This commit is contained in:
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ppocr/data/imaug/rec_img_aug.py
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932
ppocr/data/imaug/rec_img_aug.py
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# 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 math
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import cv2
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import numpy as np
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import random
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import copy
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from PIL import Image
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import PIL
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from .text_image_aug import tia_perspective, tia_stretch, tia_distort
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from .abinet_aug import (
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CVGeometry,
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CVDeterioration,
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CVColorJitter,
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SVTRGeometry,
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SVTRDeterioration,
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ParseQDeterioration,
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)
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from paddle.vision.transforms import Compose
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class RecAug(object):
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def __init__(
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self,
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tia_prob=0.4,
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crop_prob=0.4,
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reverse_prob=0.4,
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noise_prob=0.4,
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jitter_prob=0.4,
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blur_prob=0.4,
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hsv_aug_prob=0.4,
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**kwargs,
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):
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self.tia_prob = tia_prob
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self.bda = BaseDataAugmentation(
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crop_prob, reverse_prob, noise_prob, jitter_prob, blur_prob, hsv_aug_prob
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)
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def __call__(self, data):
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img = data["image"]
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h, w, _ = img.shape
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# tia
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if random.random() <= self.tia_prob:
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if h >= 20 and w >= 20:
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img = tia_distort(img, random.randint(3, 6))
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img = tia_stretch(img, random.randint(3, 6))
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img = tia_perspective(img)
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# bda
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data["image"] = img
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data = self.bda(data)
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return data
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class BaseDataAugmentation(object):
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def __init__(
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self,
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crop_prob=0.4,
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reverse_prob=0.4,
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noise_prob=0.4,
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jitter_prob=0.4,
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blur_prob=0.4,
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hsv_aug_prob=0.4,
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**kwargs,
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):
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self.crop_prob = crop_prob
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self.reverse_prob = reverse_prob
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self.noise_prob = noise_prob
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self.jitter_prob = jitter_prob
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self.blur_prob = blur_prob
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self.hsv_aug_prob = hsv_aug_prob
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# for GaussianBlur
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self.fil = cv2.getGaussianKernel(ksize=5, sigma=1, ktype=cv2.CV_32F)
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def __call__(self, data):
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img = data["image"]
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h, w, _ = img.shape
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if random.random() <= self.crop_prob and h >= 20 and w >= 20:
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img = get_crop(img)
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if random.random() <= self.blur_prob:
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# GaussianBlur
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img = cv2.sepFilter2D(img, -1, self.fil, self.fil)
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if random.random() <= self.hsv_aug_prob:
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img = hsv_aug(img)
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if random.random() <= self.jitter_prob:
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img = jitter(img)
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if random.random() <= self.noise_prob:
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img = add_gasuss_noise(img)
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if random.random() <= self.reverse_prob:
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img = 255 - img
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data["image"] = img
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return data
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class ABINetRecAug(object):
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def __init__(
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self, geometry_p=0.5, deterioration_p=0.25, colorjitter_p=0.25, **kwargs
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):
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self.transforms = Compose(
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[
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CVGeometry(
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degrees=45,
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translate=(0.0, 0.0),
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scale=(0.5, 2.0),
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shear=(45, 15),
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distortion=0.5,
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p=geometry_p,
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),
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CVDeterioration(var=20, degrees=6, factor=4, p=deterioration_p),
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CVColorJitter(
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brightness=0.5,
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contrast=0.5,
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saturation=0.5,
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hue=0.1,
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p=colorjitter_p,
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),
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]
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)
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def __call__(self, data):
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img = data["image"]
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img = self.transforms(img)
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data["image"] = img
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return data
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class RecConAug(object):
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def __init__(
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self,
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prob=0.5,
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image_shape=(32, 320, 3),
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max_text_length=25,
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ext_data_num=1,
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**kwargs,
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):
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self.ext_data_num = ext_data_num
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self.prob = prob
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self.max_text_length = max_text_length
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self.image_shape = image_shape
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self.max_wh_ratio = self.image_shape[1] / self.image_shape[0]
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def merge_ext_data(self, data, ext_data):
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ori_w = round(
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data["image"].shape[1] / data["image"].shape[0] * self.image_shape[0]
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)
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ext_w = round(
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ext_data["image"].shape[1]
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/ ext_data["image"].shape[0]
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* self.image_shape[0]
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)
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data["image"] = cv2.resize(data["image"], (ori_w, self.image_shape[0]))
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ext_data["image"] = cv2.resize(ext_data["image"], (ext_w, self.image_shape[0]))
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data["image"] = np.concatenate([data["image"], ext_data["image"]], axis=1)
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data["label"] += ext_data["label"]
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return data
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def __call__(self, data):
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rnd_num = random.random()
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if rnd_num > self.prob:
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return data
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for idx, ext_data in enumerate(data["ext_data"]):
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if len(data["label"]) + len(ext_data["label"]) > self.max_text_length:
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break
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concat_ratio = (
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data["image"].shape[1] / data["image"].shape[0]
