docs: update filenames for proper link format

Author: EscapedGibbon

blog/releases/1.0.md

@@ -149,8 +149,8 @@ The new `blackRois` and `whiteRois` properties provide more intuitive access to
 
 For more information, please visit these tutorials:
 
-- [Image segmentation with `threshold()` and `fromMask()`](../../docs/Tutorials/Image%20segmentation%20with%20threshold)
-- [Image segmentation with `watershed()`](../../docs/Tutorials/Image%20segmentation%20with%20watershed)
+- [Image segmentation with `threshold()` and `fromMask()`](../../docs/tutorials/image-segmentation-with-threshold)
+- [Image segmentation with `watershed()`](../../docs/tutorials/image-segmentation-with-watershed)
 
 ### Sobel and Scharr filters
 
@@ -266,7 +266,7 @@ The following deprecated features have been removed:
 
 - `countAlphaPixel()` - Use custom pixel counting with [`getPixel()`](https://api.image-js.org/classes/index.Image.html#getpixel 'API link on getPixel').
 - `paintLabels()` and `roi.paint()` - Features have been removed due to dependency issues. We plan to add it back in future updates.
-- `warpingFourPoints()` - Use [`getPerspectiveWarpMatrix()`](../../docs/Features/Geometry/Get%20Perspective%20Warp%20Matrix 'internal link on getPerspectiveWarp') + [`transform()`](#transform) instead.
+- `warpingFourPoints()` - Use [`getPerspectiveWarpMatrix()`](../../docs/features/geometry/get-perspective-warp-matrix 'internal link on getPerspectiveWarp') + [`transform()`](#transform) instead.
 - 32-bit color depth support and `abs()` have been removed.
 - `CMYK` and `HSL` color models have been removed.
 - `paintMasks()` has been removed. Use [`paintMask()`](https://api.image-js.org/classes/index.Image.html#paintmask 'API link on paintMask')+ a `for` loop.
@@ -303,7 +303,7 @@ const matrix = getPerspectiveWarp(sourcePoints);
 const warped = img.transform(matrix);
 ```
 
-For more details, [visit our tutorial](../../docs/Tutorials/Applying%20transform%20function%20on%20images 'internal link on transform function tutorial') on how image transformations work how they can be used.
+For more details, [visit our tutorial](../../docs/tutorials/applying-transform-function-on-images 'internal link on transform function tutorial') on how image transformations work how they can be used.
 
 ### Bicubic interpolation
 
@@ -323,11 +323,11 @@ const resized = img.resize(800, 600, { interpolation: 'bicubic' });
 const prewitt = img.derivative({ filter: 'prewitt' });
 ```
 
-**Use cases**: Object detection, image segmentation, feature extraction. You can [learn more about it here](../../docs/Features/Morphology/Morphological%20Gradient 'internal link on morphological gradient').
+**Use cases**: Object detection, image segmentation, feature extraction. You can [learn more about it here](../../docs/features/morphology/morphological-gradient 'internal link on morphological gradient').
 
 ### Migration from deprecated methods
 
-`warpingFourPoints()` has been removed. Now you have [`getPerspectiveWarp()`](../../docs/Features/Geometry/Get%20Perspective%20Warp%20Matrix 'internal link on perspective warp') that returns a matrix that can be applied on the image of interest in a new `transform()`.
+`warpingFourPoints()` has been removed. Now you have [`getPerspectiveWarp()`](../../docs/features/geometry/get-perspective-warp-matrix 'internal link on perspective warp') that returns a matrix that can be applied on the image of interest in a new `transform()`.
 
 ```ts
 // Before
@@ -338,7 +338,7 @@ const matrix = getPerspectiveWarp(corners);
 const warped = img.transform(matrix);
 ```
 
-**Use cases**: Rectification of a perspective angle of an image. You can learn more about it [here](../../docs/Features/Geometry/Get%20Perspective%20Warp%20Matrix 'internal link on perspective warp').
+**Use cases**: Rectification of a perspective angle of an image. You can learn more about it [here](../../docs/features/geometry/get-perspective-warp-matrix 'internal link on perspective warp').
 
 ### `merge()`
 

docs/Basics/Basics.md

@@ -1,3 +1,5 @@
 import DocCardList from '@theme/DocCardList';
 
+# Basics
+
 <DocCardList />

docs/Basics/Working with Images.md renamed to docs/Basics/working-with-images.md

@@ -47,28 +47,28 @@ In ImageJS main properties of an image are:
 
 - data: typed array with information about image's pixels.
 
-- [Color model](../Glossary.md#color-model 'internal link on color model'): the abstract model of how pixel colors are formed.
+- [Color model](../glossary.md#color-model 'internal link on color model'): the abstract model of how pixel colors are formed.
 
-- [Bit depth](../Glossary.md#bit-depth 'internal link on bit depth'): number of bits allocated to each channel.
+- [Bit depth](../glossary.md#bit-depth 'internal link on bit depth'): number of bits allocated to each channel.
 
