b, a float64
for _, w := range weights[y] {
i := w.index * 4
s := scanLine[i : i+4 : i+4]
aw := float64(s[3]) * w.weight
r += float64(s[0]) * aw
g += float64(s[1]) * aw
b += float64(s[2]) * aw
a += aw
}
if a != 0 {
aInv := 1 / a
j := y*dst.Stride + x*4
d := dst.Pix[j : j+4 : j+4]
d[0] = clamp(r * aInv)
d[1] = clamp(g * aInv)
d[2] = clamp(b * aInv)
d[3] = clamp(a)
}
}
}
})
return dst
}
// resizeNearest is a fast nearest-neighbor resize, no filtering.
func resizeNearest(img image.Image, width, height int) *image.NRGBA {
dst := image.NewNRGBA(image.Rect(0, 0, width, height))
dx := float64(img.Bounds().Dx()) / float64(width)
dy := float64(img.Bounds().Dy()) / float64(height)
if dx > 1 && dy > 1 {
src := newScanner(img)
parallel(0, height, func(ys <-chan int) {
for y := range ys {
srcY := int((float64(y) + 0.5) * dy)
dstOff := y * dst.Stride
for x := 0; x < width; x++ {
srcX := int((float64(x) + 0.5) * dx)
src.scan(srcX, srcY, srcX+1, srcY+1, dst.Pix[dstOff:dstOff+4])
dstOff += 4
}
}
})
} else {
src := toNRGBA(img)
parallel(0, height, func(ys <-chan int) {
for y := range ys {
srcY := int((float64(y) + 0.5) * dy)
srcOff0 := srcY * src.Stride
dstOff := y * dst.Stride
for x := 0; x < width; x++ {
srcX := int((float64(x) + 0.5) * dx)
srcOff := srcOff0 + srcX*4
copy(dst.Pix[dstOff:dstOff+4], src.Pix[srcOff:srcOff+4])
dstOff += 4
}
}
})
}
return dst
}
// Fit scales down the image using the specified resample filter to fit the specified
// maximum width and height and returns the transformed image.
//
// Example:
//
// dstImage := imaging.Fit(srcImage, 800, 600, imaging.Lanczos)
//
func Fit(img image.Image, width, height int, filter ResampleFilter) *image.NRGBA {
maxW, maxH := width, height
if maxW <= 0 || maxH <= 0 {
return &image.NRGBA{}
}
srcBounds := img.Bounds()
srcW := srcBounds.Dx()
srcH := srcBounds.Dy()
if srcW <= 0 || srcH <= 0 {
return &image.NRGBA{}
}
if srcW <= maxW && srcH <= maxH {
return Clone(img)
}
srcAspectRatio := float64(srcW) / float64(srcH)
maxAspectRatio := float64(maxW) / float64(maxH)
var newW, newH int
if srcAspectRatio > maxAspectRatio {
newW = maxW
newH = int(float64(newW) / srcAspectRatio)
} else {
newH = maxH
newW = int(float64(newH) * srcAspectRatio)
}
return Resize(img, newW, newH, filter)
}
// Fill creates an image with the specified dimensions and fills it with the scaled source image.
// To achieve the correct aspect ratio without stretching, the source image will be cropped.
//
// Example:
//
// dstImage := imaging.Fill(srcImage, 800, 600, imaging.Center, imaging.Lanczos)
//
func Fill(img image.Image, width, height int, anchor Anchor, filter ResampleFilter) *image.NRGBA {
dstW, dstH := width, height
if dstW <= 0 || dstH <= 0 {
return &image.NRGBA{}
}
srcBounds := img.Bounds()
srcW := srcBounds.Dx()
srcH := srcBounds.Dy()
if srcW <= 0 || srcH <= 0 {
return &image.NRGBA{}
}
if srcW == dstW && srcH == dstH {
return Clone(img)
}
if srcW >= 100 && srcH >= 100 {
return cropAndResize(img, dstW, dstH, anchor, filter)
}
return resizeAndCrop(img, dstW, dstH, anchor, filter)
}
// cropAndResize crops the image to the smallest possible size that has the required aspect ratio using
// the given anchor point, then scales it to the specified dimensions and returns the transformed image.
//
// This is generally faster than resizing first, but may result in inaccuracies when used on small source images.
func cropAndResize(img image.Image, width, height int, anchor Anchor, filter ResampleFilter) *image.NRGBA {
dstW, dstH := width, height
srcBounds := img.Bounds()
srcW := srcBounds.Dx()
srcH := srcBounds.Dy()
srcAspectRatio := float64(srcW) / float64(srcH)
dstAspectRatio := float64(dstW) / float64(dstH)
var tmp *image.NRGBA
if srcAspectRatio < dstAspectRatio {
cropH := float64(srcW) * float64(dstH) / float64(dstW)
tmp = CropAnchor(img, srcW, int(math.Max(1, cropH)+0.5), anchor)
} else {
cropW := float64(srcH) * float64(dstW) / float64(dstH)
tmp = CropAnchor(img, int(math.Max(1, cropW)+0.5), srcH, anchor)
}
return Resize(tmp, dstW, dstH, filter)
}
// resizeAndCrop resizes the image to the smallest possible size that will cover the specified dimensions,
// crops the resized image to the specified dimensions using the given anchor point and returns
// the transformed image.