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+ ext_data["image"].shape[1] / ext_data["image"].shape[0]
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)
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if concat_ratio > self.max_wh_ratio:
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break
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data = self.merge_ext_data(data, ext_data)
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data.pop("ext_data")
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return data
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class SVTRRecAug(object):
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def __init__(
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self,
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aug_type=0,
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geometry_p=0.5,
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deterioration_p=0.25,
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colorjitter_p=0.25,
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**kwargs,
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):
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self.transforms = Compose(
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[
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SVTRGeometry(
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aug_type=aug_type,
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degrees=45,
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translate=(0.0, 0.0),
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scale=(0.5, 2.0),
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shear=(45, 15),
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distortion=0.5,
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p=geometry_p,
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),
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SVTRDeterioration(var=20, degrees=6, factor=4, p=deterioration_p),
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CVColorJitter(
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brightness=0.5,
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contrast=0.5,
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saturation=0.5,
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hue=0.1,
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p=colorjitter_p,
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),
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]
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)
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def __call__(self, data):
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img = data["image"]
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img = self.transforms(img)
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data["image"] = img
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return data
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class ParseQRecAug(object):
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def __init__(
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self,
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aug_type=0,
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geometry_p=0.5,
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deterioration_p=0.25,
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colorjitter_p=0.25,
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**kwargs,
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):
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self.transforms = Compose(
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[
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SVTRGeometry(
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aug_type=aug_type,
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degrees=45,
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translate=(0.0, 0.0),
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scale=(0.5, 2.0),
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shear=(45, 15),
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distortion=0.5,
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p=geometry_p,
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),
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ParseQDeterioration(
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var=20, degrees=6, lam=20, radius=2.0, factor=4, p=deterioration_p
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),
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CVColorJitter(
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brightness=0.5,
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contrast=0.5,
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saturation=0.5,
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hue=0.1,
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p=colorjitter_p,
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),
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]
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)
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def __call__(self, data):
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img = data["image"]
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img = self.transforms(img)
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data["image"] = img
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return data
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class ClsResizeImg(object):
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def __init__(self, image_shape, **kwargs):
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self.image_shape = image_shape
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def __call__(self, data):
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img = data["image"]
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norm_img, _ = resize_norm_img(img, self.image_shape)
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data["image"] = norm_img
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return data
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class RecResizeImg(object):
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def __init__(
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self,
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image_shape,
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infer_mode=False,
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eval_mode=False,
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character_dict_path="./ppocr/utils/ppocr_keys_v1.txt",
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padding=True,
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**kwargs,
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):
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self.image_shape = image_shape
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self.infer_mode = infer_mode
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self.eval_mode = eval_mode
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self.character_dict_path = character_dict_path
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self.padding = padding
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def __call__(self, data):
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img = data["image"]
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if self.eval_mode or (self.infer_mode and self.character_dict_path is not None):
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norm_img, valid_ratio = resize_norm_img_chinese(img, self.image_shape)
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else:
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norm_img, valid_ratio = resize_norm_img(img, self.image_shape, self.padding)
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data["image"] = norm_img
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data["valid_ratio"] = valid_ratio
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return data
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class VLRecResizeImg(object):
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def __init__(
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self,
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image_shape,
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infer_mode=False,
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character_dict_path="./ppocr/utils/ppocr_keys_v1.txt",
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padding=True,
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**kwargs,
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):
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self.image_shape = image_shape
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self.infer_mode = infer_mode
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self.character_dict_path = character_dict_path
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self.padding = padding
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def __call__(self, data):
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img = data["image"]
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imgC, imgH, imgW = self.image_shape
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resized_image = cv2.resize(img, (imgW, imgH), interpolation=cv2.INTER_LINEAR)
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resized_w = imgW
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resized_image = resized_image.astype("float32")
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if self.image_shape[0] == 1:
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resized_image = resized_image / 255
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norm_img = resized_image[np.newaxis, :]
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else:
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norm_img = resized_image.transpose((2, 0, 1)) / 255
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valid_ratio = min(1.0, float(resized_w / imgW))
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data["image"] = norm_img
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data["valid_ratio"] = valid_ratio
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return data
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class RFLRecResizeImg(object):
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def __init__(self, image_shape, padding=True, interpolation=1, **kwargs):
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self.image_shape = image_shape
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self.padding = padding
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self.interpolation = interpolation
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if self.interpolation == 0:
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self.interpolation = cv2.INTER_NEAREST
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elif self.interpolation == 1:
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self.interpolation = cv2.INTER_LINEAR
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elif self.interpolation == 2:
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self.interpolation = cv2.INTER_CUBIC
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elif self.interpolation == 3:
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self.interpolation = cv2.INTER_AREA
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else:
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raise Exception("Unsupported interpolation type !!!")
