# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License" # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import argparse import ast import os import numpy as np 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 check_dir from .processor import cv2_to_base64 from .processor import postprocess from paddlehub.module.module import moduleinfo from paddlehub.module.module import runnable from paddlehub.module.module import serving @moduleinfo(name="falsr_a", type="CV/image_editing", author="paddlepaddle", author_email="", summary="falsr_a is a super resolution model.", version="1.2.0") class Falsr_A: def __init__(self): self.default_pretrained_model_path = os.path.join(self.directory, "falsr_a_model", "model") self._set_config() 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 reconstruct(self, images=None, paths=None, use_gpu=False, visualization=False, output_dir="falsr_a_output"): """ API for super resolution. 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. 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." ) all_data = list() for yield_data in reader(images, paths): all_data.append(yield_data) total_num = len(all_data) res = list() for i in range(total_num): image_y = np.array([all_data[i]['img_y']]) image_scale_pbpr = np.array([all_data[i]['img_scale_pbpr']]) 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_y.copy()) input_handle = predictor.get_input_handle(input_names[1]) input_handle.copy_from_cpu(image_scale_pbpr.copy()) predictor.run() output_names = predictor.get_output_names() output_handle = predictor.get_output_handle(output_names[0]) output = np.expand_dims(output_handle.copy_to_cpu(), axis=1) out = postprocess(data_out=output, org_im=all_data[i]['org_im'], org_im_shape=all_data[i]['org_im_shape'], org_im_path=all_data[i]['org_im_path'], output_dir=output_dir, visualization=visualization) 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.reconstruct(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.reconstruct(paths=[args.input_path], use_gpu=args.use_gpu, output_dir=args.output_dir, visualization=args.visualization) if args.save_dir is not None: check_dir(args.save_dir) self.save_inference_model(args.save_dir) 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='falsr_a_output', help="The directory to save output images.") self.arg_config_group.add_argument('--save_dir', type=str, default='falsr_a_save_model', help="The directory to save model.") self.arg_config_group.add_argument('--visualization', type=ast.literal_eval, default=True, 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.") def create_gradio_app(self): import gradio as gr import tempfile import os from PIL import Image def inference(image, use_gpu=False): with tempfile.TemporaryDirectory() as temp_dir: self.reconstruct(paths=[image], use_gpu=use_gpu, visualization=True, output_dir=temp_dir) return Image.open(os.path.join(temp_dir, os.listdir(temp_dir)[0])) interface = gr.Interface( inference, [gr.inputs.Image(type="filepath"), gr.Checkbox(label='use_gpu')], gr.outputs.Image(type="ndarray"), title='falsr_a') return interface