FuzzuTech Projects

━━━━━━━━━━━━━━━━━━━━ 🤖 TRUTHSCAN AI — AI Behavior Analysis Engine Using Python What if an AI could analyze your facial and behavioral patterns and generate a suspicion estimate? TRUTHSCAN AI is a futuristic experimental AI behavior analysis application built with Python and computer vision concepts. The application analyzes visual behavioral signals such as facial stability, eye movement, expression changes, head movement, and behavioral consistency through a modern desktop interface. This project combines Python, OpenCV, computer vision, real-time camera processing, behavioral metrics, and a modern GUI to create a futuristic AI software experience designed for experimentation, learning, and technology demonstrations. The project is also designed with a recording-friendly interface, making it suitable for Python project demonstrations, AI technology videos, coding content, software showcases, and YouTube Shorts. ━━━━━━━━━━━━━━━━━━━━ 🔥 What Is ...

━━━━━━━━━━━━━━━━━━━━ 📖 Introduction In this tutorial, we will build a modern Python Hand Gesture Control System using Python, OpenCV, MediaPipe, and CustomTkinter. This Python computer vision application uses a webcam to detect a human hand in real time, analyze hand landmarks, recognize different finger gestures, and perform computer actions without touching the keyboard or mouse. The application includes real-time webcam processing, hand landmark detection, finger state analysis, gesture recognition, mouse cursor movement, left click control, right click control, scrolling actions, volume control, live gesture status, camera preview, and a modern recording-friendly 9:16 CustomTkinter GUI. This Python project is perfect for learning computer vision, hand tracking, gesture recognition, OpenCV image processing, MediaPipe hand landmarks, Python automation, mouse control, keyboard control, Python threading, and modern desktop GUI development using CustomTkinte...

  Demo : Click Video 👇👇👇 📝 Features: Real-time AI detection GUI built with Tkinter Works on any PC Built for tech lovers & Python learners Includes full source code (link in YT comments) Code : import tkinter as tk from tkinter import messagebox import cv2 from PIL import Image, ImageTk import threading class FaceLockApp:     def __init__(self, root):         self.root = root         self.root.title("Face Lock System")         self.root.geometry("700x500")         self.root.configure(bg="#121212")         self.root.resizable(False, False)         # Title         self.title_label = tk.Label(root, text="🔒 Face Lock System", font=("Segoe UI", 24, "bold"), fg="#00fff7", bg="#121212")         self.title_label.pack(pady=20)         # Video frame border     ...

  Demo : Click Video 👇👇👇 📢 Features: Embed YouTube Short Include GitHub link (if public) Short write-up: Explain core logic and how Haar Cascade works SEO Tip: Use keywords like “Python face detection 2025”, “OpenCV tutorial real time” Code : import cv2 import tkinter as tk from tkinter import Label from PIL import Image, ImageTk # Haarcascade XML for face detection face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml") # Initialize webcam cap = cv2.VideoCapture(0) # Create GUI Window root = tk.Tk() root.title("Modern Face Detector - FuzzuTech") root.geometry("700x550") root.configure(bg="#1e1e1e") label = Label(root) label.pack() title = Label(root, text="Face Detector - No TensorFlow / Mediapipe", font=("Helvetica", 18), fg="white", bg="#1e1e1e") title.pack(pady=10) def detect_face():     ret, frame = cap.read()     if not ret:         return   ...

Demo : Click Video 👇👇👇 📌 Features ✅ AI-powered Sketch Converter ✅ OpenCV Image Processing ✅ No TensorFlow Required ✅ Save High-Quality Sketches ✅ Full Source Code Provided Code : import cv2 import numpy as np import tkinter as tk from tkinter import filedialog from PIL import Image, ImageTk # Global variable for storing the loaded image global_image = None   def sketch_transform(image):     gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)     inv = cv2.bitwise_not(gray)     blur = cv2.GaussianBlur(inv, (21, 21), 0)     sketch = cv2.divide(gray, 255 - blur, scale=256)     return sketch def open_image():     global global_image     file_path = filedialog.askopenfilename(filetypes=[("Image Files", "*.jpg;*.png;*.jpeg")])          if file_path:         global_image = cv2.imread(file_path)  # Store the image globally         sketch ...