# coding=utf-8 from __future__ import absolute_import from __future__ import division import ast import os import argparse 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 deeplabv3p_xception65_humanseg.processor import postprocess, base64_to_cv2, cv2_to_base64 from deeplabv3p_xception65_humanseg.data_feed import reader @moduleinfo( name="deeplabv3p_xception65_humanseg", type="CV/semantic_segmentation", author="baidu-vis", author_email="", summary="DeepLabv3+ is a semantic segmentation model.", version="1.1.0") class DeeplabV3pXception65HumanSeg(hub.Module): def _initialize(self): self.default_pretrained_model_path = os.path.join( self.directory, "deeplabv3p_xception65_humanseg_model") 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, visualization=False, output_dir='humanseg_output'): """ API for human segmentation. Args: images (list(numpy.ndarray)): images data, shape of each is [H, W, C], the color space is BGR. paths (list[str]): The paths of images. data (dict): key is 'image', the corresponding value is the path to image. batch_size (int): batch size. use_gpu (bool): Whether to use gpu. visualization (bool): Whether to save image or not. output_dir (str): The path to store output images. Returns: res (list[dict]): each element in the list is a dict, the keys and values are: save_path (str, optional): the path to save images. (Exists only if visualization is True) data (numpy.ndarray): data of post processed image. """ 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 = list() paths += data['image'] all_data = list() 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 = list() 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.copy()) output = self.gpu_predictor.run([ batch_image ]) if use_gpu else self.cpu_predictor.run([batch_image]) output = np.expand_dims(output[0].as_ndarray(), axis=1) # postprocess one by one for i in range(len(batch_data)): out = postprocess( data_out=output[i], org_im=batch_data[i]['org_im'], org_im_shape=batch_data[i]['org_im_shape'], org_im_path=batch_data[i]['org_im_path'], 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): 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=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='humanseg_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.")