From 795199cad64b287bab28434e08b39b877206b203 Mon Sep 17 00:00:00 2001
From: iamdevdhanush <dhanushdprabhu18@gmail.com>
Date: Mon, 5 Jan 2026 09:02:41 +0000
Subject: [PATCH 1/2] Added Motion Detection Project

---
 Motion-Detection | 1 +
 1 file changed, 1 insertion(+)
 create mode 160000 Motion-Detection

diff --git a/Motion-Detection b/Motion-Detection
new file mode 160000
index 0000000..e4011e7
--- /dev/null
+++ b/Motion-Detection
@@ -0,0 +1 @@
+Subproject commit e4011e7254c239ba2286c6c300c633ff52045f9e

From d3ada1e750c2d31d9b964ba0c7cd7adaf376f0e6 Mon Sep 17 00:00:00 2001
From: iamdevdhanush <dhanushdprabhu18@gmail.com>
Date: Mon, 5 Jan 2026 09:39:29 +0000
Subject: [PATCH 2/2] Convert Motion-Detection from submodule to normal folder

---
 Motion-Detection                  |  1 -
 Motion-Detection/OpenCvPROJECT.py | 52 ++++++++++++++++++++++++++++
 Motion-Detection/README.md        | 57 +++++++++++++++++++++++++++++++
 3 files changed, 109 insertions(+), 1 deletion(-)
 delete mode 160000 Motion-Detection
 create mode 100644 Motion-Detection/OpenCvPROJECT.py
 create mode 100644 Motion-Detection/README.md

diff --git a/Motion-Detection b/Motion-Detection
deleted file mode 160000
index e4011e7..0000000
--- a/Motion-Detection
+++ /dev/null
@@ -1 +0,0 @@
-Subproject commit e4011e7254c239ba2286c6c300c633ff52045f9e
diff --git a/Motion-Detection/OpenCvPROJECT.py b/Motion-Detection/OpenCvPROJECT.py
new file mode 100644
index 0000000..404de24
--- /dev/null
+++ b/Motion-Detection/OpenCvPROJECT.py
@@ -0,0 +1,52 @@
+import cv2  
+import time 
+import imutils 
+
+cam = cv2.VideoCapture(0)
+time.sleep(0)
+firstFrame = None
+area = 500
+frameUpdateInterval = 100  
+frameCount = 0
+
+while True:
+    ret, img = cam.read()  
+    if not ret:
+        break
+
+    text = "Normal"
+    img = imutils.resize(img, width=600)  
+    grayImg = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) 
+    gaussianImg = cv2.GaussianBlur(grayImg, (21, 21), 0)  
+
+    if firstFrame is None or frameCount % frameUpdateInterval == 0:
+        firstFrame = gaussianImg
+        frameCount = 0
+
+    frameCount += 1
+
+    imgDiff = cv2.absdiff(firstFrame, gaussianImg)
+
+    threshImg = cv2.threshold(imgDiff, 25, 255, cv2.THRESH_BINARY)[1]
+    threshImg = cv2.dilate(threshImg, None, iterations=2)
+
+    cnts = cv2.findContours(threshImg.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+    cnts = imutils.grab_contours(cnts)
+
+    for c in cnts:
+        if cv2.contourArea(c) < area:
+            continue
+
+        (x, y, w, h) = cv2.boundingRect(c)
+        cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)
+        text = "Moving Object detected"
+
+    cv2.putText(img, text, (10, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
+    cv2.imshow("cameraFeed", img)
+
+    key = cv2.waitKey(1) & 0xFF
+    if key == ord("q"):
+        break
+
+cam.release()
+cv2.destroyAllWindows()
diff --git a/Motion-Detection/README.md b/Motion-Detection/README.md
new file mode 100644
index 0000000..485594a
--- /dev/null
+++ b/Motion-Detection/README.md
@@ -0,0 +1,57 @@
+# 🎥 Motion Detection Using OpenCV 🕵️‍♂️
+
+This project demonstrates a **basic motion detection system** using OpenCV and Python. It captures video from a camera, processes the frames to detect moving objects, and highlights detected motion areas in real-time. 🚀
+
+---
+
+## ✨ Features
+
+- 📹 Capture video feed from the webcam.
+- 🎨 Convert frames to grayscale and apply Gaussian blur for noise reduction.
+- 🔍 Detect motion by comparing the current frame with the first frame.
+- 🔲 Highlight moving objects with bounding rectangles.
+- 📝 Display status text indicating whether motion is detected.
+- ❌ Press **`q`** to quit the application.
+
+---
+
+## 🛠️ Requirements
+
+- Python 3.x 🐍
+- OpenCV (`cv2`) 🖼️
+- Imutils 🔧
+- Time module (standard Python library) ⏰
+
+---
+
+## 📦 Installation
+
+Install required libraries using pip:
+
+```bash
+pip install opencv-python imutils
+```
+
+▶️ Usage
+
+Run the script to start the motion detection:
+
+```bash
+python motion_detection.py
+```
+
+⚙️ How It Works
+
+🥇 The first frame is captured and used as a reference.
+
+🖤 Each new frame is converted to grayscale and blurred.
+
+➖ The absolute difference between the current frame and the reference frame is calculated.
+
+⚫ Thresholding and dilation are applied to highlight differences.
+
+🔎 Contours are detected in the thresholded image.
+
+✅ If contours exceed a minimum area, bounding boxes are drawn, and motion is detected.
+
+📺 The video feed with detected motion highlighted is displayed.