Streamlining Computer Vision Pipelines with Pixeltable: From Video Processing to Model Inferences

Computer vision pipelines present unique infrastructure challenges: managing large video datasets, coordinating frame extraction, handling annotations, and deploying models efficiently. Traditional solutions often involve complex orchestration code and multiple specialized tools. Today, we’ll explore how Pixeltable’s declarative approach simplifies computer vision workflows while providing enterprise-grade features for production deployment.

The Video Processing Challenge

Consider a typical computer vision pipeline:

• Extract frames from multiple video sources
• Run object detection on selected frames
• Store and index detection results
• Update models without reprocessing everything
• Maintain annotation quality

Traditional implementations might look like this:

# Typical approach - lots of infrastructure code
def process_video(video_path):
    cap = cv2.VideoCapture(video_path)
    frames = []
    while cap.isOpened():
        ret, frame = cap.read()
        if not ret:
            break
        frames.append(frame)

# More code for frame storage, model inference, result tracking...

The Pixeltable Approach: Declarative Video Processing

import pixeltable as pxt
from pixeltable.iterators import FrameIterator
from pixeltable.functions.yolox import yolox

# Create video table
videos = pxt.create_table('videos', {
    'video': pxt.Video
})

# Create frame view - frames extracted only when needed
frames = pxt.create_view(
    'frames',
    videos,
    iterator=FrameIterator.create(
        video=videos.video,
        fps=1 # Extract 1 frame per second
    )
)

# Add object detection as computed columns
frames['detections'] = yolox(
    frames.frame,
    model_id='yolox_s',
    threshold=0.5
)

# Visualization for validation
@pxt.udf
def draw_boxes(img, detections):
    """Draw bounding boxes on frame"""
    result = img.copy()
    draw = PIL.ImageDraw.Draw(result)
    for box in detections['boxes']:
        draw.rectangle(box, outline='red', width=2)
    return result

frames['annotated'] = draw_boxes(frames.frame, frames.detections)

# Add new videos - only process new frames
videos.insert({'video': 's3://my-bucket/new-video.mp4'})

Key Features and Benefits

Efficient Video Processing

• Lazy frame extraction – only process what you need
• Automatic caching and resource management
• Incremental updates for new videos

Seamless Model Integration

• Built-in support for popular CV models
• Easy integration with custom models
• Track model versions and outputs

# Compare multiple models
frames['yolox_tiny'] = yolox(frames.frame, model_id='yolox_tiny')
frames['yolox_m'] = yolox(frames.frame, model_id='yolox_m')

Annotation Management

• Integration with Label Studio & Voxel51
• Automatic pre-annotations
• Quality control workflows

# Export to Label Studio for annotation
pxt.io.create_label_studio_project(
    frames,
    label_config="""
    <View>
      <Image name="frame" value="$frame"/>
      <RectangleLabels name="detection" toName="frame">
        <Label value="person"/>
        <Label value="car"/>
      </RectangleLabels>
    </View>
    """
)

Production-Ready Features

• Complete data lineage
• Automatic versioning
• Efficient resource utilization

# Track performance metrics
frames['eval'] = eval_detections(
    pred_boxes=frames.detections.boxes,
    gt_boxes=frames.annotations.boxes
)

Real-World Applications

1. Traffic Monitoring
   • Process multiple camera feeds
   • Real-time vehicle detection
   • Historical pattern analysis
2. Manufacturing QA
   • Defect detection
   • Production line monitoring
   • Quality trend analysis
3. Security Systems
   • Multi-camera processing
   • Object tracking
   • Alert generation

See It In Action

• Demo: Object Detection in Videos
• Tutorial: Video Processing Pipeline
• Example: Label Studio Integration
• Example: Voxel51 Integration

Building computer vision pipelines doesn’t have to be complex. With Pixeltable’s declarative approach, you can focus on what matters – building great computer vision applications – while we handle the infrastructure. Get started today and see how Pixeltable can transform your computer vision workflows.