Course Project Expectations And Grading ✓ Solved

Course Project Expectations And Grading

Develop an application that provides an API for image processing, such as uploading a picture to simulate how a colorblind person would see it or applying a frame to images. Ensure the project is hosted in its own GitHub repository, separate from other repositories. Include a clear, detailed README file with project description, requirements, and setup instructions that are easy to follow. Verify that the project runs correctly and performs the described functions as documented. Incorporate exception handling mechanisms to manage invalid user inputs or edge cases gracefully without crashing. Design meaningful self-defined classes and subclasses to organize your code effectively. Follow the PEP-8 style guide, organize your code well, use meaningful comments, and structure your functions properly. Utilize at least one external Python module installed via pip in a purposeful way. Incorporate at least one external tool or framework relevant to the project, such as MongoDB, pandas, scikit-learn, or a visualization tool like ElasticSearch or Spark, to enhance the application's functionality. Prepare and deliver a presentation, and be ready to answer follow-up questions. Write unit tests for your code, ensuring at least 20% code coverage, and include a coverage report. Your project will be graded based on the following criteria:

  • Hosting in a dedicated GitHub repo (3%)
  • Clear documentation in README (7% to 10%)
  • Correct functionality and performance (35% to 45%)
  • Robust exception handling (10% to 55%)
  • Use of self-defined classes and subclasses (5% to 60%)
  • Code organization, style, and commenting (10% to 70%)
  • Meaningful use of at least one pip-installed module (5% to 75%)
  • Utilization of an external researched tool or framework (15% to 90%)
  • Presentation and follow-up questions (5% to 95%)
  • Unit test coverage of at least 20% of code (5% to 100%)

Ensure your project demonstrates all these components effectively to achieve a high grade.

Paper For Above Instructions

The following comprehensive guide details the development, implementation, and evaluation of a robust image processing API application aligned with the provided course project expectations and grading criteria.

Introduction

The primary objective of this project is to create an API-driven application capable of performing advanced image processing tasks. By facilitating functionalities such as simulating color blindness or applying decorative frames, this project not only demonstrates technical proficiency but also emphasizes best practices in software development, documentation, testing, and project management within a collaborative environment. The core challenge involves integrating various Python modules, designing a clean object-oriented architecture, ensuring robustness through exception handling, and deploying with comprehensive documentation for reproducibility and scalability.

Project Planning and Repository Setup

Hosting the project in a dedicated GitHub repository ensures organized version control, collaborative development, and transparent access for grading. The repository should include a well-structured README file containing an overview of the project, prerequisites, installation steps, usage examples, and configuration instructions. Setting up a virtual environment for dependency management and documenting required modules contribute to ease of use and reproducibility.

Application Features and Functional Requirements

The core functionalities encompass image uploading, processing, and retrieval via API endpoints. Specific features include:

  • Simulating color blindness effects—allowing users to upload images and view how different color vision deficiencies perceive colors.
  • Applying a consistent frame or border to images—enhancing aesthetic appeal or highlighting enhancements.

Additional functionalities can include batch processing, image size adjustments, or integration with external tools for data analysis and visualization.

Implementation and Code Structure

The application should be built using Python frameworks such as Flask or FastAPI to expose RESTful endpoints. Object-oriented design principles guide the creation of classes representing images, processing algorithms, and API handlers. Subclasses extend core classes to implement specific effects or processing techniques, illustrating meaningful use of inheritance.

Example class structure:



class ImageProcessor:


def __init__(self, image_path):


self.image_path = image_path


def load_image(self):


pass


def save_image(self, output_path):


pass


class ColorBlindSimulator(ImageProcessor):


def simulate_deuteranopia(self):


pass


def simulate_protanopia(self):


pass


class FrameApplier(ImageProcessor):


def apply_frame(self, frame_style):


pass

This structure promotes code reuse and maintainability while aligning with the requirement for self-defined classes and subclasses.

External Modules and Tools Utilization

Beyond core Python, external modules such as Pillow for image manipulation (installed via pip), and other tools like pandas for data handling or scikit-learn for analysis, should be incorporated meaningfully. For instance, Pillow can handle image processing tasks, while MongoDB (via PyMongo) can store metadata or user history, and ElasticSearch can facilitate image search or indexing. Integrating these tools demonstrates external tool utilization and aligns with project criteria.

Example of using Pillow:



from PIL import Image


image = Image.open('sample.jpg')


apply transformations


image.show()

Exception Handling and Robustness

Implementing try-except blocks throughout the code ensures the program can manage invalid inputs, file errors, or processing exceptions without crashing. For example, during image upload or processing, invalid file formats or corrupted images should be caught, and user-friendly error messages should be returned via the API.

Testing and Code Quality

Unit tests are essential for validating functionality and ensuring future maintainability. Using the unittest or pytest framework, tests should cover more than 20% of the codebase, verifying core functions such as image loading, processing effects, and API endpoints. Generating a coverage report highlights areas needing improvement and affirms test adequacy.

Documentation and Presentation

The README should clearly explain setup procedures, including environment setup, dependency installation, and example API calls. A README example of testing and expected outcomes enhances usability. Preparing an articulate presentation covering development choices, architecture, challenges, and potential future enhancements will demonstrate comprehensive understanding and engagement.

Conclusion

By following these detailed guidelines—host in a structured repository, develop robust and well-documented code, incorporate meaningful external tools, implement proper testing, and prepare a professional presentation—the project will align with all grading criteria, demonstrating technical proficiency and best development practices in image processing API application development.

References

  • Grinberg, M. (2018). Flask Web Development: Developing Web Applications with Python. O'Reilly Media.
  • Van Rossum, G., & Drake Jr, F. L. (2009). Python Tutorial. Python Software Foundation.
  • McKinney, W. (2010). Data Structures for Statistical Computing in Python. Proceedings of the 9th Python in Science Conference.
  • Boisselle, H. (2021). Efficient Image Processing with Pillow. Python Software Foundation.
  • MongoDB, Inc. (2023). MongoDB Manual. Retrieved from https://docs.mongodb.com/manual/
  • Pandas Development Team. (2023). pandas documentation. https://pandas.pydata.org/pandas-docs/stable/
  • Scikit-learn Developers. (2023). Scikit-learn: Machine Learning in Python. https://scikit-learn.org/stable/
  • Elastic.co. (2023). ElasticSearch Documentation. https://www.elastic.co/guide/en/elasticsearch/reference/current/index.html
  • Apache Spark Software Foundation. (2023). Spark Documentation. https://spark.apache.org/docs/latest/
  • GitHub Docs. (2023). Hosting and managing repositories. https://docs.github.com/en/github/creating-cloning-and-archiving-repositories

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