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Fixing the markdown in the README for better readability #36

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52 changes: 26 additions & 26 deletions README.md
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Original file line number Diff line number Diff line change
@@ -1,9 +1,9 @@
##OpenCV.jl
## OpenCV.jl

The OpenCV (C++) interface for Julia.

<br>
OpenCV.jl aims to provide an interface for [OpenCV](http://opencv.org) computer vision applications (C++) directly in [Julia] (http://julia.readthedocs.org/en/latest/manual/). It relies primarily on [Cxx.jl](https://github.com/Keno/Cxx.jl), the Julia C++ foreign function interface (FFI). OpenCV.jl comes bundled with the [Qt framework](http://qt-project.org/) - though not essential, it supports many convenient GUI functions. The package also contains thin wrappers for common C++ classes (e.g., std::vector, std::string) to make the C++/Julia interface smoother.

OpenCV.jl aims to provide an interface for [OpenCV](http://opencv.org) computer vision applications (C++) directly in [Julia](http://julia.readthedocs.org/en/latest/manual/). It relies primarily on [Cxx.jl](https://github.com/Keno/Cxx.jl), the Julia C++ foreign function interface (FFI). OpenCV.jl comes bundled with the [Qt framework](http://qt-project.org/) - though not essential, it supports many convenient GUI functions. The package also contains thin wrappers for common C++ classes (e.g., std::vector, std::string) to make the C++/Julia interface smoother.

The OpenCV API is described [here](http://docs.opencv.org/2.4/modules/refman.html). OpenCV.jl is organized along the following modules:

Expand All @@ -21,9 +21,9 @@ Currently, OpenCV.jl has julia wrappers for the `core`, `imgproc`, `videoio`, `h

OpenCV.jl has OpenCL support for GPU image processing. This has been made easier recently by a smooth and transparent interface (T-API). GPU-supported code can display improvements in processing speed up to 30 fold. This is invaluable for supporting real-time applications in Julia. See section below on how to implement GPU-enabled code in OpenCV.jl.

The OpenCV API is extensively documented - rather than repeating the entire documentation here, the primary focus is on implementation of image processing and computer vision algorithms to suport Julia applications.
The OpenCV API is extensively documented - rather than repeating the entire documentation here, the primary focus is on implementation of image processing and computer vision algorithms to support Julia applications.

##Installation
## Installation

Install `julia 0.6.0` and `Cxx.jl` according to the following [instructions](https://github.com/Keno/Cxx.jl/blob/master/README.md). For Mac OSX, you can use the pre-compiled shared libraries (.dylib) and headers (.hpp) included in OpenCV.jl. However, you can also compile OpenCV from source with the instructions below.

Expand Down Expand Up @@ -78,16 +78,16 @@ $ ls opencv2

#### Linux (Ubuntu)
- See links [1](http://docs.opencv.org/trunk/doc/tutorials/introduction/linux_install/linux_install.html) and [2](https://github.com/jayrambhia/Install-OpenCV) on how to install OpenCV `3.2.0`.
- Comment out the #include statements for `flann` in `opencv2/opencv.hpp`.
- Comment out the `#include` statements for `flann` in `opencv2/opencv.hpp`.


####Download and run OpenCV.jl
#### Download and run OpenCV.jl
```julia
Pkg.clone("git://github.com/maxruby/OpenCV.jl.git")
using OpenCV
```

##Basic interface
## Basic interface
OpenCV contains hundreds of algorithms and functions. Most frequently used functions for image processing are already accessible in the current version of OpenCV.jl. For simplicity, here I focus on using functions wrapped in OpenCV.jl.

#### <span style="color:green"> Basic structures
Expand Down Expand Up @@ -192,22 +192,22 @@ gemm(m1, m2, alpha, Mat(), beta, m3, flag)
Image pixels in Mat containers are arranged in a row-major order.<br>
For a grayscale image, e.g., pixels are addressed by row, col

|col 0| col 1| col 2|col 3| col m|
|:----- |:--:| :--:| :--:| :--:| :--:|
| row 0 | 0,0| 0,1| 0,2| 0,3| 0,m|
| row 1 | 1,0| 1,1| 1,2| 1,3| 1,m|
| row 2 | 2,0| 2,1| 2,2| 2,3| 2,m|
| row n | n,0| n,1| n,2| n,3| n,m|
Image [m x n] | col 0 | col 1 | col 2 | col 3 | col m
----- | ----- | ----- | ----- | ----- | -----
__row 0__ | 0,0 | 0,1 | 0,2 | 0,3 | 0,m
__row 1__ | 1,0 | 1,1 | 1,2 | 1,3 | 1,m
__row 2__ | 2,0 | 2,1 | 2,2 | 2,3 | 2,m
__row n__ | n,0 | n,1 | n,2 | n,3 | n,m

