Description #

An image cropping processor that supports multiple cropping methods and various input and output formats.

Key features #

• Input formats:
  • URL address (image_url)
  • Base64 encoding (image_base64)
  • Binary stream (image_binary)
• Crop methods (supports multiple algorithms and modes):
  • Coordinate cropping: Precisely crops by specifying the top-left and bottom-right coordinates.
  • Ratio cropping: Crops according to width and height ratio (0–1).
  • Center cropping: Crops from the center of the image with specified dimensions.
• Interpolation algorithms: nearest, bilinear, bicubic, lanczos (used for resizing after cropping).
• Output modes:
  • Returns the Base64 string of the cropped image.
  • Optionally saves the image to TOS.
  • Optionally saves the image to a local directory.
• Format adaptation:
  • JPEG: Converts RGBA to RGB and applies quality compression.
  • PNG: Preserves the alpha channel and uses lossless compression.
  • WebP: Supports the alpha channel and uses quality or lossless compression.

Operator parameters #

Input #

Output #

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

• base64: Base64-encoded cropped image;
• image_path: Local or 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 cropping.

from __future__ import annotations

import os

import daft
from daft import col
from daft.las.functions.image.image_crop import ImageCrop
from daft.las.functions.udf import las_udf

if __name__ == "__main__":
    # Content will be saved to the specified TOS path. Therefore, you need to set environment variables to ensure permission to write to TOS, including: ACCESS_KEY, SECRET_KEY, TOS_ENDPOINT, TOS_REGION, TOS_TEST_DIR

    TOS_DIR = os.getenv("TOS_TEST_DIR", "tos_bucket")
    output_tos_dir = f"tos://{TOS_DIR}/image/image_crop"

    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.png"],
        "image_name": ["cat_crop_test"],
    }

    image_suffix = ".jpg"
    image_src_type = "image_url"
    crop_type = "center"
    crop_size = [500, 500]
    crop_ratio = [0.8, 0.8]
    crop_coords = [100, 100, 800, 800]
    quality = 85
    method = "lanczos"
    local_dir = ""

    ds = daft.from_pydict(samples)
    ds = ds.with_column(
        "image_crop",
        las_udf(
            ImageCrop,
            construct_args={
                "image_suffix": image_suffix,
                "output_dir": output_tos_dir,
                "image_src_type": image_src_type,
                "crop_type": crop_type,
                "crop_size": crop_size,
                "quality": quality,
                "method": method,
            },
            batch_size=1,
        )(col("image"), col("image_name")),
    )
    ds.show()
# ╭────────────────────────────────┬───────────────┬────────────────────────────────────────────╮
# │ image                          ┆ image_name    ┆ image_crop                                 │
# │ ---                            ┆ ---           ┆ ---                                        │
# │ String                         ┆ String        ┆ Struct[base64: String, image_path: String] │
# ╞════════════════════════════════╪═══════════════╪════════════════════════════════════════════╡
# │ https://las-ai-qa-online.tos-… ┆ cat_crop_test ┆ {base64: /9j/4AAQSkZJRgABAQAA…             │
# ╰────────────────────────────────┴───────────────┴────────────────────────────────────────────╯