Spider-Verse Classifier: A Machine Learning Approach

Introduction

Diving into the fascinating world of machine learning, the Spider-Verse Classifier offers a creative challenge: distinguishing between different Spider-Man actors using image classification techniques. This project demonstrates how machine learning can be applied to a fun, real-world scenario by building a classifier capable of identifying actors like Andrew Garfield, Tobey Maguire, and Tom Holland. In this blog, we'll walk through the project's development, from data collection to model implementation, and explore its potential uses and future enhancements.

Objective

The Spider-Verse Classifier is designed to recognize images of actors like Tobey Maguire, Andrew Garfield, and Tom Holland. The classifier employs a combination of image preprocessing, feature extraction, and machine learning techniques to achieve high accuracy in identifying these actors.

Data Collection

To build the Spider-Verse classifier, gathering a diverse and comprehensive dataset of images was crucial. For this purpose, I used a Pinterest scraper to automate the process of collecting images. This tool allowed me to efficiently fetch a wide range of images related to the Spider-Verse characters, significantly reducing the time and manual effort required for data collection.

The Pinterest scraper was particularly useful in sourcing high-quality images, which were then used to train the classifier. The automated process ensured that the dataset was varied and extensive, covering different poses and lighting conditions of the characters.

Despite this efficiency, challenges such as filtering out irrelevant images and ensuring the consistency of image quality were encountered. Nonetheless, the scraper proved to be a valuable asset in creating a robust dataset for the project.

Methodology

The classification approach involved several steps:

1. Image Processing: We used OpenCV for face detection and cropping, focusing on images with clearly visible faces to enhance accuracy.
2. Feature Extraction: Features were extracted using a combination of raw pixel values and wavelet transforms to capture different aspects of the images.
3. Model Training: A machine learning model was trained using the extracted features. We utilized traditional machine learning algorithms to build a classifier capable of distinguishing between the actors.

Implementation

Here’s a look at how the classifier was implemented:

• Data Preparation: Images were preprocessed to ensure consistency.
• Feature Extraction: The w2d function was used to apply wavelet transforms, enhancing the model’s ability to distinguish between different actors.
• Model Training: The model was trained with the processed data and evaluated to ensure high accuracy.

Below is a snippet of the code responsible for feature extraction:

python

def classify_image(image_base64_data):
    imgs = get_cropped_image_if_2_eyes(image_base64_data)
    result = []
    for img in imgs:
        scalled_raw_img = cv2.resize(img, (32, 32))
        img_har = w2d(img, 'db1', 5)
        scalled_img_har = cv2.resize(img_har, (32, 32))
        combined_img = np.vstack((scalled_raw_img.reshape(32 * 32 * 3, 1), scalled_img_har.reshape(32 * 32, 1)))
        final = combined_img.reshape(1, len_image_array).astype(float)
        result.append({
            'class': class_number_to_name(__model.predict(final)[0]),
            'class_probability': np.around(__model.predict_proba(final)*100,2).tolist()[0],
            'class_dictionary': __class_name_to_number
        })
    return result

Results

The Spider-Verse Classifier successfully identifies the actor behind the Spider-Man mask with high accuracy. For instance, in tests, the classifier consistently matched the correct actor with probabilities that clearly differentiated between the choices. Visualizations of these results, such as confusion matrices and probability distributions, highlight the model's effectiveness.

Conclusion

Through the Spider-Verse Classifier project, we’ve demonstrated how machine learning can be applied to image classification tasks with impressive results. Key learnings include the importance of high-quality data and the effectiveness of combining different feature extraction techniques. Future improvements could involve expanding the dataset and exploring more advanced algorithms to further enhance accuracy.

Call to Action

I’d love to hear your thoughts on the Spider-Verse Classifier! Feel free to leave a comment, share this post with your network, or check out other projects on my GitHub. Your feedback and support are greatly appreciated!