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def __call__(self, data):
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img = data["image"]
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img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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norm_img, valid_ratio = resize_norm_img(
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img, self.image_shape, self.padding, self.interpolation
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)
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data["image"] = norm_img
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data["valid_ratio"] = valid_ratio
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return data
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class SRNRecResizeImg(object):
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def __init__(self, image_shape, num_heads, max_text_length, **kwargs):
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self.image_shape = image_shape
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self.num_heads = num_heads
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self.max_text_length = max_text_length
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def __call__(self, data):
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img = data["image"]
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norm_img = resize_norm_img_srn(img, self.image_shape)
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data["image"] = norm_img
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[
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encoder_word_pos,
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gsrm_word_pos,
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gsrm_slf_attn_bias1,
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gsrm_slf_attn_bias2,
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] = srn_other_inputs(self.image_shape, self.num_heads, self.max_text_length)
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data["encoder_word_pos"] = encoder_word_pos
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data["gsrm_word_pos"] = gsrm_word_pos
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data["gsrm_slf_attn_bias1"] = gsrm_slf_attn_bias1
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data["gsrm_slf_attn_bias2"] = gsrm_slf_attn_bias2
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return data
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class SARRecResizeImg(object):
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def __init__(self, image_shape, width_downsample_ratio=0.25, **kwargs):
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self.image_shape = image_shape
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self.width_downsample_ratio = width_downsample_ratio
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def __call__(self, data):
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img = data["image"]
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norm_img, resize_shape, pad_shape, valid_ratio = resize_norm_img_sar(
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img, self.image_shape, self.width_downsample_ratio
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)
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data["image"] = norm_img
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data["resized_shape"] = resize_shape
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data["pad_shape"] = pad_shape
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data["valid_ratio"] = valid_ratio
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return data
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class PRENResizeImg(object):
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def __init__(self, image_shape, **kwargs):
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"""
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According to original paper's realization, it's a hard resize method here.
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So maybe you should optimize it to fit for your task better.
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"""
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self.dst_h, self.dst_w = image_shape
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def __call__(self, data):
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img = data["image"]
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resized_img = cv2.resize(
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img, (self.dst_w, self.dst_h), interpolation=cv2.INTER_LINEAR
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)
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resized_img = resized_img.transpose((2, 0, 1)) / 255
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resized_img -= 0.5
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resized_img /= 0.5
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data["image"] = resized_img.astype(np.float32)
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return data
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class SPINRecResizeImg(object):
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def __init__(
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self,
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image_shape,
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interpolation=2,
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mean=(127.5, 127.5, 127.5),
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std=(127.5, 127.5, 127.5),
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**kwargs,
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):
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self.image_shape = image_shape
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self.mean = np.array(mean, dtype=np.float32)
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self.std = np.array(std, dtype=np.float32)
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self.interpolation = interpolation
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def __call__(self, data):
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img = data["image"]
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img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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# different interpolation type corresponding the OpenCV
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if self.interpolation == 0:
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interpolation = cv2.INTER_NEAREST
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elif self.interpolation == 1:
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interpolation = cv2.INTER_LINEAR
|
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elif self.interpolation == 2:
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interpolation = cv2.INTER_CUBIC
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elif self.interpolation == 3:
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interpolation = cv2.INTER_AREA
|
||||
else:
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raise Exception("Unsupported interpolation type !!!")
