docstr: API for face detection.
  
  Args:
      images (list(numpy.ndarray)): images data, shape of each is [H, W, C]
      paths (list[str]): The paths of images.
      batch_size (int): batch size.
      use_gpu (bool): Whether to use gpu.
      output_dir (str): The path to store output images.
      visualization (bool): Whether to save image or not.
      confs_threshold (float): threshold for confidence coefficient.
      iou_threshold (float): threshold for iou.
  Returns:
      res (list[dict()]): The result of face detection and save path of images.
docstr_tok: ["API","for","face","detection",".","Args",":","images","(","list","(","numpy.ndarray",")",")",":","images","data",",","shape","of","each","is","[","H",",","W",",","C","]","paths","(","list","[","str","]",")",":","The","paths","of","images",".","batch_size","(","int",")",":","batch","size",".","use_gpu","(","bool",")",":","Whether","to","use","gpu",".","output_dir","(","str",")",":","The","path","to","store","output","images",".","visualization","(","bool",")",":","Whether","to","save","image","or","not",".","confs_threshold","(","float",")",":","threshold","for","confidence","coefficient",".","iou_threshold","(","float",")",":","threshold","for","iou",".","Returns",":","res","(","list","[","dict","(",")","]",")",":","The","result","of","face","detection","and","save","path","of","images","."]
code: def face_detection(
      self,
      images=None,
      paths=None,
      data=None,
      batch_size=1,
      use_gpu=False,
      output_dir="face_detector_640_predict_output",
      visualization=False,
      confs_threshold=0.5,
      iou_threshold=0.5,
  ):
      """
      API for face detection.
  
      Args:
          images (list(numpy.ndarray)): images data, shape of each is [H, W, C]
          paths (list[str]): The paths of images.
          batch_size (int): batch size.
          use_gpu (bool): Whether to use gpu.
          output_dir (str): The path to store output images.
          visualization (bool): Whether to save image or not.
          confs_threshold (float): threshold for confidence coefficient.
          iou_threshold (float): threshold for iou.
      Returns:
          res (list[dict()]): The result of face detection and save path of images.
      """
      if use_gpu:
          try:
              _places = os.environ["CUDA_VISIBLE_DEVICES"]
              int(_places[0])
          except:
              raise RuntimeError(
                  "Environment Variable CUDA_VISIBLE_DEVICES is not set correctly. If you wanna use gpu, please set CUDA_VISIBLE_DEVICES as cuda_device_id."
              )
  
      # compatibility with older versions
      if data and "image" in data:
          if paths is None:
              paths = []
          paths += data["image"]
  
      # get all data
      all_data = []
      for yield_data in reader(images, paths):
          all_data.append(yield_data)
  
      total_num = len(all_data)
      loop_num = int(np.ceil(total_num / batch_size))
  
      res = []
      for iter_id in range(loop_num):
          batch_data = list()
          handle_id = iter_id * batch_size
          for image_id in range(batch_size):
              try:
                  batch_data.append(all_data[handle_id + image_id])
              except:
                  pass
          # feed batch image
          batch_image = np.array(
              [data["image"] for data in batch_data]
          ).astype("float32")
          predictor = (
              self.gpu_predictor if use_gpu else self.cpu_predictor
          )
          input_names = predictor.get_input_names()
          input_handle = predictor.get_input_handle(input_names[0])
          input_handle.copy_from_cpu(batch_image)
  
          predictor.run()
          output_names = predictor.get_output_names()
          output_handle = predictor.get_output_handle(output_names[0])
          confidences = output_handle.copy_to_cpu()
  
          output_handle = predictor.get_output_handle(output_names[1])
          boxes = output_handle.copy_to_cpu()
  
          # postprocess one by one
          for i in range(len(batch_data)):
              out = postprocess(
                  confidences=confidences[i],
                  boxes=boxes[i],
                  orig_im=batch_data[i]["orig_im"],
                  orig_im_shape=batch_data[i]["orig_im_shape"],
                  orig_im_path=batch_data[i]["orig_im_path"],
                  output_dir=output_dir,
                  visualization=visualization,
                  confs_threshold=confs_threshold,
                  iou_threshold=iou_threshold,
              )
              res.append(out)
      return res
  
imports: from __future__ import absolute_import
  from __future__ import division
  import argparse
  import ast
  import os
  import numpy
  import paddle
  import paddle.static
  from paddle.inference import Config
  from paddle.inference import create_predictor
  from .data_feed import reader
  from .processor import base64_to_cv2
  from .processor import postprocess
  from paddlehub.module.module import moduleinfo
  from paddlehub.module.module import runnable
  from paddlehub.module.module import serving
label_desc: Use image node_id 3 for calling the FaceDetector640 obj's underlying member method code with example usage: obj.face_detection(images, paths, data, batch_size, use_gpu, output_dir, visualization, confs_threshold, iou_threshold) and returns: res