https://github.com/AbhiLegend/Image-Processing-with-OpenVINO

https://github.com/AbhiLegend/Image-Processing-with-OpenVINO

Last synced: about 1 month ago
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• Host: GitHub
• URL: https://github.com/AbhiLegend/Image-Processing-with-OpenVINO
• Owner: AbhiLegend
• Created: 2023-11-10T09:53:00.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2023-11-10T09:57:55.000Z (about 1 year ago)
• Last Synced: 2024-08-01T21:46:09.656Z (4 months ago)
• Language: Jupyter Notebook
• Size: 5.86 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Image-Processing-with-OpenVINO

Certainly! To create a README for the "ExperimentOpenVino.py" script, you'll want to include several key sections that explain the purpose, requirements, setup, and usage of the script. Here's a structured outline with explanations for each section:

---

### README for ExperimentOpenVino.py

#### **Overview**

Briefly describe what the script does. Mention that it's a Python script for demonstrating image processing and model inference using OpenVINO, with a focus on semantic segmentation for road images.

#### **Requirements**

List the required libraries and tools, such as OpenVINO, OpenCV, Matplotlib, Numpy, and Python itself. Specify the version of OpenVINO used (e.g., OpenVINO 2023.1.0) and mention that the script is intended for environments where these libraries are not pre-installed, such as Jupyter Notebook.

#### **Installation**

Provide instructions on how to set up the environment. This includes steps for installing OpenVINO and other dependencies, either through pip commands or by providing a requirements.txt file.

#### **Script Features**

Detail the key functionalities of the script:

- **Model Download**: Automatically downloads the `semantic-segmentation-adas-0001` model if not present.

- **OpenVINO Model Compilation**: Demonstrates how to compile the model using OpenVINO for optimized inference.

- **Image Processing Techniques**: Shows how to perform operations like Gaussian Blur, Edge Detection, and applying a Sepia filter on an image.

- **Visualization**: Explain how the script uses Matplotlib to display the original and processed images.

#### **Usage**

Give a step-by-step guide on how to run the script. Include any necessary steps to download the script, navigate to the directory, and execute it in a Python environment or Jupyter Notebook.

#### **Output**

Briefly describe what output to expect. This includes the visual comparison of the original image with its blurred, edge-detected, and sepia-toned variants.

#### **Contribution**

If you're open to contributions, provide guidelines on how others can contribute to the script. Mention the process for submitting issues, pull requests, or contact information for direct communication.

#### **License**

Specify the licensing details if applicable.

#### **Contact**

Provide your contact information or the maintainer's details for users who might have questions or need support.

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