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# Deal with the image error during image loading
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if img is None:
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return None
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img = cv2.resize(img, tuple(self.image_shape), interpolation)
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img = np.array(img, np.float32)
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img = np.expand_dims(img, -1)
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img = img.transpose((2, 0, 1))
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# normalize the image
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img = img.copy().astype(np.float32)
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mean = np.float64(self.mean.reshape(1, -1))
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stdinv = 1 / np.float64(self.std.reshape(1, -1))
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img -= mean
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img *= stdinv
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data["image"] = img
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return data
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||||
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||||
class GrayRecResizeImg(object):
|
||||
def __init__(
|
||||
self,
|
||||
image_shape,
|
||||
resize_type,
|
||||
inter_type="Image.Resampling.LANCZOS",
|
||||
scale=True,
|
||||
padding=False,
|
||||
**kwargs,
|
||||
):
|
||||
self.image_shape = image_shape
|
||||
self.resize_type = resize_type
|
||||
self.padding = padding
|
||||
self.inter_type = eval(inter_type)
|
||||
self.scale = scale
|
||||
|
||||
def __call__(self, data):
|
||||
img = data["image"]
|
||||
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
|
||||
image_shape = self.image_shape
|
||||
if self.padding:
|
||||
imgC, imgH, imgW = image_shape
|
||||
# todo: change to 0 and modified image shape
|
||||
h = img.shape[0]
|
||||
w = img.shape[1]
|
||||
ratio = w / float(h)
|
||||
if math.ceil(imgH * ratio) > imgW:
|
||||
resized_w = imgW
|
||||
else:
|
||||
resized_w = int(math.ceil(imgH * ratio))
|
||||
resized_image = cv2.resize(img, (resized_w, imgH))
|
||||
norm_img = np.expand_dims(resized_image, -1)
|
||||
norm_img = norm_img.transpose((2, 0, 1))
|
||||
resized_image = norm_img.astype(np.float32) / 128.0 - 1.0
|
||||
padding_im = np.zeros((imgC, imgH, imgW), dtype=np.float32)
|
||||
padding_im[:, :, 0:resized_w] = resized_image
|
||||
data["image"] = padding_im
|
||||
return data
|
||||
if self.resize_type == "PIL":
|
||||
image_pil = Image.fromarray(np.uint8(img))
|
||||
img = image_pil.resize(self.image_shape, self.inter_type)
|
||||
img = np.array(img)
|
||||
if self.resize_type == "OpenCV":
|
||||
img = cv2.resize(img, self.image_shape)
|
||||
norm_img = np.expand_dims(img, -1)
|
||||
norm_img = norm_img.transpose((2, 0, 1))
|
||||
if self.scale:
|
||||
data["image"] = norm_img.astype(np.float32) / 128.0 - 1.0
|
||||
else:
|
||||
data["image"] = norm_img.astype(np.float32) / 255.0
|
||||
return data
|
||||
|
||||
|
||||
class ABINetRecResizeImg(object):
|
||||
