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diff --git a/vendor/github.com/disintegration/imaging/README.md b/vendor/github.com/disintegration/imaging/README.md new file mode 100644 index 0000000..c7ee30f --- /dev/null +++ b/vendor/github.com/disintegration/imaging/README.md @@ -0,0 +1,188 @@ +# Imaging
+
+[](https://godoc.org/github.com/disintegration/imaging)
+[](https://travis-ci.org/disintegration/imaging)
+[](https://coveralls.io/github/disintegration/imaging?branch=master)
+[](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, not premultiplied by 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:
+- `NearestNeighbor` - Fastest resampling filter, no antialiasing.
+- `Box` - Simple and fast averaging filter appropriate for downscaling. When upscaling it's similar to NearestNeighbor.
+- `Linear` - Bilinear filter, smooth and reasonably fast.
+- `MitchellNetravali` - А smooth bicubic filter.
+- `CatmullRom` - A sharp bicubic filter.
+- `Gaussian` - Blurring filter that uses gaussian function, useful for noise removal.
+- `Lanczos` - High-quality resampling filter for photographic images yielding sharp results, slower than cubic filters.
+
+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:
+
+
+
+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` | 
+`imaging.Linear` | 
+`imaging.CatmullRom` | 
+`imaging.Lanczos` | 
+
+
+### 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
+-----------------------------------|----------------------------------------|---------------------------------------
+ |  | 
+
+### 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
+-----------------------------------|-------------------------------------------|------------------------------------------
+ |  | 
+
+### Gamma correction
+
+```go
+dstImage := imaging.AdjustGamma(srcImage, 0.75)
+```
+
+Original image | Gamma = 0.75 | Gamma = 1.25
+-----------------------------------|------------------------------------------|-----------------------------------------
+ |  | 
+
+### Contrast adjustment
+
+```go
+dstImage := imaging.AdjustContrast(srcImage, 20)
+```
+
+Original image | Contrast = 15 | Contrast = -15
+-----------------------------------|--------------------------------------------|-------------------------------------------
+ |  | 
+
+### Brightness adjustment
+
+```go
+dstImage := imaging.AdjustBrightness(srcImage, 20)
+```
+
+Original image | Brightness = 10 | Brightness = -10
+-----------------------------------|----------------------------------------------|---------------------------------------------
+ |  | 
+
+## 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:
+
+
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