basuru07 / Image_Processing_and_Computer_Vision_Practicals

Guide for setting up a virtual environment and kernel for Python image processing. Practical exercises cover face recognition, shapes detection, vehicle identification, face detection, and vehicle number plate detection in a Jupiter Notebook. Purpose: Hands-on experience with essential computer vision techniques.

Image-Processing-and-Computer-Vision-Practicals

First create the Virtual Environment and the kernel. 01. Go to the respective folder structure when you want to create it.

   Open Anaconda Powershell Prompt -> cd " (file path) "

02. Execute the command.

    python -m venv (type any name 01)

03. Activate the respective Virtual Environment that has been created that follwing below steps.

    .\(type any name 01)\Scripts\activate

04. After activate the relevent environment next required kernel has to be created kernel will link the customized package assembly jupitor as relevent environment.

05. Execute the below command to activate the kernel installation.

    pip install ipykernel

06. Once the kernel is install executed the command for kernel link in development environment.

    python -m ipykernel install --name (type any name 01)

• Go to the ANACONDA Navigator -> Open jupitor NoteBook.

• Then the right side of the page.

  Click new -> select the Kernel (type any name 01) -> open it
  

Then type these code in the jupitor Notebook

• Shapes Identify Practical 01
• Face Recognition Practical 02
• Vehical Numberplate Detection and Numberplate Reading Practical 03
• Vehical Detection and Object Identification Practical 04
• PNEUMONIA Identification Practical 05
• Gun Holding Detection Practical 06
• Gun Matching Practical 07
• Multi Class Image Classification Practical 08
• Transfer Learning with VGG Practical 09