def __init__(self, image_shape, **kwargs):
|
||||
self.image_shape = image_shape
|
||||
|
||||
def __call__(self, data):
|
||||
img = data["image"]
|
||||
norm_img, valid_ratio = resize_norm_img_abinet(img, self.image_shape)
|
||||
data["image"] = norm_img
|
||||
data["valid_ratio"] = valid_ratio
|
||||
return data
|
||||
|
||||
|
||||
class SVTRRecResizeImg(object):
|
||||
def __init__(self, image_shape, padding=True, **kwargs):
|
||||
self.image_shape = image_shape
|
||||
self.padding = padding
|
||||
|
||||
def __call__(self, data):
|
||||
img = data["image"]
|
||||
|
||||
norm_img, valid_ratio = resize_norm_img(img, self.image_shape, self.padding)
|
||||
data["image"] = norm_img
|
||||
data["valid_ratio"] = valid_ratio
|
||||
return data
|
||||
|
||||
|
||||
class RobustScannerRecResizeImg(object):
|
||||
def __init__(
|
||||
self, image_shape, max_text_length, width_downsample_ratio=0.25, **kwargs
|
||||
):
|
||||
self.image_shape = image_shape
|
||||
self.width_downsample_ratio = width_downsample_ratio
|
||||
self.max_text_length = max_text_length
|
||||
|
||||
def __call__(self, data):
|
||||
img = data["image"]
|
||||
norm_img, resize_shape, pad_shape, valid_ratio = resize_norm_img_sar(
|
||||
img, self.image_shape, self.width_downsample_ratio
|
||||
)
|
||||
word_positons = np.array(range(0, self.max_text_length)).astype("int64")
|
||||
data["image"] = norm_img
|
||||
data["resized_shape"] = resize_shape
|
||||
data["pad_shape"] = pad_shape
|
||||
data["valid_ratio"] = valid_ratio
|
||||
data["word_positons"] = word_positons
|
||||
return data
|
||||
|
||||
|
||||
def resize_norm_img_sar(img, image_shape, width_downsample_ratio=0.25):
|
||||
imgC, imgH, imgW_min, imgW_max = image_shape
|
||||
h = img.shape[0]
|
||||
w = img.shape[1]
|
||||
valid_ratio = 1.0
|
||||
# make sure new_width is an integral multiple of width_divisor.
|
||||
width_divisor = int(1 / width_downsample_ratio)
|
||||
# resize
|
||||
ratio = w / float(h)
|
||||
resize_w = math.ceil(imgH * ratio)
|
||||
if resize_w % width_divisor != 0:
|
||||
resize_w = round(resize_w / width_divisor) * width_divisor
|
||||
if imgW_min is not None:
|
||||
resize_w = max(imgW_min, resize_w)
|
||||
if imgW_max is not None:
|
||||
valid_ratio = min(1.0, 1.0 * resize_w / imgW_max)
|
||||
resize_w = min(imgW_max, resize_w)
|
||||
resized_image = cv2.resize(img, (resize_w, imgH))
|
||||
resized_image = resized_image.astype("float32")
|
||||
# norm
|
||||
if image_shape[0] == 1:
|
||||
resized_image = resized_image / 255
|
||||
resized_image = resized_image[np.newaxis, :]
|
||||
else:
|
||||
resized_image = resized_image.transpose((2, 0, 1)) / 255
|
||||
resized_image -= 0.5
|
||||
resized_image /= 0.5
|
||||
resize_shape = resized_image.shape
|
||||
padding_im = -1.0 * np.ones((imgC, imgH, imgW_max), dtype=np.float32)
|
||||
padding_im[:, :, 0:resize_w] = resized_image
|
||||
pad_shape = padding_im.shape
|
||||
|
||||
return padding_im, resize_shape, pad_shape, valid_ratio
|
||||
|
||||
|
||||
def resize_norm_img(img, image_shape, padding=True, interpolation=cv2.INTER_LINEAR):
|
||||
imgC, imgH, imgW = image_shape
|
||||
h = img.shape[0]
|
||||
w = img.shape[1]
|
||||
if not padding:
|
||||
resized_image = cv2.resize(img, (imgW, imgH), interpolation=interpolation)
