README.md

#### __Title: Pynovisao__
### Authors (email):
- Adair da Silva Oliveira Junior
- Alessandro dos Santos Ferreira
- Diego André Sant'Ana (diegoandresantana@gmail.com)
- Diogo Nunes Gonçalves (dnunesgoncalves@gmail.com)
- Everton Castelão Tetila (evertontetila@gmail.com)
- Fabio Prestes Cesar Rezende (fpcrezende@gmail.com)
- Felipe Silveira (eng.fe.silveira@gmail.com)
- Gabriel Kirsten Menezes (gabriel.kirsten@hotmail.com)
- Gilberto Astolfi (gilbertoastolfi@gmail.com)
- Hemerson Pistori (pistori@ucdb.br)
- Joao Vitor de Andrade Porto (jvaporto@gmail.com)
- Nícolas Alessandro de Souza Belete (nicolas.belete@gmail.com)

## Resume:

Computer Vision Tool Collection for Inovisão. This collection of tools allows the user to select an image (or folder) and realize numerous actions such as:
- Generate new Datasets and classes
- Segmentation of images
- Extract features from an image
- Extract frames from videos
- Train Machine Learning algorithms
- Classify using CNNs
- Experiment with data using Keras
- Create XML files from segments previously created.

## Open Software License: 

NPOSL-30 https://opensource.org/licenses/NPOSL-3.0 - Free for non-profit use (E.g.: Education, scientific research, etc.). Contact Inovisão's Prof. Hemerson Pistori (pistori@ucdb.br), should any interest in commercial exploration of this software arise.

## How to cite:

[1] dos Santos Ferreira, A., Freitas, D. M., da Silva, G. G., Pistori, H., & Folhes, M. T. (2017). Weed detection in soybean crops using ConvNets. Computers and Electronics in Agriculture, 143, 314-324.

## How to use:

- In order to download Pynovisao, click the download button in the top right of the screen (Compressed folder), or type the following command in a terminal:
```
 $ git clone http://git.inovisao.ucdb.br/inovisao/pynovisao
```

- From inside of this directory:
```
 [...]/pynovisao
```

- Enter the folder named **[...]/pynovisao/src** or type the following command in the terminal to do so:
```
 $ cd src
```

- Next, type the following command if you desire to run it using Python 2.7:
```
 $ python main.py
```
- Or, should you want to run it using Python 3.6:
```
 $ python3 main.py
```

- A window such as the following will open, and you can start using Pynovisão and it's features:

    ![pynovisao](data/pynovisao.png)
    
## Other options:

- Show All options available

```
 $ python main.py --help
```

- Executes the program, defining the wanted classes and it's respective colours (X11 colour names)

```
 $ python main.py --classes "Soil Soy Grass LargeLeaves" --colors "Orange SpringGreen RebeccaPurple Snow"
```

- A Linux script exists in *[...]/pynovisao/src/util* to help divide images into training, validation and testing datasets. It has not been implemented to the GUI. verificar com diego

```
 $ cd src/util
 $ chmod 755 split_data.sh
 $ ./split_data -h
```


### How to Install mudr de lugar
##Option 1, Linux-only Script
You can easily install Pynovisão utilizing the automated installation script given with it, as seen by the following steps:

- From inside of this directory:
```
 [...]/pynovisao
```

- Execute the following command:
```
$ sudo bash INSTALL.sh
```
**NOTE**: This script has been tested for Ubuntu versions 19.04 and 18.04

##Option 2, without INSTALL.sh
# Linux

Besides it's dependencies, Python 2.7.6 or Python 3.6 is needed. (Latest tested versions for this software)

- Installing the necessary dependencies on Python 3.6:
```
$ sudo apt-get update
$ sudo apt-get install libfreetype6-dev tk tk-dev python3-pip openjdk-8-jre openjdk-8-jdk weka weka-doc python3-tk python3-matplotlib
$ source ~/.bashrc
$ sudo pip3 install numpy
$ sudo pip3 install -r requirements_pip3.txt
$ sudo pip3 install tensorflow 
$ sudo pip3 install keras
```