For RGB color images, each column has 3 values (actually BGR in Mat)

|col 0| col 1| col 2| col m |
|:----- |:--:| :--:| :--:| :--:|
| row 0 |<span style="color:blue">0,0, <span style="color:green">0,0 <span style="color:red">0,0| <span style="color:blue">0,1 <span style="color:green">0,1 <span style="color:red">0,1| <span style="color:blue">0,2 <span style="color:green">0,2 <span style="color:red">0,2| <span style="color:blue">0,m <span style="color:green">0,m <span style="color:red">0,m
| row 1 | <span style="color:blue">1,0 <span style="color:green">1,0 <span style="color:red">1,0| <span style="color:blue">1,1 <span style="color:green">1,1 <span style="color:red">1,1| <span style="color:blue">1,2 <span style="color:green">1,2 <span style="color:red">1,2| <span style="color:blue">1,m <span style="color:green">1,m <span style="color:red">1,m
| row 2 | <span style="color:blue">2,0 <span style="color:green">2,0 <span style="color:red">2,0| <span style="color:blue">2,1 <span style="color:green">2,1 <span style="color:red">2,1| <span style="color:blue">2,2 <span style="color:green">2,2 <span style="color:red">2,2|<span style="color:blue">2,m <span style="color:green">2,m <span style="color:red">2,m
| row n| <span style="color:blue">n,0 <span style="color:green">n,0 <span style="color:red">n,0| <span style="color:blue">n,1 <span style="color:green">n,1 <span style="color:red">n,1| <span style="color:blue">n,2 <span style="color:green">n,2 <span style="color:red">n,2| <span style="color:blue">n,m <span style="color:green">n,m <span style="color:red">n,m

col 0 | col 1 | col 2 | col 3 | col m
----- | ----- | ----- | ----- | -----
__row 0__ | (0,0) (0,0) (0,0) | (0,1) (0,1) (0,1) | (0,2) (0,2) (0,2) | (0,m) (0,m) (0,m)
__row 1__ | (1,0) (1,0) (1,0) | (1,1) (1,1) (1,1) | (1,2) (1,2) (1,2) | (1,m) (1,m) (1,m)
__row 2__ | (2,0) (2,0) (2,0) | (2,1) (2,1) (2,1) | (2,2) (2,2) (2,2) | (2,m) (2,m) (2,m)
__row n__ | (n,0) (n,0) (n,0) | (n,1) (n,1) (n,1) | (n,2) (n,2) (n,2) | (n,m) (n,m) (n,m)
**Getting and setting selected pixel values** <br>
**Method 1**: Access pixel values using `pixget` and `pixset` functions. Here we use the`Mat::at`class method - slow but safe, intended only for checking and setting small numbers of pixels (not for scanning through the entire image). To illustrate we draw random red pixels on a blue image (i.e., turn them yellow).

Expand Down Expand Up @@ -237,7 +237,7 @@ imdisplay(img, "coloring the fast way")
closeWindows(0,27,"")
```

#### <span style="color:green">Opening and saving images
### <span style="color:green">Opening and saving images
**Read and write with full path/name**

```julia
Expand Down Expand Up @@ -267,7 +267,7 @@ ImageView.view(image)
imdisplay(converted, "converted to OpenCV Mat")
closeWindows(0,27,"")
```
#### <span style="color:green">Access image properties
### <span style="color:green">Access image properties
```julia
printMat(img) # crude printout of the entire Mat (uchar only)
total(img) # number of array elements
Expand All @@ -285,7 +285,7 @@ empty(img) # is array is empty? (true/false)
ptr(img, 10) # uchar* or typed pointer for matrix row
```

#### <span style="color:green">Basic image display (GUIs)
### <span style="color:green">Basic image display (GUIs)
```julia
# original highgui functions
namedWindow("Lena", WINDOW_AUTOSIZE)
Expand All @@ -299,7 +299,7 @@ imdisplay(img, "Lena") # optional: window resizing, key press, time
im2tile(imArray, "Tiled images") # => closeWindows
```

#### <span style="color:green">Image processing <span>
### <span style="color:green">Image processing <span>
**Resize images**

```julia
Expand Down Expand Up @@ -429,7 +429,7 @@ im2tile([img, dst], "sharpened")
closeWindows(0,27,"")
```

#### <span style="color:green">Video acquistion, streaming and writing
### <span style="color:green">Video acquistion, streaming and writing
Basic video stream display from default camera. All GUI classes/functions (e.g., videoCapture) can be easily called from OpenCV.jl to build new custom video acquisition functions.

```julia
Expand Down