|
||||
resized_w = imgW
|
||||
else:
|
||||
ratio = w / float(h)
|
||||
if math.ceil(imgH * ratio) > imgW:
|
||||
resized_w = imgW
|
||||
else:
|
||||
resized_w = int(math.ceil(imgH * ratio))
|
||||
resized_image = cv2.resize(img, (resized_w, imgH))
|
||||
resized_image = resized_image.astype("float32")
|
||||
if image_shape[0] == 1:
|
||||
resized_image = resized_image / 255
|
||||
resized_image = resized_image[np.newaxis, :]
|
||||
else:
|
||||
resized_image = resized_image.transpose((2, 0, 1)) / 255
|
||||
resized_image -= 0.5
|
||||
resized_image /= 0.5
|
||||
padding_im = np.zeros((imgC, imgH, imgW), dtype=np.float32)
|
||||
padding_im[:, :, 0:resized_w] = resized_image
|
||||
valid_ratio = min(1.0, float(resized_w / imgW))
|
||||
return padding_im, valid_ratio
|
||||
|
||||
|
||||
def resize_norm_img_chinese(img, image_shape):
|
||||
imgC, imgH, imgW = image_shape
|
||||
# todo: change to 0 and modified image shape
|
||||
max_wh_ratio = imgW * 1.0 / imgH
|
||||
h, w = img.shape[0], img.shape[1]
|
||||
ratio = w * 1.0 / h
|
||||
max_wh_ratio = max(max_wh_ratio, ratio)
|
||||
imgW = int(imgH * max_wh_ratio)
|
||||
if math.ceil(imgH * ratio) > imgW:
|
||||
resized_w = imgW
|
||||
else:
|
||||
resized_w = int(math.ceil(imgH * ratio))
|
||||
resized_image = cv2.resize(img, (resized_w, imgH))
|
||||
resized_image = resized_image.astype("float32")
|
||||
if image_shape[0] == 1:
|
||||
resized_image = resized_image / 255
|
||||
resized_image = resized_image[np.newaxis, :]
|
||||
else:
|
||||
resized_image = resized_image.transpose((2, 0, 1)) / 255
|
||||
resized_image -= 0.5
|
||||
resized_image /= 0.5
|
||||
padding_im = np.zeros((imgC, imgH, imgW), dtype=np.float32)
|
||||
padding_im[:, :, 0:resized_w] = resized_image
|
||||
valid_ratio = min(1.0, float(resized_w / imgW))
|
||||
return padding_im, valid_ratio
|
||||
|
||||
|
||||
def resize_norm_img_srn(img, image_shape):
|
||||
imgC, imgH, imgW = image_shape
|
||||
|
||||
img_black = np.zeros((imgH, imgW))
|
||||
im_hei = img.shape[0]
|
||||
im_wid = img.shape[1]
|
||||
|
||||
if im_wid <= im_hei * 1:
|
||||
img_new = cv2.resize(img, (imgH * 1, imgH))
|
||||
elif im_wid <= im_hei * 2:
|
||||
img_new = cv2.resize(img, (imgH * 2, imgH))
|
||||
elif im_wid <= im_hei * 3:
|
||||
img_new = cv2.resize(img, (imgH * 3, imgH))
|
||||
else:
|
||||
img_new = cv2.resize(img, (imgW, imgH))
|
||||
|
||||
img_np = np.asarray(img_new)
|
||||
img_np = cv2.cvtColor(img_np, cv2.COLOR_BGR2GRAY)
|
||||
img_black[:, 0 : img_np.shape[1]] = img_np
|
||||
img_black = img_black[:, :, np.newaxis]
|
||||
|
||||
row, col, c = img_black.shape
|
||||
c = 1
|
||||
|
||||
return np.reshape(img_black, (c, row, col)).astype(np.float32)
|
||||
|
||||
|
||||
def resize_norm_img_abinet(img, image_shape):
|
||||
imgC, imgH, imgW = image_shape
|
||||
|
||||
resized_image = cv2.resize(img, (imgW, imgH), interpolation=cv2.INTER_LINEAR)
|
||||
resized_w = imgW
|
||||
resized_image = resized_image.astype("float32")
|
||||
resized_image = resized_image / 255.0
|
||||
|
||||
mean = np.array([0.485, 0.456, 0.406])
|
||||
std = np.array([0.229, 0.224, 0.225])
|
||||
resized_image = (resized_image - mean[None, None, ...]) / std[None, None, ...]
