from __future__ import absolute_import from __future__ import division import argparse import ast import os import numpy as np import paddle 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 @moduleinfo(name="pyramidbox_lite_mobile", type="CV/face_detection", author="baidu-vis", author_email="", summary="PyramidBox-Lite-Mobile is a high-performance face detection model.", version="1.4.0") class PyramidBoxLiteMobile: def __init__(self): self.default_pretrained_model_path = os.path.join(self.directory, "pyramidbox_lite_mobile_face_detection", "model") self._set_config() self.processor = self def _set_config(self): """ predictor config setting """ model = self.default_pretrained_model_path + '.pdmodel' params = self.default_pretrained_model_path + '.pdiparams' cpu_config = Config(model, params) cpu_config.disable_glog_info() cpu_config.disable_gpu() self.cpu_predictor = create_predictor(cpu_config) try: _places = os.environ["CUDA_VISIBLE_DEVICES"] int(_places[0]) use_gpu = True except: use_gpu = False if use_gpu: gpu_config = Config(model, params) gpu_config.disable_glog_info() gpu_config.enable_use_gpu(memory_pool_init_size_mb=1000, device_id=0) self.gpu_predictor = create_predictor(gpu_config) def face_detection(self, images=None, paths=None, data=None, use_gpu=False, output_dir='detection_result', visualization=False, shrink=0.5, confs_threshold=0.6): """ 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. use_gpu (bool): Whether to use gpu. output_dir (str): The path to store output images. visualization (bool): Whether to save image or not. shrink (float): parameter to control the resize scale in preprocess. confs_threshold (float): confidence threshold. 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: if 'image' in data: if paths is None: paths = list() paths += data['image'] elif 'data' in data: if images is None: images = list() images += data['data'] res = list() # process one by one for element in reader(images, paths, shrink): image = np.expand_dims(element['image'], axis=0).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(image) predictor.run() output_names = predictor.get_output_names() output_handle = predictor.get_output_handle(output_names[0]) output_data = output_handle.copy_to_cpu() out = postprocess(data_out=output_data, org_im=element['org_im'], org_im_path=element['org_im_path'], image_width=element['image_width'], image_height=element['image_height'], output_dir=output_dir, visualization=visualization, shrink=shrink, confs_threshold=confs_threshold) res.append(out) return res @serving def serving_method(self, images, **kwargs): """ Run as a service. """ images_decode = [base64_to_cv2(image) for image in images] results = self.face_detection(images_decode, **kwargs) return results @runnable def run_cmd(self, argvs): """ Run as a command. """ self.parser = argparse.ArgumentParser(description="Run the {} module.".format(self.name), prog='hub run {}'.format(self.name), usage='%(prog)s', add_help=True) self.arg_input_group = self.parser.add_argument_group(title="Input options", description="Input data. Required") self.arg_config_group = self.parser.add_argument_group( title="Config options", description="Run configuration for controlling module behavior, not required.") self.add_module_config_arg() self.add_module_input_arg() args = self.parser.parse_args(argvs) results = self.face_detection(paths=[args.input_path], use_gpu=args.use_gpu, output_dir=args.output_dir, visualization=args.visualization, shrink=args.shrink, confs_threshold=args.confs_threshold) return results def add_module_config_arg(self): """ Add the command config options. """ self.arg_config_group.add_argument('--use_gpu', type=ast.literal_eval, default=False, help="whether use GPU or not") self.arg_config_group.add_argument('--output_dir', type=str, default='detection_result', help="The directory to save output images.") self.arg_config_group.add_argument('--visualization', type=ast.literal_eval, default=False, help="whether to save output as images.") def add_module_input_arg(self): """ Add the command input options. """ self.arg_input_group.add_argument('--input_path', type=str, help="path to image.") self.arg_input_group.add_argument( '--shrink', type=ast.literal_eval, default=0.5, help="resize the image to shrink * original_shape before feeding into network.") self.arg_input_group.add_argument('--confs_threshold', type=ast.literal_eval, default=0.6, help="confidence threshold.") def create_gradio_app(self): import gradio as gr import tempfile import os from PIL import Image def inference(image, shrink, confs_threshold): with tempfile.TemporaryDirectory() as temp_dir: self.face_detection(paths=[image], use_gpu=False, visualization=True, output_dir=temp_dir, shrink=shrink, confs_threshold=confs_threshold) return Image.open(os.path.join(temp_dir, os.listdir(temp_dir)[0])) interface = gr.Interface(inference, [ gr.inputs.Image(type="filepath"), gr.Slider(0.0, 1.0, 0.5, step=0.01), gr.Slider(0.0, 1.0, 0.6, step=0.01) ], gr.outputs.Image(type="ndarray"), title='pyramidbox_lite_mobile') return interface