diff --git a/docs/examples/color_channels/rgb_grayscale.jl b/docs/examples/color_channels/rgb_grayscale.jl index 2b62eff3..d0728005 100644 --- a/docs/examples/color_channels/rgb_grayscale.jl +++ b/docs/examples/color_channels/rgb_grayscale.jl @@ -17,7 +17,7 @@ rgb_image = testimage("lighthouse") gray_image = Gray.(rgb_image) mosaicview(rgb_image, gray_image; nrow = 1) -# Gray scale conversion form RGB follows a weighted sum of all channels, the coffecients are computed according to +# Gray scale conversion form RGB follows a weighted sum of all channels, the coefficients are computed according to # [Rec. ITU-R BT.601-7](https://www.itu.int/dms_pubrec/itu-r/rec/bt/R-REC-BT.601-7-201103-I!!PDF-E.pdf) rounding off to 3 decimal places # `0.299 * R + 0.587 * G + 0.114 * B` diff --git a/docs/examples/contours/contour_detection.jl b/docs/examples/contours/contour_detection.jl index eb934ca4..c3966376 100644 --- a/docs/examples/contours/contour_detection.jl +++ b/docs/examples/contours/contour_detection.jl @@ -17,12 +17,12 @@ using Images, TestImages, FileIO ## N NE E SE S SW W NW ## direction between two pixels -## rotate direction clocwise +## rotate direction clockwise function clockwise(dir) return (dir)%8 + 1 end -## rotate direction counterclocwise +## rotate direction counterclockwise function counterclockwise(dir) return (dir+6)%8 + 1 end diff --git a/docs/examples/contours/detecting_corners.jl b/docs/examples/contours/detecting_corners.jl index 70dce237..5b0f6b53 100644 --- a/docs/examples/contours/detecting_corners.jl +++ b/docs/examples/contours/detecting_corners.jl @@ -58,7 +58,7 @@ mosaicview(img_copies; nrow=1) # change abruptly at corner points, giving rise to large gradients. However, this makes them # computationally expensive. -# The *FAST (Features from Accelarated Segment Test) Corner Detector* is a feature +# The *FAST (Features from Accelerated Segment Test) Corner Detector* is a feature # detection algorithm which is designed to be computationally cheaper, and hence much faster. # It classifies a pixel $P$ as a corner if at least $n$ contiguous points out of the 16 # points in a circle around it have intensities either higher (or lower) than that of $P$ diff --git a/docs/examples/image_quality_and_benchmarks/structural_similarity_index.jl b/docs/examples/image_quality_and_benchmarks/structural_similarity_index.jl index 427cd1f7..28e79c2c 100644 --- a/docs/examples/image_quality_and_benchmarks/structural_similarity_index.jl +++ b/docs/examples/image_quality_and_benchmarks/structural_similarity_index.jl @@ -45,7 +45,7 @@ img_noise = img_orig + noise mosaicview(img_const, img_noise; nrow=1) save("assets/ssim.png", img_noise) #src -# We use the `mse` funtion defined in `ImageDistances` to calculate the mean squared +# We use the `mse` function defined in `ImageDistances` to calculate the mean squared # error between the original and the two modified images. mse(img_orig, img_const), mse(img_orig, img_noise) diff --git a/docs/examples/image_segmentation/watershed.jl b/docs/examples/image_segmentation/watershed.jl index 16633741..3a8a6a80 100644 --- a/docs/examples/image_segmentation/watershed.jl +++ b/docs/examples/image_segmentation/watershed.jl @@ -36,12 +36,12 @@ bw_transform_example = feature_transform(bw_example) # For example, the closest `true` to `bw_example[1,1]` exists at `CartesianIndex(2, 2)`, # hence it's assigned `CartesianIndex(2, 2)`. For other positions in `bw_example` it is -# processed similarily. +# processed similarly. dist = 1 .- distance_transform(bw_transform) dist_example = 1 .- distance_transform(bw_transform_example) -# | Dist(distance tranform for img) | Dist(distance transform for img_example) | +# | Dist(distance transform for img) | Dist(distance transform for img_example) | # | :---:| :-----------:| # |![](assets/contour1.png) | ![](assets/dist_example.png) | @@ -82,7 +82,7 @@ colored_labels = IndirectArray(labels, distinguishable_colors(maximum(labels))) masked_colored_labels = colored_labels .* (1 .- bw) mosaic(img, colored_labels, masked_colored_labels; nrow=1) -# Here we use `IndirectArray` to store the indexed image, for more explaination on it please +# Here we use `IndirectArray` to store the indexed image, for more explanation on it please # check the tutorial [Indexed image in 5 minutes](@ref demo_indexed_image). save("assets/watershed.gif", cat(img, colored_labels, masked_colored_labels; dims=3); fps=1) #src diff --git a/docs/examples/spatial_transformation/image_diffview.jl b/docs/examples/spatial_transformation/image_diffview.jl index 86be5551..66865ce5 100644 --- a/docs/examples/spatial_transformation/image_diffview.jl +++ b/docs/examples/spatial_transformation/image_diffview.jl @@ -5,7 +5,7 @@ # date: 2020-03-07 # --- -# This demonstration shows some common tricks in image comparision -- difference view +# This demonstration shows some common tricks in image comparison -- difference view # People with MATLAB experiences would miss the function # [`imshowpair`](https://www.mathworks.com/help/images/ref/imshowpair.html), but in JuliaImages diff --git a/docs/src/install.md b/docs/src/install.md index 62981816..b838dc61 100644 --- a/docs/src/install.md +++ b/docs/src/install.md @@ -40,7 +40,7 @@ These backends aren't exclusive to each other, so if you're a macOS user, you ca backends. And in most cases, you don't need to directly interact with these backends, instead, we use the `save` and `load` provided by the [`FileIO.jl`](https://github.com/JuliaIO/FileIO.jl) frontend. If you've installed multiple backends then `FileIO` will choose the most appropriate -backend acoording to your file format. For example, if available `ImageIO` is used to load PNG +backend according to your file format. For example, if available `ImageIO` is used to load PNG files. Adding these gives you a basic image IO setup: diff --git a/docs/src/pkgs/index.md b/docs/src/pkgs/index.md index b525384f..a07ec9d2 100644 --- a/docs/src/pkgs/index.md +++ b/docs/src/pkgs/index.md @@ -57,7 +57,7 @@ you are interested in most. !!! tip For package developers, `Images.jl` is usually a large dependency to be included in the `deps` section of - your `Project.toml`. Hence it is reccomended to add only `ImageCore` together with the exact sub-packages + your `Project.toml`. Hence it is recommended to add only `ImageCore` together with the exact sub-packages you need. You can use `@which` to find out the exact package and file a method/function belongs to. JuliaImages is not a closed ecosystem; it works nicely with many other packages outside of JuliaImages.