This bootcamp introduces participants to the development of AI-powered camera detection applications using Python, OpenCV, PyTorch, pretrained YOLO models, and FastAPI. It covers programming foundations, machine learning and deep learning concepts, image and video processing, real-time object detection, and integration with web applications.
Participants progress from foundational concepts to an end-to-end camera detection prototype that connects camera input, AI inference, backend APIs, and a web frontend. Applications include object monitoring, automated inspection, and pest detection where suitable models and labelled datasets are available.
Duration 20 Days – 140 hrs.
| Phase | Duration |
| Python Programming for AI | 3 days |
| ML & Deep Learning | 3 days |
| Computer Vision and AI-Powered Camera | 5 days |
| AI Model Deployment, REST APIs, and Web | 4 days |
| Capstone Project | 5 days |
| Total | 20 days / 140 hours |
Objectives
- Develop Python programs for data handling, image processing, and AI application development.
- Explain the fundamentals of machine learning, deep learning, and object detection.
- Process images, recorded videos, and live camera streams using OpenCV.
- Implement object detection using pretrained YOLO models.
- Apply basic object tracking, counting, and event detection.
- Prepare datasets and explain the requirements for fine-tuning detection models.
- Interpret detection metrics and identify model limitations.
- Develop REST APIs that expose AI inference functionality.
- Integrate detection results into a web application.
- Build and document an end-to-end AI-powered camera detection prototype.
Target Audience
- Software developers building AI-enabled applications.
- Python programmers entering computer vision development.
- Web developers integrating AI inference into existing applications.
- Engineers developing camera monitoring and automated inspection solutions.
- Technical teams working on pest monitoring or similar object detection applications.
- AI and machine learning practitioners seeking experience with camera-based systems.
Prerequisites
- Participants should have basic programming knowledge and web development experience.
- The client should identify the intended camera detection use case before the training begins.
- Suitable development machines, cameras, and computing resources must be available.
- Custom model fine-tuning depends on the availability and quality of labelled data.
- The capstone deliverable is a proof of concept; production readiness, accuracy, scalability, and real-time performance depend on the specific requirements and hardware.
Course Outline
Phase 1: Python Programming Fundamentals for AI – Development (3D, 21 hrs.)
Day 1: Python Programming Essentials
- Python installation and development environment setup
- Variables, data types, operators, and type conversion
- Conditional statements and loops
- Functions, parameters, return values, and scope
- Hands-on programming exercises
Day 2: Python Data Structures and Application Development
- Lists, tuples, dictionaries, and sets
- Strings, comprehensions, and common built-in functions
- Modules, packages, and exception handling
- File handling and JSON processing
- Object-oriented programming fundamentals
Day 3: Python Libraries for AI Development
- Virtual environments and package management
- NumPy arrays, indexing, slicing, and basic numerical operations
- Introduction to Pandas for data handling
- Installing and configuring OpenCV and PyTorch
- Reading images and videos using Python
- Introduction to AI development workflows
Expected Outcome: Participants can write Python programs, manage the
development environment, and perform basic image and data processing.
Phase 2: Machine Learning and Deep Learning – Fundamentals (3D, 21 hrs.)
Day 4: AI and Machine Learning Fundamentals
- Artificial Intelligence, Machine Learning, and Deep Learning
- Supervised and unsupervised learning
- Dataset, features, labels, and model predictions
- Classification and regression overview
- AI applications, capabilities, and limitations
Day 5: Deep Learning and Neural Networks
- Neural network architecture and layers
- Weights, biases, activation functions, and loss functions
- Training, validation, and testing workflows
- CNN fundamentals for image-related tasks
- Pretrained models and transfer learning
Day 6: Model Evaluation and Object Detection Concepts
- Overfitting, underfitting, and generalization
- Confusion matrix, precision, recall, and F1-score
- Image classification versus object detection
- Bounding boxes, confidence scores, and Intersection over Union (IoU)
- Introduction to object detection evaluation metrics
- Overview of pretrained AI models and inference
Expected Outcome: Participants understand the AI model development lifecycle,
common evaluation metrics, and the principles behind object detection.
Phase 3: Computer Vision and AI-Powered Camera – Object Detection (5D, 35 hrs.)
