YOLOv5 Object Detection

example for alert.txt

etc/README.md

@@ -72,4 +72,28 @@ python etc/simulator.py
 **Note:**  
 Edit the `projectName` variable to match the handler function you want to test. You can expand this script to test additional handlers or scenarios as needed.
 
-Feel free to add or update resources here as needed for documentation, configuration, or project support.
+## yolo_vision.py
+
+**Purpose:**  
+`yolo_vision.py` provides real-time object detection and movement tracking using a Raspberry Pi camera and YOLOv5. It is designed for integration with the Mesh Bot project, outputting alerts to both the console and an optional `alert.txt` file for further use (such as with Meshtastic).
+
+**Features:**  
+- Ignores specified object classes (e.g., "bed", "chair") to reduce false positives.
+- Configurable detection confidence threshold and movement sensitivity.
+- Tracks object movement direction (left, right, stationary).
+- Fuse counter: only alerts after an object is detected for several consecutive frames.
+- Optionally writes the latest alert (without timestamp) to a specified file, overwriting previous alerts.
+
+**Configuration:**  
+- `LOW_RES_MODE`: Use low or high camera resolution for CPU savings.
+- `IGNORE_CLASSES`: List of object classes to ignore.
+- `CONFIDENCE_THRESHOLD`: Minimum confidence for reporting detections.
+- `MOVEMENT_THRESHOLD`: Minimum pixel movement to consider as "moving".
+- `ALERT_FUSE_COUNT`: Number of consecutive detections before alerting.
+- `ALERT_FILE_PATH`: Path to alert file (set to `None` to disable file output).
+
+**Usage:**  
+Run this script to monitor the camera feed and generate alerts for detected and moving objects. Alerts are printed to the console and, if configured, written to `alert.txt` for integration with other systems.
+
+---
+