func resizeAndCrop(img image.Image, width, height int, anchor Anchor, filter ResampleFilter) *image.NRGBA {
dstW, dstH := width, height
srcBounds := img.Bounds()
srcW := srcBounds.Dx()
srcH := srcBounds.Dy()
srcAspectRatio := float64(srcW) / float64(srcH)
dstAspectRatio := float64(dstW) / float64(dstH)
var tmp *image.NRGBA
if srcAspectRatio < dstAspectRatio {
tmp = Resize(img, dstW, 0, filter)
} else {
tmp = Resize(img, 0, dstH, filter)
}
return CropAnchor(tmp, dstW, dstH, anchor)
}
// Thumbnail scales the image up or down using the specified resample filter, crops it
// to the specified width and hight and returns the transformed image.
//
// Example:
//
// dstImage := imaging.Thumbnail(srcImage, 100, 100, imaging.Lanczos)
//
func Thumbnail(img image.Image, width, height int, filter ResampleFilter) *image.NRGBA {
return Fill(img, width, height, Center, filter)
}
// ResampleFilter specifies a resampling filter to be used for image resizing.
//
// General filter recommendations:
//
// - Lanczos
// A high-quality resampling filter for photographic images yielding sharp results.
//
// - CatmullRom
// A sharp cubic filter that is faster than Lanczos filter while providing similar results.
//
// - MitchellNetravali
// A cubic filter that produces smoother results with less ringing artifacts than CatmullRom.
//
// - Linear
// Bilinear resampling filter, produces a smooth output. Faster than cubic filters.
//
// - Box
// Simple and fast averaging filter appropriate for downscaling.
// When upscaling it's similar to NearestNeighbor.
//
// - NearestNeighbor
// Fastest resampling filter, no antialiasing.
//
type ResampleFilter struct {
Support float64
Kernel func(float64) float64
}
// NearestNeighbor is a nearest-neighbor filter (no anti-aliasing).
var NearestNeighbor ResampleFilter
// Box filter (averaging pixels).
var Box ResampleFilter
// Linear filter.
var Linear ResampleFilter
// Hermite cubic spline filter (BC-spline; B=0; C=0).
var Hermite ResampleFilter
// MitchellNetravali is Mitchell-Netravali cubic filter (BC-spline; B=1/3; C=1/3).
var MitchellNetravali ResampleFilter
// CatmullRom is a Catmull-Rom - sharp cubic filter (BC-spline; B=0; C=0.5).
var CatmullRom ResampleFilter
// BSpline is a smooth cubic filter (BC-spline; B=1; C=0).
var BSpline ResampleFilter
// Gaussian is a Gaussian blurring filter.
var Gaussian ResampleFilter
// Bartlett is a Bartlett-windowed sinc filter (3 lobes).
var Bartlett ResampleFilter
// Lanczos filter (3 lobes).
var Lanczos ResampleFilter
// Hann is a Hann-windowed sinc filter (3 lobes).
var Hann ResampleFilter
// Hamming is a Hamming-windowed sinc filter (3 lobes).
var Hamming ResampleFilter
// Blackman is a Blackman-windowed sinc filter (3 lobes).
var Blackman ResampleFilter
// Welch is a Welch-windowed sinc filter (parabolic window, 3 lobes).
var Welch ResampleFilter
// Cosine is a Cosine-windowed sinc filter (3 lobes).
var Cosine ResampleFilter
func bcspline(x, b, c float64) float64 {
var y float64
x = math.Abs(x)
if x < 1.0 {
y = ((12-9*b-6*c)*x*x*x + (-18+12*b+6*c)*x*x + (6 - 2*b)) / 6
} else if x < 2.0 {
y = ((-b-6*c)*x*x*x + (6*b+30*c)*x*x + (-12*b-48*c)*x + (8*b + 24*c)) / 6
}
return y
}
func sinc(x float64) float64 {
if x == 0 {
return 1
}
return math.Sin(math.Pi*x) / (math.Pi * x)
}
func init() {
NearestNeighbor = ResampleFilter{
Support: 0.0, // special case - not applying the filter
}
Box = ResampleFilter{
Support: 0.5,
Kernel: func(x float64) float64 {
x = math.Abs(x)
if x <= 0.5 {
return 1.0
}
return 0
},
}
Linear = ResampleFilter{
Support: 1.0,
Kernel: func(x float64) float64 {
