Commit f9abb032 authored by Fábio Prestes's avatar Fábio Prestes

Finalizacao readme v6 tentado fazer formatacao funcionar

parent 616170bb
......@@ -36,6 +36,7 @@ NPOSL-30 https://opensource.org/licenses/NPOSL-3.0 - Free for non-profit use (E.
## How to Install
- **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:
......@@ -94,7 +95,8 @@ Finally, it is necessary to configure your CNNCaffe.
- A example file can be found in **[...]/pynovisao/examples/labels.txt**.
## How to use:
####Opening the software
#### Opening the software
- 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
......@@ -143,54 +145,54 @@ Now you are able to run Pynovisão!
$ ./split_data -h
```
##File
#Open Image (Shortcut: Ctrl + O)
## File
# Open Image (Shortcut: Ctrl + O)
Opens a file selection windows and allows the user to choose a desired image to work upon.
#Restore Image (Shortcut: Ctrl + R)
# Restore Image (Shortcut: Ctrl + R)
Restores the selected image to it's original state.
#Close Image (Shortcut: Ctrl + W)
# Close Image (Shortcut: Ctrl + W)
Closes the currently selected image.
#Quit (Shortcut: Ctrl + Q)
# Quit (Shortcut: Ctrl + Q)
Closes Pynovisão.
##View
#Show Image Axis (Shortcut: Not Defined)
## View
# Show Image Axis (Shortcut: Not Defined)
Shows a X/Y axis on the Image.
#Show Image Toolbar (Shortcut: Not Defined)
# Show Image Toolbar (Shortcut: Not Defined)
Shows a list of all the images in the selected folder.
#Show Log (Shortcut: Not Defined)
# Show Log (Shortcut: Not Defined)
Shows a log with information about the current processes and Traceback errors should they happen.
##Dataset
#Add new class (Shortcut: Ctrl + A)
## Dataset
# Add new class (Shortcut: Ctrl + A)
Create a new class. This will create a new folder in the /data folder.
#Set Dataset Path (Shortcut: Ctrl + D)
# Set Dataset Path (Shortcut: Ctrl + D)
Choose the folder with the desired images.
#Dataset Generator (Shortcut: Not Defined)
# Dataset Generator (Shortcut: Not Defined)
Creates a new dataset utilizing the selected folder.
##Segmentation
#Choose Segmenter (Shortcut: Not Defined)
## Segmentation
# Choose Segmenter (Shortcut: Not Defined)
Choose the desired segmentation method. Please research the desired method before segmenting. The Default option is SLIC.
#Configure (Shortcut: Ctrl + G)
# Configure (Shortcut: Ctrl + G)
Configure the parameters for the segmentation.
- Segments: Number of total segments the image should be split into.
- Sigma: How "square" the segment is.
- Compactness: How spread out across the image one segment will be. A higher compactness will result in more clearly separated borders.
- Border Color: The color of the created segments' borders. This is only visual, it will not affect the resulting segment.
- Border Outline: Will create a border for the segment borders.
#Execute (Shortcut: Ctrl + S)
# Execute (Shortcut: Ctrl + S)
Execute the chosen segmentation method with the desired parameters.
Once Segmented, the user can manually click on the desired segments and they will be saved in data/demo/**name-of-the-class**/**name-of-the-image**_**number-of-the-segment**.tif.
#Assign using labeled image (Shortcut: Ctrl + L)
# Assign using labeled image (Shortcut: Ctrl + L)
Use a mask/bicolor image created using a labelling software (LabelMe/LabelImg) and applies it to the original/selected image, and generates all the correct segments inside such mask.
#Execute folder (Shortcut: Not Defined)
# Execute folder (Shortcut: Not Defined)
Same as the Execute command, however it realizes the segmentation on an entire folder at once.
#Create .XML File (Shortcut: Not Defined)
# Create .XML File (Shortcut: Not Defined)
Will create a .xml file using the chosen segments. The .xml will be saved in data/XML/**name-of-the-image**.xml
##Feature Extraction
#Select Extractors (Shortcut: Ctrl + E)
## Feature Extraction
# Select Extractors (Shortcut: Ctrl + E)
Select the desired extractors to use. The currently available extractors are:
- Color Statistics;
- Gray-Level Co-Ocurrence Matrix;
......@@ -201,46 +203,46 @@ Select the desired extractors to use. The currently available extractors are:
- Gabor Filter Bank;
- K-Curvature Angles.
Please research what each extractor does, and choose accordingly. By default all extractors are chosen.
#Execute (Shortcut: Ctrl + F)
# Execute (Shortcut: Ctrl + F)
Execute the chosen Extractors. It will create a training.arff file in the data/demo folder.
#Extract Frames (Shortcut: Ctrl + V)
# Extract Frames (Shortcut: Ctrl + V)
Will extract frames from a video. The user must choose the folder where the desired videos are, and the destination folder where the consequent frames will be extracted to.
##Training
#Choose Classifier (Shortcut: Not Defined)
## Training
# Choose Classifier (Shortcut: Not Defined)
Choose the desired classifier to use. Only one can be chosen at a time.
- CNNKeras
- CNNPseudoLabel
- SEGNETKeras
If the user is interested in implementing it's own classifiers into Pynovisão, please go to **Implementing a new classifier in Pynovisão**
#Configure (Shortcut: Not Defined)
# Configure (Shortcut: Not Defined)
Choose the desired parameters for the currently selected classifier.
Each classifier has it's own parameters and configurations, and therefore must be extensibly research should the desired result be achieved.
#Execute (Shortcut: Ctrl + T)
# Execute (Shortcut: Ctrl + T)
Train the selected classifier utilizing al the chosen parameters and the training.arff file created previously.
##Classification
#Load h5 weights (Shortcut: Not Defined)
## Classification
# Load h5 weights (Shortcut: Not Defined)
*Only used for CNN classifiers* Take a previously created weight .h5 file and use it for this classification.
#Execute (Shortcut: Ctrl + C)
# Execute (Shortcut: Ctrl + C)
Execute the current classifier over the currently selected image.
#Execute folder (Shortcut: Not Defined)
# Execute folder (Shortcut: Not Defined)
Same as the previous command, however executes all the image files inside a selected folder at once.
##Experimenter
#Ground Truth (Shortcut: Not Defined)
## Experimenter
# Ground Truth (Shortcut: Not Defined)
Utilizes the currently selected image as the ground truth for the experimentations.
#Execute Graphical Confusion Matrix (Shortcut: Not Defined)
# Execute Graphical Confusion Matrix (Shortcut: Not Defined)
For each classifier, creates a graphic with it's confusion matrix for the choen dataset.
#Cross Validation (Shortcut: Ctrl + X)
# Cross Validation (Shortcut: Ctrl + X)
Performs cross validation utilizing the previously experimented classifiers.
#Experimenter All (Shortcut: Ctrl + P)
# Experimenter All (Shortcut: Ctrl + P)
Runs all Weka classifiers and experiments with them.
##XML
#Configure folders (Shortcut: Not Defined)
## XML
# Configure folders (Shortcut: Not Defined)
Choose the target folder for the original images and the other target folder for the segments to be searched and conevrted into a .xml file.
#Execute Conversion (Shortcut: Not Defined)
# Execute Conversion (Shortcut: Not Defined)
Executes the conversion using the two given folders. The file with the annotations will be saved in *[...]/pynovisao/data/XML*, with the name ***image** + .xml*.
### Implementing a new classifier in Pynovisão
......
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