Computer Vision
First of all you should know the basic syntax of Python. To start with computer vision you need to have a basic knowledge about Deep Learning, neural neworks, CNN , RCNN , fast RCNN , faster RCNN.
Here are some resourses,
Deep learning, CNN (Startup)
[1] https://www.youtube.com/watch?v=vq2nnJ4g6N0&t=1546s
RCNN, fast RCNN, faster RCNN (Startup)
[1] https://www.youtube.com/watch?v=u6aEYuemt0M&t=238s
[2] Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks by Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun
Step1: Lets start with OpenCV for the neural networks. For this install OpenCV in Ubuntu. Refer to this documentation for installation : http://docs.opencv.org/trunk/d7/d9f/tutorial_linux_install.html
The repository contains an example of a neural network made with OpenCV called "RedEyeOpenCV" which
-converts the img into gray scale,
-splits and merge the image into BGR components,
-puts boxes one eyes, puts text on the image,
-save the given image in a different format eg.png.
Run it by typing "python RedEyes.py" on terminal.
The repository also contains a GUI made with opencv called "trackbar.py" that makes colours.
Run it by typing "python trackbar.py" on terminal.
Step2: Make a neural network that detect faces and eyes in images using opencv and haarcascades.
Find a folder named "Eyes and face Detection".
Run it by typing "python face.py" and "python eye_and_face_detection.py" respectively on terminal.
Step3: Start making GUI for neural networks. For this, use OpenCV along with 'Tkinter' which is a standard package in Python. Insure OpenCV is installed on Linux.
Repository contains 'Tkinter' named folder which contains files that demonstrates some features of tkinter.
-"camera.py" opens a videoa and contains a button through which you can capture any moment of the video and save.
-"askfile.py" contains GUI for file browse.
-"btton.py" is a GUI for button and pops a window on click.
Run the files by typing "python camera.py", "python askfile.py" and "python btton.py" respectively.
Step4: Make a network to detect faces in videos or webcam on live stream.
Face Detection in a video along with GUI is made and the faces are cropped and saved in a folder.
-Use OpenCV to read and perform various operations.
-Do 'Object Detection' using Haar feature-based cascade classifiers. Repository contains the cascade folder for eye and face detection.
-For GUI of the neural network, use Tkinter and OpenCV.
Repository contains Video-Face-Detection folder.
Run the files by typing "python gui.py" on terminal.