import CDM.detect_text.ocr as ocr
from CDM.detect_text.Text import Text
import numpy as np
import cv2
import json
import time
import os
from os.path import join as pjoin
# from paddleocr import PaddleOCR
import pytesseract

# paddle_model = PaddleOCR(use_angle_cls=True, lang="en") #'ch' for chinese and english, 'en' for english


def save_detection_json(file_path, texts, img_shape):
    f_out = open(file_path, 'w')
    output = {'img_shape': img_shape, 'texts': []}
    for text in texts:
        c = {'id': text.id, 'content': text.content}
        loc = text.location
        c['column_min'], c['row_min'], c['column_max'], c['row_max'] = loc['left'], loc['top'], loc['right'], loc['bottom']
        c['width'] = text.width
        c['height'] = text.height
        output['texts'].append(c)
    json.dump(output, f_out, indent=4)


def visualize_texts(org_img, texts, shown_resize_height=None, show=False, write_path=None):
    img = org_img.copy()
    for text in texts:
        text.visualize_element(img, line=2)

    img_resize = img
    if shown_resize_height is not None:
        img_resize = cv2.resize(img, (int(shown_resize_height * (img.shape[1]/img.shape[0])), shown_resize_height))

    if show:
        cv2.imshow('texts', img_resize)
        cv2.waitKey(0)
        cv2.destroyWindow('texts')
    if write_path is not None:
        cv2.imwrite(write_path, img)


def text_sentences_recognition(texts):
    '''
    Merge separate words detected by Google ocr into a sentence
    '''
    changed = True
    while changed:
        changed = False
        temp_set = []
        for text_a in texts:
            merged = False
            for text_b in temp_set:
                if text_a.is_on_same_line(text_b, 'h', bias_justify=0.2 * min(text_a.height, text_b.height), bias_gap=2 * max(text_a.word_width, text_b.word_width)):
                    text_b.merge_text(text_a)
                    merged = True
                    changed = True
                    break
            if not merged:
                temp_set.append(text_a)
        texts = temp_set.copy()

    for i, text in enumerate(texts):
        text.id = i
    return texts


def merge_intersected_texts(texts):
    '''
    Merge intersected texts (sentences or words)
    '''
    changed = True
    while changed:
        changed = False
        temp_set = []
        for text_a in texts:
            merged = False
            for text_b in temp_set:
                if text_a.is_intersected(text_b, bias=2):
                    text_b.merge_text(text_a)
                    merged = True
                    changed = True
                    break
            if not merged:
                temp_set.append(text_a)
        texts = temp_set.copy()
    return texts


def text_cvt_orc_format(ocr_result):
    texts = []
    if ocr_result is not None:
        for i, result in enumerate(ocr_result):
            error = False
            x_coordinates = []
            y_coordinates = []
            text_location = result['boundingPoly']['vertices']
            content = result['description']
            for loc in text_location:
                if 'x' not in loc or 'y' not in loc:
                    error = True
                    break
                x_coordinates.append(loc['x'])
                y_coordinates.append(loc['y'])
            if error: continue
            location = {'left': min(x_coordinates), 'top': min(y_coordinates),
                        'right': max(x_coordinates), 'bottom': max(y_coordinates)}
            texts.append(Text(i, content, location))
    return texts


def text_cvt_orc_format_paddle(paddle_result):
    texts = []
    for i, line in enumerate(paddle_result):
        points = np.array(line[0])
        # points = points * 5
        location = {'left': int(min(points[:, 0])), 'top': int(min(points[:, 1])), 'right': int(max(points[:, 0])),
                    'bottom': int(max(points[:, 1]))}
        content = line[1][0]
        texts.append(Text(i, content, location))
    return texts


def text_cvt_orc_format_tesseract(tesseract_result):
    # texts = []
    # i_real = 0
    # for i, line in enumerate(tesseract_result['text']):
    #     content = line.strip()
    #     location = {
    #         'left': int(tesseract_result['left'][i]),
    #         'top': int(tesseract_result['top'][i]),
    #         'right': int(tesseract_result['left'][i]) + int(tesseract_result['width'][i]),
    #         'bottom': int(tesseract_result['top'][i]) + int(tesseract_result['height'][i])
    #     }
    #     if len(content) > 0:
    #         texts.append(Text(i_real, content, location))
    #         i_real = i_real + 1

