In the bustling world of online marketplaces, product images hold the key to attracting potential customers and driving sales. Picture this: a seller uploads an image of their remarkable product, but alas! It appears misaligned, dimly lit, or lacking that captivating appeal. Such images can hamper the overall user experience, leaving buyers disenchanted. But fear not, for a groundbreaking solution awaits! To tackle this challenge, this assignment aims to develop an automated Image processing model. This will enable image detection, auto-rotation of images for proper alignment, and hence will enhances seller-uploaded images. Get ready to transform online marketing into an extraordinary realm, where products shine and the marketplace experience becomes captivating.
The aim of this assignment is to develop a model which can be used to automatically detect, rotate for proper alignment, and enhance the image uploaded by a seller in an online marketplace. Below are the steps and methods that you may follow to proceed for this assignment:
Data collection: Build a dataset containing the variety of images that usually uploaded by the sellers for training and testing your ML models. Make sure to include the images with different orientations, quality, background to train the model for all conditions.
Conversion of image to matrix: Images are stored in the form of matrices or tensors for using in ML methods, one can use libraries like OpenCV or PIL for this purpose to get the matrices in the compatible form for the ML method that you will use.
Image recognistion and detection: Image recognition is a fundamental task in machine learning that involves identifying and categorizing objects or patterns within images. There are a few approachable methods that can be used to get started with image recognition tasks. Two popular techniques are Haar cascades and HOG (Histogram of Oriented Gradients) + SVM (Support Vector Machine).
Rotation: Methods like SIFT (Scale-Invariant Feature Transform) or SURF (Speeded-Up Robust Features) combined with algorithms like RANSAC (Random Sample Consensus) can be used for properly aligning the detected objects in the image.
Image enhancement and background blur( Bonus Task ): Use methods like Histogram Equalization (it improves the contrast and enhances the image details) and Median Filter(a non-linear filtering method that replaces each pixel’s value with the median of its neighbourhood, effectively reducing noise and blurring the background).
Training and Testing: Split your dataset into training and testing sets. Use the training set to train your models. During the training process, the model iteratively learns from the training data by adjusting its parameters based on the calculated loss. Evaluate the performance of your models using the testing set and iterate on your approach, if necessary, till the no. of iterations reach the maximum limit or the calculated loss is brought down.
Deployment and integration with platform: After the training and testing process, when the model starts performing well on new data then it is ready to be deployed and integrated with the online marketplace platform. This involves implementing the image processing pipeline and integrating it with the platform’s existing infrastructure.
Improvement: The model can be improved by periodically training and re-deploying the improved model utilising the live data from the platform itself.