🔍 Welcome to OpenCV's Object Detection Guide

Object detection is a key computer vision task in OpenCV, enabling identification and localization of objects within images or videos. Below are core concepts and methods for Python developers.

🧠 Core Concepts

  • What is Object Detection?
    Detecting objects involves both classifying (identifying what the object is) and localizing (finding its position via bounding boxes).

    object_detection_basics

  • Common Use Cases

    • Real-time video surveillance
    • Autonomous vehicle navigation
    • Industrial quality inspection
    • Augmented reality applications

🚀 Popular Methods in OpenCV

  1. YOLO (You Only Look Once)
    A fast, real-time detection model.

    YOLO_model
    Example: `cv2.dnn.readNetFromDarknet('yolov3.cfg', 'yolov3.weights')`

  2. SSD (Single Shot MultiBox Detector)
    Simplifies detection with a single network pass.

    SSD_architecture
    Example: `cv2.dnn.blobFromImage(image, 1, (300, 300), (104, 117, 123))`

  3. Haar Cascade
    Traditional method for simple object recognition (e.g., faces, cars).

    Haar_cascade_example
    Example: `cv2.CascadeClassifier('haarcascade_frontalface_default.xml')`

📜 Example Code Snippet

import cv2

# Load pre-trained model
net = cv2.dnn.readNetFromTensorflow('frozen_inference_graph.pb', 'graph.pbtxt')

# Process image
image = cv2.imread('input.jpg')
blob = cv2.dnn.blobFromImage(image, 1/255.0, (300, 300), swapRB=True, crop=False)
net.setInput(blob)
detections = net.forward()

# Draw bounding boxes
for detection in detections:
    if detection[1] > 0.5:  # Confidence threshold
        x, y, w, h = map(int, detection[2:6] * [image.shape[1], image.shape[0], image.shape[1], image.shape[0]])
        cv2.rectangle(image, (x, y), (x+w, y+h), (0, 255, 0), 2)

cv2.imshow('Detection', image)
cv2.waitKey(0)

📚 Expand Your Knowledge

For deeper insights into OpenCV's detection capabilities:

Stay tuned for updates on cutting-edge detection algorithms! 🌟