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+# 📜 Brightness control
+
+
+
+
+## 🎯 AIM
+To develop a real-time brightness control system using hand gestures, leveraging OpenCV and MediaPipe for hand detection and brightness adjustment.
+
+
+## 📊 DATASET LINK
+No dataset used
+
+## 📓 NOTEBOOK LINK
+
+[https://drive.google.com/file/d/1q7kraajGykfc2Kb6-84dCOjkrDGhIQcy/view?usp=sharing](https://drive.google.com/file/d/1q7kraajGykfc2Kb6-84dCOjkrDGhIQcy/view?usp=sharing)
+
+
+## ⚙️ TECH STACK
+
+| **Category** | **Technologies** |
+|--------------------------|---------------------------------------------|
+| **Languages** | Python |
+| **Libraries/Frameworks** | OpenCV, NumPy, MediaPipe, cvzone |
+| **Tools** | Jupyter Notebook, Local Python IDE |
+
+
+---
+
+## 📝 DESCRIPTION
+!!! info "What is the requirement of the project?"
+ - The project requires a webcam to capture real-time video and detect hand gestures for brightness control.
+
+??? info "How is it beneficial and used?"
+ - Allows users to control screen brightness without physical touch, making it useful for touchless interfaces.
+ - Ideal for applications in smart home systems and assistive technologies.
+
+??? info "How did you start approaching this project? (Initial thoughts and planning)"
+ - Identified the need for a touchless brightness control system.
+ - Selected OpenCV for video processing and MediaPipe for efficient hand tracking.
+ - Developed a prototype to calculate brightness based on hand distance.
+
+??? info "Mention any additional resources used (blogs, books, chapters, articles, research papers, etc.)."
+ - OpenCV documentation for video processing.
+ - MediaPipe's official guide for hand tracking.
+
+
+---
+
+## 🔍 EXPLANATION
+
+### 🧩 DETAILS OF THE DIFFERENT FEATURES
+
+#### 🛠 Developed Features
+
+| Feature Name | Description | Reason |
+|--------------|-------------|----------|
+| Hand Detection | Detects hand gestures in real-time | To control brightness with gestures |
+| Distance Calculation | Calculates distance between fingers | To adjust brightness dynamically |
+| Brightness Mapping | Maps hand distance to brightness levels | Ensures smooth adjustment of brightness |
+
+
+---
+
+### 🛤 PROJECT WORKFLOW
+
+!!! success "Project workflow"
+
+ ``` mermaid
+ graph LR
+ A[Start] --> B[Initialize Webcam];
+ B --> C[Detect Hand Gestures];
+ C --> D[Calculate Distance];
+ D --> E[Adjust Brightness];
+ E --> F[Display Output];
+ ```
+
+=== "Step 1"
+- Initialize the webcam using OpenCV.
+
+
+=== "Step 2"
+- Use MediaPipe to detect hands in the video feed.
+
+=== "Step 3"
+- Calculate the distance between two fingers (e.g., thumb and index).
+
+=== "Step 4"
+- Map the distance to a brightness range.
+
+=== "Step 5"
+- Display the adjusted brightness on the video feed.
+
+---
+
+### 🖥 CODE EXPLANATION
+
+=== "Section 1: Webcam Initialization"
+- The program begins by setting up the webcam to capture frames with a resolution of 640x480 pixels. This ensures consistent processing and visualization of the video stream.
+
+ ```python
+ cap = cv2.VideoCapture(0)
+ cap.set(3, 640) # Set width
+ cap.set(4, 480) # Set height
+ ```
+
+=== "Section 2: Hand Detection and Brightness Control"
+- Using the `HandDetector` from `cvzone`, the program tracks one hand (maxHands=1). The brightness of the video frame is dynamically adjusted based on the distance between the thumb and index finger.
+
+ ```python
+ detector = HandDetector(detectionCon=0.8, maxHands=1)
+ brightness = 1.0 # Default brightness level
+ ```
+
+- The HandDetector detects hand landmarks in each frame with a confidence threshold of 0.8. The initial brightness is set to 1.0 (normal).
+
+ ```python
+ hands, img = detector.findHands(frame, flipType=False)
+
+ if hands:
+ hand = hands[0]
+ lm_list = hand['lmList']
+ if len(lm_list) > 8:
+ thumb_tip = lm_list[4]
+ index_tip = lm_list[8]
+ distance = int(((thumb_tip[0] - index_tip[0]) ** 2 + (thumb_tip[1] - index_tip[1]) ** 2) ** 0.5)
+ brightness = np.interp(distance, [20, 200], [0, 1])
+ ```
+
+- The program calculates the distance between the thumb tip (`lmList[4]`) and index finger tip (`lmList[8]`). This distance is mapped to a brightness range of 0 to 1 using np.interp.
+
+=== "Section 3: Brightness Adjustment and Display "
+
+- The captured frame's brightness is modified by scaling the value (V) channel in the HSV color space according to the calculated brightness level.
+
+ ```python
+ hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
+ hsv[..., 2] = np.clip(hsv[..., 2] * brightness, 0, 255).astype(np.uint8)
+ frame_bright = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)
+ cv2.imshow("Brightness Controller", frame_bright)
+ ```
+
+- This technique ensures smooth, real-time brightness adjustments based on the user's hand gestures. The output frame is displayed with the adjusted brightness level.
+
+
+---
+
+### ⚖️ PROJECT TRADE-OFFS AND SOLUTIONS
+
+
+=== "Trade Off 1"
+ - Real-time processing vs. computational efficiency: Optimized hand detection by limiting the maximum number of detectable hands to 1.
+
+
+=== "Trade Off 2"
+ - Precision in brightness control vs. usability: Adjusted mapping function to ensure smooth transitions.
+
+---
+
+## 🖼 SCREENSHOTS
+??? tip "Working of the model"
+
+ === "Image Topic"
+
+
+
+
+---
+
+## ✅ CONCLUSION
+
+### 🔑 KEY LEARNINGS
+!!! tip "Insights gained from the data"
+- Improved understanding of real-time video processing.
+- Learned to integrate gesture detection with hardware functionalities.
+
+??? tip "Improvements in understanding machine learning concepts"
+- Gained insights into MediaPipe's efficient hand detection algorithms.
+
+---
+
+### 🌍 USE CASES
+=== "Smart Homes"
+- Touchless brightness control for smart home displays.
+
+=== "Assistive Technologies"
+- Brightness adjustment for users with limited mobility.
diff --git a/docs/projects/computer-vision/index.md b/docs/projects/computer-vision/index.md
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+