|
||||
resized_image = resized_image.transpose((2, 0, 1))
|
||||
resized_image = resized_image.astype("float32")
|
||||
|
||||
valid_ratio = min(1.0, float(resized_w / imgW))
|
||||
return resized_image, valid_ratio
|
||||
|
||||
|
||||
def srn_other_inputs(image_shape, num_heads, max_text_length):
|
||||
imgC, imgH, imgW = image_shape
|
||||
feature_dim = int((imgH / 8) * (imgW / 8))
|
||||
|
||||
encoder_word_pos = (
|
||||
np.array(range(0, feature_dim)).reshape((feature_dim, 1)).astype("int64")
|
||||
)
|
||||
gsrm_word_pos = (
|
||||
np.array(range(0, max_text_length))
|
||||
.reshape((max_text_length, 1))
|
||||
.astype("int64")
|
||||
)
|
||||
|
||||
gsrm_attn_bias_data = np.ones((1, max_text_length, max_text_length))
|
||||
gsrm_slf_attn_bias1 = np.triu(gsrm_attn_bias_data, 1).reshape(
|
||||
[1, max_text_length, max_text_length]
|
||||
)
|
||||
gsrm_slf_attn_bias1 = np.tile(gsrm_slf_attn_bias1, [num_heads, 1, 1]) * [-1e9]
|
||||
|
||||
gsrm_slf_attn_bias2 = np.tril(gsrm_attn_bias_data, -1).reshape(
|
||||
[1, max_text_length, max_text_length]
|
||||
)
|
||||
gsrm_slf_attn_bias2 = np.tile(gsrm_slf_attn_bias2, [num_heads, 1, 1]) * [-1e9]
|
||||
|
||||
return [encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1, gsrm_slf_attn_bias2]
|
||||
|
||||
|
||||
def flag():
|
||||
"""
|
||||
flag
|
||||
"""
|
||||
return 1 if random.random() > 0.5000001 else -1
|
||||
|
||||
|
||||
def hsv_aug(img):
|
||||
"""
|
||||
cvtColor
|
||||
"""
|
||||
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
|
||||
delta = 0.001 * random.random() * flag()
|
||||
hsv[:, :, 2] = hsv[:, :, 2] * (1 + delta)
|
||||
new_img = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)
|
||||
return new_img
|
||||
|
||||
|
||||
def blur(img):
|
||||
"""
|
||||
blur
|
||||
"""
|
||||
h, w, _ = img.shape
|
||||
if h > 10 and w > 10:
|
||||
return cv2.GaussianBlur(img, (5, 5), 1)
|
||||
else:
|
||||
return img
|
||||
|
||||
|
||||
def jitter(img):
|
||||
"""
|
||||
jitter
|
||||
"""
|
||||
w, h, _ = img.shape
|
||||
if h > 10 and w > 10:
|
||||
thres = min(w, h)
|
||||
s = int(random.random() * thres * 0.01)
|
||||
src_img = img.copy()
|
||||
for i in range(s):
|
||||
img[i:, i:, :] = src_img[: w - i, : h - i, :]
|
||||
return img
|
||||
else:
|
||||
return img
|
||||
|
||||
|
||||
def add_gasuss_noise(image, mean=0, var=0.1):
|
||||
"""
|
||||
Gasuss noise
|
||||
"""
|
||||
|
||||
noise = np.random.normal(mean, var**0.5, image.shape)
|
||||
out = image + 0.5 * noise
|
||||
out = np.clip(out, 0, 255)
|
||||
out = np.uint8(out)
|
||||
return out
|
||||
|
||||
|
||||
def get_crop(image):
|
||||
"""
|
||||
random crop
|
||||
"""
|
||||
h, w, _ = image.shape
|
||||
top_min = 1
|
||||
top_max = 8
|
||||
top_crop = int(random.randint(top_min, top_max))
|
||||
top_crop = min(top_crop, h - 1)
|
||||
crop_img = image.copy()
|
||||
ratio = random.randint(0, 1)
|
||||
if ratio:
|
||||
crop_img = crop_img[top_crop:h, :, :]
|
||||
else:
|
||||
crop_img = crop_img[0 : h - top_crop, :, :]
|
||||
return crop_img
|
||||
|
||||
|
||||
def rad(x):