x = math.Abs(x)
if x < 1.0 {
return 1.0 - x
}
return 0
},
}
Hermite = ResampleFilter{
Support: 1.0,
Kernel: func(x float64) float64 {
x = math.Abs(x)
if x < 1.0 {
return bcspline(x, 0.0, 0.0)
}
return 0
},
}
MitchellNetravali = ResampleFilter{
Support: 2.0,
Kernel: func(x float64) float64 {
x = math.Abs(x)
if x < 2.0 {
return bcspline(x, 1.0/3.0, 1.0/3.0)
}
return 0
},
}
CatmullRom = ResampleFilter{
Support: 2.0,
Kernel: func(x float64) float64 {
x = math.Abs(x)
if x < 2.0 {
return bcspline(x, 0.0, 0.5)
}
return 0
},
}
BSpline = ResampleFilter{
Support: 2.0,
Kernel: func(x float64) float64 {
x = math.Abs(x)
if x < 2.0 {
return bcspline(x, 1.0, 0.0)
}
return 0
},
}
Gaussian = ResampleFilter{
Support: 2.0,
Kernel: func(x float64) float64 {
x = math.Abs(x)
if x < 2.0 {
return math.Exp(-2 * x * x)
}
return 0
},
}
Bartlett = ResampleFilter{
Support: 3.0,
Kernel: func(x float64) float64 {
x = math.Abs(x)
if x < 3.0 {
return sinc(x) * (3.0 - x) / 3.0
}
return 0
},
}
Lanczos = ResampleFilter{
Support: 3.0,
Kernel: func(x float64) float64 {
x = math.Abs(x)
if x < 3.0 {
return sinc(x) * sinc(x/3.0)
}
return 0
},
}
Hann = ResampleFilter{
Support: 3.0,
Kernel: func(x float64) float64 {
x = math.Abs(x)
if x < 3.0 {
return sinc(x) * (0.5 + 0.5*math.Cos(math.Pi*x/3.0))
}
return 0
},
}
Hamming = ResampleFilter{
Support: 3.0,
Kernel: func(x float64) float64 {
x = math.Abs(x)
if x < 3.0 {
return sinc(x) * (0.54 + 0.46*math.Cos(math.Pi*x/3.0))
}
return 0
},
}
Blackman = ResampleFilter{
Support: 3.0,
Kernel: func(x float64) float64 {
x = math.Abs(x)
if x < 3.0 {
return sinc(x) * (0.42 - 0.5*math.Cos(math.Pi*x/3.0+math.Pi) + 0.08*math.Cos(2.0*math.Pi*x/3.0))
}
return 0
},
}
Welch = ResampleFilter{
Support: 3.0,
Kernel: func(x float64) float64 {
x = math.Abs(x)
if x < 3.0 {
return sinc(x) * (1.0 - (x * x / 9.0))
}
return 0
},
}
Cosine = ResampleFilter{
Support: 3.0,
Kernel: func(x float64) float64 {
x = math.Abs(x)
if x < 3.0 {
return sinc(x) * math.Cos((math.Pi/2.0)*(x/3.0))
}
return 0
},
}
}

285
vendor/github.com/disintegration/imaging/scanner.go generated vendored Normal file
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package imaging
import (
"image"
"image/color"
)
type scanner struct {
image image.Image
w, h int
palette []color.NRGBA
}
func newScanner(img image.Image) *scanner {
s := &scanner{
image: img,
w: img.Bounds().Dx(),
h: img.Bounds().Dy(),
}
if img, ok := img.(*image.Paletted); ok {
s.palette = make([]color.NRGBA, len(img.Palette))
for i := 0; i < len(img.Palette); i++ {
s.palette[i] = color.NRGBAModel.Convert(img.Palette[i]).(color.NRGBA)
}
}
return s
}
// scan scans the given rectangular region of the image into dst.
func (s *scanner) scan(x1, y1, x2, y2 int, dst []uint8) {
switch img := s.image.(type) {
case *image.NRGBA:
size := (x2 - x1) * 4
j := 0
i := y1*img.Stride + x1*4
if size == 4 {
for y := y1; y < y2; y++ {
d := dst[j : j+4 : j+4]
s := img.Pix[i : i+4 : i+4]
d[0] = s[0]
d[1] = s[1]
d[2] = s[2]
d[3] = s[3]
j += size
i += img.Stride
}
} else {
for y := y1; y < y2; y++ {
copy(dst[j:j+size], img.Pix[i:i+size])
j += size
i += img.Stride
}
}
case *image.NRGBA64:
j := 0
for y := y1; y < y2; y++ {
i := y*img.Stride + x1*8
for x := x1; x < x2; x++ {
s := img.Pix[i : i+8 : i+8]
d := dst[j : j+4 : j+4]
d[0] = s[0]
d[1] = s[2]
d[2] = s[4]
d[3] = s[6]
j += 4
i += 8
}
}
case *image.RGBA:
j := 0
for y := y1; y < y2; y++ {
i := y*img.Stride + x1*4
for x := x1; x < x2; x++ {
d := dst[j : j+4 : j+4]
a := img.Pix[i+3]
switch a {
case 0:
d[0] = 0
d[1] = 0
d[2] = 0
d[3] = a
case 0xff:
s := img.Pix[i : i+4 : i+4]
