shit

scripts/image_filters.js

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+// Image filter script for JSCA
+// This script demonstrates how to apply basic image filters to the camera feed
+
+function applyGrayscale(image) {
+    var width = image.getWidth();
+    var height = image.getHeight();
+    var result = new java.awt.image.BufferedImage(
+        width, height, 
+        java.awt.image.BufferedImage.TYPE_BYTE_GRAY
+    );
+    
+    var g = result.getGraphics();
+    g.drawImage(image, 0, 0, null);
+    g.dispose();
+    
+    return result;
+}
+
+function applySepia(image) {
+    var width = image.getWidth();
+    var height = image.getHeight();
+    var result = new java.awt.image.BufferedImage(
+        width, height,
+        java.awt.image.BufferedImage.TYPE_INT_RGB
+    );
+    
+    for (var y = 0; y < height; y++) {
+        for (var x = 0; x < width; x++) {
+            var rgb = image.getRGB(x, y);
+            var r = (rgb >> 16) & 0xFF;
+            var g = (rgb >> 8) & 0xFF;
+            var b = rgb & 0xFF;
+            
+            var tr = Math.min(255, (r * 0.393 + g * 0.769 + b * 0.189));
+            var tg = Math.min(255, (r * 0.349 + g * 0.686 + b * 0.168));
+            var tb = Math.min(255, (r * 0.272 + g * 0.534 + b * 0.131));
+            
+            result.setRGB(x, y, (tr << 16) | (tg << 8) | tb);
+        }
+    }
+    
+    return result;
+}
+
+function applyInvert(image) {
+    var width = image.getWidth();
+    var height = image.getHeight();
+    var result = new java.awt.image.BufferedImage(
+        width, height,
+        java.awt.image.BufferedImage.TYPE_INT_RGB
+    );
+    
+    for (var y = 0; y < height; y++) {
+        for (var x = 0; x < width; x++) {
+            var rgb = image.getRGB(x, y);
+            result.setRGB(x, y, ~rgb);
+        }
+    }
+    
+    return result;
+}
+
+// Apply the selected filter to the input image
+var filterName = "grayscale"; // Change this to "sepia" or "invert" for different effects
+var result;
+
+switch (filterName) {
+    case "grayscale":
+        result = applyGrayscale(inputImage);
+        break;
+    case "sepia":
+        result = applySepia(inputImage);
+        break;
+    case "invert":
+        result = applyInvert(inputImage);
+        break;
+    default:
+        print("Unknown filter: " + filterName);
+        result = inputImage;
+}
+
+// Return the filtered image
+result; 

scripts/motion_detection.py

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+#!/usr/bin/env python3
+
+import sys
+import cv2
+import numpy as np
+
+def detect_motion(image_path):
+    # Read the input image
+    frame = cv2.imread(image_path)
+    if frame is None:
+        print("Error: Could not read image file")
+        sys.exit(1)
+
+    # Convert to grayscale
+    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+    
+    # Apply Gaussian blur to reduce noise
+    blur = cv2.GaussianBlur(gray, (21, 21), 0)
+    
+    # Threshold the image to detect significant changes
+    _, thresh = cv2.threshold(blur, 20, 255, cv2.THRESH_BINARY)
+    
+    # Find contours in the thresholded image
+    contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+    
+    # Filter contours based on area to remove noise
+    min_area = 500
+    motion_detected = False
+    
+    for contour in contours:
+        if cv2.contourArea(contour) > min_area:
+            motion_detected = True
+            # Draw rectangle around motion area
+            (x, y, w, h) = cv2.boundingRect(contour)
+            cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
+    
+    # Save the output image with motion detection boxes
+    output_path = image_path.replace('.jpg', '_motion.jpg')
+    cv2.imwrite(output_path, frame)
+    
+    # Return result
+    if motion_detected:
+        print("Motion detected!")
+        print(f"Output saved to: {output_path}")
+    else:
+        print("No motion detected")
+
+if __name__ == "__main__":
+    if len(sys.argv) != 2:
+        print("Usage: python motion_detection.py <image_path>")
+        sys.exit(1)
+    
+    detect_motion(sys.argv[1])