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A project aimed to address challenges in ICU care by leveraging machine learning and computer vision. The primary goal is to develop a system capable of extracting vital signs information from patient monitor images obtained through CCTV footage or dedicated cameras

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Cloudphysician

Read this document for the motivation to make this project.

Dependencies

  • Python3: Ensure that you have Python 3 installed on your system. You can download and install Python 3 from the official Python website: https://www.python.org.
  • pip: pip is the package installer for Python. It is usually installed by default when you install Python. However, make sure you have pip installed and it is up to date. You can check the version of pip by running the following command:
    pip --version
    

Installation

To install and use Cloudphysician, follow the steps given below:

  • Fork the Cloudphysician repository by clicking the "Fork" button at the top right corner of the repository page. This will create a copy of the repository under your GitHub account.
  • Clone the forked repository to your local machine:
    git clone https://github.com/{YOUR-USERNAME}/Cloudphysician
    
  • Navigate to the project directory:
    cd Cloudphysician
    
  • Install the necessary Python packages by running the following command:
    pip install -r requirements.txt
    

(NOTE: It is recommended to install these requirements in a new python environment)

How to use?

Follow the steps given below:

Output

Input Image

sample_image.jpeg

Result

results.jpeg

Future Developement

Contributions

Contributions to Cloudphysician are welcome! If you encounter any issues or have suggestions for improvements, please feel free to open an issue or submit a pull request on the GitHub repository.

Author

Abhishek Singh Kushwaha

Kriti Gupta

Bhavik Shangari

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A project aimed to address challenges in ICU care by leveraging machine learning and computer vision. The primary goal is to develop a system capable of extracting vital signs information from patient monitor images obtained through CCTV footage or dedicated cameras

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