The image resampling operator is used to perform size resampling (downsampling only) on the input image and save the result to the TOS directory specified by the user. Supports four interpolation algorithms (nearest/bilinear/bicubic/lanczos) and .jpg / .png output formats. Suitable for image preprocessing, data normalization, offline dataset construction, and similar scenarios.

Operator ID: las_image_resample

Core capabilities #

1. Multiple interpolation algorithms

• nearest: fastest speed, suitable for pixel-style images
• bilinear: balanced speed and quality
• bicubic: smoother, high-quality scaling
• lanczos: superior anti-aliasing, suitable for photos

2. URL / TOS input support

• image_src_type=image_url: input public URL
• image_src_type=image_tos: input tos:// address

3. Output to TOS

• tos_dir is required and specifies the output directory (folder level)
• The output file name is generated by the server (image_name can assist with naming), and the _resample suffix is appended for identification.

4. Output format and DPI control

• Output formats supported: .jpg / .png
• Supports setting output DPI (target_dpi) with effect display

Supported regions #

• Beijing: cn-beijing
• Shanghai: cn-shanghai
• Guangzhou: cn-guangzhou

Operator performance #

Input requirements #

Output requirements #

• Billing standards

  Itemization	Billing standards description
  Billing item	Usage is calculated based on the number of input images, and billed accordingly.
  Billing type	Usage-based billing, unit: CNY/million images, billed hourly based on actual usage.
  Unit price	Depends on the resolution of the input images.

• Billing details
  Billing formula: Total cost = unit price * usage

  Detailed scenarios	Unit price
  Input image resolution: 16k	600 CNY/million images
  Input image resolution: 12k	350 CNY/million images
  Input image resolution: 4k	50 CNY/million images

Interface description #

Invoke the las_image_resample operator to resample the input image (downsampling only) and save the result to the TOS directory specified by the user.

Request parameters #

Response parameters #

Examples #

Request example #

# Please set INPUT_IMAGE_URL to an accessible image URL
export INPUT_IMAGE_URL="https://example.com/sample.jpg"
# Please set OUTPUT_TOS_DIR to a writable TOS directory (folder) under the current account
export OUTPUT_TOS_DIR="tos://xxxx/las_image_resample/output"

curl --location "${LAS_ENDPOINT}/api/v1/process" \
--header "Content-Type: application/json" \
--header "Authorization: Bearer $LAS_API_KEY" \
--data @- <<EOF
{
  "operator_id": "las_image_resample",
  "operator_version": "v1",
  "data": {
    "image_src_type": "image_url",
    "image": "$INPUT_IMAGE_URL",
    "tos_dir": "$OUTPUT_TOS_DIR",
    "image_suffix": ".jpg",
    "target_size": [200, 200],
    "target_dpi": [72, 72],
    "method": "lanczos"
  }
}
EOF

Response example #

{
  "metadata": {
    "task_status": "COMPLETED",
    "business_code": "0",
    "error_msg": "",
    "request_id": "c7b29d78a99f88beda5497753ed60816"
  },
  "data": {
    "image_path": "tos://xxxx/las_image_resample/output/sample_resample.jpg"
  }
}

Operator parameters #

Input #

Output #

An array of dictionaries containing processing results. Each element includes:

• base64: base64 encoding of the resampled image;
• image_path: local/TOS storage path (valid when output directory is configured)

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 resampling.

from __future__ import annotations

import os

import daft
from daft import col
from daft.las.functions.image.image_resample import ImageResample
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_name": ["cat_ip_adapter"],
    }

    image_suffix = ".jpg"
    image_src_type = "image_url"
    target_size = (200, 200)
    target_dpi = (72, 72)
    method = "lanczos"
    local_dir = ""
    tos_dir = ""
    num_gpus = 0

    ds = daft.from_pydict(samples)
    ds = ds.with_column(
        "image_resample",
        las_udf(
            ImageResample,
            construct_args={
                "image_suffix": image_suffix,
                "tos_dir": tos_dir,
                "local_dir": local_dir,
                "image_src_type": image_src_type,
                "target_size": target_size,
                "target_dpi": target_dpi,
                "method": method,
            },
            num_gpus=num_gpus,
            batch_size=1,
        )(col("image"), col("image_name")),
    )

    ds.show()

    # ╭────────────────────────────────┬────────────────┬────────────────────────────────────────╮
    # │ image                          ┆ image_name     ┆ image_resample                         │
    # │ ---                            ┆ ---            ┆ ---                                    │
    # │ Utf8                           ┆ Utf8           ┆ Struct[base64: Utf8, image_path: Utf8] │
    # ╞════════════════════════════════╪════════════════╪════════════════════════════════════════╡
    # │ https://las-cn-beijing-public… ┆ cat_ip_adapter ┆ {base64: iVBORw0KGgoAAAANSUhE…         │
    # ╰────────────────────────────────┴────────────────┴────────────────────────────────────────╯