|
||||
"""
|
||||
rad
|
||||
"""
|
||||
return x * np.pi / 180
|
||||
|
||||
|
||||
def get_warpR(config):
|
||||
"""
|
||||
get_warpR
|
||||
"""
|
||||
anglex, angley, anglez, fov, w, h, r = (
|
||||
config.anglex,
|
||||
config.angley,
|
||||
config.anglez,
|
||||
config.fov,
|
||||
config.w,
|
||||
config.h,
|
||||
config.r,
|
||||
)
|
||||
if w > 69 and w < 112:
|
||||
anglex = anglex * 1.5
|
||||
|
||||
z = np.sqrt(w**2 + h**2) / 2 / np.tan(rad(fov / 2))
|
||||
# Homogeneous coordinate transformation matrix
|
||||
rx = np.array(
|
||||
[
|
||||
[1, 0, 0, 0],
|
||||
[0, np.cos(rad(anglex)), -np.sin(rad(anglex)), 0],
|
||||
[
|
||||
0,
|
||||
-np.sin(rad(anglex)),
|
||||
np.cos(rad(anglex)),
|
||||
0,
|
||||
],
|
||||
[0, 0, 0, 1],
|
||||
],
|
||||
np.float32,
|
||||
)
|
||||
ry = np.array(
|
||||
[
|
||||
[np.cos(rad(angley)), 0, np.sin(rad(angley)), 0],
|
||||
[0, 1, 0, 0],
|
||||
[
|
||||
-np.sin(rad(angley)),
|
||||
0,
|
||||
np.cos(rad(angley)),
|
||||
0,
|
||||
],
|
||||
[0, 0, 0, 1],
|
||||
],
|
||||
np.float32,
|
||||
)
|
||||
rz = np.array(
|
||||
[
|
||||
[np.cos(rad(anglez)), np.sin(rad(anglez)), 0, 0],
|
||||
[-np.sin(rad(anglez)), np.cos(rad(anglez)), 0, 0],
|
||||
[0, 0, 1, 0],
|
||||
[0, 0, 0, 1],
|
||||
],
|
||||
np.float32,
|
||||
)
|
||||
r = rx.dot(ry).dot(rz)
|
||||
# generate 4 points
|
||||
pcenter = np.array([h / 2, w / 2, 0, 0], np.float32)
|
||||
p1 = np.array([0, 0, 0, 0], np.float32) - pcenter
|
||||
p2 = np.array([w, 0, 0, 0], np.float32) - pcenter
|
||||
p3 = np.array([0, h, 0, 0], np.float32) - pcenter
|
||||
p4 = np.array([w, h, 0, 0], np.float32) - pcenter
|
||||
dst1 = r.dot(p1)
|
||||
dst2 = r.dot(p2)
|
||||
dst3 = r.dot(p3)
|
||||
dst4 = r.dot(p4)
|
||||
list_dst = np.array([dst1, dst2, dst3, dst4])
|
||||
org = np.array([[0, 0], [w, 0], [0, h], [w, h]], np.float32)
|
||||
dst = np.zeros((4, 2), np.float32)
|
||||
# Project onto the image plane
|
||||
dst[:, 0] = list_dst[:, 0] * z / (z - list_dst[:, 2]) + pcenter[0]
|
||||
dst[:, 1] = list_dst[:, 1] * z / (z - list_dst[:, 2]) + pcenter[1]
|
||||
|
||||
warpR = cv2.getPerspectiveTransform(org, dst)
|
||||
|
||||
dst1, dst2, dst3, dst4 = dst
|
||||
r1 = int(min(dst1[1], dst2[1]))
|
||||
r2 = int(max(dst3[1], dst4[1]))
|
||||
c1 = int(min(dst1[0], dst3[0]))
|
||||
c2 = int(max(dst2[0], dst4[0]))
|
||||
|
||||
try:
|
||||
ratio = min(1.0 * h / (r2 - r1), 1.0 * w / (c2 - c1))
|
||||
|
||||
dx = -c1
|
||||
dy = -r1
|
||||
T1 = np.float32([[1.0, 0, dx], [0, 1.0, dy], [0, 0, 1.0 / ratio]])
|
||||
ret = T1.dot(warpR)
|
||||
except:
|
||||
ratio = 1.0
|
||||
T1 = np.float32([[1.0, 0, 0], [0, 1.0, 0], [0, 0, 1.0]])
|
||||
ret = T1
|
||||
return ret, (-r1, -c1), ratio, dst
|
||||
|
||||
|
||||
def get_warpAffine(config):
|
||||
"""
|
||||
get_warpAffine
|
||||
"""
|
||||
anglez = config.anglez
|
||||
rz = np.array(
|
||||
[
|
||||
[np.cos(rad(anglez)), np.sin(rad(anglez)), 0],
|
||||
[-np.sin(rad(anglez)), np.cos(rad(anglez)), 0],
|
||||
],
|
||||
np.float32,
|
||||
)
|
||||
return rz
|
||||
Reference in New Issue
Block a user