rec_img_aug.py

# 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 math
import cv2
import numpy as np
import random
from PIL import Image
from .text_image_aug import tia_perspective, tia_stretch, tia_distort


class RecAug(object):
    def __init__(self, use_tia=True, aug_prob=0.4, **kwargs):
        self.use_tia = use_tia
        self.aug_prob = aug_prob

    def __call__(self, data):
        img = data['image']
        img = warp(img, 10, self.use_tia, self.aug_prob)
        data['image'] = img
        return data


class ClsResizeImg(object):
    def __init__(self, image_shape, **kwargs):
        self.image_shape = image_shape

    def __call__(self, data):
        img = data['image']
        norm_img = resize_norm_img(img, self.image_shape)
        data['image'] = norm_img
        return data


class NRTRRecResizeImg(object):
    def __init__(self, image_shape, resize_type, **kwargs):
        self.image_shape = image_shape
        self.resize_type = resize_type

    def __call__(self, data):
        img = data['image']
        if self.resize_type == 'PIL':
            image_pil = Image.fromarray(np.uint8(img))
            img = image_pil.resize(self.image_shape, Image.ANTIALIAS)
            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))
        data['image'] = norm_img.astype(np.float32) / 128. - 1.
        return data


class RecResizeImg(object):
    def __init__(self,
                 image_shape,
                 infer_mode=False,
                 character_type='ch',
                 **kwargs):
        self.image_shape = image_shape
        self.infer_mode = infer_mode
        self.character_type = character_type

    def __call__(self, data):
        img = data['image']
        if self.infer_mode and self.character_type == "ch":
            norm_img = resize_norm_img_chinese(img, self.image_shape)
        else:
            norm_img = resize_norm_img(img, self.image_shape)
        data['image'] = norm_img
        return data


class SEEDResize(object):
    def __init__(self, image_shape, infer_mode=False, **kwargs):
        self.image_shape = image_shape
        self.infer_mode = infer_mode

    def __call__(self, data):
        img = data['image']
        norm_img = resize_no_padding_img(img, self.image_shape)
        data['image'] = norm_img
        return data


class SRNRecResizeImg(object):
    def __init__(self, image_shape, num_heads, max_text_length, **kwargs):
        self.image_shape = image_shape
        self.num_heads = num_heads
        self.max_text_length = max_text_length

    def __call__(self, data):
        img = data['image']
        norm_img = resize_norm_img_srn(img, self.image_shape)
        data['image'] = norm_img
        [encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1, gsrm_slf_attn_bias2] = \
            srn_other_inputs(self.image_shape, self.num_heads, self.max_text_length)

        data['encoder_word_pos'] = encoder_word_pos
        data['gsrm_word_pos'] = gsrm_word_pos
        data['gsrm_slf_attn_bias1'] = gsrm_slf_attn_bias1
        data['gsrm_slf_attn_bias2'] = gsrm_slf_attn_bias2
        return data


class SARRecResizeImg(object):
    def __init__(self, image_shape, width_downsample_ratio=0.25, **kwargs):
        self.image_shape = image_shape
        self.width_downsample_ratio = width_downsample_ratio

    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)
        data['image'] = norm_img
        data['resized_shape'] = resize_shape
        data['pad_shape'] = pad_shape
        data['valid_ratio'] = valid_ratio
        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):
    imgC, imgH, imgW = 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))
    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
    return padding_im


def resize_no_padding_img(img, image_shape):
    imgC, imgH, imgW = image_shape
    resized_image = cv2.resize(
        img, (imgW, imgH), interpolation=cv2.INTER_LINEAR)
    resized_image = resized_image.astype('float32')
    resized_image = resized_image.transpose((2, 0, 1)) / 255
    resized_image -= 0.5
    resized_image /= 0.5
    return resized_image


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(32 * 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
    return padding_im


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 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 cvtColor(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


class Config:
    """
    Config
    """

    def __init__(self, use_tia):
        self.anglex = random.random() * 30
        self.angley = random.random() * 15
        self.anglez = random.random() * 10
        self.fov = 42
        self.r = 0
        self.shearx = random.random() * 0.3
        self.sheary = random.random() * 0.05
        self.borderMode = cv2.BORDER_REPLICATE
        self.use_tia = use_tia

    def make(self, w, h, ang):
        """
        make
        """
        self.anglex = random.random() * 5 * flag()
        self.angley = random.random() * 5 * flag()
        self.anglez = -1 * random.random() * int(ang) * flag()
        self.fov = 42
        self.r = 0
        self.shearx = 0
        self.sheary = 0
        self.borderMode = cv2.BORDER_REPLICATE
        self.w = w
        self.h = h

        self.perspective = self.use_tia
        self.stretch = self.use_tia
        self.distort = self.use_tia

        self.crop = True
        self.affine = False
        self.reverse = True
        self.noise = True
        self.jitter = True
        self.blur = True
        self.color = True


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, dx], [0, 1., dy], [0, 0, 1.0 / ratio]])
        ret = T1.dot(warpR)
    except:
        ratio = 1.0
        T1 = np.float32([[1., 0, 0], [0, 1., 0], [0, 0, 1.]])
        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


def warp(img, ang, use_tia=True, prob=0.4):
    """
    warp
    """
    h, w, _ = img.shape
    config = Config(use_tia=use_tia)
    config.make(w, h, ang)
    new_img = img

    if config.distort:
        img_height, img_width = img.shape[0:2]
        if random.random() <= prob and img_height >= 20 and img_width >= 20:
            new_img = tia_distort(new_img, random.randint(3, 6))

    if config.stretch:
        img_height, img_width = img.shape[0:2]
        if random.random() <= prob and img_height >= 20 and img_width >= 20:
            new_img = tia_stretch(new_img, random.randint(3, 6))

    if config.perspective:
        if random.random() <= prob:
            new_img = tia_perspective(new_img)

    if config.crop:
        img_height, img_width = img.shape[0:2]
        if random.random() <= prob and img_height >= 20 and img_width >= 20:
            new_img = get_crop(new_img)

    if config.blur:
        if random.random() <= prob:
            new_img = blur(new_img)
    if config.color:
        if random.random() <= prob:
            new_img = cvtColor(new_img)
    if config.jitter:
        new_img = jitter(new_img)
    if config.noise:
        if random.random() <= prob:
            new_img = add_gasuss_noise(new_img)
    if config.reverse:
        if random.random() <= prob:
            new_img = 255 - new_img
    return new_img