etc/yolo_vision.py

@@ -0,0 +1,137 @@
+#!/usr/bin/env python3
+# YOLOv5 Object Detection with Movement Tracking using Raspberry Pi AI Camera
+# Adapted from Ultralytics YOLOv5 and Picamera2 examples
+# Requirements: torch, torchvision, pillow, picamera2, numpy
+# Adjust settings below as needed, indended for meshing-around alert.txt output to meshtastic
+# 2025 K7MHI Kelly Keeton
+import torch
+from PIL import Image
+import numpy as np
+import time
+from picamera2 import Picamera2
+import warnings
+import sys
+import datetime
+
+# Suppress FutureWarnings from libraries
+warnings.filterwarnings("ignore", category=FutureWarning)
+
+# Load YOLOv5 model, other options include 'yolov5m', 'yolov5l', 'yolov5x'
+model = torch.hub.load("ultralytics/yolov5", "yolov5s")
+
+LOW_RES_MODE = 0  # 1 for low res (320x240), 0 for high res (640x480)
+IGNORE_CLASSES = ["bed", "chair"]  # Add object names to ignore
+CONFIDENCE_THRESHOLD = 0.8  # Only show detections above this confidence
+MOVEMENT_THRESHOLD = 50     # Pixels to consider as movement (adjust as needed)
+IGNORE_STATIONARY = True   # Whether to ignore stationary objects in output
+ALERT_FUSE_COUNT = 5  # Number of consecutive detections before alerting
+ALERT_FILE_PATH = "alert.txt"  # e.g., "/opt/meshing-around/alert.txt" or None for no file output
+
+picam2 = Picamera2()
+if LOW_RES_MODE:
+    picam2.preview_configuration.main.size = (320, 240)
+else:
+    picam2.preview_configuration.main.size = (640, 480)
+picam2.preview_configuration.main.format = "RGB888"
+picam2.configure("preview")
+picam2.start()
+
+print("="*40)
+print("  Sentinal Vision 3000 Booting Up!")
+print("="*40)
+time.sleep(1)
+
+def alert_output(msg, alert_file_path=ALERT_FILE_PATH):
+    print(msg)
+    if alert_file_path:
+        # Remove timestamp for file output
+        msg_no_time = " ".join(msg.split("] ")[1:]) if "] " in msg else msg
+        with open(alert_file_path, "w") as f:  # Use "a" to append instead of overwrite
+            f.write(msg_no_time + "\n")
+
+try:
+    i = 0 # Frame counter if zero will be infinite
+    system_normal_printed = False # system nominal flag, if true disables printing
+    while True:
+        i += 1
+        frame = picam2.capture_array()
+        img = Image.fromarray(frame)
+
+        results = model(img)
+        df = results.pandas().xyxy[0]
+        df = df[df['confidence'] >= CONFIDENCE_THRESHOLD]  # Filter by confidence
+        df = df[~df['name'].isin(IGNORE_CLASSES)]          # Filter out ignored classes
+        counts = df['name'].value_counts()
+        if counts.empty:
+            if not system_normal_printed:
+                print("System nominal: No objects detected.")
+                system_normal_printed = True
+            continue  # Skip the rest of the loop if nothing detected
+        if counts.sum() > ALERT_FUSE_COUNT:
+            system_normal_printed = False  # Reset flag if something is detected
+
+        # Movement tracking
+        if not hasattr(__builtins__, 'prev_centers'):
+            __builtins__.prev_centers = {}
+        if not hasattr(__builtins__, 'stationary_reported'):
+            __builtins__.stationary_reported = set()
+        if not hasattr(__builtins__, 'fuse_counters'):
+            __builtins__.fuse_counters = {}
+
+        timestamp = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+        current_centers = {}
+        detected_this_frame = set()
+
+        for idx, row in df.iterrows():
+            obj_id = f"{row['name']}_{idx}"
+            x_center = (row['xmin'] + row['xmax']) / 2
+            current_centers[obj_id] = x_center
+            detected_this_frame.add(obj_id)
+
+            prev_x = __builtins__.prev_centers.get(obj_id)
+            direction = ""
+            count = counts[row['name']]
+
+            # Fuse logic
+            fuse_counters = __builtins__.fuse_counters
+            if obj_id not in fuse_counters:
+                fuse_counters[obj_id] = 1
+            else:
+                fuse_counters[obj_id] += 1
+
+            if fuse_counters[obj_id] < ALERT_FUSE_COUNT:
+                continue  # Don't alert yet
+
+            if prev_x is not None:
+                delta = x_center - prev_x
+                if abs(delta) < MOVEMENT_THRESHOLD:
+                    direction = "stationary"
+                    if IGNORE_STATIONARY:
+                        if obj_id not in __builtins__.stationary_reported:
+                            alert_output(f"[{timestamp}] {count} {row['name']} {direction}")
+                            __builtins__.stationary_reported.add(obj_id)
+                    else:
+                        alert_output(f"[{timestamp}] {count} {row['name']} {direction}")
+                else:
+                    direction = "moving right" if delta > 0 else "moving left"
+                    alert_output(f"[{timestamp}] {count} {row['name']} {direction}")
+                    __builtins__.stationary_reported.discard(obj_id)
+            else:
+                direction = "detected"
+                alert_output(f"[{timestamp}] {count} {row['name']} {direction}")
+
+        # Reset fuse counters for objects not detected in this frame
+        for obj_id in list(__builtins__.fuse_counters.keys()):
+            if obj_id not in detected_this_frame:
+                __builtins__.fuse_counters[obj_id] = 0
+
+        __builtins__.prev_centers = current_centers
+
+        time.sleep(1)  # Adjust frame rate as needed
+except KeyboardInterrupt:
+    print("\nInterrupted by user. Shutting down...")
+except Exception as e:
+    print(f"\nAn error occurred: {e}", file=sys.stderr)
+finally:
+    picam2.close()
+    print("Camera closed. Goodbye!")