# https://www.cnblogs.com/HL-space/p/10588423.html
import os
import cv2
import numpy as np


# 预处理
def imgProcess(path):
 img = cv2.imread(path)
 # 统一规定大小
 img = cv2.resize(img, (640, 480))
 # 高斯模糊
 img_Gas = cv2.GaussianBlur(img, (5, 5), 0)
 # RGB通道分离
 img_B = cv2.split(img_Gas)[0]
 img_G = cv2.split(img_Gas)[1]
 img_R = cv2.split(img_Gas)[2]
 # 读取灰度图和HSV空间图
 img_gray = cv2.cvtColor(img_Gas, cv2.COLOR_BGR2GRAY)
 img_HSV = cv2.cvtColor(img_Gas, cv2.COLOR_BGR2HSV)
 return img, img_Gas, img_B, img_G, img_R, img_gray, img_HSV


# 初步识别
def preIdentification(img_gray, img_HSV, img_B, img_R):
 for i in range(480):
 for j in range(640):
 # 普通蓝色车牌,同时排除透明反光物质的干扰
 if ((img_HSV[:, :, 0][i, j]-115)**2 < 15**2) and (img_B[i, j] > 70) and (img_R[i, j] < 40):
 img_gray[i, j] = 255
 else:
 img_gray[i, j] = 0
 # 定义核
 kernel_small = np.ones((3, 3))
 kernel_big = np.ones((7, 7))

 img_gray = cv2.GaussianBlur(img_gray, (5, 5), 0) # 高斯平滑
 img_di = cv2.dilate(img_gray, kernel_small, iterations=5) # 腐蚀5次
 img_close = cv2.morphologyEx(img_di, cv2.MORPH_CLOSE, kernel_big) # 闭操作
 img_close = cv2.GaussianBlur(img_close, (5, 5), 0) # 高斯平滑
 _, img_bin = cv2.threshold(img_close, 100, 255, cv2.THRESH_BINARY) # 二值化
 return img_bin


# 定位
def fixPosition(img, img_bin):
 # 检测所有外轮廓,只留矩形的四个顶点
 contours, _ = cv2.findContours(img_bin, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
 #形状及大小筛选校验
 det_x_max = 0
 det_y_max = 0
 num = 0
 for i in range(len(contours)):
 x_min = np.min(contours[i][ :, :, 0])
 x_max = np.max(contours[i][ :, :, 0])
 y_min = np.min(contours[i][ :, :, 1])
 y_max = np.max(contours[i][ :, :, 1])
 det_x = x_max - x_min
 det_y = y_max - y_min
 if (det_x / det_y > 1.8) and (det_x > det_x_max ) and (det_y > det_y_max ):
 det_y_max = det_y
 det_x_max = det_x
 num = i
 # 获取最可疑区域轮廓点集
 points = np.array(contours[num][:, 0])
 return points


def findVertices(points):
 # 获取最小外接矩阵,中心点坐标,宽高,旋转角度
 rect = cv2.minAreaRect(points)
 # 获取矩形四个顶点,浮点型
 box = cv2.boxPoints(rect)
 # 取整
 box = np.int0(box)
 # 获取四个顶点坐标
 left_point_x = np.min(box[:, 0])
 right_point_x = np.max(box[:, 0])
 top_point_y = np.min(box[:, 1])
 bottom_point_y = np.max(box[:, 1])

 left_point_y = box[:, 1][np.where(box[:, 0] == left_point_x)][0]
 right_point_y = box[:, 1][np.where(box[:, 0] == right_point_x)][0]
 top_point_x = box[:, 0][np.where(box[:, 1] == top_point_y)][0]
 bottom_point_x = box[:, 0][np.where(box[:, 1] == bottom_point_y)][0]
 # 上下左右四个点坐标
 vertices = np.array([[top_point_x, top_point_y], [bottom_point_x, bottom_point_y], [left_point_x, left_point_y], [right_point_x, right_point_y]])
 return vertices, rect


def tiltCorrection(vertices, rect):
 # 畸变情况1
 if rect[2] > -45:
 new_right_point_x = vertices[0, 0]
 new_right_point_y = int(vertices[1, 1] - (vertices[0, 0]- vertices[1, 0]) / (vertices[3, 0] - vertices[1, 0]) * (vertices[1, 1] - vertices[3, 1]))
 new_left_point_x = vertices[1, 0]
 new_left_point_y = int(vertices[0, 1] + (vertices[0, 0] - vertices[1, 0]) / (vertices[0, 0] - vertices[2, 0]) * (vertices[2, 1] - vertices[0, 1]))
 # 校正后的四个顶点坐标
 point_set_1 = np.float32([[440, 0],[0, 0],[0, 140],[440, 140]])
 # 畸变情况2
 elif rect[2] < -45:
 new_right_point_x = vertices[1, 0]
 new_right_point_y = int(vertices[0, 1] + (vertices[1, 0] - vertices[0, 0]) / (vertices[3, 0] - vertices[0, 0]) * (vertices[3, 1] - vertices[0, 1]))
 new_left_point_x = vertices[0, 0]
 new_left_point_y = int(vertices[1, 1] - (vertices[1, 0] - vertices[0, 0]) / (vertices[1, 0] - vertices[2, 0]) * (vertices[1, 1] - vertices[2, 1]))
 # 校正后的四个顶点坐标
 point_set_1 = np.float32([[0, 0],[0, 140],[440, 140],[440, 0]])

 # 校正前平行四边形四个顶点坐标
 new_box = np.array([(vertices[0, 0], vertices[0, 1]), (new_left_point_x, new_left_point_y), (vertices[1, 0], vertices[1, 1]), (new_right_point_x, new_right_point_y)])
 point_set_0 = np.float32(new_box)
 return point_set_0, point_set_1, new_box


def transform(img, point_set_0, point_set_1):
 # 变换矩阵
 mat = cv2.getPerspectiveTransform(point_set_0, point_set_1)
 # 投影变换
 lic = cv2.warpPerspective(img, mat, (440, 140))
 return lic


def main():
 path = os.getcwd()+"\\img\\0.jpg"
 # 图像预处理
 img, img_Gas, img_B, img_G, img_R, img_gray, img_HSV = imgProcess(path)
 # 初步识别
 img_bin = preIdentification(img_gray, img_HSV, img_B, img_R)
 points = fixPosition(img, img_bin)
 vertices, rect = findVertices(points)
 point_set_0, point_set_1, new_box = tiltCorrection(vertices, rect)
 img_draw = cv2.drawContours(img.copy(), [new_box], -1, (0,0,255), 3)
 lic = transform(img, point_set_0, point_set_1)
 # 原图上框出车牌
 cv2.namedWindow("Image")
 cv2.imshow("Image", img_draw)
 # 二值化图像
 cv2.namedWindow("Image_Bin")
 cv2.imshow("Image_Bin", img_bin)
 # 显示校正后的车牌
 cv2.namedWindow("Lic")
 cv2.imshow("Lic", lic)
 # 暂停、关闭窗口
 cv2.waitKey(0)
 cv2.destroyAllWindows()


if __name__ == '__main__':
 main()