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build(deps): bump github.com/google/go-tika from 0.3.0 to 0.3.1

Browse Source 
Bumps [github.com/google/go-tika](https://github.com/google/go-tika) from 0.3.0 to 0.3.1.
- [Release notes](https://github.com/google/go-tika/releases)
- [Commits](https://github.com/google/go-tika/compare/v0.3.0...v0.3.1)

---
updated-dependencies:
- dependency-name: github.com/google/go-tika
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
This commit is contained in:
dependabot[bot]
2024-04-26 13:54:11 +00:00
committed by GitHub
parent 46549dd98a
commit 6881b63b12
4 changed files with 236 additions and 235 deletions
Download Patch File Download Diff File Expand all files Collapse all files

2
go.mod
View File

@@ -46,7 +46,7 @@ require (
github.com/golang-jwt/jwt/v4 v4.5.0
github.com/golang/protobuf v1.5.4
github.com/google/go-cmp v0.6.0
github.com/google/go-tika v0.3.0
github.com/google/go-tika v0.3.1
github.com/google/uuid v1.6.0
github.com/gookit/config/v2 v2.2.5
github.com/gorilla/mux v1.8.1

5
go.sum
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@@ -1367,8 +1367,8 @@ github.com/google/go-pkcs11 v0.2.0/go.mod h1:6eQoGcuNJpa7jnd5pMGdkSaQpNDYvPlXWMc
github.com/google/go-querystring v1.0.0/go.mod h1:odCYkC5MyYFN7vkCjXpyrEuKhc/BUO6wN/zVPAxq5ck=
github.com/google/go-querystring v1.1.0 h1:AnCroh3fv4ZBgVIf1Iwtovgjaw/GiKJo8M8yD/fhyJ8=
github.com/google/go-querystring v1.1.0/go.mod h1:Kcdr2DB4koayq7X8pmAG4sNG59So17icRSOU623lUBU=
github.com/google/go-tika v0.3.0 h1:JncwikDcIJrSwwoTjWg6NE7g3IW7XTrI6ZeeOGp03Y8=
github.com/google/go-tika v0.3.0/go.mod h1:k/Afo/0kDgo9g/9gjIIUBoDXgyGgYO3JAISqowoJdVE=
github.com/google/go-tika v0.3.1 h1:l+jr10hDhZjcgxFRfcQChRLo1bPXQeLFluMyvDhXTTA=
github.com/google/go-tika v0.3.1/go.mod h1:DJh5N8qxXIl85QkqmXknd+PeeRkUOTbvwyYf7ieDz6c=
github.com/google/gofuzz v1.0.0/go.mod h1:dBl0BpW6vV/+mYPU4Po3pmUjxk6FQPldtuIdl/M65Eg=
github.com/google/martian v2.1.0+incompatible/go.mod h1:9I4somxYTbIHy5NJKHRl3wXiIaQGbYVAs8BPL6v8lEs=
github.com/google/martian/v3 v3.0.0/go.mod h1:y5Zk1BBys9G+gd6Jrk0W3cC1+ELVxBWuIGO+w/tUAp0=
@@ -2322,6 +2322,7 @@ golang.org/x/net v0.12.0/go.mod h1:zEVYFnQC7m/vmpQFELhcD1EWkZlX69l4oqgmer6hfKA=
golang.org/x/net v0.14.0/go.mod h1:PpSgVXXLK0OxS0F31C1/tv6XNguvCrnXIDrFMspZIUI=
golang.org/x/net v0.21.0/go.mod h1:bIjVDfnllIU7BJ2DNgfnXvpSvtn8VRwhlsaeUTyUS44=
golang.org/x/net v0.22.0/go.mod h1:JKghWKKOSdJwpW2GEx0Ja7fmaKnMsbu+MWVZTokSYmg=
golang.org/x/net v0.23.0/go.mod h1:JKghWKKOSdJwpW2GEx0Ja7fmaKnMsbu+MWVZTokSYmg=
golang.org/x/net v0.24.0 h1:1PcaxkF854Fu3+lvBIx5SYn9wRlBzzcnHZSiaFFAb0w=
golang.org/x/net v0.24.0/go.mod h1:2Q7sJY5mzlzWjKtYUEXSlBWCdyaioyXzRB2RtU8KVE8=
golang.org/x/oauth2 v0.0.0-20180821212333-d2e6202438be/go.mod h1:N/0e6XlmueqKjAGxoOufVs8QHGRruUQn6yWY3a++T0U=

462
vendor/github.com/kovidgoyal/imaging/README.md generated vendored
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@@ -1,231 +1,231 @@
# 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/kovidgoyal/imaging
## Documentation
https://pkg.go.dev/github.com/kovidgoyal/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)
### Hue adjustment
```go
dstImage := imaging.AdjustHue(srcImage, 20)
```
Original image | Hue = 60 | Hue = -60
-----------------------------------|----------------------------------------------|---------------------------------------------
![srcImage](testdata/flowers_small.png) | ![dstImage](testdata/out_hue_p60.png) | ![dstImage](testdata/out_hue_m60.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 standard `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/kovidgoyal/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/kovidgoyal/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)
# 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/kovidgoyal/imaging
## Documentation
https://pkg.go.dev/github.com/kovidgoyal/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)
### Hue adjustment
```go
dstImage := imaging.AdjustHue(srcImage, 20)
```
Original image | Hue = 60 | Hue = -60
-----------------------------------|----------------------------------------------|---------------------------------------------
![srcImage](testdata/flowers_small.png) | ![dstImage](testdata/out_hue_p60.png) | ![dstImage](testdata/out_hue_m60.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 standard `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/kovidgoyal/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/kovidgoyal/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)

2
vendor/modules.txt vendored
View File

@@ -1104,7 +1104,7 @@ github.com/google/go-cmp/cmp/internal/value
# github.com/google/go-querystring v1.1.0
## explicit; go 1.10
github.com/google/go-querystring/query
# github.com/google/go-tika v0.3.0
# github.com/google/go-tika v0.3.1
## explicit; go 1.11
github.com/google/go-tika/tika
# github.com/google/pprof v0.0.0-20210720184732-4bb14d4b1be1