Practical Computer Vision Applications

Real-World CV Applications

• Face Detection & Recognition: MTCNN/RetinaFace for detection, ArcFace/FaceNet for recognition — identify individuals from facial features. Used in security, authentication, social media
• Pose Estimation: OpenPose, MediaPipe, HRNet — detect human body keypoints (joints). Used in fitness apps, gaming, sports analytics, AR try-on
• Optical Character Recognition (OCR): Tesseract, PaddleOCR, TrOCR — extract text from images. Used in document scanning, license plate recognition, receipt processing
• Video Analysis: Action recognition, object tracking, anomaly detection — process temporal sequences. Used in surveillance, sports analytics, content moderation
• Medical Imaging: Tumor detection, retinal analysis, X-ray classification — CNNs achieve radiologist-level accuracy in specific tasks
• Generative Vision: GANs (image synthesis), Stable Diffusion (text-to-image), Neural Style Transfer — creating and manipulating images with AI

Computer Vision Tools & Frameworks

• OpenCV: The standard library for classical CV — image processing, feature detection, video I/O. Over 2500 algorithms
• PyTorch + torchvision: Deep learning with pre-trained models (ResNet, YOLO), datasets (COCO, ImageNet), and transforms
• TensorFlow + TF Hub: Production ML with model serving. TF Lite for mobile/edge deployment
• Hugging Face Transformers: Access SOTA vision models (ViT, DINOv2, SAM) with simple APIs
• Ultralytics (YOLOv8+): Production-ready object detection in one line of code — train, validate, predict, export
• Roboflow: Dataset management, annotation, augmentation — upload images and get a trained model

Try It Yourself: Model Evaluation

# ML Model Evaluation

actual =    [1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0]
predicted = [1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0]

tp = sum(1 for a, p in zip(actual, predicted) if a == 1 and p == 1)
tn = sum(1 for a, p in zip(actual, predicted) if a == 0 and p == 0)
fp = sum(1 for a, p in zip(actual, predicted) if a == 0 and p == 1)
fn = sum(1 for a, p in zip(actual, predicted) if a == 1 and p == 0)

accuracy = (tp + tn) / len(actual)
precision = tp / (tp + fp) if (tp + fp) > 0 else 0
recall = tp / (tp + fn) if (tp + fn) > 0 else 0
f1 = 2 * precision * recall / (precision + recall) if (precision + recall) > 0 else 0

print("Model Evaluation")
print("TP=" + str(tp) + "  FP=" + str(fp))
print("FN=" + str(fn) + "  TN=" + str(tn))
print("Accuracy: " + str(round(accuracy*100,1)) + "%")
print("Precision: " + str(round(precision*100,1)) + "%")
print("Recall: " + str(round(recall*100,1)) + "%")
print("F1: " + str(round(f1*100,1)) + "%")

Quick Quiz — Computer Vision