294 lines
9.9 KiB
Python
294 lines
9.9 KiB
Python
'''
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基于形状和色调的检测车牌号并提取车牌号图片
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'''
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import cv2
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import numpy as np
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from numpy.linalg import norm
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import sys
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import os
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import json
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SZ = 20 # 训练图片长宽
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MAX_WIDTH = 1000 # 原始图片最大宽度
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Min_Area = 2000 # 车牌区域允许最大面积
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PROVINCE_START = 1000
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def point_limit(point):
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if point[0] < 0:
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point[0] = 0
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if point[1] < 0:
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point[1] = 0
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def accurate_place(card_img_hsv, limit1, limit2, color,cfg):
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row_num, col_num = card_img_hsv.shape[:2]
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xl = col_num
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xr = 0
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yh = 0
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yl = row_num
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#col_num_limit = cfg["col_num_limit"]
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row_num_limit = cfg["row_num_limit"]
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col_num_limit = col_num * 0.8 if color != "green" else col_num * 0.5 # 绿色有渐变
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for i in range(row_num):
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count = 0
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for j in range(col_num):
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H = card_img_hsv.item(i, j, 0)
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S = card_img_hsv.item(i, j, 1)
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V = card_img_hsv.item(i, j, 2)
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if limit1 < H <= limit2 and 34 < S and 46 < V:
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count += 1
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if count > col_num_limit:
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if yl > i:
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yl = i
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if yh < i:
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yh = i
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for j in range(col_num):
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count = 0
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for i in range(row_num):
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H = card_img_hsv.item(i, j, 0)
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S = card_img_hsv.item(i, j, 1)
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V = card_img_hsv.item(i, j, 2)
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if limit1 < H <= limit2 and 34 < S and 46 < V:
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count += 1
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if count > row_num - row_num_limit:
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if xl > j:
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xl = j
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if xr < j:
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xr = j
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return xl, xr, yh, yl
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def CaridDetect(car_pic):
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# 加载图片
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img = cv2.imread(car_pic)
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pic_hight, pic_width = img.shape[:2]
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if pic_width > MAX_WIDTH:
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resize_rate = MAX_WIDTH / pic_width
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img = cv2.resize(img, (MAX_WIDTH, int(pic_hight*resize_rate)), interpolation=cv2.INTER_AREA)
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# 车牌识别的部分参数保存在js中，便于根据图片分辨率做调整
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f = open('config.js')
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j = json.load(f)
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for c in j["config"]:
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if c["open"]:
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cfg = c.copy()
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break
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else:
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raise RuntimeError('[ ERROR ] 没有设置有效配置参数.')
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blur = cfg["blur"]
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# 高斯去噪
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if blur > 0:
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img = cv2.GaussianBlur(img, (blur, blur), 0) #图片分辨率调整
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oldimg = img
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img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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#equ = cv2.equalizeHist(img)
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#img = np.hstack((img, equ))
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# 去掉图像中不会是车牌的区域
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kernel = np.ones((20, 20), np.uint8)
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# morphologyEx 形态学变化函数
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img_opening = cv2.morphologyEx(img, cv2.MORPH_OPEN, kernel)
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img_opening = cv2.addWeighted(img, 1, img_opening, -1, 0);
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# 找到图像边缘 Canny边缘检测
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ret, img_thresh = cv2.threshold(img_opening, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
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img_edge = cv2.Canny(img_thresh, 100, 200)
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# 使用开运算和闭运算让图像边缘成为一个整体
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kernel = np.ones((cfg["morphologyr"], cfg["morphologyc"]), np.uint8)
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img_edge1 = cv2.morphologyEx(img_edge, cv2.MORPH_CLOSE, kernel)
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img_edge2 = cv2.morphologyEx(img_edge1, cv2.MORPH_OPEN, kernel)
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# 查找图像边缘整体形成的矩形区域，可能有很多，车牌就在其中一个矩形区域中
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# cv2.findContours()函数来查找检测物体的轮廓
