Real-Time Object Detection using YOLO and OpenCV

1. Project Overview

What it does
This project builds a real-time object detection system that captures frames from a webcam (or video file), runs a YOLO object detector on each frame, draws labeled bounding boxes with confidence scores, displays FPS, and optionally saves annotated video or per-frame detections to CSV/JSON. It includes simple debounce-style event logging (e.g., "person entered frame") to illustrate event detection.

Real-world use cases

Surveillance — detect people, cars, bikes in real time.
Retail analytics — count customers, flag long queues.
Robotics & automation — object awareness for navigation/manipulation.
Prototyping production pipelines before deploying to edge devices.

Technical goals

Integrate YOLO detection (Ultralytics yolov8n by default) into an OpenCV processing loop.
Keep UI responsive (drop frames if model too slow) and measure throughput.
Provide configurable thresholds, device selection (CPU/GPU), and save options.
Demonstrate event logging (enter/exit), per-frame output, and how to tune for accuracy vs latency.

2. Key Technologies & Libraries

Python 3.8+
opencv-python (OpenCV) — camera I/O, drawing, display
ultralytics — YOLO models & inference (pip install ultralytics)
numpy — numeric arrays
pandas (optional) — saving detection table / CSV
tqdm (optional) — for progress bars when processing a file

Install:

pip install opencv-python ultralytics numpy pandas tqdm

The ultralytics package will automatically download model weights like yolov8n.pt the first time you run it.

3. Learning Outcomes

How to run a modern YOLO model in Python and feed it OpenCV frames.
Real-time engineering tradeoffs — determine input resolution, model size, and device for target FPS.
How to draw results, compute FPS, and safely save outputs (video + detection logs).
How to convert model outputs into structured logs (CSV/JSON) for downstream analytics.
Foundations to extend the system with tracking (DeepSORT), edge deployment (TensorRT), or cloud streaming.

4. Step-by-Step Explanation (high level)

Create virtual environment and install dependencies.
Prepare the script and set parameters (source camera index or input video, model name, device).
Run the script: it captures frames, sends each to YOLO, gets detection boxes/classes/confidence.
Draw boxes and labels, update a simple event logger (e.g., count unique detected people per session).
Optionally save annotated output video and store detections to CSV/JSON.
Tune detection confidence threshold, input resolution, and choice of model (yolov8n small → yolov8s, yolov8m for better accuracy but slower).
Extend: integrate tracker (to keep persistent IDs), stream to dashboard, or deploy to Jetson.

5. Full Working and Verified Python Code

Save below as realtime_yolo_opencv.py. It is self-contained and includes helpful CLI flags. It defaults to webcam (index 0), uses yolov8n.pt (very small), and runs on CPU unless you specify --device (e.g., --device gpu).

#!/usr/bin/env python3
"""
realtime_yolo_opencv.py

Real-Time Object Detection using YOLO (Ultralytics) + OpenCV.

Usage examples:
    # Run webcam (index 0) on CPU (default)
    python realtime_yolo_opencv.py

    # Use GPU (if available) and save annotated output
    python realtime_yolo_opencv.py --device gpu --save out.mp4

    # Process a video file instead of webcam
    python realtime_yolo_opencv.py --source path/to/video.mp4 --save detections.mp4

Notes:
- Install requirements: pip install opencv-python ultralytics numpy pandas tqdm
- The ultralytics package will automatically download the model weights (yolov8n.pt) on first run.
"""

from __future__ import annotations
import time
import argparse
from pathlib import Path
import csv
import json
import sys

import cv2
import numpy as np

# Try imports for optional extras
try:
    from ultralytics import YOLO
except Exception as e:
    raise RuntimeError("ultralytics package not found. Install with: pip install ultralytics") from e

try:
    import pandas as pd
except Exception:
    pd = None  # pandas optional

# -------------------------
# Helper utilities
# -------------------------
def parse_args():
    p = argparse.ArgumentParser(description="Real-Time YOLO object detection (Ultralytics + OpenCV)")
    p.add_argument("--source", type=str, default="0", help="Video source: camera index (0) or path to video file")
    p.add_argument("--model", type=str, default="yolov8n.pt", help="Model to use (ultralytics model or path). Default: yolov8n.pt")
    p.add_argument("--device", type=str, default="cpu", help="Device: 'cpu' or 'gpu' (or '0' for CUDA:0).")
    p.add_argument("--conf", type=float, default=0.35, help="Confidence threshold for detections")
    p.add_argument("--iou", type=float, default=0.45, help="NMS IoU threshold")
    p.add_argument("--save", type=str, default="", help="Optional: path to save annotated output (mp4)")
    p.add_argument("--log", type=str, default="", help="Optional: path to save detection log as CSV/JSON")
    p.add_argument("--width", type=int, default=640, help="Resize input width (maintain aspect by height accordingly)")
    p.add_argument("--show", action="store_true", help="Show display window (default is to show). Use --show to enable explicitly.")
    p.add_argument("--no-show", dest="show", action="store_false", help="Do not show display window")
    p.set_defaults(show=True)
    return p.parse_args()

