fix bugs, format arguments

Author: mronian

docs/src/function_reference.md

@@ -61,7 +61,6 @@ ASM
 IDM
 glcm_entropy
 energy
-contrast
 dissimilarity
 correlation
 glcm_mean_ref

docs/src/tutorials/freak.md

@@ -13,7 +13,7 @@ Let us take a look at a simple example where the FREAK descriptor is used to mat
 
 First, lets define warping functions to transform and rotate the image.
 
-```@example 1
+```@example 3
 function _warp(img, transx, transy)
     res = zeros(eltype(img), size(img))
     for i in 1:size(img, 1) - transx
@@ -46,56 +46,56 @@ nothing # hide
 
 Now, let us create the two images we will match using FREAK.
 
-```@example 1
+```@example 3
 
 using ImageFeatures, TestImages, Images, ImageDraw
 
 img = testimage("lighthouse")
-img_array_1 = convert(Array{Gray}, img)
+img_array_1 = convert(Array{Images.Gray}, img)
 img_temp_2 = _warp(img_array_1, 5 * pi / 6)
 img_array_2 = _warp(img_temp_2, 50, 40)
 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).
 
-```@example 1
+```@example 3
 keypoints_1 = Keypoints(fastcorners(img_array_1, 12, 0.35))
 keypoints_2 = Keypoints(fastcorners(img_array_2, 12, 0.35))
 nothing # hide
 ```
 
 To create the BRIEF descriptor, we first need to define the parameters by calling the [`BRIEF`](@ref) constructor.
 
-```@example 1
+```@example 3
 freak_params = FREAK()
 nothing # hide
 ```
 
 Now pass the image with the keypoints and the parameters to the [`create_descriptor`](@ref) function.
 
-```@example 1
+```@example 3
 desc_1, ret_keypoints_1 = create_descriptor(img_array_1, keypoints_1, freak_params)
 desc_2, ret_keypoints_2 = create_descriptor(img_array_2, keypoints_2, freak_params)
 nothing # hide
 ```
 
 The obtained descriptors can be used to find the matches between the two images using the [`match_keypoints`](@ref) function.
 
-```@example 1
+```@example 3
 matches = match_keypoints(ret_keypoints_1, ret_keypoints_2, desc_1, desc_2, 0.1)
 nothing # hide
 ```
 
 We can use the [ImageDraw.jl](https://github.com/JuliaImages/ImageDraw.jl) package to view the results.
 
-```@example 1
+```@example 3
 
 grid = hcat(img_array_1, img_array_2)
 offset = CartesianIndex(0, 768)
 map(m_i -> line!(grid, m_i[1], m_i[2] + offset), matches)
-save("brief_example.jpg", grid); nothing # hide
+save("freak_example.jpg", grid); nothing # hide
 
 ```
 
-![](brief_example.jpg)
+![](freak_example.jpg)

docs/src/tutorials/orb.md

@@ -1,6 +1,7 @@
 The *ORB* descriptor is a somewhat similar to [BRIEF](brief). It doesn’t have an elaborate sampling pattern as [BRISK](brisk) or [FREAK](freak). 
 
 However, there are two main differences between ORB and BRIEF:
+
 - ORB uses an orientation compensation mechanism, making it rotation invariant.
 - ORB learns the optimal sampling pairs, whereas BRIEF uses randomly chosen sampling pairs.
 
@@ -18,7 +19,7 @@ Let us take a look at a simple example where the ORB descriptor is used to match
 
 First, lets define warping functions to transform and rotate the image.
 
-```@example 1
+```@example 2
 function _warp(img, transx, transy)
     res = zeros(eltype(img), size(img))
     for i in 1:size(img, 1) - transx
@@ -51,11 +52,12 @@ nothing # hide
 
 Now, let us create the two images we will match using ORB. 
 
-```@example 1
+```@example 2
 
 using ImageFeatures, TestImages, Images, ImageDraw
 
 img = testimage("lighthouse")
+img_array_1 = convert(Array{Images.Gray}, img)
 img_temp_2 = _warp(img_array_1, 5 * pi / 6)
 img_array_2 = _warp(img_temp_2, 50, 40)
         
@@ -64,29 +66,29 @@ nothing # hide
 
 The ORB descriptor calculates the keypoints as well as the descriptor, unlike [BRIEF](brief). To create the ORB descriptor, we first need to define the parameters by calling the [`ORB`](@ref) constructor.
 
-```@example 1
+```@example 2
 orb_params = ORB(num_keypoints = 1000)
 nothing # hide
 ```
 
 Now pass the image with the parameters to the [`create_descriptor`](@ref) function.
 
-```@example 1
+```@example 2
 desc_1, ret_keypoints_1 = create_descriptor(img_array_1, orb_params)
 desc_2, ret_keypoints_2 = create_descriptor(img_array_2, orb_params)
 nothing # hide
 ```
 
 The obtained descriptors can be used to find the matches between the two images using the [`match_keypoints`](@ref) function.
 
-```@example 1
+```@example 2
 matches = match_keypoints(ret_keypoints_1, ret_keypoints_2, desc_1, desc_2, 0.2)
 nothing # hide
 ```
 
 We can use the [ImageDraw.jl](https://github.com/JuliaImages/ImageDraw.jl) package to view the results.
 
