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Image Processing and Computer Vision Toolbox

A Module of Image Processing and Computer Vision Toolbox for Scilab 6.0
(23224 downloads for this version - 63212 downloads for all versions)
Details
Version
2.0
A more recent valid version with binaries for Scilab 6.0 exists: 4.1
Author
Tan Chin Luh
Owner Organization
Trity Technologies
Maintainer
Chin Luh Tan
Category
License
Creation Date
July 16, 2018
Source created on
Scilab 6.0.x
Binaries available on
Scilab 6.0.x:
Linux 64-bit MacOSX Windows 64-bit
Install command
--> atomsInstall("IPCV")
Description
            IPCV - Image Processing and Computer Vision Toolbox for Scilab 

This module is inspired by SIVP module and now has became independent module to
work with Scilab 6.0!

-------------------------------------------------------
PLEASE SCROLL DOWN TO THE "NEWS" SECTION FOR THE INSTALLATION
INSTRUCTIONS ESPECIALLY FOR LINUX USER.
-------------------------------------------------------
            
Files (5)
[116.51 MB]
Linux 64-bit binary for Scilab 6.0.x
Tested on Ubuntu 18.04 64-bit, pre-requisites:

sudo apt-get install build-essential cmake unzip pkg-config libjpeg-dev
libpng-dev libtiff-dev libavcodec-dev libavformat-dev libswscale-dev libv4l-dev
libxvidcore-dev libx264-dev libgtk-3-dev libatlas-base-dev gfortran

[48.21 MB]
MacOSX binary for Scilab 6.0.x
Tested only on Mac OS 10.13 High Sierra

Need to install hdf5, following lines install hdf5 with brew

# Install brew, this will install Xcode command line tools as well
/usr/bin/ruby -e "$(curl -fsSL
https://raw.githubusercontent.com/Homebrew/install/master/install)"

# Install hdf5 with other prerequisites
brew install hdf5

# Create folder and link the hdf5 to it so that it could be located by IPCV
sudo mkdir /sw
sudo mkdir /sw/lib
sudo ln /usr/local/lib/libhdf5.dylib /sw/lib/libhdf5.9.dylib
[14.53 MB]
Source code archive

[53.19 MB]
Windows 64-bit binary for Scilab 6.0.x

[102.20 MB]
Miscellaneous file
Patch file for various Linux Distribution.

IPCV Installation
1. Install Scilab or download Scilab binary
2. Launch Scilab and run "atomsInstall('IPCV')";
3. Restart Scilab. You will get error on not able to load the libraries.

How to apply the patch:
1. Download patch_ipcv2.zip
2. Unzip it into a folder
3. Launch Scilab again
4. CD to the folder where you unzip the patch_ipcv.zip
5. Run following commands:
--> load patch_ipcv
--> patch_ipcv('centos-lib.zip')

centos-lib.zip is the patch for the centos 7.5. Other version not tested
News (5)
Comments (4)     Leave a comment 
Comment from Egge_d Dd -- April 11, 2019, 03:29:28 PM    
Hello

is there any chance to control exposure time with USB-cameras using camopen and imread?

This script works fine with the attached USB-camera
n = camopen(0);
sleep(200);
im = camread(n); //get a frame
imshow(im);
camcloseall();

However I can not controll exposure time and get therefore in some cases bad pictures. I
can change the camera settings (and also save these settings on the camera) with the
camera software. However it seems, that camopen and camread ignores these settings or
sends its own settings.

The goals would be to take a picuture and check the exposure (e.g. using
histogram-function) if the picture is to dark or to bright. Based on this, a second
picture should be taken with the corrected exposure time.

Is there a better place to post this question (e.g. forum)?

best regards
egge_d
Comment from Eduardo Inglez -- June 27, 2019, 09:19:58 PM    
Hi,

Are there plans to develop features to train a CNN from scratch or apply transfer 
learning?

Rgs

Eduardo
Answer from Chin Luh Tan -- July 2, 2019, 02:21:31 PM    
Hi, 

Currently IPCV 4.1 is being developed, in which transfer learning already been shown in 
this post: https://www.linkedin.com/pulse/deep-learning-series-transfer-scilab-modules-
tan-chin-luh/

Training CNN from scratch is in the plan as well, lacking of resources is the main reason 
of the delay. 

rgds,
CL
Answer from Chin Luh Tan -- July 2, 2019, 03:14:04 PM    
Hi, 

Currently IPCV 4.1 is being developed, in which transfer learning already been shown in 
this post: https://www.linkedin.com/pulse/deep-learning-series-transfer-scilab-modules-
tan-chin-luh/

Training CNN from scratch is in the plan as well, lacking of resources is the main reason 
of the delay. 

rgds,
CL
Comment from Eduardo Inglez -- July 2, 2019, 09:01:54 PM    
Good news. Good luck

Rgs

Eduardo
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