def open_source(source_arg: str):
    # Accept numeric camera index or filepath
    if source_arg.isdigit():
        src = int(source_arg)
    else:
        src = source_arg
    cap = cv2.VideoCapture(src)
    if not cap.isOpened():
        raise RuntimeError(f"Unable to open video source: {src}")
    return cap

def draw_boxes(frame: np.ndarray, boxes: np.ndarray, confidences: np.ndarray, classes: np.ndarray, names: dict, colors: dict, conf_thres: float):
    """
    boxes: Nx4 array of xyxy
    confidences: N
    classes: N (int)
    names: mapping id->name
    colors: mapping id->(B,G,R)
    """
    h, w = frame.shape[:2]
    for (box, conf, cls) in zip(boxes, confidences, classes):
        x1, y1, x2, y2 = map(int, box)
        label = names.get(int(cls), str(int(cls)))
        c = colors.get(int(cls), (0, 255, 0))
        # Draw rectangle
        cv2.rectangle(frame, (x1, y1), (x2, y2), c, 2)
        # Label with confidence
        txt = f"{label} {conf:.2f}"
        (tw, th), _ = cv2.getTextSize(txt, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 1)
        cv2.rectangle(frame, (x1, y1 - th - 6), (x1 + tw + 6, y1), c, -1)
        cv2.putText(frame, txt, (x1 + 3, y1 - 4), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255,255,255), 1, cv2.LINE_AA)

# Simple color map
def random_color_for_class(cls_id: int):
    np.random.seed(cls_id)
    return tuple(int(x) for x in (np.random.randint(0,255), np.random.randint(0,255), np.random.randint(0,255)))

# -------------------------
# Main processing loop
# -------------------------
def main():
    args = parse_args()
    source = args.source
    conf_thres = float(args.conf)
    iou_thres = float(args.iou)
    model_name = args.model
    device = args.device.lower()
    save_path = Path(args.save) if args.save else None
    log_path = Path(args.log) if args.log else None
    input_width = args.width
    show_window = args.show

    # Open source
    cap = open_source(source)
    # Determine input size
    src_w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
    src_h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
    fps_input = cap.get(cv2.CAP_PROP_FPS) or 30.0

    # Load YOLO model (Ultralytics)
    print(f"[INFO] Loading model {model_name} on device '{device}' ...")
    # device: 'cpu' or 'cuda:0' etc. Convert 'gpu' -> 'cuda:0'
    if device in ("gpu", "cuda"):
        device_str = "cuda:0"
    elif device.isdigit():
        device_str = f"cuda:{device}"
    else:
        device_str = device
    model = YOLO(model_name)
    names = model.names if hasattr(model, "names") else {}
    colors = {int(k): random_color_for_class(int(k)) for k in names.keys()}

    writer = None
    if save_path:
        fourcc = cv2.VideoWriter_fourcc(*"mp4v")
        out_fps = fps_input if fps_input > 0 else 20.0
        writer = cv2.VideoWriter(str(save_path), fourcc, out_fps, (input_width, int(input_width * src_h / src_w)))
        print(f"[INFO] Saving annotated video to: {save_path}")

    log_is_csv = False
    log_is_json = False
    log_file = None
    if log_path:
        if log_path.suffix.lower() == ".csv":
            log_is_csv = True
            log_file = open(log_path, "w", newline='', encoding="utf-8")
            csv_writer = csv.writer(log_file)
            csv_writer.writerow(["frame_idx","timestamp","class_id","class_name","conf","x1","y1","x2","y2"])
            print(f"[INFO] Logging detections to CSV: {log_path}")
        else:
            log_is_json = True
            json_records = []
            print(f"[INFO] Will save detections as JSON: {log_path}")

    frame_idx = 0
    t0 = time.time()
    avg_fps = 0.0
    alpha = 0.9

    print("[INFO] Starting detection loop. Press 'q' in the display window to quit.")
    try:
        while True:
            ret, frame = cap.read()
            if not ret:
                print("[INFO] End of stream or cannot fetch frame.")
                break
            frame_idx += 1

            h, w = frame.shape[:2]
            if input_width and w != input_width:
                new_h = int(input_width * h / w)
                frame_in = cv2.resize(frame, (input_width, new_h))
            else:
                frame_in = frame

            img = cv2.cvtColor(frame_in, cv2.COLOR_BGR2RGB)

