Visualize object detections

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Draw bounding boxes on images to visualize object detection results.

Problem

You’ve run object detection on images but need to visualize the results—see where objects were detected and verify the model’s accuracy.

Solution

What’s in this recipe:

Run object detection with YOLOX
Draw bounding boxes on images
Color-code by object class

You create a pipeline that detects objects and then draws the results on the original image.

Setup

%pip install -qU pixeltable pixeltable-yolox

import pixeltable as pxt
from pixeltable.functions.vision import bboxes_draw
from pixeltable.functions.yolox import yolox

# Create a fresh directory
pxt.drop_dir('viz_demo', force=True)
pxt.create_dir('viz_demo')

Connected to Pixeltable database at: postgresql+psycopg://postgres:@/pixeltable?host=/Users/pjlb/.pixeltable/pgdata
Created directory ‘viz_demo’.
<pixeltable.catalog.dir.Dir at 0x138534d00>

Create detection and visualization pipeline

# Create table for images
images = pxt.create_table('viz_demo/images', {'image': pxt.Image})

Created table ‘images’.

# Step 1: Run object detection
images.add_computed_column(
    detections=yolox(images.image, model_id='yolox_m', threshold=0.5)
)

Added 0 column values with 0 errors.
No rows affected.

# Step 2: Draw bounding boxes on the image
# Note: bboxes_draw takes image, boxes, and labels (scores are not used for drawing)
images.add_computed_column(
    annotated=bboxes_draw(
        images.image,
        images.detections.bboxes,
        labels=images.detections.labels,
    )
)

Added 0 column values with 0 errors.
No rows affected.

Detect and visualize

# Insert sample images
base_url = 'https://raw.githubusercontent.com/pixeltable/pixeltable/main/docs/resources/images'

image_urls = [
    f'{base_url}/000000000036.jpg',  # cats
    f'{base_url}/000000000139.jpg',  # elephants
]

images.insert([{'image': url} for url in image_urls])

Inserting rows into `images`: 0 rows [00:00, ? rows/s]
Inserting rows into `images`: 2 rows [00:00, 236.29 rows/s]
Inserted 2 rows with 0 errors.
2 rows inserted, 8 values computed.

# View original vs annotated images side by side
images.select(images.image, images.annotated).collect()

# View detection details
images.select(images.detections).collect()

Explanation

Pipeline flow:

Image → YOLOX detection → Bounding boxes + labels → bboxes_draw → Annotated image

Detection output format: The yolox function returns a dict with:

bboxes - List of [x1, y1, x2, y2] coordinates
labels - List of class names (e.g., “cat”, “dog”)
scores - List of confidence scores (0-1)

YOLOX model options:

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How-To

Problem

Solution

Setup

Create detection and visualization pipeline

Detect and visualize

Explanation

See also

​Problem

​Solution

​Setup

​Create detection and visualization pipeline

​Detect and visualize

​Explanation

​See also

Problem

Solution

Setup

Create detection and visualization pipeline

Detect and visualize

Explanation

See also