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Get Started in Under 5 Minutes

This guide gets you from zero to running computer vision applications with PixelFlow. You’ll install the library, run your first detection, and see the power of unified CV workflows.

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Installation

pip install pixelflow

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Your First PixelFlow Application

Here’s how simple it is to get professional computer vision results: 
import pixelflow as pf
import cv2
from ultralytics import YOLO

# Load any YOLO model
model = YOLO('yolov8n.pt')  # Downloads automatically first time

# Load your image
image = cv2.imread('your_image.jpg')

# Run detection
results = model(image)

# Convert to PixelFlow format
detections = pf.from_ultralytics(results[0])

# Professional annotations in one line
annotated = pf.annotate.box(image, detections)

# Display or save result
cv2.imshow('PixelFlow Result', annotated)
cv2.waitKey(0)
All examples use the same PixelFlow workflow: Model Output � Convert � Annotate. This pattern works across every supported framework.

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Advanced Features in 3 More Lines

Once you have basic detection working, PixelFlow’s advanced features are just as simple:

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Object Tracking

# Add multi-object tracking
from pixelflow.tracker import ByteTracker

tracker = ByteTracker()
tracked_detections = tracker.update(detections, image)
annotated = pf.annotate.box(image, tracked_detections, show_ids=True)

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Zone-Based Filtering

# Filter detections by spatial zones
from pixelflow.zones import Zones

zones = Zones.from_polygons([[(100, 100), (400, 100), (400, 300), (100, 300)]])
filtered_detections = detections.filter_by_zones(zones)
annotated = pf.annotate.zones(image, zones, detections=filtered_detections)

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Privacy Protection

# Blur faces for privacy compliance
person_detections = detections.filter_by_class([0])  # Person class = 0 in COCO
privacy_safe = pf.annotate.blur(image, person_detections)

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Complete Working Example

Here’s a full script that demonstrates PixelFlow’s power:
complete_example.py
import pixelflow as pf
import cv2
from ultralytics import YOLO
from pixelflow.tracker import ByteTracker
from pixelflow.zones import Zones

# Setup
model = YOLO('yolov8n.pt')
tracker = ByteTracker()

# Define a zone (rectangle from top-left to bottom-right)
zone_polygon = [(200, 200), (600, 200), (600, 400), (200, 400)]
zones = Zones.from_polygons([zone_polygon])

# Process image
image = cv2.imread('busy_street.jpg')
results = model(image)

# PixelFlow pipeline
detections = pf.from_ultralytics(results[0])
tracked_detections = tracker.update(detections, image)
zone_detections = tracked_detections.filter_by_zones(zones)

# Professional visualization
annotated = image.copy()
annotated = pf.annotate.zones(annotated, zones, alpha=0.3)
annotated = pf.annotate.box(annotated, zone_detections, show_ids=True)
annotated = pf.annotate.label(annotated, zone_detections, show_confidence=True)

# Display results
cv2.imshow('PixelFlow Complete Example', annotated)
cv2.waitKey(0)
cv2.destroyAllWindows()

print(f"Detected {len(zone_detections)} objects in the zone")

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Next Steps

You’re now ready to build production computer vision applications! Here’s where to go next:

Detections

Master the unified detection format that works with any model

Annotations

Explore all 20+ professional annotation functions

Object Tracking

Add multi-object tracking to your applications

Spatial Analytics

Use zones and crossings for location-based insights
Pro Tip: PixelFlow’s modular design means you can use any component independently. Start with basic detection and annotations, then add tracking, zones, and advanced features as needed.