feat: initial implementation of People Counter web app

- Add Flask application with MJPEG video streaming
- Implement OpenCV DNN face detection module
- Add zone-based entry/exit tracking with cooldown mechanism
- Create web interface with real-time WebSocket updates
- Add model download script and comprehensive README
- Include OpenCV DNN model files for face detection

download_models.py

@@ -0,0 +1,78 @@
+#!/usr/bin/env python3
+"""
+Script to download OpenCV DNN face detection model files.
+Downloads the required prototxt and caffemodel files for face detection.
+"""
+
+import os
+import urllib.request
+from pathlib import Path
+
+
+def download_file(url, destination):
+    """Download a file from URL to destination."""
+    print(f"Downloading {os.path.basename(destination)}...")
+    try:
+        urllib.request.urlretrieve(url, destination)
+        print(f"✓ Successfully downloaded {os.path.basename(destination)}")
+        return True
+    except Exception as e:
+        print(f"✗ Error downloading {os.path.basename(destination)}: {e}")
+        return False
+
+
+def main():
+    """Main function to download model files."""
+    # Create models directory if it doesn't exist
+    models_dir = Path("models")
+    models_dir.mkdir(exist_ok=True)
+    
+    # Model file URLs
+    prototxt_url = "https://raw.githubusercontent.com/opencv/opencv/master/samples/dnn/face_detector/deploy.prototxt"
+    model_url = "https://raw.githubusercontent.com/opencv/opencv_3rdparty/dnn_samples_face_detector_20170830/res10_300x300_ssd_iter_140000.caffemodel"
+    
+    # Destination paths
+    prototxt_path = models_dir / "deploy.prototxt"
+    model_path = models_dir / "res10_300x300_ssd_iter_140000.caffemodel"
+    
+    print("=" * 60)
+    print("OpenCV DNN Face Detection Model Downloader")
+    print("=" * 60)
+    print()
+    
+    # Check if files already exist
+    if prototxt_path.exists():
+        print(f"⚠ {prototxt_path.name} already exists. Skipping download.")
+    else:
+        success = download_file(prototxt_url, prototxt_path)
+        if not success:
+            print("\nAlternative: You can manually download deploy.prototxt from:")
+            print("https://github.com/opencv/opencv/blob/master/samples/dnn/face_detector/deploy.prototxt")
+            print()
+    
+    if model_path.exists():
+        print(f"⚠ {model_path.name} already exists. Skipping download.")
+    else:
+        success = download_file(model_url, model_path)
+        if not success:
+            print("\n⚠ Warning: The caffemodel file is large (~10MB) and may require manual download.")
+            print("Alternative download methods:")
+            print("1. Using wget:")
+            print(f"   wget -O {model_path} {model_url}")
+            print("2. Using curl:")
+            print(f"   curl -L -o {model_path} {model_url}")
+            print("3. Direct browser download:")
+            print(f"   {model_url}")
+            print()
+    
+    print()
+    print("=" * 60)
+    if prototxt_path.exists() and model_path.exists():
+        print("✓ All model files are ready!")
+    else:
+        print("⚠ Some files may be missing. Please check the files above.")
+    print("=" * 60)
+
+
+if __name__ == "__main__":
+    main()