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try:
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contours, hierarchy = cv2.findContours(img_edge2, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
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except ValueError:
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image, contours, hierarchy = cv2.findContours(img_edge2, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
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contours = [cnt for cnt in contours if cv2.contourArea(cnt) > Min_Area]
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# print('[ INFO ] len(contours): {}'.format(len(contours)))
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# 一一排除不是车牌的矩形区域，找到最小外接矩形的长宽比复合车牌条件的边缘检测到的物体
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car_contours = []
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for cnt in contours:
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rect = cv2.minAreaRect(cnt)
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# 生成最小外接矩形，点集 cnt 存放的就是该四边形的4个顶点坐标（点集里面有4个点）
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# 函数 cv2.minAreaRect() 返回一个Box2D结构rect：（最小外接矩形的中心（x，y），（宽度，高度），旋转角度），
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# 但是要绘制这个矩形，我们需要矩形的4个顶点坐标box, 通过函数 cv2.boxPoints() 获得，
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# 返回形式[ [x0,y0], [x1,y1], [x2,y2], [x3,y3] ]。
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# 得到的最小外接矩形的4个顶点顺序、中心坐标、宽度、高度、旋转角度（是度数形式，不是弧度数）
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# https://blog.csdn.net/lanyuelvyun/article/details/76614872
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area_width, area_height = rect[1]
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if area_width < area_height:
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area_width, area_height = area_height, area_width
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wh_ratio = area_width / area_height
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#print(wh_ratio)
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# 要求矩形区域长宽比在2到5.5之间，2到5.5是车牌的长宽比，其余的矩形排除 一般的比例是3.5
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if wh_ratio > 2 and wh_ratio < 5.5:
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car_contours.append(rect)
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box = cv2.boxPoints(rect)
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box = np.int0(box)
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#oldimg = cv2.drawContours(oldimg, [box], 0, (0, 0, 255), 2)
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#cv2.imshow("edge4", oldimg)
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#print(rect)
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# print("[ INFo ] len(car_contours): {}".format(len(car_contours)))
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# print("[ INFO ] 精确定位.")
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card_imgs = []
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# 矩形区域可能是倾斜的矩形，需要矫正，以便使用颜色定位
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# 这个就是为什么我们不选择YOLO,SSD或其他的目标检测算法来检测车牌号的原因！！！（给自己偷懒找个台阶 :) )
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for rect in car_contours:
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if rect[2] > -1 and rect[2] < 1:#创造角度，使得左、高、右、低拿到正确的值
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angle = 1
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else:
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angle = rect[2]
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rect = (rect[0], (rect[1][0]+5, rect[1][1]+5), angle)#扩大rect范围，避免车牌边缘被排除
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box = cv2.boxPoints(rect)
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# 避免边界超出图像边界
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heigth_point = right_point = [0, 0]
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left_point = low_point = [pic_width, pic_hight]
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for point in box:
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if left_point[0] > point[0]:
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left_point = point
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if low_point[1] > point[1]:
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low_point = point
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if heigth_point[1] < point[1]:
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heigth_point = point
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if right_point[0] < point[0]:
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right_point = point
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if left_point[1] <= right_point[1]: # 正角度
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new_right_point = [right_point[0], heigth_point[1]]
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pts2 = np.float32([left_point, heigth_point, new_right_point])#字符只是高度需要改变
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pts1 = np.float32([left_point, heigth_point, right_point])
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M = cv2.getAffineTransform(pts1, pts2) # 仿射变换
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dst = cv2.warpAffine(oldimg, M, (pic_width, pic_hight))
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point_limit(new_right_point)
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point_limit(heigth_point)
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point_limit(left_point)
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card_img = dst[int(left_point[1]):int(heigth_point[1]), int(left_point[0]):int(new_right_point[0])]
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card_imgs.append(card_img)
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#cv2.imshow("card", card_img)
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#cv2.waitKey(0)
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elif left_point[1] > right_point[1]: # 负角度
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new_left_point = [left_point[0], heigth_point[1]]
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pts2 = np.float32([new_left_point, heigth_point, right_point])#字符只是高度需要改变
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pts1 = np.float32([left_point, heigth_point, right_point])
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M = cv2.getAffineTransform(pts1, pts2)
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dst = cv2.warpAffine(oldimg, M, (pic_width, pic_hight))
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point_limit(right_point)
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point_limit(heigth_point)
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point_limit(new_left_point)
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card_img = dst[int(right_point[1]):int(heigth_point[1]), int(new_left_point[0]):int(right_point[0])]
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card_imgs.append(card_img)
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#cv2.imshow("card", card_img)
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#cv2.waitKey(0)