- Installing the necessary dependencies on Python 2.7:
```
$ sudo apt-get update
$ sudo apt-get install libfreetype6-dev tk tk-dev python-pip openjdk-8-jre openjdk-8-jdk weka weka-doc python-tk python-matplotlib
$ source ~/.bashrc
$ sudo pip install numpy
$ sudo pip install -r requirements_pip3.txt
$ sudo pip install tensorflow 
$ sudo pip install keras
```

# Windows

- Instale o [Anaconda](http://continuum.io/downloads) que contém todas dependências, inclusive o Python. Basta fazer o download do arquivo .exe e executá-lo.
- Opencv 2.7
- python-weka-wrapper ( Classification )
- WiP

# Windows
WiP
 - [OpenCV-Python](https://opencv-python-tutroals.readthedocs.org/en/latest/py_tutorials/py_setup/py_setup_in_windows/py_setup_in_windows.html#install-opencv-python-in-windows).
	1. Baixe o [Opencv](https://opencv-python-tutroals.readthedocs.org/en/latest/py_tutorials/py_setup/py_setup_in_windows/py_setup_in_windows.html#install-opencv-python-in-windows)
	2. Extraia os arquivos no local desejado.
	3. Vá para a pasta opencv/build/python/2.7.
	4. Cipie o arquivo cv2.pyd para C:/Python27/lib/site-packeges.
	5. Abra o terminal e digite python para executar o interpretador.
	6. Digite:
    	
      ```
        >>> import cv2
        >>> print cv2.__version__
      ```
    
# Windows
WiP
Instale .Net 4.0 (se já não estiver instalado)

Instale Windows SDK 7.1

Abra o prompt de comando do Windows SDK (não o prompt de comando convencional!) e instale javabridge e python-weka-wrapper
```
> set MSSdk=1
> set DISTUTILS_USE_SDK=1
> pip install javabridge
> pip install python-weka-wrapper
```

Agora você pode executar python-weka-wrapper usando o prompt de comando convencional também.
# Mais informações
- http://pythonhosted.org/python-weka-wrapper/install.html
- http://pythonhosted.org/python-weka-wrapper/troubleshooting.html

### Como instalar o caffe ( Opcional )

## Ubuntu / Windows
WiP
Para poder utilizar o classificador CNNCaffe, uma ConvNet baseada na topologia AlexNet, é necessário instalar o software Caffe.

A instalação do software Caffe é mais complexa que as instalações descritas anteriormente e pode ser encontrada detalhada no link abaixo:
-  http://caffe.berkeleyvision.org/installation.html

Após realizar a instalação do software Caffe, para realizar a classificação, você precisa realizar o treinamento da sua rede no software, pois não há interface no Pynovisao para o treinamento da ConvNet.

O tutorial para o treinamento pode ser encontrado no link abaixo:
- http://caffe.berkeleyvision.org/gathered/examples/imagenet.html

Por fim será necessário configurar sua CNNCaffe.
- Para os campos ModelDef, ModelWeights e MeanImage, você deverá fornecer os caminhos relativos ao seu treinamento realizado no passo anterior.
- Para o campo LabelsFile você deve fornecer o caminho de um arquivo que descrava nominalmente as classes na ordem 0, 1, ..., n-1, onde n é o número de classes que você treinou. 
- Um arquivo de exemplo pode ser encontrado em examples/labels.txt.

### Implementing a new classifier in Pynovisão

In this section we shall show the steps needed to implement a new classifier into Pynovisão. As an example, we are using **Syntactic**, of type **KTESTABLE** and vocabulary size as an hyperparameter.

Inicially, you need to create a class where all the types of your classifier are in a dictionary (Key, Value). The class must be created inside *[...]/pynovisao/src/classification/*. As an example, look for the *SyntacticAlias* in *[...]/pynovisao/src/classification/syntactic_alias.py*.