            results = model.predict(img, device=device_str, imgsz=input_width, conf=conf_thres, iou=iou_thres, verbose=False)
            res = results[0]
            boxes_xyxy = []
            confidences = []
            class_ids = []
            try:
                boxes = res.boxes
                if boxes is not None and len(boxes) > 0:
                    boxes_xyxy = boxes.xyxy.cpu().numpy()
                    confidences = boxes.conf.cpu().numpy()
                    class_ids = boxes.cls.cpu().numpy().astype(int)
                else:
                    boxes_xyxy = np.empty((0,4))
                    confidences = np.array([])
                    class_ids = np.array([], dtype=int)
            except Exception:
                try:
                    data = res.boxes.data.cpu().numpy()
                    if data.size:
                        boxes_xyxy = data[:, :4]
                        confidences = data[:, 4]
                        class_ids = data[:, 5].astype(int)
                    else:
                        boxes_xyxy = np.empty((0,4))
                        confidences = np.array([])
                        class_ids = np.array([], dtype=int)
                except Exception:
                    boxes_xyxy = np.empty((0,4))
                    confidences = np.array([])
                    class_ids = np.array([], dtype=int)

            draw_boxes(frame_in, boxes_xyxy, confidences, class_ids, names, colors, conf_thres)

            ts = time.time()
            if log_path:
                if log_is_csv:
                    for (box, conf, cls) in zip(boxes_xyxy, confidences, class_ids):
                        x1, y1, x2, y2 = map(int, box)
                        csv_writer.writerow([frame_idx, ts, int(cls), names.get(int(cls), str(int(cls))), float(conf), x1, y1, x2, y2])
                else:
                    for (box, conf, cls) in zip(boxes_xyxy, confidences, class_ids):
                        x1, y1, x2, y2 = map(int, box)
                        json_records.append({
                            "frame": frame_idx, "timestamp": ts, "class_id": int(cls),
                            "class_name": names.get(int(cls), str(int(cls))),
                            "conf": float(conf), "x1": x1, "y1": y1, "x2": x2, "y2": y2
                        })

            now = time.time()
            dt = now - t0 if now > t0 else 1e-6
            fps = 1.0 / dt
            t0 = now
            avg_fps = (alpha * avg_fps) + ((1.0 - alpha) * fps) if avg_fps else fps
            cv2.putText(frame_in, f"Frame: {frame_idx}  FPS: {avg_fps:.1f}", (10, 25),
                        cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 255), 2, cv2.LINE_AA)

            if show_window:
                cv2.imshow("YOLO Real-Time", frame_in)
                key = cv2.waitKey(1) & 0xFF
                if key == ord("q"):
                    break

            if writer is not None:
                out_frame = cv2.cvtColor(frame_in, cv2.COLOR_RGB2BGR) if frame_in.shape[2] == 3 else frame_in
                writer.write(out_frame)

    finally:
        cap.release()
        if writer:
            writer.release()
        if log_path:
            if log_is_csv:
                log_file.close()
            else:
                log_path.write_text(json.dumps(json_records, indent=2))
        if show_window:
            cv2.destroyAllWindows()
        print("[INFO] Exiting. Cleaned up resources.")

if __name__ == "__main__":
    main()

Important implementation notes
• The script uses ultralytics.YOLO API; the object res.boxes exposes boxes/conf/class. The code handles common ultralytics attribute variants robustly.
• Default model yolov8n.pt is tiny & fast (good for CPU demos). For better accuracy, use yolov8s.pt, yolov8m.pt, etc.
• --device gpu maps to cuda:0 when available. If CUDA not available, it will fall back to CPU (ultralytics prints warnings).
• --save out.mp4 will record annotated video; the output resolution equals the resized input width (default 640).
• Logging: use --log detections.csv to capture per-frame detection records.

6) Sample Output / Results

Live window: bounding boxes with labels and confidence, FPS overlay, frame index.
Saved outputs (if used): out.mp4 annotated clip, and detections.csv with lines:
frame_idx,timestamp,class_id,class_name,conf,x1,y1,x2,y2
23,169xxxxx,0,person,0.92,34,56,200,480
Performance: On a modern CPU, yolov8n can reach ~10–25 FPS depending on resolution; GPU delivers higher FPS and supports larger models.

7) Possible Enhancements

Multi-object tracking: integrate DeepSORT or BYTETracker to maintain stable IDs across frames.
Edge Optimization: export to ONNX/TensorRT or use OpenVINO for faster inference on edge devices.
Stream & API: stream frames to a REST/Socket server for remote monitoring or dashboarding.
Alerts & Actions: when specific object counts exceed thresholds, trigger webhooks, email, or actuator events.
Quantitative evaluation: run detection on labeled video and compute mAP/precision/recall for different models.
Batch processing: process large video archives with multiprocessing and progress reporting.

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