import os
import sys
import subprocess

__dir__ = os.path.dirname(os.path.abspath(__file__))
sys.path.append(__dir__)
sys.path.append(os.path.abspath(os.path.join(__dir__, '../..')))

os.environ["FLAGS_allocator_strategy"] = 'auto_growth'


import cv2
import copy
import numpy as np
import time
import utility as utility
from tools.infer import predict_rec as predict_rec
from tools.infer import predict_det as predict_det


class TextSystem(object):
    def __init__(self, args):
        print(args)
        self.text_detector = predict_det.TextDetector(args)
        self.text_recognizer = predict_rec.TextRecognizer(args)
        self.drop_score = args.drop_score

    def get_rotate_crop_image(self, img, points):
        '''
        img_height, img_width = img.shape[0:2]
        left = int(np.min(points[:, 0]))
        right = int(np.max(points[:, 0]))
        top = int(np.min(points[:, 1]))
        bottom = int(np.max(points[:, 1]))
        img_crop = img[top:bottom, left:right, :].copy()
        points[:, 0] = points[:, 0] - left
        points[:, 1] = points[:, 1] - top
        '''
        img_crop_width = int(
            max(
                np.linalg.norm(points[0] - points[1]),
                np.linalg.norm(points[2] - points[3])))
        img_crop_height = int(
            max(
                np.linalg.norm(points[0] - points[3]),
                np.linalg.norm(points[1] - points[2])))
        pts_std = np.float32([[0, 0], [img_crop_width, 0],
                              [img_crop_width, img_crop_height],
                              [0, img_crop_height]])
        M = cv2.getPerspectiveTransform(points, pts_std)
        dst_img = cv2.warpPerspective(
            img,
            M, (img_crop_width, img_crop_height),
            borderMode=cv2.BORDER_REPLICATE,
            flags=cv2.INTER_CUBIC)
        dst_img_height, dst_img_width = dst_img.shape[0:2]
        if dst_img_height * 1.0 / dst_img_width >= 1.5:
            dst_img = np.rot90(dst_img)
        return dst_img

    def __call__(self, img):
        ori_im = img.copy()
        dt_boxes, elapse = self.text_detector(img)
        print("dt_boxes num : {}, elapse : {}".format(
            len(dt_boxes), elapse))
        if dt_boxes is None:
            return None, None
        img_crop_list = []

        dt_boxes = sorted_boxes(dt_boxes)

        for bno in range(len(dt_boxes)):
            tmp_box = copy.deepcopy(dt_boxes[bno])
            img_crop = self.get_rotate_crop_image(ori_im, tmp_box)
            img_crop_list.append(img_crop)
            print("cls num  : {}, elapse : {}".format(
                len(img_crop_list), elapse))

        rec_res, elapse = self.text_recognizer(img_crop_list)
        print("rec_res num  : {}, elapse : {}".format(
            len(rec_res), elapse))
        # self.print_draw_crop_rec_res(img_crop_list, rec_res)
        filter_boxes, filter_rec_res = [], []
        for box, rec_result in zip(dt_boxes, rec_res):
            text, score = rec_result
            if score >= self.drop_score:
                filter_boxes.append(box)
                filter_rec_res.append(rec_result)
        return filter_boxes, filter_rec_res


def sorted_boxes(dt_boxes):
    """
    Sort text boxes in order from top to bottom, left to right
    args:
        dt_boxes(array):detected text boxes with shape [4, 2]
    return:
        sorted boxes(array) with shape [4, 2]
    """
    num_boxes = dt_boxes.shape[0]
    sorted_boxes = sorted(dt_boxes, key=lambda x: (x[0][1], x[0][0]))
    _boxes = list(sorted_boxes)

    for i in range(num_boxes - 1):
        if abs(_boxes[i + 1][0][1] - _boxes[i][0][1]) < 10 and \
                (_boxes[i + 1][0][0] < _boxes[i][0][0]):
            tmp = _boxes[i]
            _boxes[i] = _boxes[i + 1]
            _boxes[i + 1] = tmp
    return _boxes


def main(args):
    text_sys = TextSystem(args)
    image_file = r'C:\Users\DELL\Downloads\AI_FL\ocr\doc\imgs\passport.jpg'
    img = cv2.imread(image_file)
    if img is None:
        print("error in loading image:{}".format(image_file))
        # continue
    starttime = time.time()
    dt_boxes, rec_res = text_sys(img)
    elapse = time.time() - starttime
    print("Predict time of %s: %.3fs" % (image_file, elapse))

    for text, score in rec_res:
        print("{}, {:.3f}".format(text, score))

if __name__ == "__main__":
    args = utility.parse_args()
    main(args)