d[0] = s[0]
d[1] = s[1]
d[2] = s[2]
d[3] = a
default:
s := img.Pix[i : i+4 : i+4]
r16 := uint16(s[0])
g16 := uint16(s[1])
b16 := uint16(s[2])
a16 := uint16(a)
d[0] = uint8(r16 * 0xff / a16)
d[1] = uint8(g16 * 0xff / a16)
d[2] = uint8(b16 * 0xff / a16)
d[3] = a
}
j += 4
i += 4
}
}
case *image.RGBA64:
j := 0
for y := y1; y < y2; y++ {
i := y*img.Stride + x1*8
for x := x1; x < x2; x++ {
s := img.Pix[i : i+8 : i+8]
d := dst[j : j+4 : j+4]
a := s[6]
switch a {
case 0:
d[0] = 0
d[1] = 0
d[2] = 0
case 0xff:
d[0] = s[0]
d[1] = s[2]
d[2] = s[4]
default:
r32 := uint32(s[0])<<8 | uint32(s[1])
g32 := uint32(s[2])<<8 | uint32(s[3])
b32 := uint32(s[4])<<8 | uint32(s[5])
a32 := uint32(s[6])<<8 | uint32(s[7])
d[0] = uint8((r32 * 0xffff / a32) >> 8)
d[1] = uint8((g32 * 0xffff / a32) >> 8)
d[2] = uint8((b32 * 0xffff / a32) >> 8)
}
d[3] = a
j += 4
i += 8
}
}
case *image.Gray:
j := 0
for y := y1; y < y2; y++ {
i := y*img.Stride + x1
for x := x1; x < x2; x++ {
c := img.Pix[i]
d := dst[j : j+4 : j+4]
d[0] = c
d[1] = c
d[2] = c
d[3] = 0xff
j += 4
i++
}
}
case *image.Gray16:
j := 0
for y := y1; y < y2; y++ {
i := y*img.Stride + x1*2
for x := x1; x < x2; x++ {
c := img.Pix[i]
d := dst[j : j+4 : j+4]
d[0] = c
d[1] = c
d[2] = c
d[3] = 0xff
j += 4
i += 2
}
}
case *image.YCbCr:
j := 0
x1 += img.Rect.Min.X
x2 += img.Rect.Min.X
y1 += img.Rect.Min.Y
y2 += img.Rect.Min.Y
hy := img.Rect.Min.Y / 2
hx := img.Rect.Min.X / 2
for y := y1; y < y2; y++ {
iy := (y-img.Rect.Min.Y)*img.YStride + (x1 - img.Rect.Min.X)
var yBase int
switch img.SubsampleRatio {
case image.YCbCrSubsampleRatio444, image.YCbCrSubsampleRatio422:
yBase = (y - img.Rect.Min.Y) * img.CStride
case image.YCbCrSubsampleRatio420, image.YCbCrSubsampleRatio440:
yBase = (y/2 - hy) * img.CStride
}
for x := x1; x < x2; x++ {
var ic int
switch img.SubsampleRatio {
case image.YCbCrSubsampleRatio444, image.YCbCrSubsampleRatio440:
ic = yBase + (x - img.Rect.Min.X)
case image.YCbCrSubsampleRatio422, image.YCbCrSubsampleRatio420:
ic = yBase + (x/2 - hx)
default:
ic = img.COffset(x, y)
}
yy1 := int32(img.Y[iy]) * 0x10101
cb1 := int32(img.Cb[ic]) - 128
cr1 := int32(img.Cr[ic]) - 128
r := yy1 + 91881*cr1
if uint32(r)&0xff000000 == 0 {
r >>= 16
} else {
r = ^(r >> 31)
}
g := yy1 - 22554*cb1 - 46802*cr1
if uint32(g)&0xff000000 == 0 {
g >>= 16
} else {
g = ^(g >> 31)
}
b := yy1 + 116130*cb1
if uint32(b)&0xff000000 == 0 {
b >>= 16
} else {
b = ^(b >> 31)
}
d := dst[j : j+4 : j+4]
d[0] = uint8(r)
d[1] = uint8(g)
d[2] = uint8(b)
d[3] = 0xff
iy++
j += 4
}
}
case *image.Paletted:
j := 0
for y := y1; y < y2; y++ {
i := y*img.Stride + x1
for x := x1; x < x2; x++ {
c := s.palette[img.Pix[i]]
d := dst[j : j+4 : j+4]
d[0] = c.R
d[1] = c.G
d[2] = c.B
d[3] = c.A
j += 4
i++
}
}
default:
j := 0
b := s.image.Bounds()
x1 += b.Min.X
x2 += b.Min.X
y1 += b.Min.Y
y2 += b.Min.Y
for y := y1; y < y2; y++ {
for x := x1; x < x2; x++ {
r16, g16, b16, a16 := s.image.At(x, y).RGBA()
d := dst[j : j+4 : j+4]
switch a16 {
case 0xffff:
d[0] = uint8(r16 >> 8)
d[1] = uint8(g16 >> 8)
d[2] = uint8(b16 >> 8)
d[3] = 0xff
case 0:
d[0] = 0
d[1] = 0
d[2] = 0
d[3] = 0
default:
d[0] = uint8(((r16 * 0xffff) / a16) >> 8)
d[1] = uint8(((g16 * 0xffff) / a16) >> 8)
d[2] = uint8(((b16 * 0xffff) / a16) >> 8)
d[3] = uint8(a16 >> 8)
}
j += 4
}
}
}
}

249
vendor/github.com/disintegration/imaging/tools.go generated vendored Normal file
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package imaging
import (
"bytes"
"image"
"image/color"
"math"
)
// New creates a new image with the specified width and height, and fills it with the specified color.
