Small tweaks to docs

Author: timholy

docs/src/framework.md

@@ -1,14 +1,15 @@
 # High-level overview of the framework
 
-The `Feature` and `Keypoint` types are the fundamental types in ImageFeatures.jl. `Feature` stores the `keypoint` and its `orientation` and `scale`. A vector of the `Feature` type is denoted by the `Features` type and similary a vector of `Keypoint` type is denoted by the `Keypoints` type. We provide multiple methods for easily transitioning between the two types. 
+The `Feature` and `Keypoint` types are the main fundamental types in ImageFeatures.jl.
+`Feature` stores the `keypoint` and its `orientation` and `scale`. A vector of the `Feature` type is denoted by the `Features` type and similary a vector of `Keypoint` type is denoted by the `Keypoints` type. We provide multiple methods for easily transitioning between the two types.
 
-`Keypoints` or `Features` can be generate from an image of boolean values by `Keypoints(boolean_image)` or `Features(boolean_image)` where the boolean_image may be obtained from a feature detection algorithm for eg. the result of a corner detector. All feature detectors in ImageFeatures.jl directly return `Features`.
+`Keypoints` or `Features` can be generated from an image of boolean values by `Keypoints(boolean_image)` or `Features(boolean_image)` where the boolean_image may be obtained from a feature detection algorithm for, e.g., the result of a corner detector. All feature detectors in ImageFeatures.jl directly return `Features`.
 
 A keypoint may be converted to a feature or vice versa by directly passing it to the respective method eg. `Keypoint(feature_A)` or `Feature(keypoint_A)`.
 
 ```julia
-descriptor, ret_keypoints = create_descriptor(img, dparams)
-descriptor, ret_keypoints = create_descriptor(img, keypoints, dparams)
+descriptor, ret_keypoints = create_descriptor(img, params)
+descriptor, ret_keypoints = create_descriptor(img, keypoints, params)
 ```
 
-Depending on the algorithm , the `create_descriptor` API can be used to directly create a feature descriptor from the image (eg. ORB, BRISK) or from the keypoints (eg. BRIEF, FREAK). In case of the latter, the keypoints can be obtained using algorithms such as FAST. The `dparams` argument is dependent on the algorithm chosen and its type is the name of the algorithm.
+Depending on the algorithm , the `create_descriptor` API can be used to directly create a feature descriptor from the image (eg. ORB, BRISK) or from the keypoints (eg. BRIEF, FREAK). In case of the latter, the keypoints can be obtained using algorithms such as FAST. The `params` argument is dependent on the algorithm chosen and its type is the name of the algorithm.

docs/src/function_reference.md

@@ -15,10 +15,13 @@ FREAK
 BRISK
 ```
 
-## Corners
+## Corners and edges
 
 ```@docs
 corner_orientations
+fastcorners
+canny
+phase
 ```
 
 ## BRIEF Sampling Patterns
@@ -87,6 +90,7 @@ multi_block_lbp
 # Misc
 
 ```@docs
+HOG
 hough_transform_standard
 hough_circle_gradient
 ```

docs/src/tutorials/brief.md

@@ -31,7 +31,7 @@ img2 = warp(img1, trans, axes(img1));
 nothing # hide
 ```
 
-To calculate the descriptors, we first need to get the keypoints. For this tutorial, we will use the FAST corners to generate keypoints (see [`fastcorners`](@ref).
+To calculate the descriptors, we first need to get the keypoints. For this tutorial, we will use the FAST corners to generate keypoints (see [`fastcorners`](@ref)).
 
 ```@example 1
 keypoints_1 = Keypoints(fastcorners(img1, 12, 0.4))

docs/src/tutorials/brisk.md

@@ -29,7 +29,7 @@ img2 = warp(img1, tform, axes(img1))
 nothing # hide
 ```
 
-To calculate the descriptors, we first need to get the keypoints. For this tutorial, we will use the FAST corners to generate keypoints (see [`fastcorners`](@ref).
+To calculate the descriptors, we first need to get the keypoints. For this tutorial, we will use the FAST corners to generate keypoints (see [`fastcorners`](@ref)).
 
 ```@example 4
 features_1 = Features(fastcorners(img1, 12, 0.35))

docs/src/tutorials/freak.md

@@ -25,7 +25,7 @@ img2 = warp(img1, tform, axes(img1))
 nothing # hide
 ```
 
-To calculate the descriptors, we first need to get the keypoints. For this tutorial, we will use the FAST corners to generate keypoints (see [`fastcorners`](@ref).
+To calculate the descriptors, we first need to get the keypoints. For this tutorial, we will use the FAST corners to generate keypoints (see [`fastcorners`](@ref)).
 
 ```@example 3
 keypoints_1 = Keypoints(fastcorners(img1, 12, 0.35))

docs/src/tutorials/object_detection.md

@@ -13,15 +13,15 @@ Download the script to get the training data [here](https://drive.google.com/fil
 ```julia
 using Images, ImageFeatures
 
-path_to_tutorial = ""
-pos_examples = "path_to_tutorial/tutorial/humans/"
-neg_examples = "path_to_tutorial/tutorial/not_humans/"
+path_to_tutorial = ""    # specify this path
+pos_examples = "$path_to_tutorial/tutorial/humans/"
+neg_examples = "$path_to_tutorial/tutorial/not_humans/"
 
 n_pos = length(readdir(pos_examples))   # number of positive training examples
 n_neg = length(readdir(neg_examples))   # number of negative training examples
 n = n_pos + n_neg                       # number of training examples 
-data = Array{Float64}(3780, n)          # Array to store HOG descriptor of each image. Each image in our training data has size 128x64 and so has a 3780 length 
-labels = Vector{Int}(n)                 # Vector to store label (1=human, 0=not human) of each image.
+data = Array{Float64}(undef, 3780, n)   # Array to store HOG descriptor of each image. Each image in our training data has size 128x64 and so has a 3780 length 
+labels = Vector{Int}(undef, n)          # Vector to store label (1=human, 0=not human) of each image.
 
 for (i, file) in enumerate([readdir(pos_examples); readdir(neg_examples)])
     filename = "$(i <= n_pos ? pos_examples : neg_examples )/$file"