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Facial Recognition OpenCV

Developed By Parag Bomble

Access the original repository on Github : Facial-Recognition-OpenCV

Email for any issues : parag@null.net


The Facial Expression Detection Tool is an advanced computer vision application developed using Python, OpenCV, and Convolutional neural network. This tool enables efficient detection of faces in real-time and accurate analysis of facial expressions displayed by individuals.

Usage :

Face Detection: The tool utilizes OpenCV's pre-trained deep learning models, such as Single Shot Multibox Detector (SSD) or You Only Look Once (YOLO), to perform rapid and precise face detection within input videos or images.

Facial Expression Analysis: Once a face is detected, the tool employs Neural Networking models, trained on extensive datasets, to accurately recognize and analyze emotional expressions exhibited by individuals. These models utilize facial landmarks and feature extraction to achieve exceptional accuracy.

Real-Time Emotion Analysis: The tool continuously processes frames from the video feed, enabling real-time emotion analysis of multiple faces. This functionality is advantageous for applications involving emotion-driven user interfaces, customer sentiment analysis, and social behavior studies.

Graphical and Textual Output: Our tool presents the detected facial expressions through both graphical visualizations and textual feedback. This dual representation facilitates intuitive analysis and interpretation of the identified emotions.

The Facial Expression Detection Tool empowers projects with cutting-edge facial analysis capabilities, making it a valuable asset for emotion-aware applications, human-computer interaction research, and sentiment analysis. Leveraging Python, OpenCV, and Neural Networking, this tool ensures remarkable accuracy, efficiency, and adaptability for real-time emotion analysis requirement.



Reinforcement-Learning-CMD-interface

Developed By Parag Bomble

Developed By ParagBML Access the original repository on Github : Reinforcement-Learning-CMD-interface
Email for any issues : parag@null.net
Usage: 
This is an implementation of the AlphaZero algorithm for playing Gomoku (also called Gobang or Five in a Row) from pure self-play training. The game Gomoku is much simpler than Go or chess, so that we can focus on the training scheme and obtain a AI model on a single PC in a few hours.

    Each move with 400 Monte Carlo tree search algorithm playouts:



    REQ

    • Python >= 2.7
    • Numpy >= 1.11

    python human_play.py

    python train.py

    With PyTorch or TensorFlow, modify the file [train.py]

    from policy_value_net import PolicyValueNet  # Theano and Lasagne

    and execute: python train.py

    The models (best_policy.model and current_policy.model) will be saved every a few updates (default 50).



    Hand Gesture Detector OpenCV/Python

    Developed By Parag Bomble

    Access the original repository on Github : Hand-Gesture-Detector-OpenCV/Python

    Email for any issues : parag@null.net

    The Hand Gesture Detection Tool leverages the robustness of OpenCV to facilitate instantaneous detection and classification of an array of hand gestures in real-time. It effectively accommodates various video input sources, including webcams.

    The Hand Gesture Detection Tool is designed to accommodate customization, allowing users to define and recognize their own hand gestures or seamlessly integrate the tool into their projects.

    • Clone or download the repository to your local machine.
    • Install the requisite dependencies, as meticulously outlined in the documentation.
    • Execute the provided example scripts to witness the tool's functionality in real-time.
    • Customize and integrate the tool into your specific applications as warranted.

    Vehicle Speed Detection OpenCV PY

    Developed By Parag Bomble

    Access the original repository on Github : Vehicle Speed Detection

    Vehicle Speed Detection OpenCV with Python

    Includes : Vehicle Cascade detecting datasets.

    Email for any issues : parag@null.net

    Developed by ParagBml 

    Apache 2.0 Licensed. 

    Prerequisites Libraries: import cv2 import dlib import time import math

    Usage:

    • Import Live Vehicle passing footage or live Webcam. Run Speed_detector.py

    Cascade detectors can be changed by customized RGB codes in the (z,y,x) format.



    Solana DevNet Token Distro

    Developed By Parag Bomble

    Access the original repository on Github : Solana-DevNet-Token-Distro

    Email for any issues : parag@null.net

    Solana Developer Network Token Distributor offers a streamlined approach to creating and managing token distributions for your projects or applications on the Solana Developer Network.

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