Description #

Image face detection operator

Main features #

• Automatically detects faces in images and returns a list of bounding rectangles (x, y, w, h)
• Supports multiple input methods including URL, local path, Base64, and binary
• Outputs a list of face bounding rectangles
• Can run in both CPU and GPU environments

Applicable scenarios #

• Privacy compliance: Desensitize and mask faces in images before publishing
• Data preprocessing: Generate versions of training or evaluation datasets with face bounding rectangles
• Content review/display: Automatically process images containing sensitive faces with bounding rectangles

Caution #

• If processing of a single sample fails, the result for that sample returns None and does not affect other samples in the same batch

Operator parameters #

Input #

Output #

A structured result array, where each element is a list of face bounding boxes detected in the image.

• Each bounding box is a quadruple (x1, y1, x2, y2), representing the coordinates of the top-left and bottom-right corners.
• If no face is detected in the image, the corresponding element is None.

Parameters #

If a parameter does not have a default value, it is required

Examples #

The following code demonstrates how to use daft to run the operator for face detection in images.

from __future__ import annotations

import os

import daft
from daft import col
from daft.las.functions.image import ImageFaceDetect
from daft.las.functions.udf import las_udf

if __name__ == "__main__":
    if os.getenv("DAFT_RUNNER", "native") == "ray":
        import logging

        import ray

        def configure_logging():
            logging.basicConfig(
                level=logging.INFO,
                format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
                datefmt="%Y-%m-%d %H:%M:%S.%s".format(),
            )
            logging.getLogger("tracing.span").setLevel(logging.WARNING)
            logging.getLogger("daft_io.stats").setLevel(logging.WARNING)
            logging.getLogger("DaftStatisticsManager").setLevel(logging.WARNING)
            logging.getLogger("DaftFlotillaScheduler").setLevel(logging.WARNING)
            logging.getLogger("DaftFlotillaDispatcher").setLevel(logging.WARNING)

        ray.init(dashboard_host="0.0.0.0", runtime_env={"worker_process_setup_hook": configure_logging})
        daft.set_runner_ray()

    daft.set_execution_config(actor_udf_ready_timeout=600)
    daft.set_execution_config(min_cpu_per_task=0)

    tos_dir_url = os.getenv("TOS_DIR_URL", "las-cn-beijing-public-online.tos-cn-beijing.volces.com")
    samples = {"image_path": [f"https://{tos_dir_url}/public/shared_image_dataset/mengnalisa.png"]}
    ds = daft.from_pydict(samples)

    model_path = os.getenv("MODEL_PATH", "/opt/las/models")
    constructor_kwargs = {
        "model_path": model_path,
    }

    ds = ds.with_column(
        "face_bounding_boxes",
        las_udf(
            ImageFaceDetect,
            construct_args=constructor_kwargs,
            num_gpus=0,
            batch_size=1,
            concurrency=1,
        )(col("image_path")),
    )

    ds.show()

    # ╭────────────────────────────────┬──────────────────────╮
    # │ image_path                     ┆ face_bounding_boxes  │
    # │ ---                            ┆ ---                  │
    # │ String                         ┆ List[List[Int32]]    │
    # ╞════════════════════════════════╪══════════════════════╡
    # │ https://las-cn-beijing-public… ┆ [[99, 36, 166, 116]] │
    # ╰────────────────────────────────┴──────────────────────╯