# 图像的透视校正
import cv2
import numpy as np
import math

# 读入待处理的图像并显示
img = cv2.imread("./data/shu3.jpg")
cv2.imshow("1_yuanshituxima", img)

# 灰度化 并显示
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
cv2.imshow("2.huidu", img_gray)

# 模糊化处理 去掉过小的细节
blurred = cv2.GaussianBlur(img_gray, (5, 5), 0)
cv2.imshow("3.mohu", blurred)

# 膨胀-抖小的细节合并
dilate = cv2.dilate(blurred, (3, 3))
cv2.imshow("4_pengzhang", dilate)

# 边缘提取
canny = cv2.Canny(dilate, 50, 240)
cv2.imshow("5_bianyuntiqu", canny)

# 轮廓检测
imag, cnts, hie = cv2.findContours(canny.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

# 绘制轮廓
img_cnt = cv2.drawContours(img.copy(), cnts, -1, (0, 0, 255), 2)
cv2.imshow("6_lunkuxian", img_cnt)

docCnt = None  # 定义一个空数组

# 计算面积, 排序
if len(cnts) > 0:
    cnts = sorted(cnts, key=cv2.contourArea, reverse=True)
    for c in cnts:
        peri = cv2.arcLength(c, True)  # 求出该轮廓线的周长
        approx = cv2.approxPolyDP(c, 0.02 * peri, True)  # 轮廓线的多边形组合
        if len(approx) == 4:  # 找到边为4的多边形
            docCnt = approx  # 将找到的多边形赋值给另外一个变量docCnt
            break

# 绘制找到的四边形的点,画圆
points = []
for peak in docCnt:
    peak = peak[0]
    print("peak:", peak)
    cv2.circle(img_cnt, tuple(peak), 10, (0, 255, 0), 2)  # 圆所在的图像, 圆心, 半径, 颜色, 线宽
    points.append(peak)

cv2.imshow("7-sibianxingdingdian", img_cnt)
print("points:", points)

# 校正
# 原纸张逆时针方向的4个角点
src = np.float32([points[0], points[1], points[2], points[3]])

# 根据勾股定理计算宽度,再做透视变换
h = int(math.sqrt((points[1][0] - points[0][0]) ** 2 + (points[1][1] - points[0][1]) ** 2))  # 高度
w = int(math.sqrt((points[2][0] - points[1][0]) ** 2 + (points[2][1] - points[1][1]) ** 2))  # 宽度
print("w, h", w, h)

dst = np.float32([[0, 0], [0, h], [w, h], [w, 0]])
M = cv2.getPerspectiveTransform(src, dst)  # 生成透视矩阵
result = cv2.warpPerspective(img.copy(), M, (w, h))  # 透视变换

cv2.imshow("8_leguo", result)
cv2.waitKey()
cv2.destroyAllWindows()