This project involves analyzing a dataset of approximately 25,000 images for an academic study.

• The main objective is to identify the number of unique images and the number of duplicates in the dataset.

• Additionally, the project aims to extract several features from the images, which include:

  • Dominant color
  • Saturation
  • Brightness
  • Presence of humans
  • Age category of humans
  • Facial expressions
  • General sentiment

To run the code in this repository, you will need to have Python 3 installed on your machine. You can install Python by following the instructions on the official Python website.

Next, you will need to install the required Python packages. You can do this by running the following command in your terminal:

Protip: To ensure efficient development and avoid conflicts between different Python projects, it is recommended to install required modules inside a virtual environment (virtualenv) and run Visual Studio Code (VS Code) using the virtualenv's Python interpreter.

pip install -r requirements.txt

To run the analysis on the dataset, simply run the src/main_parallel.py.

Once the analysis is complete, a CSV file called image_duplicates_{case}.csv will be generated in the data/processed, which contains the image IDs and their corresponding feature values.

• To visualize the unqiue images and their # of duplicates.
  • Simply run display_unique_imgs.py after generating the .csv file by the above step.

Image-Recognition-Analysis
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├───src                                 >     Code Scripts for each milestones
│  ├──milestone_1      
|  .      
|  └──milestone_x  
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├───config.py                           >     Control variables for test + disp
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├───main.py                             >     Main file to perform analysis on the unique images
├───main_parallel.py                    >     -multiprocessing enabled-
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├──data                                 >     Input(raw) data + intermediate results (processed)
│  ├──processed
│  └──raw
│     ├──test
│     └──test_full
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├──results                              >     Complete output for each milestone (.csv + timing + visualization)
│  ├──milestone_1
|  .      
|  └──milestone_x   
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└──Readme.MD                            >     Self Explanatory XD

This project was developed as part of an academic study and was made possible by the following resources:

• The OpenCV library for image processing.
• The imagehash library for Perceptual Image Hashing.
• The ColorThief library for color palette extraction.

This project is licensed under the BSD 2-Clause License - see the LICENSE file for details.