# Copyright (c) 2021 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 os import time import argparse from typing import Callable, Union, List, Tuple from PIL import Image import numpy as np import cv2 import scipy import paddle import paddle.nn as nn import paddle.nn.functional as F from paddlehub.module.module import moduleinfo import paddlehub.vision.transforms as T from paddlehub.module.module import moduleinfo, runnable, serving from skimage.transform import resize from gfm_resnet34_matting.gfm import GFM import gfm_resnet34_matting.processor as P @moduleinfo( name="gfm_resnet34_matting", type="CV/matting", author="paddlepaddle", author_email="", summary="gfm_resnet34_matting is an animal matting model.", version="1.0.0") class GFMResNet34(nn.Layer): """ The GFM implementation based on PaddlePaddle. The original article refers to： Bridging Composite and Real: Towards End-to-end Deep Image Matting [IJCV-2021] Main network file (GFM). Github repo: https://github.com/JizhiziLi/GFM Paper link (Arxiv): https://arxiv.org/abs/2010.16188 """ def __init__(self, pretrained: str=None): super(GFMResNet34, self).__init__() self.model = GFM() self.resize_by_short = P.ResizeByShort(1080) if pretrained is not None: model_dict = paddle.load(pretrained) self.model.set_dict(model_dict) print("load custom parameters success") else: checkpoint = os.path.join(self.directory, 'model.pdparams') model_dict = paddle.load(checkpoint) self.model.set_dict(model_dict) print("load pretrained parameters success") def preprocess(self, img: Union[str, np.ndarray], h: int, w: int) -> paddle.Tensor: if min(h, w) > 1080: img = self.resize_by_short(img) tensor_img = self.scale_image(img, h, w) return tensor_img def scale_image(self, img: np.ndarray, h: int, w: int, ratio: float = 1/3): new_h = min(1600, h - (h % 32)) new_w = min(1600, w - (w % 32)) resize_h = int(h*ratio) resize_w = int(w*ratio) new_h = min(1600, resize_h - (resize_h % 32)) new_w = min(1600, resize_w - (resize_w % 32)) scale_img = resize(img,(new_h,new_w)) * 255 tensor_img = paddle.to_tensor(scale_img.astype(np.float32)[np.newaxis, :, :, :]) tensor_img = tensor_img.transpose([0,3,1,2]) return tensor_img def inference_img_scale(self, input: paddle.Tensor) -> List[paddle.Tensor]: pred_global, pred_local, pred_fusion = self.model(input) pred_global = P.gen_trimap_from_segmap_e2e(pred_global) pred_local = pred_local.numpy()[0,0,:,:] pred_fusion = pred_fusion.numpy()[0,0,:,:] return pred_global, pred_local, pred_fusion def predict(self, image_list: list, visualization: bool =True, save_path: str = "gfm_resnet34_matting_output"): self.model.eval() result = [] with paddle.no_grad(): for i, img in enumerate(image_list): if isinstance(img, str): img = np.array(Image.open(img))[:,:,:3] else: img = img[:,:,::-1] h, w, _ = img.shape tensor_img = self.preprocess(img, h, w) pred_glance_1, pred_focus_1, pred_fusion_1 = self.inference_img_scale(tensor_img) pred_glance_1 = resize(pred_glance_1,(h,w)) * 255.0 tensor_img = self.scale_image(img, h, w, 1/2) pred_glance_2, pred_focus_2, pred_fusion_2 = self.inference_img_scale(tensor_img) pred_focus_2 = resize(pred_focus_2,(h,w)) pred_fusion = P.get_masked_local_from_global_test(pred_glance_1, pred_focus_2) pred_fusion = (pred_fusion * 255).astype(np.uint8) if visualization: if not os.path.exists(save_path): os.makedirs(save_path) img_name = str(time.time()) + '.png' image_save_path = os.path.join(save_path, img_name) cv2.imwrite(image_save_path, pred_fusion) result.append(pred_fusion) return result @serving def serving_method(self, images: str, **kwargs): """ Run as a service. """ images_decode = [P.base64_to_cv2(image) for image in images] outputs = self.predict(image_list=images_decode, **kwargs) serving_data = [P.cv2_to_base64(outputs[i]) for i in range(len(outputs))] results = {'data': serving_data} return results @runnable def run_cmd(self, argvs: list): """ 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.predict(image_list=[args.input_path], save_path=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( '--output_dir', type=str, default="gfm_resnet34_matting_output", help="The directory to save output images.") self.arg_config_group.add_argument( '--visualization', type=bool, 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.")