Day 7: Computer Vision with OpenCV
- Digital image representation and color spaces
- Image reading, display, resizing, and cropping
- Image filtering and noise reduction
- Thresholding, edge detection, and contours
- Image preprocessing and enhancement
Day 8: Video Processing and Camera Integration
- Reading and processing video files
- Frame extraction and frame-by-frame processing
- USB webcam integration using OpenCV
- Introduction to IP cameras and RTSP streams
- Camera error handling and frame-rate measurement
Day 9: YOLO-Based Object Detection
- Object detection pipeline and YOLO overview
- Setting up a pretrained YOLO-family model
- Image and video inference
- Bounding boxes, class labels, and confidence thresholds
- Visualizing and saving detection results
Day 10: Real-Time Detection and Object Tracking
- Live camera-based object detection
- Processing and displaying real-time detection results
- Introduction to object tracking and object IDs
- Basic object counting and event detection
- Measuring inference latency and frames per second
- Handling common detection errors
Day 11: Custom Detection and Model Evaluation
- Defining target objects and detection requirements
- Dataset collection and annotation fundamentals
- Dataset organization and annotation formats
- Introduction to fine-tuning pretrained YOLO models
- Model validation and evaluation
- Testing detection under different lighting and environmental conditions
Expected Outcome: Participants build a working Python application that detects
selected objects in images, recorded videos, or a supported live camera feed.
Basic model customization is covered where suitable data and computing
resources are available.
Phase 4: AI Model Deployment, REST APIs, and Web – Application Integration (4D, 28 hrs.)
Day 12: AI Inference Application Development
- Designing an AI inference pipeline
- Loading and executing pretrained models
- Separating model inference from application logic
- Processing image uploads and video frames
- Logging, configuration, and error handling
Day 13: Building REST APIs with FastAPI
- REST API fundamentals and FastAPI setup
- Request and response models
- Image upload and inference endpoints
- Returning detection labels, confidence scores, and bounding boxes as JSON
- API validation and interactive documentation
Day 14: Web Application Integration
- Frontend-to-backend communication
- Calling AI APIs using JavaScript
- Integrating with the client’s existing web framework
- Displaying detection results and annotated images
- Handling API errors and presenting inference status
Day 15: Camera Streaming and Deployment Fundamentals
- Camera-to-inference-to-frontend architecture
- Streaming and WebSocket concepts
- Basic latency and resource optimization
- Managing concurrent requests
- Deployment configuration, logging, and security fundamentals
Expected Outcome: Participants integrate AI inference into a web application
through APIs and demonstrate detection results through a frontend interface.
Phase 5: End-to-End AI Camera Detection Capstone – Project (5D, 35 hrs.)
Day 16: Requirements Analysis and System Design
- Identify the client’s target detection use case
- Define object classes, camera inputs, and success criteria
- Design the system architecture
- Select the model and deployment approach
- Prepare a project plan and test scenarios
Day 17: Camera and AI Detection Pipeline
- Implement camera or video input
- Integrate the selected detection model
- Process frames and generate detection metadata
- Display bounding boxes and labels
- Test the detection pipeline
Day 18: Backend and Frontend Integration
- Implement inference API endpoints
- Connect the AI service to the web application
- Display detection results and object information
- Handle errors and camera interruptions
- Validate end-to-end application functionality
Day 19: Testing and Performance Optimization
- Functional testing and edge-case testing
- Measure inference latency and throughput
- Test different input conditions
- Optimize model settings and frame processing
- Document limitations and outstanding issues
Day 20: Final Demonstration and Knowledge Transfer
- Demonstrate the completed prototype
- Review the application architecture and source code
- Present test results and performance observations
- Document setup instructions and API usage
- Identify production-readiness gaps and future enhancements
Expected Outcome: A demonstrable proof of concept integrating camera input, AI
object detection, backend inference, and a web frontend, supported by source
code, technical documentation, test results, and a list of recommended next steps.
Recommended Technology Stack
- Programming: Python
- Computer Vision: OpenCV
- Object Detection: Pretrained YOLO-family model
- Deep Learning: PyTorch
- Backend API: FastAPI
- Frontend Integration: JavaScript and the client’s existing web framework
- Optional Deployment: ONNX Runtime or Docker, depending on requirements
Training Methodology
- Instructor-led technical sessions
- Live coding and guided demonstrations
- Hands-on programming exercises
- Image, video, and camera-based experiments
- API development and web integration labs
- Capstone project development and final demonstration
Scope note: This is a training program with a hands-on proof-of-concept deliverable. Production deployment, guaranteed detection accuracy, and integration into a live business system would require separate requirements, validation, and acceptance planning.