The next step is creating the .py file for your classifier in your directory *[...]/pynovisao/src/classification/*, for example, *syntactic.py*.
In this newly-created file you must implement your classifier class extending the class **Classifier**, which is implemented in the file *[...]/pynovisao/src/classification/classifier.py*.
See the example below:

```python
#syntactic.py
#minimal required imports
from collections import OrderedDict
from util.config import Config
from util.utils import TimeUtils
from classifier import Classifier

class Syntactic(Classifier):
    """Class for syntactic classifier"""
```

In the contructor class you must inform default values for the parameters. In the case fo the example below, **classname** is the type of classifier and **options** is the size of the alphabet. Besides, some attributes must be inicialized: **self.classname** and **self.options**. The attribute **self.dataset** (optional) is the path to the training and testing dataset which tells the user in the GUI. Having this attribute in the class is important to get access to the dataset in any of the methods and is initialized in the method **train** discussed later.

```python
def __init__(self, classname="KTESTABLE", options='32'):

        self.classname = Config("ClassName", classname, str)
        self.options = Config("Options", options, str)
        self.dataset = None
        self.reset()
```

The methods **get_name**, **get_config**, **set_config**, **get_summary_config** and **must_train** have default implementations, as seen in example in *[..]/pynovisao/src/classification/classifier.py*.

The **train** method must be implemented in order to train your classifier. The **dataset** parameter is given the path to the training images. Within the method, the value of the attribute self.dataset, declared as optional in the constructor, is altered to the current training directory.

```python
def train(self, dataset, training_data, force = False):              
        
        dataset += '/'
        # Attribute which retains the dataset path.
        self.dataset = dataset 
  	    # The two tests below are default.
        
        if self.data is not None and not force:
            return 
        
        if self.data is not None:
            self.reset()
		
	   # Implement here your training.
```

The **classify** method must be implemented should you want your classifier to be able to predict classes for images. The **dataset** parameter is given the training images, and **test_dir** is given the temporary folder path created by Pynovisão, where the testing images are located. This folder is created within the **dataset** directory and, to acesss it, just concatenate **dataset** and **test_dir** as show in the example below. The parameter test_data is a .arff file with data for the testing images.
 
 This method must return a list containing all the predicted classes by the classifier. E.g.: [‘weed’,’weed’,’target_stain’, ‘weed’]

```python
def classify(self, dataset, test_dir, test_data):
      
	   # Directory retaining the testing images.
       path_test = dataset + '/' + test_dir + '/'        
        
       # Implement heere the prediction algorithm for your classifier.
 
       return # A list with the predicted classes
```

The **cross_validate** must be implemented and return a string (info) with the metrics.
Obs.: The attribute **self.dataset**, updated in **train**, can be used in **cross_validate** to access the training images folder.

```python
def cross_validate(self, detail = True):
        start_time = TimeUtils.get_time()        
        info =  "Scheme:\t%s %s\n" % (str(self.classifier.classname) , "".join([str(option) for option in self.classifier.options]))
	  
	   # Implement here the cross validation.
	   return info
```
The **reset** method must also be implemented in default form, as seen below.

```python
def reset(self):
        self.data = None
        self.classifier = None
```

After implementing your classifier, you must configure it in Pynovisão by modifying **[...]/pynovisao/src/classification/__init__.py**.

Should utility classes be necessary, they must be created in **[...]/pynovisao/src/util/**. They must also be registered as modules in **[...]/pynovisao/src/util/__init__.py**.


Should any problem related to the number of processes arise, add these two variables in your terminal:

```
export OMP_NUM_THREADS=**number of threads your cpu has**
export KMP_AFFINITY="verbose,explicit,proclist=[0,3,5,9,12,15,18,21],granularity=core"
```

### Como utilizar as ferramentas de anotação XML

For those that wish to create .xml files during the process of segmentation, Pynovisão is now capable of doing so.
After following the previous steps for segmenting an image, after choosing all the desired segments, click on *Segmentation ->  Create .XML file* and the file with the annotations will be saved in *[...]/pynovisao/data/XML*, with the name ***image** + .xml*.

Should the user want to use previously segmented images, it is possible to have Pynovisão search for the position of such segments and create the corresponding Bounding Box.
To make use of this feature:
- Separate the segments and full images from each other. It is not necessary, but it will help with execution time.
- Open Pynovisão.
- Select *XML -> Configure folders*.
- Choose the desired image and segment folders you wish to use.
- Click *Save All Directories*.
- With your desired folders chosen, click *XML -> Execute Conversion*
- The log (Should it be shown by the user) will update for every concluded image.
- After it is explicitly said the process has ended, the .xml files will be found in *[...]/pynovisao/data/XML* with the name ***imagem** + .xml*.