import numpy as np
import cv2
from collections import Counter

"""
方案1， 实验版本，还未成功
主要思想是将线段提取出主要的斜率，并确定名片方向
张晋伟 - 21215158
"""


def CrossPoint(line1, line2):
    """找两条线的交点"""
    x0, y0, x1, y1 = line1[0]
    x2, y2, x3, y3 = line2[0]
    dx1 = x1 - x0
    dy1 = y1 - y0
    dx2 = x3 - x2
    dy2 = y3 - y2
    D1 = x1 * y0 - x0 * y1
    D2 = x3 * y2 - x2 * y3
    y = float(dy1 * D2 - D1 * dy2) / (dy1 * dx2 - dx1 * dy2)
    x = float(y * dx1 - D1) / dy1
    return (int(x), int(y))


def SortPoint(points):
    """对四个交点进行排序"""
    sp = sorted(points, key=lambda x: (int(x[1]), int(x[0])))
    if sp[0][0] > sp[1][0]:
        sp[0], sp[1] = sp[1], sp[0]
    if sp[2][0] > sp[3][0]:
        sp[2], sp[3] = sp[3], sp[2]
    return sp


def drawlines(lines, original_pic):
    """将线们绘制在图形上"""
    i = 1
    for line in lines:  # 3.将检测的线画出来（注意是极坐标噢）
        # print("line[" + str(i - 1) + "]=", line)
        x1, y1, x2, y2 = line[0]  # 两点确定一条直线，这里就是通过遍历得到的两个点的数据 （x1,y1）(x2,y2)
        cv2.line(original_pic, (x1, y1), (x2, y2), (0, 0, 255), 2)  # 在原图上画线
        i = i + 1
    cv2.imshow("line_detect", original_pic)  # 显示最后的成果图


def x_or_y_check(x0, y0, x1, y1):
    if abs(x1 - x0) > abs(y1 - y0):
        return 1  # 横线
    else:
        return 0  # 竖线


def get_function(x0, y0, x1, y1):
    """两点确定一条直线 y = ax + b（对于横线）   x = cy + d(对于竖线）"""
    length = ((x0 - x1)**2 + (y1 - y0)**2)**0.5
    x_or_y = x_or_y_check(x0, y0, x1, y1)
    if x_or_y:  # 横线
        a = (y1 - y0) / abs(x1 - x0)
        b = min([y1, y0]) + a * min([x1, x0])
    else:   # 竖线
        a = (x1 - x0) / abs(y1 - y0)
        b = min([x1, x0]) + a * min([y1, y0])
    return x_or_y, a, b, length, [x0, y0, x1, y1]  # 返回： 竖线还是横线， 斜率， 偏移


def find_4_lines(lines):
    """从线段列表中找到最拟合的4条边框"""
    lines_pool = []  # 构造一个横线方程池
    # lines_pool_y = []  # 构造一个竖线方程池
    for line in lines:
        x0, y0, x1, y1 = line[0]
        x_or_y, a, b, l, x = get_function(x0, y0, x1, y1)  # 获得本条直线的表达式
        print(x_or_y, a, b, round(a*5, 1), b//100, l, x)
        #key = str(x_or_y) + '_' + str(round(a*5, 1)) + '_' + str(b//100)
        if b//100 not in (2, 3, 4, 5, 6, 7) and abs(a) <= 0.35:   # b为截距，过滤截距以过滤掉名片中间的字所产生的线。
            lines_pool.append([x])

    lines_pool.sort()
    result = np.array(lines_pool)
    return result


def imgcorr(src):
    rgbsrc = src.copy()
    gray = cv2.cvtColor(rgbsrc, cv2.COLOR_RGB2GRAY)
    # blurimg = cv2.GaussianBlur(gray, (3, 3), 0)
    ret, thresh = cv2.threshold(gray, 200, 255, cv2.THRESH_TRUNC)
    Cannyimg = cv2.Canny(thresh, 50, 80)
    # cv2.imshow('Cannyimg', Cannyimg)
    lines = cv2.HoughLinesP(Cannyimg, 0.7, theta=np.pi / 180, threshold=100, minLineLength=100, maxLineGap=10)
    result_line = find_4_lines(lines)
    drawlines(result_line, rgbsrc)  # 画出找到的线
    # for i in range(int(np.size(lines) / 4)):
    #     for x1, y1, x2, y2 in lines[i]:
    #         cv2.line(rgbsrc, (x1, y1), (x2, y2), (255, 255, 0), 3)
    #     cv2.imshow('lines', rgbsrc)
    # points = np.zeros((4, 2), dtype="float32")
    # points[0] = CrossPoint(lines[0], lines[2])
    # points[1] = CrossPoint(lines[0], lines[3])
    # points[2] = CrossPoint(lines[1], lines[2])
    # points[3] = CrossPoint(lines[1], lines[3])
    # sp = SortPoint(points)
    # width = int(np.sqrt(((sp[0][0] - sp[1][0]) ** 2) + (sp[0][1] - sp[1][1]) ** 2))
    # height = int(np.sqrt(((sp[0][0] - sp[2][0]) ** 2) + (sp[0][1] - sp[2][1]) ** 2))
    # dstrect = np.array([
    #     [0, 0],
    #     [width - 1, 0],
    #     [0, height - 1],
    #     [width - 1, height - 1]
    #     ], dtype="float32")
    # transform = cv2.getPerspectiveTransform(np.array(sp), dstrect)
    # warpedimg = cv2.warpPerspective(src, transform, (width, height))
    # return warpedimg


if __name__ == '__main__':
    src = cv2.imread(r"D:\Users\zhangjinwei\Desktop\books\master\first_semester\1_digital_pic\hw2\HW2\4.jpg")
    dst = imgcorr(src)
    # cv2.imshow("Image", dst)
    cv2.waitKey(0)
    # cv2.imwrite(r"D:\Users\zhangjinwei\Desktop\books\master\first_semester\1_digital_pic\hw2\mingpian_data\4_1.jpg", dst)