import os
import sys
import subprocess

__dir__ = os.path.dirname(os.path.abspath(__file__))
sys.path.append(__dir__)
sys.path.insert(0, os.path.abspath(os.path.join(__dir__, '../..')))

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

import cv2
import copy
import numpy as np
import json
import time
import logging
from PIL import Image
import tools.infer.utility as utility
import tools.infer.predict_rec as predict_rec
import tools.infer.predict_det as predict_det
import tools.infer.predict_cls as predict_cls
from ppocr.utils.utility import get_image_file_list, check_and_read_gif
from ppocr.utils.logging import get_logger
from tools.infer.utility import draw_ocr_box_txt, get_rotate_crop_image
logger = get_logger()


class TextSystem(object):
    def __init__(self, args):
        if not args.show_log:
            logger.setLevel(logging.INFO)

        self.text_detector = predict_det.TextDetector(args)
        self.text_recognizer = predict_rec.TextRecognizer(args)
        self.use_angle_cls = args.use_angle_cls
        self.drop_score = args.drop_score
        if self.use_angle_cls:
            self.text_classifier = predict_cls.TextClassifier(args)

        self.args = args

    def __call__(self, img, cls=True):
        ori_im = img.copy()
        dt_boxes, elapse = self.text_detector(img)

        # logger.debug("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 = get_rotate_crop_image(ori_im, tmp_box)
            img_crop_list.append(img_crop)
        if self.use_angle_cls and cls:
            img_crop_list, angle_list, elapse = self.text_classifier(
                img_crop_list)
            logger.debug("cls num  : {}, elapse : {}".format(
                len(img_crop_list), elapse))

        rec_res, elapse = self.text_recognizer(img_crop_list)
        # logger.debug("rec_res num  : {}, elapse : {}".format(
        #     len(rec_res), elapse))

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


def main(args):
    image_file_list = get_image_file_list(args.image_dir)
    image_file_list = image_file_list[args.process_id::args.total_process_num]
    text_sys = TextSystem(args)
    is_visualize = False
    font_path = args.vis_font_path
    drop_score = args.drop_score
    draw_img_save_dir = args.draw_img_save_dir
    os.makedirs(draw_img_save_dir, exist_ok=True)
    save_results = []

    # warm up
    if args.warmup:
        warmup_file_list = get_image_file_list("./warmup_images_5/")
        warmup_file_rec_list = get_image_file_list("./warmup_images_rec/")
        startwarm = time.time()
        for warmup_file in warmup_file_list:
            print(warmup_file)
            img_warm = cv2.imread(warmup_file)
            res = text_sys.text_detector(img_warm)
        for warmup_file_rec in warmup_file_rec_list:
            print(warmup_file_rec)
            img_warm_rec = cv2.imread(warmup_file_rec)
            max_batnum = 24
            min_batnum = 8
            if os.environ.get("OCR_REC_MAX_BATNUM") is not None:
                max_batnum = int(os.environ.get("OCR_REC_MAX_BATNUM"))
            if os.environ.get("OCR_REC_MIN_BATNUM") is not None:
                min_batnum = int(os.environ.get("OCR_REC_MIN_BATNUM"))
            assert max_batnum / min_batnum == int(max_batnum / min_batnum), "max_batnum must be multiple of min_batnum."
        
            for bn in range(int(max_batnum / min_batnum)):
                img_rec_list = []
                for i in range(min_batnum * (bn + 1)):
                    img_rec_list.append(img_warm_rec)
                cls_results = text_sys.text_classifier(img_rec_list)
                rec_results = text_sys.text_recognizer(img_rec_list)
        elapsewarm = time.time() - startwarm
        logger.debug("warmup time:{}".format(elapsewarm))

    total_time = 0
    _st = time.time()
    for idx, image_file in enumerate(image_file_list):

        img, flag = check_and_read_gif(image_file)
        if not flag:
            img = cv2.imread(image_file)
        if img is None:
            logger.debug("error in loading image:{}".format(image_file))
            continue
        starttime = time.time()
        dt_boxes, rec_res = text_sys(img)
        elapse = time.time() - starttime
        total_time += elapse

        logger.debug(
            str(idx) + "  Predict time of %s: %.3fs" % (image_file, elapse))
        for text, score in rec_res:
            logger.debug("{}, {:.3f}".format(text, score))

        res = [{
            "transcription": rec_res[idx][0],
            "points": np.array(dt_boxes[idx]).astype(np.int32).tolist(),
        } for idx in range(len(dt_boxes))]
        save_pred = os.path.basename(image_file) + "\t" + json.dumps(
            res, ensure_ascii=False) + "\n"
        save_results.append(save_pred)

        if is_visualize:
            image = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
            boxes = dt_boxes
            txts = [rec_res[i][0] for i in range(len(rec_res))]
            scores = [rec_res[i][1] for i in range(len(rec_res))]

            draw_img = draw_ocr_box_txt(
                image,
                boxes,
                txts,
                scores,
                drop_score=drop_score,
                font_path=font_path)
            if flag:
                image_file = image_file[:-3] + "png"
            cv2.imwrite(
                os.path.join(draw_img_save_dir, os.path.basename(image_file)),
                draw_img[:, :, ::-1])
            logger.debug("The visualized image saved in {}".format(
                os.path.join(draw_img_save_dir, os.path.basename(image_file))))

    logger.info("The predict total time is {}".format(time.time() - _st))

    if args.total_process_num > 1:
        save_results_path = os.path.join(draw_img_save_dir, f"system_results_{args.process_id}.txt")
    else:
        save_results_path = os.path.join(draw_img_save_dir, "system_results.txt")

    with open(save_results_path, 'w', encoding='utf-8') as f:
        f.writelines(save_results)


if __name__ == "__main__":
    args = utility.parse_args()
    if args.use_mp:
        p_list = []
        total_process_num = args.total_process_num
        for process_id in range(total_process_num):
            cmd = [sys.executable, "-u"] + sys.argv + [
                "--process_id={}".format(process_id),
                "--use_mp={}".format(False)
            ]
            p = subprocess.Popen(cmd, stdout=sys.stdout, stderr=sys.stdout)
            p_list.append(p)
        for p in p_list:
            p.wait()
    else:
        main(args)
