> ## Documentation Index
> Fetch the complete documentation index at: https://docs.datamarkin.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Quickstart

> Get productive with PixelFlow in under 5 minutes. From installation to your first computer vision application.

# 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.

## Installation

<Tabs>
  <Tab title="pip install">
    ```bash theme={null}
    pip install pixelflow
    ```
  </Tab>

  <Tab title="Development">
    ```bash theme={null}
    git clone https://github.com/datamarkin/pixelflow
    cd pixelflow
    pip install -e .
    ```
  </Tab>
</Tabs>

## Your First PixelFlow Application

Here's how simple it is to get professional computer vision results:

<CodeGroup>
  ```python YOLO + PixelFlow theme={null}
  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)
  ```

  ```python Detectron2 + PixelFlow theme={null}
  import pixelflow as pf
  import cv2
  from detectron2 import model_zoo
  from detectron2.engine import DefaultPredictor
  from detectron2.config import get_cfg

  # Setup Detectron2 model
  cfg = get_cfg()
  cfg.merge_from_file(model_zoo.get_config_file("COCO-Detection/faster_rcnn_R_50_FPN_3x.yaml"))
  cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url("COCO-Detection/faster_rcnn_R_50_FPN_3x.yaml")
  cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5
  predictor = DefaultPredictor(cfg)

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

  # Run detection
  outputs = predictor(image)

  # Same PixelFlow workflow
  detections = pf.from_detectron2(outputs)
  annotated = pf.annotate.box(image, detections)

  cv2.imshow('PixelFlow Result', annotated)
  cv2.waitKey(0)
  ```

  ```python MediaPipe + PixelFlow theme={null}
  import pixelflow as pf
  import cv2
  import mediapipe as mp

  # Setup MediaPipe
  mp_pose = mp.solutions.pose.Pose()

  # Load your image
  image = cv2.imread('your_image.jpg')
  rgb_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

  # Run pose detection
  results = mp_pose.process(rgb_image)

  # Unified PixelFlow interface
  detections = pf.from_mediapipe(results, image.shape)
  annotated = pf.annotate.keypoint(image, detections)

  cv2.imshow('PixelFlow Result', annotated)
  cv2.waitKey(0)
  ```
</CodeGroup>

<Note>
  All examples use the same PixelFlow workflow: **Model Output** � **Convert** � **Annotate**. This pattern works across every supported framework.
</Note>

## Advanced Features in 3 More Lines

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

### Object Tracking

```python theme={null}
# 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)
```

### Zone-Based Filtering

```python theme={null}
# 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)
```

### Privacy Protection

```python theme={null}
# 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)
```

## Complete Working Example

Here's a full script that demonstrates PixelFlow's power:

```python complete_example.py theme={null}
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")
```

## Next Steps

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

<CardGroup cols={2}>
  <Card title="Detections" icon="target" href="/docs/detections/detections">
    Master the unified detection format that works with any model
  </Card>

  <Card title="Annotations" icon="pen" href="/docs/annotators/introduction">
    Explore all 20+ professional annotation functions
  </Card>

  <Card title="Object Tracking" icon="route" href="/docs/tracker/bytetrack">
    Add multi-object tracking to your applications
  </Card>

  <Card title="Spatial Analytics" icon="map" href="/docs/zones">
    Use zones and crossings for location-based insights
  </Card>
</CardGroup>

<Tip>
  **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.
</Tip>