-```@example 1
+```@example 2
 
 grid = hcat(img_array_1, img_array_2)
 offset = CartesianIndex(0, 768)

src/ImageFeatures.jl

@@ -40,7 +40,6 @@ export
 	IDM,
 	glcm_entropy,
 	energy,
-	contrast,
 	dissimilarity,
 	correlation,
 	glcm_mean_ref,

src/brief.jl

@@ -5,11 +5,11 @@ brief_params = BRIEF([size = 128], [window = 9], [sigma = 2 ^ 0.5], [sampling_ty
 
 | Argument | Type | Description |
 |----------|------|-------------|
-| `size` | `Int` | Size of the descriptor |
-| `window` | `Int` | Size of sampling window | 
-| `sigma` | `Float64` | Value of sigma used for inital gaussian smoothing of image | 
-| `sampling_type` | `Function` | Type of sampling used for building the descriptor (See [BRIEF Sampling Patterns](BRIEF Sampling Patterns)) |
-| `seed` | `Int` | Random seed used for generating the sampling pairs. For matching two descriptors, the seed used to build both should be same. | 
+| **size** | Int | Size of the descriptor |
+| **window** | Int | Size of sampling window | 
+| **sigma** | Float64 | Value of sigma used for inital gaussian smoothing of image | 
+| **sampling_type** | Function | Type of sampling used for building the descriptor (See [BRIEF Sampling Patterns](#brief-sampling-patterns)) |
+| **seed** | Int | Random seed used for generating the sampling pairs. For matching two descriptors, the seed used to build both should be same. | 
 
 """
 type BRIEF{F} <: DescriptorParams

src/core.jl

@@ -38,7 +38,7 @@ hamming_distance(desc_1, desc_2) = mean(desc_1 $ desc_2)
 matches = match_keypoints(keypoints_1, keypoints_2, desc_1, desc_2, threshold = 0.1)
 ```
 
-Finds matched keypoints using the [hamming_distance](`hamming_distance`) function having distance value less than `threshold`.
+Finds matched keypoints using the [`hamming_distance`](@ref) function having distance value less than `threshold`.
 """
 function match_keypoints(keypoints_1::Keypoints, keypoints_2::Keypoints, desc_1, desc_2, threshold::Float64 = 0.1)
     smaller = desc_1

src/freak.jl

@@ -5,7 +5,7 @@ freak_params = FREAK([pattern_scale = 22.0])
 
 | Argument | Type | Description |
 |----------|------|-------------|
-| `pattern_scale` | `Float64` | Scaling factor for the sampling window | 
+| **pattern_scale** | Float64 | Scaling factor for the sampling window | 
 """
 type FREAK{S, T, O} <: DescriptorParams
     pattern_scale::Float64

src/orb.jl

@@ -5,13 +5,13 @@ orb_params = ORB([num_keypoints = 500], [n_fast = 12], [threshold = 0.25], [harr
 
 | Argument | Type | Description |
 |----------|------|-------------|
-| `num_keypoints` | `Int` | Number of keypoints to extract and size of the descriptor calculated |
-| `n_fast` | `Int` | Number of consecutive pixels used for finding corners with FAST. See [`fastcorners`] | 
-| `threshold` | `Float64` | Threshold used to find corners in FAST. See [`fastcorners`] | 
-| `harris_factor` | `Float64` | Harris factor `k` used to rank keypoints by harris responses and extract the best ones |
-| `downsample` | `Float64` | Downsampling parameter used while building the gaussian pyramid. See [`gaussian_pyramid`] in Images.jl | 
-| `levels` | `Int` | Number of levels in the gaussian pyramid.  See [`gaussian_pyramid`] in Images.jl | 
-| `sigma` | `Float64` | Used for gaussian smoothing in each level of the gaussian pyramid.  See [`gaussian_pyramid`] in Images.jl | 
+| **num_keypoints** | Int | Number of keypoints to extract and size of the descriptor calculated |
+| **n_fast** | Int | Number of consecutive pixels used for finding corners with FAST. See [`fastcorners`] | 
+| **threshold** | Float64 | Threshold used to find corners in FAST. See [`fastcorners`] | 
+| **harris_factor** | Float64 | Harris factor `k` used to rank keypoints by harris responses and extract the best ones |
+| **downsample** | Float64 | Downsampling parameter used while building the gaussian pyramid. See [`gaussian_pyramid`] in Images.jl | 
+| **levels** | Int | Number of levels in the gaussian pyramid.  See [`gaussian_pyramid`] in Images.jl | 
+| **sigma** | Float64 | Used for gaussian smoothing in each level of the gaussian pyramid.  See [`gaussian_pyramid`] in Images.jl | 
 """
 type ORB <: DescriptorParams
     num_keypoints::Int