    # Extract line boxes
    texts = []
    i_real = 0
    line_boxes = []
    n_boxes = len(tesseract_result['level'])
    for i in range(n_boxes):
        if tesseract_result['level'][i] == 4 and len(tesseract_result['text'][i].strip()) > 0:
            # (x, y, w, h) = (tesseract_result['left'][i], tesseract_result['top'][i], tesseract_result['width'][i], tesseract_result['height'][i])
            content = tesseract_result['text'][i].strip()
            location = {
                'left': int(tesseract_result['left'][i]),
                'top': int(tesseract_result['top'][i]),
                'right': int(tesseract_result['left'][i]) + int(tesseract_result['width'][i]),
                'bottom': int(tesseract_result['top'][i]) + int(tesseract_result['height'][i])
            }
            texts.append(Text(i_real, content, location))
            i_real = i_real + 1
    # print("ocr result: ", texts)

    return texts

def text_cvt_orc_format_tesseract_by_line(data):

    # line_data = []
    line_num = None
    line_text = []
    line_box = [0, 0, 0, 0]
    texts = []
    i_real = 0

    for i in range(len(data['level'])):
        # check if the level is word
        if data['level'][i] == 5:
            if line_num != data['line_num'][i]:
                if line_num is not None:  # append the previous line data to line_data
                    content = ' '.join(line_text)
                    location = {
                        'left': line_box[0],
                        'top': line_box[1],
                        'right': line_box[2],
                        'bottom': line_box[3]
                    }
                    texts.append(Text(i_real, content, location))
                    i_real = i_real + 1

                # start a new line
                line_num = data['line_num'][i]
                line_text = [data['text'][i]]
                line_box = [
                    data['left'][i],
                    data['top'][i],
                    data['left'][i] + data['width'][i],
                    data['top'][i] + data['height'][i],
                ]
            else:  # add a word to the current line
                line_text.append(data['text'][i])
                line_box[2] = max(line_box[2], data['left'][i] + data['width'][i])
                line_box[3] = max(line_box[3], data['top'][i] + data['height'][i])

        # append the last line data to line_data
    if line_text:
        content = ' '.join(line_text)
        location = {
            'left': line_box[0],
            'top': line_box[1],
            'right': line_box[2],
            'bottom': line_box[3]
        }
        texts.append(Text(i_real, content, location))
        i_real = i_real + 1

    return texts


def text_filter_noise(texts):
    valid_texts = []
    for text in texts:
        if len(text.content) <= 1 and text.content.lower() not in ['a', ',', '.', '!', '?', '$', '%', ':', '&', '+']:
            continue
        valid_texts.append(text)
    return valid_texts


def text_detection(input_file='../data/input/30800.jpg', output_file='../data/output', show=False, method='google', paddle_model=None):
    '''
    :param method: google or paddle
    :param paddle_model: the preload paddle model for paddle ocr
    '''
    start = time.time()
    name = input_file.split('/')[-1][:-4]
    ocr_root = pjoin(output_file, 'ocr')
    img = cv2.imread(input_file)
    if img is None:
        print("imread nothing!")

    # resize the img to speed up the ocr
    # img = cv2.resize(img, (int(img.shape[1]/5), int(img.shape[0]/5)))
    # cv2.imshow("img", img)
    # cv2.waitKey(0)

    if method == 'google':
        print('*** Detect Text through Google OCR ***')
        ocr_result = ocr.ocr_detection_google(input_file)
        texts = text_cvt_orc_format(ocr_result)
        texts = merge_intersected_texts(texts)
        texts = text_filter_noise(texts)
        texts = text_sentences_recognition(texts)
        ocr_time_cost = time.time() - start
    elif method == 'paddle':
        # The import of the paddle ocr can be separate to the beginning of the program if you decide to use this method
        # from paddleocr import PaddleOCR
        print('*** Detect Text through Paddle OCR ***')
        # if paddle_model is None:
            # paddle_model = PaddleOCR(use_angle_cls=True, lang="en") #'ch' for chinese and english, 'en' for english
            # None
        result = paddle_model.ocr(input_file, cls=True)
        ocr_time_cost = time.time() - start
        texts = text_cvt_orc_format_paddle(result)

    elif method == 'pytesseract':

        img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

        # Perform OCR using Tesseract
        result = pytesseract.image_to_data(img_rgb, output_type=pytesseract.Output.DICT)
        print("ocr result: ", result)

        ocr_time_cost = time.time() - start

        # Convert the Tesseract result to the desired format
        texts = text_cvt_orc_format_tesseract_by_line(result)
        print("texts: ", texts)
    else:
        raise ValueError('Method has to be "google" or "paddle" or "pytesseract"')

    visualize_texts(img, texts, shown_resize_height=800, show=show, write_path=pjoin(ocr_root, name+'.png'))
    save_detection_json(pjoin(ocr_root, name+'.json'), texts, img.shape)
    # ocr_time_cost = time.time() - start
    print("[Text Detection Completed in %.3f s] Input: %s Output: %s" % (ocr_time_cost, input_file, pjoin(ocr_root, name+'.json')))

    # print("!!! detected content !!!")
    # for text in texts:
    #     print(text.content)

    return ocr_time_cost


# text_detection()