140 lines
5.8 KiB
Python
140 lines
5.8 KiB
Python
# 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 numpy as np
|
|
import cv2
|
|
import math
|
|
import paddle
|
|
|
|
from arch import style_text_rec
|
|
from utils.sys_funcs import check_gpu
|
|
from utils.logging import get_logger
|
|
|
|
|
|
class StyleTextRecPredictor(object):
|
|
def __init__(self, config):
|
|
algorithm = config['Predictor']['algorithm']
|
|
assert algorithm in ["StyleTextRec"
|
|
], "Generator {} not supported.".format(algorithm)
|
|
use_gpu = config["Global"]['use_gpu']
|
|
check_gpu(use_gpu)
|
|
paddle.set_device('gpu' if use_gpu else 'cpu')
|
|
self.logger = get_logger()
|
|
self.generator = getattr(style_text_rec, algorithm)(config)
|
|
self.height = config["Global"]["image_height"]
|
|
self.width = config["Global"]["image_width"]
|
|
self.scale = config["Predictor"]["scale"]
|
|
self.mean = config["Predictor"]["mean"]
|
|
self.std = config["Predictor"]["std"]
|
|
self.expand_result = config["Predictor"]["expand_result"]
|
|
|
|
def reshape_to_same_height(self, img_list):
|
|
h = img_list[0].shape[0]
|
|
for idx in range(1, len(img_list)):
|
|
new_w = round(1.0 * img_list[idx].shape[1] /
|
|
img_list[idx].shape[0] * h)
|
|
img_list[idx] = cv2.resize(img_list[idx], (new_w, h))
|
|
return img_list
|
|
|
|
def predict_single_image(self, style_input, text_input):
|
|
style_input = self.rep_style_input(style_input, text_input)
|
|
tensor_style_input = self.preprocess(style_input)
|
|
tensor_text_input = self.preprocess(text_input)
|
|
style_text_result = self.generator.forward(tensor_style_input,
|
|
tensor_text_input)
|
|
fake_fusion = self.postprocess(style_text_result["fake_fusion"])
|
|
fake_text = self.postprocess(style_text_result["fake_text"])
|
|
fake_sk = self.postprocess(style_text_result["fake_sk"])
|
|
fake_bg = self.postprocess(style_text_result["fake_bg"])
|
|
bbox = self.get_text_boundary(fake_text)
|
|
if bbox:
|
|
left, right, top, bottom = bbox
|
|
fake_fusion = fake_fusion[top:bottom, left:right, :]
|
|
fake_text = fake_text[top:bottom, left:right, :]
|
|
fake_sk = fake_sk[top:bottom, left:right, :]
|
|
fake_bg = fake_bg[top:bottom, left:right, :]
|
|
|
|
# fake_fusion = self.crop_by_text(img_fake_fusion, img_fake_text)
|
|
return {
|
|
"fake_fusion": fake_fusion,
|
|
"fake_text": fake_text,
|
|
"fake_sk": fake_sk,
|
|
"fake_bg": fake_bg,
|
|
}
|
|
|
|
def predict(self, style_input, text_input_list):
|
|
if not isinstance(text_input_list, (tuple, list)):
|
|
return self.predict_single_image(style_input, text_input_list)
|
|
|
|
synth_result_list = []
|
|
for text_input in text_input_list:
|
|
synth_result = self.predict_single_image(style_input, text_input)
|
|
synth_result_list.append(synth_result)
|
|
|
|
for key in synth_result:
|
|
res = [r[key] for r in synth_result_list]
|
|
res = self.reshape_to_same_height(res)
|
|
synth_result[key] = np.concatenate(res, axis=1)
|
|
return synth_result
|
|
|
|
def preprocess(self, img):
|
|
img = (img.astype('float32') * self.scale - self.mean) / self.std
|
|
img_height, img_width, channel = img.shape
|
|
assert channel == 3, "Please use an rgb image."
|
|
ratio = img_width / float(img_height)
|
|
if math.ceil(self.height * ratio) > self.width:
|
|
resized_w = self.width
|
|
else:
|
|
resized_w = int(math.ceil(self.height * ratio))
|
|
img = cv2.resize(img, (resized_w, self.height))
|
|
|
|
new_img = np.zeros([self.height, self.width, 3]).astype('float32')
|
|
new_img[:, 0:resized_w, :] = img
|
|
img = new_img.transpose((2, 0, 1))
|
|
img = img[np.newaxis, :, :, :]
|
|
return paddle.to_tensor(img)
|
|
|
|
def postprocess(self, tensor):
|
|
img = tensor.numpy()[0]
|
|
img = img.transpose((1, 2, 0))
|
|
img = (img * self.std + self.mean) / self.scale
|
|
img = np.maximum(img, 0.0)
|
|
img = np.minimum(img, 255.0)
|
|
img = img.astype('uint8')
|
|
return img
|
|
|
|
def rep_style_input(self, style_input, text_input):
|
|
rep_num = int(1.2 * (text_input.shape[1] / text_input.shape[0]) /
|
|
(style_input.shape[1] / style_input.shape[0])) + 1
|
|
style_input = np.tile(style_input, reps=[1, rep_num, 1])
|
|
max_width = int(self.width / self.height * style_input.shape[0])
|
|
style_input = style_input[:, :max_width, :]
|
|
return style_input
|
|
|
|
def get_text_boundary(self, text_img):
|
|
img_height = text_img.shape[0]
|
|
img_width = text_img.shape[1]
|
|
bounder = 3
|
|
text_canny_img = cv2.Canny(text_img, 10, 20)
|
|
edge_num_h = text_canny_img.sum(axis=0)
|
|
no_zero_list_h = np.where(edge_num_h > 0)[0]
|
|
edge_num_w = text_canny_img.sum(axis=1)
|
|
no_zero_list_w = np.where(edge_num_w > 0)[0]
|
|
if len(no_zero_list_h) == 0 or len(no_zero_list_w) == 0:
|
|
return None
|
|
left = max(no_zero_list_h[0] - bounder, 0)
|
|
right = min(no_zero_list_h[-1] + bounder, img_width)
|
|
top = max(no_zero_list_w[0] - bounder, 0)
|
|
bottom = min(no_zero_list_w[-1] + bounder, img_height)
|
|
return [left, right, top, bottom]
|