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简介

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图像人脸数量算子：用 OpenCV Haar 正脸级联检测图像中的人脸并计数，逐行输出人脸数量，过滤交给 pipeline。

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功能描述

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算子参数

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输入

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输入含义
images图像输入列，内容形式由 image_src_type 决定（路径/URL 字符串、base64 字符串、bytes）
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输出

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输出含义
face_countint32，检出人脸数（无脸为 0）。该行处理失败时为 None
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参数

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参数名称类型默认值描述
image_src_typestr\"image_url\"输入图像编码形式：image_url / image_base64 / image_binary
cv_classifierstr\"\"Haar 级联 xml 路径，空字符串用 OpenCV 自带的 haarcascade_frontalface_alt.xml
scale_factorfloat1.1detectMultiScale 的 scaleFactor
min_neighborsint3detectMultiScale 的 minNeighbors
min_sizetuple[int,int] 或 NoneNone最小人脸尺寸，None 表示不限制（不传 minSize）
max_sizetuple[int,int] 或 NoneNone最大人脸尺寸，None 表示不限制（不传 maxSize）
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注意事项

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调用示例

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\n Python\n \n
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1from __future__ import annotations\n2\n3import os\n4\n5import daft\n6from daft import col\n7\n8from daft.aihc.common.udf import aihc_udf\n9from daft.aihc.functions.image.image_face_count import ImageFaceCount\n10\n11if __name__ == \"__main__\":\n12    if os.getenv(\"DAFT_RUNNER\", \"native\") == \"ray\":\n13        import ray\n14        ray.init(dashboard_host=\"0.0.0.0\", ignore_reinit_error=True)\n15        daft.set_runner_ray()\n16    daft.set_execution_config(actor_udf_ready_timeout=6000, min_cpu_per_task=0)\n17\n18samples = {\"image\": [\"bos://your-bucket/sample.jpg\"]}\n19    ds = daft.from_pydict(samples)\n20    ds = ds.with_column(\n21        \"face_count\",\n22        aihc_udf(\n23            ImageFaceCount,\n24            construct_args={\n25                \"image_src_type\": \"image_url\",\n26                \"scale_factor\": 1.1,\n27                \"min_neighbors\": 3,\n28            },\n29            num_cpus=1,\n30            concurrency=4,\n31            batch_size=32,\n32        )(col(\"image\")),\n33    )\n34    ds = ds.where(col(\"face_count\") == 1)\n35    ds.show()
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