pictograph 1.32.0

Pictograph SDK — agent-native computer vision annotation platform

Pictograph Python SDK

Official Python SDK for Pictograph Context Engine - a powerful computer vision annotation platform for creating high-quality training datasets.

Features

• Simple, intuitive API - Get started with just a few lines of code
• Dataset management - List, download, and manage annotation datasets
• Image operations - Upload images, retrieve metadata, and manage assets
• Annotation tools - Get, save, and delete annotations in Pictograph JSON format
• Batch operations - Download entire datasets with parallel processing
• Async client - pictograph.AsyncClient mirrors the full API surface for asyncio apps (HTTP/2, coroutine methods, async for pagers)
• Local export - pictograph datasets export <name> --format coco|yolo|pascal_voc|… writes a dataset ZIP in any of the 8 Pictograph formats, built by Pictograph's own server-side converters
• Offline format converters - pictograph.formats reads/writes COCO, YOLO & Pascal VOC annotations locally (no API call, no third-party SDK) — bring an existing dataset straight into Pictograph's typed models
• One-call annotation import - pictograph.pipelines.import_coco_annotations / import_yolo_annotations / import_pascal_voc_annotations parse a local COCO/YOLO/Pascal VOC file, create any missing classes, match images by filename, and bulk-save — the whole recipe in one call (async twins under pictograph.aio.pipelines)
• Local model evaluation - pictograph.metrics.evaluate_detections scores predictions vs ground truth (per-class + overall precision / recall / F1 by IoU matching), offline, no third-party library
• Visualization - Draw annotations onto images with draw_annotations (Pillow-only, no extra deps)
• PyTorch adapter - client.datasets.as_pytorch(...) yields a ready-to-train torch.Dataset
• Automatic retries - Built-in retry logic for transient failures
• Rate limiting - Automatic handling of API rate limits
• Type hints - Full type annotations for better IDE support

Installation

pip install pictograph

Optional extras pull in heavier dependencies only when you need them:

pip install 'pictograph[torch]'         # client.datasets.as_pytorch(...)
pip install 'pictograph[cli]'           # the `pictograph` command-line tool
pip install 'pictograph[all]'           # everything

Quick Start

from pictograph import Client

# Initialize the client with your API key
client = Client(api_key="pk_live_your_key_here")

# List all datasets
datasets = client.datasets.list()
for dataset in datasets:
    print(f"{dataset['name']}: {dataset['image_count']} images")

# Download a complete dataset
client.datasets.download(
    "dataset-uuid",
    output_dir="./my_dataset",
    mode="full"  # Download images + annotations
)

# Upload an image
result = client.images.upload(
    "dataset-uuid",
    "/path/to/image.jpg",
    folder_path="/train/images"
)
print(f"Uploaded: {result['image_id']}")

# Get annotations for an image
annotations = client.annotations.get("image-uuid")
for ann in annotations:
    print(f"{ann['name']}: {ann['type']}")

# Save annotations
client.annotations.save("image-uuid", [
    {
        "id": "ann-1",
        "name": "person",
        "type": "bbox",
        "bbox": [100, 200, 50, 80],  # [x, y, width, height]
        "confidence": 1.0
    }
])

Authentication

Get your API key from the Pictograph dashboard.

from pictograph import Client

client = Client(api_key="pk_live_your_key_here")

You can also configure the base URL and timeout:

client = Client(
    api_key="pk_live_your_key_here",
    base_url="https://your-instance.pictograph.io",
    timeout=60,
    max_retries=5
)

Usage

Working with Datasets

List Datasets

# List all datasets
datasets = client.datasets.list()

# With pagination
datasets = client.datasets.list(limit=50, offset=0)

Get Dataset Details

# Get basic info
dataset = client.datasets.get("dataset-uuid")
print(dataset['name'], dataset['image_count'])

# Get with images included
dataset = client.datasets.get(
    "dataset-uuid",
    include_images=True,
    images_limit=1000
)
for img in dataset['images']:
    print(img['filename'], img['image_url'])

List Images in Dataset

# List all images
images = client.datasets.list_images("dataset-uuid")

# Filter by annotation status
completed_images = client.datasets.list_images(
    "dataset-uuid",
    status="complete",
    limit=500
)

Download Dataset

# Download everything (images + annotations)
result = client.datasets.download(
    "dataset-uuid",
    output_dir="./dataset",
    mode="full",
    max_workers=20,  # Parallel downloads
    show_progress=True
)
print(f"Downloaded {result['images_downloaded']} images")

# Download only annotations
result = client.datasets.download(
    "dataset-uuid",
    output_dir="./annotations",
    mode="annotations_only"
)

# Download only completed images
result = client.datasets.download(
    "dataset-uuid",
    output_dir="./completed",
    mode="full",
    status_filter="complete"
)

Working with Images

Get Image Metadata

image = client.images.get("image-uuid")
print(image['filename'])
print(image['image_url'])  # CDN URL for viewing
print(image['annotation_count'])