-- [Number of channels](../Glossary.md#channel 'internal link on channels'): number of color channels that each pixel has. Grey image has one, RGB-type of image has three.
+- [Number of channels](../glossary.md#channel 'internal link on channels'): number of color channels that each pixel has. Grey image has one, RGB-type of image has three.
 
-- [Number of components](../Glossary.md#component 'internal link on components'): number of color channels that each pixel has but without alpha channel.
+- [Number of components](../glossary.md#component 'internal link on components'): number of color channels that each pixel has but without alpha channel.
 
-- [Alpha channel](../Glossary.md#alpha-channel 'internal link on alpha-channel'): channel that represents the transparency or opacity levels of pixels.
+- [Alpha channel](../glossary.md#alpha-channel 'internal link on alpha-channel'): channel that represents the transparency or opacity levels of pixels.
 
-- [Metadata](../Glossary.md#metadata 'internal link on metadata'): data about data. A basic example would be date and time when an image was taken.
+- [Metadata](../glossary.md#metadata 'internal link on metadata'): data about data. A basic example would be date and time when an image was taken.
 
 ### Features
 
 Currently, there are several ways of processing an image:
 
-- [Filtering](../Features/Filters/Filters.md 'internal link on filters'): filters usually apply some sort of [kernel](../Glossary.md#kernel 'internal link on kernel') to change an image.
+- [Filtering](../features/filters/filters.md 'internal link on filters'): filters usually apply some sort of [kernel](../glossary.md#kernel 'internal link on kernel') to change an image.
 
-- [Comparison](../Features/Comparison/Comparison.md 'internal link on comparison'): these features compare two images for further feature matching between the two.
+- [Comparison](../features/comparison/comparison.md 'internal link on comparison'): these features compare two images for further feature matching between the two.
 
-- [Geometry](../Features/Geometry/Geometry.md 'internal link on geometry'): this part of ImageJS allows rotating and resizing an image.
+- [Geometry](../features/geometry/geometry.md 'internal link on geometry'): this part of ImageJS allows rotating and resizing an image.
 
-- [Morphology](../Features/Morphology/Morphology.md 'internal link on morphology'): enables shape analysis and shape identification.
+- [Morphology](../features/morphology/morphology.md 'internal link on morphology'): enables shape analysis and shape identification.
 
-- [ROI analysis](../Features/Regions%20of%20interest/Regions%20of%20interest.md 'internal link on roi analysis'): these features allow targeting and extracting relevant information from specific regions of interest.
+- [ROI analysis](../features/regions-of-interest/regions-of-interest.md 'internal link on roi analysis'): these features allow targeting and extracting relevant information from specific regions of interest.

docs/Basics/Working with Masks.md renamed to docs/Basics/working-with-masks.md

@@ -23,7 +23,7 @@ const mask = new Mask(500, 500); // Creates a simple mask filled with 0s of size
 
 ### Use `threshold()` method
 
-Another approach is to obtain a mask by using [`threshold` method](../Features/Operations/Threshold.md 'internal link on threshold') on an image.
+Another approach is to obtain a mask by using [`threshold` method](../features/operations/threshold.md 'internal link on threshold') on an image.
 
 ```ts
 const mask = image.threshold(); // returns a mask
@@ -39,7 +39,7 @@ In most cases, thresholding is your go-to method to get a mask from an image.
 
 ### Use `cannyEdgeDetector()` method
 
-There is also a third way to get a mask. It is to use [`cannyEdgeDetector` method](../Features/Morphology/Canny%20Edge%20Detector.md 'internal link on canny edge detector').
+There is also a third way to get a mask. It is to use [`cannyEdgeDetector` method](../features/morphology/canny-edge-detector.md 'internal link on canny edge detector').
 
 ```ts
 const mask = image.cannyEdgeDetector(); // returns a mask

docs/Basics/Working with ROIs.md renamed to docs/Basics/working-with-rois.md

@@ -4,10 +4,10 @@ _A region of interest (ROI) represents an area of contiguous pixels within the d
 
 There are currently two ways ROIs can be generated in ImageJS:
 
-- From [masks](./Working%20with%20Masks.md 'internal link on working with mask') by identifying contiguous black or white pixels within it.
+- From [masks](./working-with-masks.md 'internal link on working with mask') by identifying contiguous black or white pixels within it.
 <!-- TODO: add links to the relevant sections once they exist -->
-- By identifying starting points of interest (for example by finding and filtering local extrema) and running the [watershed algorithm](../Features/Operations/Watershed.md 'internal link on watershed') on them.
-- By identifying starting points of interest (for example by finding and filtering local extrema) and running the [watershed algorithm](../Features/Operations/Watershed.md 'internal link on watershed') on them.
+- By identifying starting points of interest (for example by finding and filtering local extrema) and running the [watershed algorithm](../features/operations/watershed.md 'internal link on watershed') on them.
+- By identifying starting points of interest (for example by finding and filtering local extrema) and running the [watershed algorithm](../features/operations/watershed.md 'internal link on watershed') on them.
 
 ROIs identify and characterize regions within images, which has wide applications in image analysis.