# coding=utf-8 from __future__ import absolute_import from __future__ import division import ast import argparse import os import numpy as np import paddle.fluid as fluid import paddlehub as hub from paddle.fluid.core import PaddleTensor, AnalysisConfig, create_paddle_predictor from paddlehub.module.module import moduleinfo, runnable, serving from pyramidbox_lite_mobile_mask.data_feed import reader from pyramidbox_lite_mobile_mask.processor import postprocess, base64_to_cv2 @moduleinfo( name="pyramidbox_lite_mobile_mask", type="CV/face_detection", author="baidu-vis", author_email="", summary= "Pyramidbox-Lite-Mobile-Mask is a high-performance face detection model used to detect whether people wear masks.", version="1.3.0") class PyramidBoxLiteMobileMask(hub.Module): def _initialize(self, face_detector_module=None): """ Args: face_detector_module (class): module to detect face. """ self.default_pretrained_model_path = os.path.join(self.directory, "pyramidbox_lite_mobile_mask_model") if face_detector_module is None: self.face_detector = hub.Module(name='pyramidbox_lite_mobile') else: self.face_detector = face_detector_module self._set_config() self.processor = self def _set_config(self): """ predictor config setting """ cpu_config = AnalysisConfig(self.default_pretrained_model_path) cpu_config.disable_glog_info() cpu_config.disable_gpu() self.cpu_predictor = create_paddle_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 = AnalysisConfig(self.default_pretrained_model_path) gpu_config.disable_glog_info() gpu_config.enable_use_gpu(memory_pool_init_size_mb=1000, device_id=0) self.gpu_predictor = create_paddle_predictor(gpu_config) def set_face_detector_module(self, face_detector_module): """ Set face detector. Args: face_detector_module (class): module to detect face. """ self.face_detector = face_detector_module def get_face_detector_module(self): return self.face_detector def face_detection(self, images=None, paths=None, data=None, batch_size=1, use_gpu=False, visualization=False, output_dir='detection_result', use_multi_scale=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], color space must be BGR. paths (list[str]): The paths of images. batch_size (int): batch size of image tensor to be fed into the later classification network. use_gpu (bool): Whether to use gpu. visualization (bool): Whether to save image or not. output_dir (str): The path to store output images. use_multi_scale (bool): whether to enable multi-scale face detection. Enabling multi-scale face detection can increase the accuracy to detect faces, however, it reduce the prediction speed for the increase model calculation. 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'] # get all data all_element = list() for yield_data in reader(self.face_detector, shrink, confs_threshold, images, paths, use_gpu, use_multi_scale): all_element.append(yield_data) image_list = list() element_image_num = list() for i in range(len(all_element)): element_image = [handled['image'] for handled in all_element[i]['preprocessed']] element_image_num.append(len(element_image)) image_list.extend(element_image) total_num = len(image_list) loop_num = int(np.ceil(total_num / batch_size)) predict_out = np.zeros((1, 2)) for iter_id in range(loop_num): batch_data = list() handle_id = iter_id * batch_size for element_id in range(batch_size): try: batch_data.append(image_list[handle_id + element_id]) except: pass image_arr = np.squeeze(np.array(batch_data), axis=1) image_tensor = PaddleTensor(image_arr.copy()) data_out = self.gpu_predictor.run([image_tensor]) if use_gpu else self.cpu_predictor.run([image_tensor]) # len(data_out) == 1 # data_out[0].as_ndarray().shape == (-1, 2) data_out = data_out[0].as_ndarray() predict_out = np.concatenate((predict_out, data_out)) predict_out = predict_out[1:] # postprocess one by one res = list() for i in range(len(all_element)): detect_faces_list = [handled['face'] for handled in all_element[i]['preprocessed']] interval_left = sum(element_image_num[0:i]) interval_right = interval_left + element_image_num[i] out = postprocess( confidence_out=predict_out[interval_left:interval_right], org_im=all_element[i]['org_im'], org_im_path=all_element[i]['org_im_path'], detected_faces=detect_faces_list, output_dir=output_dir, visualization=visualization) res.append(out) return res def save_inference_model(self, dirname, model_filename=None, params_filename=None, combined=True): classifier_dir = os.path.join(dirname, 'mask_detector') detector_dir = os.path.join(dirname, 'pyramidbox_lite') self._save_classifier_model(classifier_dir, model_filename, params_filename, combined) self._save_detector_model(detector_dir, model_filename, params_filename, combined) def _save_detector_model(self, dirname, model_filename=None, params_filename=None, combined=True): self.face_detector.save_inference_model(dirname, model_filename, params_filename, combined) def _save_classifier_model(self, dirname, model_filename=None, params_filename=None, combined=True): if combined: model_filename = "__model__" if not model_filename else model_filename params_filename = "__params__" if not params_filename else params_filename place = fluid.CPUPlace() exe = fluid.Executor(place) program, feeded_var_names, target_vars = fluid.io.load_inference_model( dirname=self.default_pretrained_model_path, executor=exe) fluid.io.save_inference_model( dirname=dirname, main_program=program, executor=exe, feeded_var_names=feeded_var_names, target_vars=target_vars, model_filename=model_filename, params_filename=params_filename) @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.")