Scale-Up: Customer Identification and Product Count Keeper

Description

Scale-Up: This project is made for enhancing the customer experience in retail stores, malls and small shops, for better management and sales. In this project there are two separate modules:

1. Customer Face Recognition: This module deals with identifying a customer entering into a store and matching with database. Attendees will have access to the details of customer with budget slab ,previous purchase record,preferences.
   Attendees can also edit/change details of old customer with one click in real time if they want to add on something , also they can add some additional detail on the basis of their personal experience with that particular customer , so that next time that customer can be served better.
   Also for new customers, attendees can add the details with just some clicks.
   Using this product at retail stores will help store management to build a better relationship with their customers by knowing their purchase history and inclination towards premium and non-premium brands.
2. Product Recognition and Counter: Product counter uses CCTV for counting products and displaying at the screens of attendees so that store's manangent can fill their stocks effectively. It can help in maintaing stocks on time. Stores will require less man-power for keeping counts. It will stores to keep tracks and set trends for their best selling products.

Applications and Uses

1. With the help of this product, retail stores can keep track of customers behaviour and interests.
2. Attendees can provide a personalised shopping experience for the customers, by knowing their names, budget slab and previous purchase records.
3. Customers will have their shopping done efficiently in time and this will build a sense of trust between retails and customers.
4. Also the product count keeper can help the staff to manage their stocks while helping the management in reducing man-power needed to keep counts and maintain the stocks.

WorkFlow

Screenshots

Installation

• Clone repo using git clone https://github.com/Isha427/Engage-22.git
• Download YoloV3.weights: Click here in Components/objectDetection in the cloned repo
• Install all the required dependencies using pip install -r requirements.txt in terminal
• Import engage.sql in the MySQL server from repository and run Apache and MySQL server
• Run index.php
• Run app.py from Components/face_recognition for customer recognition module
• For object recognition stop app.py using ctrl+C in terminal and run Real time object count (YOLOV3).py from Components/objectDetection

Technologies Used

1. OpenCV
2. YoloV3
3. MySQL
4. PHP
5. Flask
6. Bootstrap

Agile Methodology