# 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 ace2p.processor import get_palette, postprocess, base64_to_cv2, cv2_to_base64 from ace2p.data_feed import reader @moduleinfo( name="ace2p", type="CV/semantic-segmentation", author="baidu-idl", author_email="", summary="ACE2P is an image segmentation model for human parsing solution.", version="1.1.0") class ACE2P(hub.Module): def _initialize(self): self.default_pretrained_model_path = os.path.join( self.directory, "ace2p_human_parsing") # label list label_list_file = os.path.join(self.directory, 'label_list.txt') with open(label_list_file, "r") as file: content = file.read() self.label_list = content.split("\n") # palette used in postprocess self.palette = get_palette(len(self.label_list)) self._set_config() 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 segmentation(self, images=None, paths=None, data=None, batch_size=1, use_gpu=False, output_dir='ace2p_output', visualization=False): """ API for human parsing. Args: images (list[numpy.ndarray]): images data, shape of each is [H, W, C], color space is BGR. 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 output images or not. Returns: res (list[dict]): The result of human parsing and original 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 = [] scale = (473, 473) # size of preprocessed image. rotation = 0 # rotation angle, used for obtaining affine matrix in preprocess. for yield_data in reader(images, paths, scale, rotation): 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]) batch_image = PaddleTensor(batch_image.astype('float32')) data_out = self.gpu_predictor.run([ batch_image ]) if use_gpu else self.cpu_predictor.run([batch_image]) # postprocess one by one for i in range(len(batch_data)): out = postprocess( data_out=data_out[0].as_ndarray()[i], org_im=batch_data[i]['org_im'], org_im_path=batch_data[i]['org_im_path'], image_info=batch_data[i]['image_info'], output_dir=output_dir, visualization=visualization, palette=self.palette) res.append(out) return res def save_inference_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.segmentation(images_decode, **kwargs) results = [{ 'data': cv2_to_base64(result['data']) } for result in results] 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.segmentation( paths=[args.input_path], batch_size=args.batch_size, use_gpu=args.use_gpu, output_dir=args.output_dir, visualization=args.visualization) 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='ace2p_output', 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.") self.arg_config_group.add_argument( '--batch_size', type=ast.literal_eval, default=1, help="batch size.") 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.")