func New(width, height int, fillColor color.Color) *image.NRGBA {
if width <= 0 || height <= 0 {
return &image.NRGBA{}
}
c := color.NRGBAModel.Convert(fillColor).(color.NRGBA)
if (c == color.NRGBA{0, 0, 0, 0}) {
return image.NewNRGBA(image.Rect(0, 0, width, height))
}
return &image.NRGBA{
Pix: bytes.Repeat([]byte{c.R, c.G, c.B, c.A}, width*height),
Stride: 4 * width,
Rect: image.Rect(0, 0, width, height),
}
}
// Clone returns a copy of the given image.
func Clone(img image.Image) *image.NRGBA {
src := newScanner(img)
dst := image.NewNRGBA(image.Rect(0, 0, src.w, src.h))
size := src.w * 4
parallel(0, src.h, func(ys <-chan int) {
for y := range ys {
i := y * dst.Stride
src.scan(0, y, src.w, y+1, dst.Pix[i:i+size])
}
})
return dst
}
// Anchor is the anchor point for image alignment.
type Anchor int
// Anchor point positions.
const (
Center Anchor = iota
TopLeft
Top
TopRight
Left
Right
BottomLeft
Bottom
BottomRight
)
func anchorPt(b image.Rectangle, w, h int, anchor Anchor) image.Point {
var x, y int
switch anchor {
case TopLeft:
x = b.Min.X
y = b.Min.Y
case Top:
x = b.Min.X + (b.Dx()-w)/2
y = b.Min.Y
case TopRight:
x = b.Max.X - w
y = b.Min.Y
case Left:
x = b.Min.X
y = b.Min.Y + (b.Dy()-h)/2
case Right:
x = b.Max.X - w
y = b.Min.Y + (b.Dy()-h)/2
case BottomLeft:
x = b.Min.X
y = b.Max.Y - h
case Bottom:
x = b.Min.X + (b.Dx()-w)/2
y = b.Max.Y - h
case BottomRight:
x = b.Max.X - w
y = b.Max.Y - h
default:
x = b.Min.X + (b.Dx()-w)/2
y = b.Min.Y + (b.Dy()-h)/2
}
return image.Pt(x, y)
}
// Crop cuts out a rectangular region with the specified bounds
// from the image and returns the cropped image.
func Crop(img image.Image, rect image.Rectangle) *image.NRGBA {
r := rect.Intersect(img.Bounds()).Sub(img.Bounds().Min)
if r.Empty() {
return &image.NRGBA{}
}
src := newScanner(img)
dst := image.NewNRGBA(image.Rect(0, 0, r.Dx(), r.Dy()))
rowSize := r.Dx() * 4
parallel(r.Min.Y, r.Max.Y, func(ys <-chan int) {
for y := range ys {
i := (y - r.Min.Y) * dst.Stride
src.scan(r.Min.X, y, r.Max.X, y+1, dst.Pix[i:i+rowSize])
}
})
return dst
}
// CropAnchor cuts out a rectangular region with the specified size
// from the image using the specified anchor point and returns the cropped image.
func CropAnchor(img image.Image, width, height int, anchor Anchor) *image.NRGBA {
srcBounds := img.Bounds()
pt := anchorPt(srcBounds, width, height, anchor)
r := image.Rect(0, 0, width, height).Add(pt)
b := srcBounds.Intersect(r)
return Crop(img, b)
}
// CropCenter cuts out a rectangular region with the specified size
// from the center of the image and returns the cropped image.
func CropCenter(img image.Image, width, height int) *image.NRGBA {
return CropAnchor(img, width, height, Center)
}
// Paste pastes the img image to the background image at the specified position and returns the combined image.
func Paste(background, img image.Image, pos image.Point) *image.NRGBA {
dst := Clone(background)
pos = pos.Sub(background.Bounds().Min)
pasteRect := image.Rectangle{Min: pos, Max: pos.Add(img.Bounds().Size())}
interRect := pasteRect.Intersect(dst.Bounds())
if interRect.Empty() {
return dst
}
src := newScanner(img)
parallel(interRect.Min.Y, interRect.Max.Y, func(ys <-chan int) {
for y := range ys {
x1 := interRect.Min.X - pasteRect.Min.X
x2 := interRect.Max.X - pasteRect.Min.X
y1 := y - pasteRect.Min.Y
y2 := y1 + 1
i1 := y*dst.Stride + interRect.Min.X*4
i2 := i1 + interRect.Dx()*4
src.scan(x1, y1, x2, y2, dst.Pix[i1:i2])
}
})
return dst
}
// PasteCenter pastes the img image to the center of the background image and returns the combined image.
func PasteCenter(background, img image.Image) *image.NRGBA {
bgBounds := background.Bounds()
bgW := bgBounds.Dx()
bgH := bgBounds.Dy()
bgMinX := bgBounds.Min.X
bgMinY := bgBounds.Min.Y
centerX := bgMinX + bgW/2
centerY := bgMinY + bgH/2
x0 := centerX - img.Bounds().Dx()/2
y0 := centerY - img.Bounds().Dy()/2
return Paste(background, img, image.Pt(x0, y0))
}
// Overlay draws the img image over the background image at given position
// and returns the combined image. Opacity parameter is the opacity of the img
// image layer, used to compose the images, it must be from 0.0 to 1.0.
