VisionLab VCL Tutorials: Getting Started with Core ToolsVisionLab VCL is a modular computer vision toolkit designed to accelerate image and video analysis workflows. This tutorial-oriented guide covers the essential components, common workflows, and practical examples to help you get started quickly and build reliable vision systems.
Who this guide is for
- Developers and engineers new to VisionLab VCL who want a hands-on introduction.
- Computer vision researchers looking for a concise reference to core modules.
- Students and hobbyists building vision projects (detection, tracking, measurement).
Prerequisites
- Basic experience with programming (Python, C++, or the VisionLab-supported language).
- Familiarity with core computer vision concepts: images, filters, feature detection, and basic linear algebra.
- VisionLab VCL installed on your system (refer to official docs for installation steps).
- A development environment with access to sample images or video.
Core concepts and architecture
VisionLab VCL follows a pipeline-based architecture built around modular components:
- Modules — self-contained units (readers, preprocessors, detectors, trackers, analyzers).
- Pipelines — ordered chains of modules that process frames or images sequentially.
- Datasets — collections of images or video streams, with optional annotations.
- Connectors — interfaces for I/O: file readers, camera inputs, and cloud sources.
- Visualizers — components that render results on images, video overlays, or dashboards.
This modularity makes it easy to swap algorithms (for example, replace a detector module) without reworking the whole pipeline.
Common modules you’ll encounter
- Input/Output: ImageReader, VideoReader, CameraCapture, DatasetLoader
- Preprocessing: Resize, Crop, ColorConvert, Denoise, CLAHE (contrast-limited adaptive histogram equalization)
- Feature & Detection: EdgeDetector, CornerDetector, DeepDetector (model-based), TemplateMatcher
- Postprocessing: NonMaxSuppression, Morphology (erode/dilate), ContourExtractor
- Tracking & Association: KalmanTracker, SORT, IOUTracker, ReIDMatcher
- Measurement & Analysis: ObjectSizer, PoseEstimator, OpticalFlow, ActivityClassifier
- Utilities: Logger, Profiler, MetricEvaluator (precision/recall/mAP), Exporter
Quickstart: Building your first pipeline (conceptual)
- Choose an input connector (VideoReader for a file or CameraCapture for a live feed).
- Add preprocessing modules (Resize to a standard resolution, ColorConvert to RGB).
- Insert a detection module (DeepDetector or TemplateMatcher).
- Apply postprocessing (NonMaxSuppression, thresholding).
- Optionally attach a tracking module (KalmanTracker) to maintain object IDs across frames.
- Visualize or export results (draw bounding boxes, output JSON annotations).
Example: Python-style pseudocode
from visionlab import Pipeline, VideoReader, Resize, DeepDetector, NonMaxSuppression, KalmanTracker, Visualizer, JSONExporter # Create pipeline pipe = Pipeline() # Input pipe.add(VideoReader("videos/traffic.mp4")) # Preprocess pipe.add(Resize(1280, 720)) # Detection pipe.add(DeepDetector(model="yolov5s", conf=0.4)) # Postprocess pipe.add(NonMaxSuppression(iou_thresh=0.5)) # Tracking pipe.add(KalmanTracker(max_lost=30)) # Output pipe.add(Visualizer(draw_labels=True)) pipe.add(JSONExporter("results/traffic.json")) # Run pipe.run() Notes: variable/module names are illustrative. Replace with actual VisionLab VCL API calls per documentation.
Practical tutorials
1) Basic object detection on images
- Goal: detect objects in still images and export bounding boxes to JSON.
- Steps:
- Use ImageReader to load images from a folder.
- Apply Resize and ColorConvert.
- Run DeepDetector with a pretrained model.
- Apply NonMaxSuppression and confidence threshold.
- Use Exporter to save bounding boxes and class IDs.
Tips:
- Standardize image sizes to improve detector throughput.
- Run inference in batches if supported.
2) Real-time detection + tracking on webcam
- Goal: run detection on a webcam with persistent IDs.
- Steps:
- CameraCapture with desired frame rate.
- Lightweight preprocessor (resize + normalize).
- Use a fast detector or a tiny model.
- Attach SORT or KalmanTracker for ID continuity.
- Visualize results overlayed on frames.
Performance tips:
- Use GPU acceleration and mixed precision if available.
- Skip frames (process every Nth frame) to reduce latency while tracking intermediate frames with optical flow.
3) Measuring objects and distance estimation
- Goal: estimate object sizes and approximate distance using simple monocular cues.
- Steps:
- Calibrate camera or provide focal length and sensor size.
- Detect objects and measure pixel height/width.
- Convert pixel measurements into real-world units using pinhole camera math:
- Distance ≈ (real_height * focal_length) / pixel_height
- Optionally refine with depth models or stereo inputs.
4) Activity classification from tracked objects
- Goal: classify behavior (e.g., walking vs running) using tracked trajectories.
- Steps:
- Detect and track objects to extract per-object trajectories (sequence of centroids).
- Compute motion features: speed, acceleration, direction variance.
- Feed features to a classifier (SVM, random forest, or a small neural net).
- Attach predictions to objects and visualize labels.
Debugging and performance tuning
- Profiling: enable the Profiler module to locate slow stages.
- Memory: batch sizes and model input dimensions directly affect memory — reduce sizes if out-of-memory.
- Accuracy vs speed: trade off model size, input resolution, and NMS thresholds.
- False positives: adjust confidence threshold, use class-specific thresholds, or add a verification stage (e.g., reclassification).
- Drift in tracking: tune association thresholds (IOU, appearance distance) and re-identification settings.
Evaluation and metrics
Use MetricEvaluator to compute:
- Detection: precision, recall, mAP (IoU thresholds configurable).
- Tracking: MOTA, MOTP, ID switches, fragmentation.
- Classification: accuracy, F1-score, confusion matrix.
Export results in standard formats (COCO, MOT, Pascal VOC) for external benchmarking.
Model integration and custom models
- Import custom models (ONNX, TorchScript, TensorRT) into DeepDetector.
- Fine-tune pretrained backbones with VisionLab’s training utilities or export datasets for external training.
- Use model-agnostic interfaces so you can benchmark different architectures easily.
Saving and sharing pipelines
- Serialize pipelines to YAML/JSON including module configs and model paths.
- Share serialized pipelines with colleagues to reproduce experiments.
- Use versioning for models and pipelines to track changes.
Troubleshooting checklist (quick)
- No detections: check model path, labels mapping, and confidence threshold.
- Slow pipeline: enable GPU, reduce resolution, or use smaller models.
- Broken visualizer: confirm frame format (BGR vs RGB) and image dimensions.
- Poor tracking: increase detector frequency, tune IOU/appearance thresholds, or use stronger ReID features.
Next steps and learning resources
- Experiment with different detectors and trackers on your dataset.
- Collect annotated data and fine-tune models for domain-specific performance.
- Benchmark with standard datasets (COCO, MOT) to measure progress.
- Consult VisionLab VCL API docs for exact function signatures and supported model formats.
If you want, I can: provide concrete code for a specific language (Python or C++), create a ready-to-run example using a public model (ONNX/Torch), or walk through camera calibration math step-by-step. Which would you like?
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