AI & Machine Learningintermediate

Computer Vision for Real-World Apps

Build practical computer vision applications using modern deep learning techniques.

PH
PlayHveTech Education Platform
November 28, 2025
40 min read
3.2K views

Computer Vision for Real-World Apps

Build practical computer vision applications using modern deep learning techniques.

Introduction

Computer Vision (CV) enables computers to "see" and interpret the visual world. From self-driving cars to medical imaging, CV is transforming industries.

In this tutorial, we will build a real-time object detection system using YOLO (You Only Look Once) and OpenCV.

Prerequisites

  • Python 3.8+
  • Basic understanding of Convolutional Neural Networks (CNNs)

Setting Up

We'll use the ultralytics library for YOLOv8 and opencv-python for image processing.

pip install ultralytics opencv-python

Object Detection with YOLOv8

YOLO is famous for its speed and accuracy. It treats object detection as a single regression problem, straight from image pixels to bounding box coordinates and class probabilities.

from ultralytics import YOLO
import cv2

# Load a pretrained model
model = YOLO('yolov8n.pt')  # 'n' for nano, smallest and fastest

# Run inference on an image
results = model('https://ultralytics.com/images/bus.jpg')  # predict on an image

# Show the results
for r in results:
    im_array = r.plot()  # plot a BGR numpy array of predictions
    im = Image.fromarray(im_array[..., ::-1])  # RGB PIL image
    im.show()  # show image

Real-Time Detection on Webcam

Now let's hook this up to a webcam feed.

cap = cv2.VideoCapture(0)

while True:
    ret, frame = cap.read()
    if not ret:
        break

    # Run inference
    results = model(frame, stream=True)

    # Visualize results
    for r in results:
        boxes = r.boxes
        for box in boxes:
            # Bounding Box
            x1, y1, x2, y2 = box.xyxy[0]
            x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)

            # Class Name
            cls = int(box.cls[0])
            name = model.names[cls]

            # Draw
            cv2.rectangle(frame, (x1, y1), (x2, y2), (255, 0, 255), 3)
            cv2.putText(frame, name, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (255, 0, 255), 2)

    cv2.imshow('Webcam', frame)

    if cv2.waitKey(1) == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()

Advanced: Custom Training

To detect custom objects (e.g., detecting defects on a manufacturing line), you need to train YOLO on your own dataset.

  1. Collect images.
  2. Annotate them using tools like LabelImg or Roboflow.
  3. Train the model:
model.train(data='custom_dataset.yaml', epochs=100)

Conclusion

Computer vision is more accessible than ever. With pretrained models like YOLO, you can build powerful applications with just a few lines of code.

PH

Written by PlayHve

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