//
// Examples:
//
// // Draw spriteImage over backgroundImage at the given position (x=50, y=50).
// dstImage := imaging.Overlay(backgroundImage, spriteImage, image.Pt(50, 50), 1.0)
//
// // Blend two opaque images of the same size.
// dstImage := imaging.Overlay(imageOne, imageTwo, image.Pt(0, 0), 0.5)
//
func Overlay(background, img image.Image, pos image.Point, opacity float64) *image.NRGBA {
opacity = math.Min(math.Max(opacity, 0.0), 1.0) // Ensure 0.0 <= opacity <= 1.0.
dst := Clone(background)
pos = pos.Sub(background.Bounds().Min)
pasteRect := image.Rectangle{Min: pos, Max: pos.Add(img.Bounds().Size())}
interRect := pasteRect.Intersect(dst.Bounds())
if interRect.Empty() {
return dst
}
src := newScanner(img)
parallel(interRect.Min.Y, interRect.Max.Y, func(ys <-chan int) {
scanLine := make([]uint8, interRect.Dx()*4)
for y := range ys {
x1 := interRect.Min.X - pasteRect.Min.X
x2 := interRect.Max.X - pasteRect.Min.X
y1 := y - pasteRect.Min.Y
y2 := y1 + 1
src.scan(x1, y1, x2, y2, scanLine)
i := y*dst.Stride + interRect.Min.X*4
j := 0
for x := interRect.Min.X; x < interRect.Max.X; x++ {
d := dst.Pix[i : i+4 : i+4]
r1 := float64(d[0])
g1 := float64(d[1])
b1 := float64(d[2])
a1 := float64(d[3])
s := scanLine[j : j+4 : j+4]
r2 := float64(s[0])
g2 := float64(s[1])
b2 := float64(s[2])
a2 := float64(s[3])
coef2 := opacity * a2 / 255
coef1 := (1 - coef2) * a1 / 255
coefSum := coef1 + coef2
coef1 /= coefSum
coef2 /= coefSum
d[0] = uint8(r1*coef1 + r2*coef2)
d[1] = uint8(g1*coef1 + g2*coef2)
d[2] = uint8(b1*coef1 + b2*coef2)
d[3] = uint8(math.Min(a1+a2*opacity*(255-a1)/255, 255))
i += 4
j += 4
}
}
})
return dst
}
// OverlayCenter overlays the img image to the center of the background image and
// returns the combined image. Opacity parameter is the opacity of the img
// image layer, used to compose the images, it must be from 0.0 to 1.0.
func OverlayCenter(background, img image.Image, opacity float64) *image.NRGBA {
bgBounds := background.Bounds()
bgW := bgBounds.Dx()
bgH := bgBounds.Dy()
bgMinX := bgBounds.Min.X
bgMinY := bgBounds.Min.Y
centerX := bgMinX + bgW/2
centerY := bgMinY + bgH/2
x0 := centerX - img.Bounds().Dx()/2
y0 := centerY - img.Bounds().Dy()/2
return Overlay(background, img, image.Point{x0, y0}, opacity)
}

268
vendor/github.com/disintegration/imaging/transform.go generated vendored Normal file
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package imaging
import (
"image"
"image/color"
"math"
)
// FlipH flips the image horizontally (from left to right) and returns the transformed image.
func FlipH(img image.Image) *image.NRGBA {
src := newScanner(img)
dstW := src.w
dstH := src.h
rowSize := dstW * 4
dst := image.NewNRGBA(image.Rect(0, 0, dstW, dstH))
parallel(0, dstH, func(ys <-chan int) {
for dstY := range ys {
i := dstY * dst.Stride
srcY := dstY
src.scan(0, srcY, src.w, srcY+1, dst.Pix[i:i+rowSize])
reverse(dst.Pix[i : i+rowSize])
}
})
return dst
}
// FlipV flips the image vertically (from top to bottom) and returns the transformed image.
func FlipV(img image.Image) *image.NRGBA {
src := newScanner(img)
dstW := src.w
dstH := src.h
rowSize := dstW * 4
dst := image.NewNRGBA(image.Rect(0, 0, dstW, dstH))
parallel(0, dstH, func(ys <-chan int) {
for dstY := range ys {
i := dstY * dst.Stride
srcY := dstH - dstY - 1
src.scan(0, srcY, src.w, srcY+1, dst.Pix[i:i+rowSize])
}
})
return dst
}
// Transpose flips the image horizontally and rotates 90 degrees counter-clockwise.
func Transpose(img image.Image) *image.NRGBA {
src := newScanner(img)
dstW := src.h
dstH := src.w
rowSize := dstW * 4
dst := image.NewNRGBA(image.Rect(0, 0, dstW, dstH))
parallel(0, dstH, func(ys <-chan int) {
for dstY := range ys {
i := dstY * dst.Stride
srcX := dstY
src.scan(srcX, 0, srcX+1, src.h, dst.Pix[i:i+rowSize])
}
})
return dst
}
// Transverse flips the image vertically and rotates 90 degrees counter-clockwise.