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# 开始使用颜色定位，排除不是车牌的矩形，目前只识别蓝、绿、黄车牌
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colors = []
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for card_index,card_img in enumerate(card_imgs):
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green = yello = blue = black = white = 0
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card_img_hsv = cv2.cvtColor(card_img, cv2.COLOR_BGR2HSV)
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#有转换失败的可能，原因来自于上面矫正矩形出错
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if card_img_hsv is None:
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continue
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row_num, col_num= card_img_hsv.shape[:2]
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card_img_count = row_num * col_num
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for i in range(row_num):
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for j in range(col_num):
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H = card_img_hsv.item(i, j, 0)
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S = card_img_hsv.item(i, j, 1)
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V = card_img_hsv.item(i, j, 2)
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if 11 < H <= 34 and S > 34:#图片分辨率调整
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yello += 1
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elif 35 < H <= 99 and S > 34:#图片分辨率调整
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green += 1
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elif 99 < H <= 124 and S > 34:#图片分辨率调整
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blue += 1
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if 0 < H <180 and 0 < S < 255 and 0 < V < 46:
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black += 1
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elif 0 < H <180 and 0 < S < 43 and 221 < V < 225:
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white += 1
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color = "no"
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limit1 = limit2 = 0
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if yello*2 >= card_img_count:
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color = "yello"
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limit1 = 11
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limit2 = 34#有的图片有色偏偏绿
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elif green*2 >= card_img_count:
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color = "green"
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limit1 = 35
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limit2 = 99
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elif blue*2 >= card_img_count:
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color = "blue"
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limit1 = 100
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limit2 = 124#有的图片有色偏偏紫
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elif black + white >= card_img_count*0.7: #TODO
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color = "bw"
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# print("[ INFO ] color: {}".format(color))
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colors.append(color)
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# print(blue, green, yello, black, white, card_img_count)
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#cv2.imshow("color", card_img)
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#cv2.waitKey(0)
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if limit1 == 0:
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continue
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#以上为确定车牌颜色
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#以下为根据车牌颜色再定位，缩小边缘非车牌边界
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xl, xr, yh, yl = accurate_place(card_img_hsv, limit1, limit2, color,cfg)
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if yl == yh and xl == xr:
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continue
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need_accurate = False
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if yl >= yh:
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yl = 0
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yh = row_num
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need_accurate = True
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if xl >= xr:
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xl = 0
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xr = col_num
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need_accurate = True
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card_imgs[card_index] = card_img[yl:yh, xl:xr] if color != "green" or yl < (yh-yl)//4 else card_img[yl-(yh-yl)//4:yh, xl:xr]
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if need_accurate:#可能x或y方向未缩小，需要再试一次
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card_img = card_imgs[card_index]
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card_img_hsv = cv2.cvtColor(card_img, cv2.COLOR_BGR2HSV)
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xl, xr, yh, yl = accurate_place(card_img_hsv, limit1, limit2, color,cfg)
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if yl == yh and xl == xr:
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continue
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if yl >= yh:
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yl = 0
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yh = row_num
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if xl >= xr:
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xl = 0
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xr = col_num
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card_imgs[card_index] = card_img[yl:yh, xl:xr] if color != "green" or yl < (yh-yl)//4 else card_img[yl-(yh-yl)//4:yh, xl:xr]
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roi = card_img
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card_color = color
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labels = (int(right_point[1]), int(heigth_point[1]), int(left_point[0]), int(right_point[0]))
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return roi,labels, card_color#定位的车牌图像、车牌颜色
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if __name__ == '__main__':
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for pic_file in os.listdir("./test_img"):
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roi, label,color = CaridDetect(os.path.join("./test_img",pic_file))
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cv2.imwrite(os.path.join("./result",pic_file),roi)
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print("*"*50)
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print("[ ROI ] {}".format(roi))
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print("[ Color ] {}".format(color))
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print("[ Label ] {}".format(label))
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