Upload Image

# Simple upload
result = client.images.upload(
    "dataset-uuid",
    "/path/to/image.jpg"
)

# Upload to specific folder
result = client.images.upload(
    "dataset-uuid",
    "/path/to/image.jpg",
    folder_path="/train/images",
    filename="custom_name.jpg"
)

print(result['image_id'])

Delete Image

# Archive (soft delete)
client.images.delete("image-uuid")

# Permanent delete
client.images.delete("image-uuid", permanent=True)

Working with Annotations

Pictograph uses a JSON format that supports multiple annotation types:

• bbox - Bounding boxes [x, y, width, height]
• polygon - Polygons [[x1, y1], [x2, y2], ...]
• polyline - Polylines [[x1, y1], [x2, y2], ...]
• keypoint - Single points [x, y]

Get Annotations

annotations = client.annotations.get("image-uuid")
for ann in annotations:
    print(ann['id'], ann['name'], ann['type'])
    if ann['type'] == 'bbox':
        x, y, width, height = ann['bbox']
        print(f"  Bbox: ({x}, {y}) - {width}x{height}")

Save Annotations

# Bounding box
annotations = [
    {
        "id": "ann-1",
        "name": "person",
        "type": "bbox",
        "bbox": [100, 200, 50, 80],
        "confidence": 1.0
    }
]
result = client.annotations.save("image-uuid", annotations)
print(result['new_count'])

# Polygon
annotations = [
    {
        "id": "ann-2",
        "name": "car",
        "type": "polygon",
        "polygon": [[10, 20], [30, 40], [50, 60], [10, 20]],
        "confidence": 1.0
    }
]
client.annotations.save("image-uuid", annotations)

# Multiple annotations
annotations = [
    {"id": "ann-1", "name": "person", "type": "bbox", "bbox": [100, 200, 50, 80]},
    {"id": "ann-2", "name": "car", "type": "polygon", "polygon": [[10,20], [30,40], [50,60], [10,20]]},
    {"id": "ann-3", "name": "road", "type": "polyline", "polyline": [[0,100], [50,100], [100,100]]},
    {"id": "ann-4", "name": "landmark", "type": "keypoint", "keypoint": [150, 200]}
]
client.annotations.save("image-uuid", annotations)

Helper Methods

# Create properly formatted annotations
bbox = client.annotations.create_bbox(
    "ann-1",
    "person",
    [100, 200, 50, 80],
    confidence=0.95
)

polygon = client.annotations.create_polygon(
    "ann-2",
    "car",
    [[10, 20], [30, 40], [50, 60], [10, 20]]
)

polyline = client.annotations.create_polyline(
    "ann-3",
    "road",
    [[0, 100], [50, 100], [100, 100]]
)

keypoint = client.annotations.create_keypoint(
    "ann-4",
    "landmark",
    [150, 200]
)

# Save them all
client.annotations.save("image-uuid", [bbox, polygon, polyline, keypoint])

Delete Annotations

# Delete all annotations for an image
result = client.annotations.delete("image-uuid")
print(result['deleted_count'])

Context Manager

Use the client as a context manager to ensure proper cleanup:

with Client(api_key="pk_live_your_key_here") as client:
    datasets = client.datasets.list()
    # Client session automatically closed when done

Async client

pictograph.AsyncClient is the asyncio twin of Client. It mirrors the exact same resource surface (datasets, images, annotations, exports, training, models, deployments, credits, organizations, projects, folders, batch, search, auto_annotate, video, connectors, api_keys, webhooks, workflows) — every I/O method is a coroutine (await it), and every iter(...) accessor returns an async pager you consume with async for. It runs over HTTP/2 on a single shared connection pool, so concurrent calls multiplex efficiently.

import asyncio
from pictograph import AsyncClient


async def main() -> None:
    async with AsyncClient(api_key="pk_live_your_key_here") as client:
        # await any resource method
        datasets = await client.datasets.list(limit=5)

        # async-for the auto-paging iterators
        async for img in client.images.iter(datasets[0].id, folder_path="/train"):
            print(img.filename, img.annotation_count)

        # fan out concurrent requests with asyncio.gather
        insights = await asyncio.gather(*(client.datasets.insights(d.name) for d in datasets))
        for d, health in zip(datasets, insights):
            print(d.name, health.total_annotations)


asyncio.run(main())

Retries, idempotency keys, typed errors, streaming downloads, and poll helpers (await client.training.create(..., wait=True), await client.exports.wait_for_completion(...)) behave identically to the sync client. Call await client.aclose() if you are not using the async with form.

Async batteries-included pipelines live under pictograph.aio.pipelines — the data-ingestion flows where concurrency is a real win: await upload_dataset_from_folder(async_client, "my-set", "./images") uploads a folder concurrently, and await import_coco_annotations(async_client, "my-set", "instances_val.json") runs the chunked bulk-saves concurrently.