func Transverse(img image.Image) *image.NRGBA {
src := newScanner(img)
dstW := src.h
dstH := src.w
rowSize := dstW * 4
dst := image.NewNRGBA(image.Rect(0, 0, dstW, dstH))
parallel(0, dstH, func(ys <-chan int) {
for dstY := range ys {
i := dstY * dst.Stride
srcX := dstH - dstY - 1
src.scan(srcX, 0, srcX+1, src.h, dst.Pix[i:i+rowSize])
reverse(dst.Pix[i : i+rowSize])
}
})
return dst
}
// Rotate90 rotates the image 90 degrees counter-clockwise and returns the transformed image.
func Rotate90(img image.Image) *image.NRGBA {
src := newScanner(img)
dstW := src.h
dstH := src.w
rowSize := dstW * 4
dst := image.NewNRGBA(image.Rect(0, 0, dstW, dstH))
parallel(0, dstH, func(ys <-chan int) {
for dstY := range ys {
i := dstY * dst.Stride
srcX := dstH - dstY - 1
src.scan(srcX, 0, srcX+1, src.h, dst.Pix[i:i+rowSize])
}
})
return dst
}
// Rotate180 rotates the image 180 degrees counter-clockwise and returns the transformed image.
func Rotate180(img image.Image) *image.NRGBA {
src := newScanner(img)
dstW := src.w
dstH := src.h
rowSize := dstW * 4
dst := image.NewNRGBA(image.Rect(0, 0, dstW, dstH))
parallel(0, dstH, func(ys <-chan int) {
for dstY := range ys {
i := dstY * dst.Stride
srcY := dstH - dstY - 1
src.scan(0, srcY, src.w, srcY+1, dst.Pix[i:i+rowSize])
reverse(dst.Pix[i : i+rowSize])
}
})
return dst
}
// Rotate270 rotates the image 270 degrees counter-clockwise and returns the transformed image.
func Rotate270(img image.Image) *image.NRGBA {
src := newScanner(img)
dstW := src.h
dstH := src.w
rowSize := dstW * 4
dst := image.NewNRGBA(image.Rect(0, 0, dstW, dstH))
parallel(0, dstH, func(ys <-chan int) {
for dstY := range ys {
i := dstY * dst.Stride
srcX := dstY
src.scan(srcX, 0, srcX+1, src.h, dst.Pix[i:i+rowSize])
reverse(dst.Pix[i : i+rowSize])
}
})
return dst
}
// Rotate rotates an image by the given angle counter-clockwise .
// The angle parameter is the rotation angle in degrees.
// The bgColor parameter specifies the color of the uncovered zone after the rotation.
func Rotate(img image.Image, angle float64, bgColor color.Color) *image.NRGBA {
angle = angle - math.Floor(angle/360)*360
switch angle {
case 0:
return Clone(img)
case 90:
return Rotate90(img)
case 180:
return Rotate180(img)
case 270:
return Rotate270(img)
}
src := toNRGBA(img)
srcW := src.Bounds().Max.X
srcH := src.Bounds().Max.Y
dstW, dstH := rotatedSize(srcW, srcH, angle)
dst := image.NewNRGBA(image.Rect(0, 0, dstW, dstH))
if dstW <= 0 || dstH <= 0 {
return dst
}
srcXOff := float64(srcW)/2 - 0.5
srcYOff := float64(srcH)/2 - 0.5
dstXOff := float64(dstW)/2 - 0.5
dstYOff := float64(dstH)/2 - 0.5
bgColorNRGBA := color.NRGBAModel.Convert(bgColor).(color.NRGBA)
sin, cos := math.Sincos(math.Pi * angle / 180)
parallel(0, dstH, func(ys <-chan int) {
for dstY := range ys {
for dstX := 0; dstX < dstW; dstX++ {
xf, yf := rotatePoint(float64(dstX)-dstXOff, float64(dstY)-dstYOff, sin, cos)
xf, yf = xf+srcXOff, yf+srcYOff
interpolatePoint(dst, dstX, dstY, src, xf, yf, bgColorNRGBA)
}
}
})
return dst
}
func rotatePoint(x, y, sin, cos float64) (float64, float64) {
return x*cos - y*sin, x*sin + y*cos
}
func rotatedSize(w, h int, angle float64) (int, int) {
if w <= 0 || h <= 0 {
return 0, 0
}
sin, cos := math.Sincos(math.Pi * angle / 180)
x1, y1 := rotatePoint(float64(w-1), 0, sin, cos)
x2, y2 := rotatePoint(float64(w-1), float64(h-1), sin, cos)
x3, y3 := rotatePoint(0, float64(h-1), sin, cos)
minx := math.Min(x1, math.Min(x2, math.Min(x3, 0)))
maxx := math.Max(x1, math.Max(x2, math.Max(x3, 0)))
miny := math.Min(y1, math.Min(y2, math.Min(y3, 0)))
maxy := math.Max(y1, math.Max(y2, math.Max(y3, 0)))
neww := maxx - minx + 1
if neww-math.Floor(neww) > 0.1 {
neww++
}
newh := maxy - miny + 1
