# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve. # # 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 os import cv2 import glob from tqdm import tqdm import numpy as np from PIL import Image import paddle import paddle.nn as nn from paddlehub.module.module import moduleinfo, serving, Module from realsr.rrdb import RRDBNet import realsr.utils as U @moduleinfo( name="realsr", type="CV/image_editing", author="paddlepaddle", author_email="", summary="realsr is a super resolution model", version="1.0.0") class RealSRPredictor(Module): def _initialize(self, output='output', weight_path=None, load_checkpoint: str = None): #super(RealSRPredictor, self).__init__() self.input = input self.output = os.path.join(output, 'RealSR') self.model = RRDBNet(3, 3, 64, 23) if load_checkpoint is not None: state_dict = paddle.load(load_checkpoint) self.model.load_dict(state_dict) print("load custom checkpoint success") else: checkpoint = os.path.join(self.directory, 'DF2K_JPEG.pdparams') state_dict = paddle.load(checkpoint) self.model.load_dict(state_dict) print("load pretrained checkpoint success") self.model.eval() def norm(self, img): img = np.array(img).transpose([2, 0, 1]).astype('float32') / 255.0 return img.astype('float32') def denorm(self, img): img = img.transpose((1, 2, 0)) return (img * 255).clip(0, 255).astype('uint8') def run_image(self, img): if isinstance(img, str): ori_img = Image.open(img).convert('RGB') elif isinstance(img, np.ndarray): # ori_img = Image.fromarray(img).convert('RGB') ori_img = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB)) elif isinstance(img, Image.Image): ori_img = img img = self.norm(ori_img) x = paddle.to_tensor(img[np.newaxis, ...]) out = self.model(x) pred_img = self.denorm(out.numpy()[0]) # pred_img = Image.fromarray(pred_img) pred_img = cv2.cvtColor(pred_img, cv2.COLOR_RGB2BGR) return pred_img def run_video(self, video): base_name = os.path.basename(video).split('.')[0] output_path = os.path.join(self.output, base_name) pred_frame_path = os.path.join(output_path, 'frames_pred') if not os.path.exists(output_path): os.makedirs(output_path) if not os.path.exists(pred_frame_path): os.makedirs(pred_frame_path) cap = cv2.VideoCapture(video) fps = cap.get(cv2.CAP_PROP_FPS) out_path = U.video2frames(video, output_path) frames = sorted(glob.glob(os.path.join(out_path, '*.png'))) for frame in tqdm(frames): pred_img = self.run_image(frame) pred_img = cv2.cvtColor(pred_img, cv2.COLOR_BGR2RGB) pred_img = Image.fromarray(pred_img) frame_name = os.path.basename(frame) pred_img.save(os.path.join(pred_frame_path, frame_name)) frame_pattern_combined = os.path.join(pred_frame_path, '%08d.png') vid_out_path = os.path.join(output_path, '{}_realsr_out.mp4'.format(base_name)) U.frames2video(frame_pattern_combined, vid_out_path, str(int(fps))) print("save result at {}".format(vid_out_path)) return frame_pattern_combined, vid_out_path def predict(self, input): if not os.path.exists(self.output): os.makedirs(self.output) if not U.is_image(input): return self.run_video(input) else: pred_img = self.run_image(input) out_path = None if self.output: final = cv2.cvtColor(pred_img, cv2.COLOR_BGR2RGB) final = Image.fromarray(final) base_name = os.path.splitext(os.path.basename(input))[0] out_path = os.path.join(self.output, base_name + '.png') final.save(out_path) print('save result at {}'.format(out_path)) return pred_img, out_path @serving def serving_method(self, images, **kwargs): """ Run as a service. """ images_decode = U.base64_to_cv2(images) results = self.run_image(img=images_decode) results = U.cv2_to_base64(results) return results