Operator ID: daft.las.functions.image.image_sharpness.ImageSharpness

Description #

Image sharpness calculation processor that supports multiple sharpness evaluation methods.

Key features #

• Provides four professional-grade sharpness evaluation methods:
  • Laplacian variance method (laplacian) – Edge detection based on second-order derivatives, simple and fast computation
  • Tenengrad method (tenengrad) – Edge strength based on Sobel gradients, sensitive to noise
  • Brenner method (brenner) – Simple calculation based on the grayscale difference of adjacent pixels, the fastest method
  • FFT high-frequency energy method (fft_highfreq) – Sharpness evaluation based on frequency domain analysis, more sensitive to blurring
• Multi-format input support:
  • URL address (image_url)
  • Base64 encoding (image_base64)
  • Binary stream (image_binary)
• High-precision calculation:
  • Supports processing images of various sizes and formats
  • Automatically handles NaN values and exception cases

Operator parameters #

Input #

Output #

Floating-point array containing the sharpness calculation results

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 image sharpness calculation.

from __future__ import annotations

import os

import daft
from daft import col
from daft.las.functions.image.image_sharpness import ImageSharpness
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": [
            f"https://{tos_dir_url}/public/shared_image_dataset/cat_ip_adapter.jpeg"
        ],
    }

    image_src_type = "image_url"
    method = "laplacian"
    num_gpus = 0

    ds = daft.from_pydict(samples)
    ds = ds.with_column(
        "image_sharpness",
        las_udf(
            ImageSharpness,
            construct_args={
                "image_src_type": image_src_type,
                "method": method,
            },
            num_gpus=num_gpus,
            batch_size=1,
        )(col("image")),
    )

    ds.show()

    # ╭────────────────────────────────┬───────────────────────╮
    # │ image                          ┆ image_sharpness       │
    # │ ---                            ┆ ---                   │
    # │ Utf8                           ┆ Float64               │
    # ╞════════════════════════════════╪═══════════════════════╡
    # │ https://las-cn-beijing-public… ┆ 1234.56               │
    # ╰────────────────────────────────┴───────────────────────╯