Local model evaluation (pictograph.metrics)

Measure how good a model is against a labeled set — offline, no server round-trip, no third-party library. evaluate_detections matches predicted annotations to ground truth by IoU and returns per-class and overall precision / recall / F1.

from pictograph import Client
from pictograph.metrics import evaluate_detections

client = Client()
ground_truth = {img_id: client.annotations.get(img_id) for img_id in image_ids}
predictions = {img_id: run_my_model(img_id) for img_id in image_ids}  # your model's output

result = evaluate_detections(predictions, ground_truth, iou_threshold=0.5)
print(f"overall  P={result.precision:.3f}  R={result.recall:.3f}  F1={result.f1:.3f}")
for name, m in sorted(result.per_class.items()):
    print(f"  {name:<12} P={m.precision:.3f}  R={m.recall:.3f}  support={m.support}")

Predictions can come from any source — client.auto_annotate, a deployed model's /predict, or local pictograph.get_model(...).predict(...).

Offline format converters (pictograph.formats)

Convert between COCO / YOLO and Pictograph's typed Annotation models entirely on your machine — no API round-trip, no third-party dependency. Handy for bringing an existing COCO/YOLO dataset into Pictograph, or emitting those formats from annotations you already hold.

from pictograph import Client
from pictograph.formats import from_coco, to_yolo

client = Client(api_key="pk_live_your_key_here")

# Parse a local COCO file into Pictograph's models.
imp = from_coco("instances_val.json")   # -> CocoImport(annotations, class_names)

# Or emit YOLO label text for one image (normalized to its pixel size).
yolo_txt = to_yolo(imp.annotations["a.jpg"], imp.class_names, image_width=640, image_height=480)

To go from a local COCO/YOLO file to annotations saved on a dataset in one call (create missing classes, match images by filename, chunked bulk-save, per-image report), use the import pipelines:

from pictograph.pipelines import import_coco_annotations

# The dataset must already exist and hold the images the file references.
report = import_coco_annotations(client, "my-set", "instances_val.json")
print(report.images_saved, "images annotated;", len(report.unmatched_files), "unmatched")

from_coco / to_coco handle bounding boxes (exact round-trip), polygon segmentation, and keypoints; from_yolo / to_yolo handle detection and segmentation labels; from_pascal_voc / to_pascal_voc handle the Pascal VOC per-image XML (bounding boxes). For hole-accurate COCO (RLE) or a full dataset ZIP in any of the 8 formats, use the server-side export instead (client.exports.create(..., format="coco")).

Error Handling

The SDK raises specific exceptions for different error types:

from pictograph import Client, AuthenticationError, RateLimitError, NotFoundError

client = Client(api_key="pk_live_your_key_here")

try:
    dataset = client.datasets.get("invalid-uuid")
except AuthenticationError:
    print("Invalid API key")
except RateLimitError as e:
    print(f"Rate limited. Retry after {e.retry_after} seconds")
except NotFoundError:
    print("Dataset not found")
except Exception as e:
    print(f"Unexpected error: {e}")

Advanced Usage

Batch Processing

from concurrent.futures import ThreadPoolExecutor

# Upload multiple images in parallel
def upload_image(image_path):
    return client.images.upload("dataset-uuid", image_path)

image_paths = ["img1.jpg", "img2.jpg", "img3.jpg"]
with ThreadPoolExecutor(max_workers=10) as executor:
    results = list(executor.map(upload_image, image_paths))

print(f"Uploaded {len(results)} images")

Custom Metadata

# Add custom metadata to annotations
annotation = client.annotations.create_bbox(
    "ann-1",
    "person",
    [100, 200, 50, 80],
    metadata={
        "annotator": "[e-mail]",
        "difficulty": "easy",
        "verified": True
    }
)
client.annotations.save("image-uuid", [annotation])

Local dataset export

Export a dataset to any of the 8 Pictograph formats — built by Pictograph's own server-side converters, with no third-party dependency — from the SDK or the CLI:

from pictograph import Client

client = Client()
export = client.exports.create(
    "road-signs", "road-signs-coco", format="coco", include_images=True
)
client.exports.download("road-signs", export.name, "road-signs-coco.zip")

The same from the CLI (pip install 'pictograph[cli]'):

pictograph datasets export road-signs --format coco -o ./out
pictograph datasets export road-signs --format yolo --include-images -o ./out

Formats: pictograph, coco, yolo, pascal_voc, darwin, cvat, labelme, csv.

Visualization

draw_annotations renders Pictograph annotations onto an image using only Pillow (a base dependency) — no extra install, no third-party renderer:

from pictograph import Client, draw_annotations

client = Client()
annotations = client.annotations.get("image-uuid")
draw_annotations("photo.jpg", annotations).save("photo.annotated.png")

All four annotation types render (bbox / polygon / polyline / keypoint), each class a stable color.

Rate Limits

The SDK automatically handles rate limits:

• Free tier: 1,000 requests/hour
• Core tier: 5,000 requests/hour
• Pro tier: 20,000 requests/hour
• Enterprise tier: 100,000 requests/hour

If you hit a rate limit, the SDK will automatically wait and retry (if retry time < 2 minutes).

Requirements

• Python 3.8+
• requests >= 2.31.0
• Pillow >= 10.0.0
• tqdm >= 4.65.0

Support

• Documentation: https://pictograph.io/docs
• Email: [e-mail]
• Issues: https://github.com/pictograph-labs/pictograph-sdk/issues

License

MIT License - see LICENSE file for details.