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diff --git a/vendor/github.com/disintegration/imaging/README.md b/vendor/github.com/disintegration/imaging/README.md deleted file mode 100644 index c7ee30f..0000000 --- a/vendor/github.com/disintegration/imaging/README.md +++ /dev/null @@ -1,188 +0,0 @@ -# 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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