117 lines
4.9 KiB
Python
117 lines
4.9 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import numpy as np
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import cv2
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import math
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import paddle
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from arch import style_text_rec
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from utils.sys_funcs import check_gpu
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from utils.logging import get_logger
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class StyleTextRecPredictor(object):
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def __init__(self, config):
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algorithm = config['Predictor']['algorithm']
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assert algorithm in ["StyleTextRec"
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], "Generator {} not supported.".format(algorithm)
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use_gpu = config["Global"]['use_gpu']
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check_gpu(use_gpu)
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paddle.set_device('gpu' if use_gpu else 'cpu')
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self.logger = get_logger()
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self.generator = getattr(style_text_rec, algorithm)(config)
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self.height = config["Global"]["image_height"]
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self.width = config["Global"]["image_width"]
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self.scale = config["Predictor"]["scale"]
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self.mean = config["Predictor"]["mean"]
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self.std = config["Predictor"]["std"]
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self.expand_result = config["Predictor"]["expand_result"]
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def predict(self, style_input, text_input):
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style_input = self.rep_style_input(style_input, text_input)
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tensor_style_input = self.preprocess(style_input)
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tensor_text_input = self.preprocess(text_input)
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style_text_result = self.generator.forward(tensor_style_input,
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tensor_text_input)
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fake_fusion = self.postprocess(style_text_result["fake_fusion"])
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fake_text = self.postprocess(style_text_result["fake_text"])
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fake_sk = self.postprocess(style_text_result["fake_sk"])
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fake_bg = self.postprocess(style_text_result["fake_bg"])
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bbox = self.get_text_boundary(fake_text)
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if bbox:
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left, right, top, bottom = bbox
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fake_fusion = fake_fusion[top:bottom, left:right, :]
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fake_text = fake_text[top:bottom, left:right, :]
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fake_sk = fake_sk[top:bottom, left:right, :]
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fake_bg = fake_bg[top:bottom, left:right, :]
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# fake_fusion = self.crop_by_text(img_fake_fusion, img_fake_text)
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return {
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"fake_fusion": fake_fusion,
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"fake_text": fake_text,
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"fake_sk": fake_sk,
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"fake_bg": fake_bg,
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}
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def preprocess(self, img):
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img = (img.astype('float32') * self.scale - self.mean) / self.std
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img_height, img_width, channel = img.shape
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assert channel == 3, "Please use an rgb image."
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ratio = img_width / float(img_height)
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if math.ceil(self.height * ratio) > self.width:
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resized_w = self.width
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else:
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resized_w = int(math.ceil(self.height * ratio))
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img = cv2.resize(img, (resized_w, self.height))
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new_img = np.zeros([self.height, self.width, 3]).astype('float32')
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new_img[:, 0:resized_w, :] = img
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img = new_img.transpose((2, 0, 1))
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img = img[np.newaxis, :, :, :]
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return paddle.to_tensor(img)
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def postprocess(self, tensor):
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img = tensor.numpy()[0]
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img = img.transpose((1, 2, 0))
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img = (img * self.std + self.mean) / self.scale
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img = np.maximum(img, 0.0)
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img = np.minimum(img, 255.0)
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img = img.astype('uint8')
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return img
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def rep_style_input(self, style_input, text_input):
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rep_num = int(1.2 * (text_input.shape[1] / text_input.shape[0]) /
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(style_input.shape[1] / style_input.shape[0])) + 1
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style_input = np.tile(style_input, reps=[1, rep_num, 1])
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max_width = int(self.width / self.height * style_input.shape[0])
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style_input = style_input[:, :max_width, :]
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return style_input
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def get_text_boundary(self, text_img):
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img_height = text_img.shape[0]
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img_width = text_img.shape[1]
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bounder = 3
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text_canny_img = cv2.Canny(text_img, 10, 20)
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edge_num_h = text_canny_img.sum(axis=0)
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no_zero_list_h = np.where(edge_num_h > 0)[0]
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edge_num_w = text_canny_img.sum(axis=1)
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no_zero_list_w = np.where(edge_num_w > 0)[0]
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if len(no_zero_list_h) == 0 or len(no_zero_list_w) == 0:
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return None
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left = max(no_zero_list_h[0] - bounder, 0)
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right = min(no_zero_list_h[-1] + bounder, img_width)
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top = max(no_zero_list_w[0] - bounder, 0)
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bottom = min(no_zero_list_w[-1] + bounder, img_height)
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return [left, right, top, bottom]
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