This project is a toy project for training and quality assurance purposes. It involves developing a simple Flask API that processes an image (or a hardcoded image) of a motherboard and detects memory modules present on it. The API will return the image with bounding boxes drawn around each detected memory module.

Project Overview

Input Types:
- Image upload via the Flask API.
- A hardcoded image for testing purposes.
Dataset:
- 20 pictures of motherboards with memory.
- 20 pictures of motherboards without memory.
Output:
- An annotated image with bounding boxes around each detected memory module. For example, if there are two memory modules, two boxes are drawn; if only one is detected, then one box is drawn.
Annotation Tool Suggestion:
- We suggest using makesense.ai for manual annotation if needed.

Task Details

The developer is required to research and answer the following questions as part of the task:

Algorithm Choice:
- Which algorithm will you use for detecting the memory modules?
- Why do you choose this particular algorithm?
Hardware Considerations:
- Does CPU or GPU have an impact on your decision? Please explain.
Video Input:
- What if a video is provided instead of single images?
- Does your approach change when processing videos? Please describe your approach.

Proposed Flask API Implementation

API Endpoints:
- An endpoint for uploading images which processes and returns the annotated image.
- An endpoint parameter for using a hardcoded image for testing purposes.
Processing Workflow:
- Receive an image (either via file upload or from a hardcoded source).
- Apply the chosen object detection algorithm to detect memory modules.
- Draw bounding boxes around each detected memory module.
- Return the annotated image to the user.

Data Set:

Dataset in on the training folder. And there is memory and no_memory subfolder in it.