#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 ppocr.utils.utility import initial_logger
logger = initial_logger()

from .text_image_aug.augment import tia_distort, tia_stretch, tia_perspective


def get_bounding_box_rect(pos):
    left = min(pos[0])
    right = max(pos[0])
    top = min(pos[1])
    bottom = max(pos[1])
    return [left, top, right, bottom]


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_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 get_img_data(value):
    """get_img_data"""
    if not value:
        return None
    imgdata = np.frombuffer(value, dtype='uint8')
    if imgdata is None:
        return None
    imgori = cv2.imdecode(imgdata, 1)
    if imgori is None:
        return None
    return imgori


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, ):
        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

    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 = True
        self.stretch = True
        self.distort = True

        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):
    """
    warp
    """
    h, w, _ = img.shape
    config = Config()
    config.make(w, h, ang)
    new_img = img

    prob = 0.4

    if config.distort:
        img_height, img_width = img.shape[0:2]
        if random.random() <= prob and img_height >= 20 and img_width >= 20:
            try:
                new_img = tia_distort(new_img, random.randint(3, 6))
            except:
                logger.warning(
                    "Exception occured during tia_distort, pass it...")

    if config.stretch:
        img_height, img_width = img.shape[0:2]
        if random.random() <= prob and img_height >= 20 and img_width >= 20:
            try:
                new_img = tia_stretch(new_img, random.randint(3, 6))
            except:
                logger.warning(
                    "Exception occured during tia_stretch, pass it...")

    if config.perspective:
        if random.random() <= prob:
            try:
                new_img = tia_perspective(new_img)
            except:
                logger.warning(
                    "Exception occured during tia_perspective, pass it...")

    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


def process_image(img,
                  image_shape,
                  label=None,
                  char_ops=None,
                  loss_type=None,
                  max_text_length=None,
                  tps=None,
                  infer_mode=False,
                  distort=False):
    if distort:
        img = warp(img, 10)
    if infer_mode and char_ops.character_type == "ch" and not tps:
        norm_img = resize_norm_img_chinese(img, image_shape)
    else:
        norm_img = resize_norm_img(img, image_shape)

    norm_img = norm_img[np.newaxis, :]
    if label is not None:
        # char_num = char_ops.get_char_num()
        text = char_ops.encode(label)
        if len(text) == 0 or len(text) > max_text_length:
            logger.info(
                "Warning in ppocr/data/rec/img_tools.py: Wrong data type."
                "Excepted string with length between 1 and {}, but "
                "got '{}'. Label is '{}'".format(max_text_length,
                                                 len(text), label))
            return None
        else:
            if loss_type == "ctc":
                text = text.reshape(-1, 1)
                return (norm_img, text)
            elif loss_type == "attention":
                beg_flag_idx = char_ops.get_beg_end_flag_idx("beg")
                end_flag_idx = char_ops.get_beg_end_flag_idx("end")
                beg_text = np.append(beg_flag_idx, text)
                end_text = np.append(text, end_flag_idx)
                beg_text = beg_text.reshape(-1, 1)
                end_text = end_text.reshape(-1, 1)
                return (norm_img, beg_text, end_text)
            else:
                assert False, "Unsupport loss_type %s in process_image"\
                    % loss_type
    return (norm_img)


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, char_num):

    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')

    lbl_weight = np.array([int(char_num - 1)] * max_text_length).reshape(
        (-1, 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, 1, max_text_length, max_text_length])
    gsrm_slf_attn_bias1 = np.tile(gsrm_slf_attn_bias1,
                                  [1, num_heads, 1, 1]) * [-1e9]

    gsrm_slf_attn_bias2 = np.tril(gsrm_attn_bias_data, -1).reshape(
        [-1, 1, max_text_length, max_text_length])
    gsrm_slf_attn_bias2 = np.tile(gsrm_slf_attn_bias2,
                                  [1, num_heads, 1, 1]) * [-1e9]

    encoder_word_pos = encoder_word_pos[np.newaxis, :]
    gsrm_word_pos = gsrm_word_pos[np.newaxis, :]

    return [
        lbl_weight, encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1,
        gsrm_slf_attn_bias2
    ]


def process_image_srn(img,
                      image_shape,
                      num_heads,
                      max_text_length,
                      label=None,
                      char_ops=None,
                      loss_type=None):
    norm_img = resize_norm_img_srn(img, image_shape)
    norm_img = norm_img[np.newaxis, :]
    char_num = char_ops.get_char_num()

    [lbl_weight, encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1, gsrm_slf_attn_bias2] = \
        srn_other_inputs(image_shape, num_heads, max_text_length,char_num)

    if label is not None:
        text = char_ops.encode(label)
        if len(text) == 0 or len(text) > max_text_length:
            return None
        else:
            if loss_type == "srn":
                text_padded = [int(char_num - 1)] * max_text_length
                for i in range(len(text)):
                    text_padded[i] = text[i]
                    lbl_weight[i] = [1.0]
                text_padded = np.array(text_padded)
                text = text_padded.reshape(-1, 1)
                return (norm_img, text, encoder_word_pos, gsrm_word_pos,
                        gsrm_slf_attn_bias1, gsrm_slf_attn_bias2, lbl_weight)
            else:
                assert False, "Unsupport loss_type %s in process_image"\
                    % loss_type
    return (norm_img, encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1,
            gsrm_slf_attn_bias2)