if newh-math.Floor(newh) > 0.1 {
newh++
}
return int(neww), int(newh)
}
func interpolatePoint(dst *image.NRGBA, dstX, dstY int, src *image.NRGBA, xf, yf float64, bgColor color.NRGBA) {
j := dstY*dst.Stride + dstX*4
d := dst.Pix[j : j+4 : j+4]
x0 := int(math.Floor(xf))
y0 := int(math.Floor(yf))
bounds := src.Bounds()
if !image.Pt(x0, y0).In(image.Rect(bounds.Min.X-1, bounds.Min.Y-1, bounds.Max.X, bounds.Max.Y)) {
d[0] = bgColor.R
d[1] = bgColor.G
d[2] = bgColor.B
d[3] = bgColor.A
return
}
xq := xf - float64(x0)
yq := yf - float64(y0)
points := [4]image.Point{
{x0, y0},
{x0 + 1, y0},
{x0, y0 + 1},
{x0 + 1, y0 + 1},
}
weights := [4]float64{
(1 - xq) * (1 - yq),
xq * (1 - yq),
(1 - xq) * yq,
xq * yq,
}
var r, g, b, a float64
for i := 0; i < 4; i++ {
p := points[i]
w := weights[i]
if p.In(bounds) {
i := p.Y*src.Stride + p.X*4
s := src.Pix[i : i+4 : i+4]
wa := float64(s[3]) * w
r += float64(s[0]) * wa
g += float64(s[1]) * wa
b += float64(s[2]) * wa
a += wa
} else {
wa := float64(bgColor.A) * w
r += float64(bgColor.R) * wa
g += float64(bgColor.G) * wa
b += float64(bgColor.B) * wa
a += wa
}
}
if a != 0 {
aInv := 1 / a
d[0] = clamp(r * aInv)
d[1] = clamp(g * aInv)
d[2] = clamp(b * aInv)
d[3] = clamp(a)
}
}

167
vendor/github.com/disintegration/imaging/utils.go generated vendored Normal file
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package imaging
import (
"image"
"math"
"runtime"
"sync"
)
// parallel processes the data in separate goroutines.
func parallel(start, stop int, fn func(<-chan int)) {
count := stop - start
if count < 1 {
return
}
procs := runtime.GOMAXPROCS(0)
if procs > count {
procs = count
}
c := make(chan int, count)
for i := start; i < stop; i++ {
c <- i
}
close(c)
var wg sync.WaitGroup
for i := 0; i < procs; i++ {
wg.Add(1)
go func() {
defer wg.Done()
fn(c)
}()
}
wg.Wait()
}
// absint returns the absolute value of i.
func absint(i int) int {
if i < 0 {
return -i
}
return i
}
// clamp rounds and clamps float64 value to fit into uint8.
func clamp(x float64) uint8 {
v := int64(x + 0.5)
if v > 255 {
return 255
}
if v > 0 {
return uint8(v)
}
return 0
}
func reverse(pix []uint8) {
if len(pix) <= 4 {
return
}
i := 0
j := len(pix) - 4
for i < j {
pi := pix[i : i+4 : i+4]
pj := pix[j : j+4 : j+4]
pi[0], pj[0] = pj[0], pi[0]
pi[1], pj[1] = pj[1], pi[1]
pi[2], pj[2] = pj[2], pi[2]
pi[3], pj[3] = pj[3], pi[3]
i += 4
j -= 4
}
}
func toNRGBA(img image.Image) *image.NRGBA {
if img, ok := img.(*image.NRGBA); ok {
return &image.NRGBA{
Pix: img.Pix,
Stride: img.Stride,
Rect: img.Rect.Sub(img.Rect.Min),
}
}
return Clone(img)
}
// rgbToHSL converts a color from RGB to HSL.
func rgbToHSL(r, g, b uint8) (float64, float64, float64) {
rr := float64(r) / 255
gg := float64(g) / 255
bb := float64(b) / 255
max := math.Max(rr, math.Max(gg, bb))
min := math.Min(rr, math.Min(gg, bb))
l := (max + min) / 2
if max == min {
return 0, 0, l
}
var h, s float64
d := max - min
if l > 0.5 {
s = d / (2 - max - min)
} else {
s = d / (max + min)
}
switch max {
case rr:
h = (gg - bb) / d
if g < b {
h += 6
}
case gg:
h = (bb-rr)/d + 2
case bb:
h = (rr-gg)/d + 4
}
h /= 6
return h, s, l
}
// hslToRGB converts a color from HSL to RGB.
func hslToRGB(h, s, l float64) (uint8, uint8, uint8) {
var r, g, b float64
if s == 0 {
v := clamp(l * 255)
return v, v, v
}
var q float64
if l < 0.5 {
q = l * (1 + s)
} else {
q = l + s - l*s
}
p := 2*l - q
r = hueToRGB(p, q, h+1/3.0)
g = hueToRGB(p, q, h)
b = hueToRGB(p, q, h-1/3.0)
return clamp(r * 255), clamp(g * 255), clamp(b * 255)
}
func hueToRGB(p, q, t float64) float64 {
if t < 0 {
t++
}
if t > 1 {
t--
}
if t < 1/6.0 {
return p + (q-p)*6*t
}
if t < 1/2.0 {
return q
}
if t < 2/3.0 {
return p + (q-p)*(2/3.0-t)*6
}
return p
}