# -*- coding: utf-8 -*-


# 提取电压任务  在返回的图片上截取出液晶屏幕的大小尺寸
__author__ = '上海色宁'

import cv2
import numpy as np


def task_init_load_digits():

    # 加载数字模板
    path = './templates'    # 这个地方就是获取当前运行目录 获取函数在主函数里面
    # 和模板的数字进行匹配
    digits.append([cv2.imread(path + "/@-0.jpg", cv2.IMREAD_GRAYSCALE), '@'])
    digits.append([cv2.imread(path + "/@-1.jpg", cv2.IMREAD_GRAYSCALE), '@'])
    digits.append([cv2.imread(path + "/@-2.jpg", cv2.IMREAD_GRAYSCALE), '@'])
    digits.append([cv2.imread(path + "/0-0.jpg", cv2.IMREAD_GRAYSCALE), 0])
    digits.append([cv2.imread(path + "/0-1.jpg", cv2.IMREAD_GRAYSCALE), 0])
    digits.append([cv2.imread(path + "/0-2.jpg", cv2.IMREAD_GRAYSCALE), 0])
    digits.append([cv2.imread(path + "/0-3.jpg", cv2.IMREAD_GRAYSCALE), 0])  
    digits.append([cv2.imread(path + "/1-0.jpg", cv2.IMREAD_GRAYSCALE), 1])
    digits.append([cv2.imread(path + "/1-1.jpg", cv2.IMREAD_GRAYSCALE), 1])
    digits.append([cv2.imread(path + "/2-0.jpg", cv2.IMREAD_GRAYSCALE), 2])
    digits.append([cv2.imread(path + "/2-1.jpg", cv2.IMREAD_GRAYSCALE), 2])
    digits.append([cv2.imread(path + "/3-0.jpg", cv2.IMREAD_GRAYSCALE), 3])
    digits.append([cv2.imread(path + "/4-0.jpg", cv2.IMREAD_GRAYSCALE), 4])
    digits.append([cv2.imread(path + "/5-0.jpg", cv2.IMREAD_GRAYSCALE), 5])
    digits.append([cv2.imread(path + "/5-1.jpg", cv2.IMREAD_GRAYSCALE), 5])
    digits.append([cv2.imread(path + "/5-2.jpg", cv2.IMREAD_GRAYSCALE), 5])
    digits.append([cv2.imread(path + "/6-0.jpg", cv2.IMREAD_GRAYSCALE), 6])
    digits.append([cv2.imread(path + "/7-0.jpg", cv2.IMREAD_GRAYSCALE), 7])
    digits.append([cv2.imread(path + "/8-0.jpg", cv2.IMREAD_GRAYSCALE), 8])
    digits.append([cv2.imread(path + "/8-1.jpg", cv2.IMREAD_GRAYSCALE), 8])
    digits.append([cv2.imread(path + "/8-2.jpg", cv2.IMREAD_GRAYSCALE), 8])
    digits.append([cv2.imread(path + "/8-3.jpg", cv2.IMREAD_GRAYSCALE), 8])
    digits.append([cv2.imread(path + "/8-4.jpg", cv2.IMREAD_GRAYSCALE), 8])
    digits.append([cv2.imread(path + "/9-0.jpg", cv2.IMREAD_GRAYSCALE), 9])
    digits.append([cv2.imread(path + "/9-1.jpg", cv2.IMREAD_GRAYSCALE), 9])
    digits.append([cv2.imread(path + "/9-2.jpg", cv2.IMREAD_GRAYSCALE), 9])
    digits.append([cv2.imread(path + "/9-3.jpg", cv2.IMREAD_GRAYSCALE), 9])
    digits.append([cv2.imread(path + "/9-4.jpg", cv2.IMREAD_GRAYSCALE), 9])


digits = []


def get_most_simmilar_digit(target_img,app):
    source = []
    #img_gray = cv2.cvtColor(target_img, cv2.COLOR_BGR2GRAY)
    #ret, binary = cv2.threshold(img_gray,0,255,cv2.THRESH_BINARY | cv2.THRESH_OTSU)
    # 循环对比模板的图片
    app.logger.info("start identify")
    if len(digits) < 5:
        
        task_init_load_digits()
    for digitROI in digits:
        
        res = cv2.matchTemplate(target_img, digitROI[0], cv2.TM_CCOEFF_NORMED)
        max_val = cv2.minMaxLoc(res)[1]
        source.append(max_val)
    #print(max(source))
    if max(source) > 0.3:
        app.logger.info(digits[source.index(max(source))][1])
        return digits[source.index(max(source))][1]
    else:
        return "@"




def extrct_digits_from_frame(frame, col_start, col_end, rows_start, rows_end,app):
    image = frame[col_start:col_end, rows_start:rows_end]
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    thresh = cv2.threshold(gray, 0, 255,
                           cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]
    kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (6, 6))
    counter = cv2.morphologyEx(thresh.copy(), cv2.MORPH_OPEN, kernel)
    contours, hierarchy = cv2.findContours(counter, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
    return_numbers = []
    contours_count = 0
    image_h = image.shape[0]
    image_w = image.shape[1]
    digit = -1
    for c in contours:
        x, y, w, h = cv2.boundingRect(c)
        if hierarchy[0][contours_count][3] ==0 and h > image_h * 0.3:
            if w>image_w/4:
                half_width = int(w/2)
                one_number = get_most_simmilar_digit(cv2.threshold(cv2.resize(
                gray[y:y+h, x:x+half_width], (20, 30)), 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1],app)
                if one_number != '@':
                    return_numbers.append([x, one_number])
                else:
                    return digit, False
                one_number = get_most_simmilar_digit(cv2.threshold(cv2.resize(
                gray[y:y+h, x+half_width:x+w], (20, 30)), 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1],app)
                if one_number != '@':
                    return_numbers.append([x+half_width, one_number])
                else:
                    return digit, False
            else:
                one_number = get_most_simmilar_digit(cv2.threshold(cv2.resize(
                gray[y:y+h, x:x+w], (20, 30)), 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1],app)
                if one_number != '@':
                    return_numbers.append([x, one_number])
                else:
                    return digit, False
        contours_count +=1
    if len(return_numbers)==0:
            return digit,False
    print(return_numbers)
    return_numbers = np.array(sorted(return_numbers))
    return_numbers = return_numbers[:, -1]
    if len(return_numbers) < 3:
        return digit, False
    elif len(return_numbers) == 3:
        digit = return_numbers[0] + \
            return_numbers[1]*0.1+return_numbers[2]*0.01
    elif len(return_numbers) == 4:
        digit = return_numbers[0]*10+return_numbers[1] + \
            return_numbers[2]*0.1+return_numbers[3]*0.1
    return round(digit, 2), True