具体步骤
1、将采集到的彩色车牌图像转换成灰度图
2、灰度化的图像利用高斯平滑处理后，再对其进行中直滤波
3、使用sobel算子对图像进行边缘检测
4、对二值化的图像进行腐蚀，膨胀，开运算，闭运算的形态学组合变换
5、对形态学变换后的图像进行轮廓查找，根据车牌的长宽比提取车牌
代码实现
图像灰度化
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
高斯平滑，中值滤波处理
gaussian = cv2.GaussianBlur(gray, (3, 3), 0, 0, cv2.BORDER_DEFAULT) median = cv2.medianBlur(gaussian, 5)
Sobel边缘检测
sobel = cv2.Sobel(median, cv2.CV_8U, 1, 0, ksize = 3)
二值化
ret, binary = cv2.threshold(sobel, 170, 255, cv2.THRESH_BINARY)
对二值化的图像进行腐蚀，膨胀，开运算，闭运算的形态学组合变换
# 膨胀和腐蚀操作的核函数 element1 = cv2.getStructuringElement(cv2.MORPH_RECT, (9, 1)) element2 = cv2.getStructuringElement(cv2.MORPH_RECT, (8, 6)) # 膨胀一次，让轮廓突出 dilation = cv2.dilate(binary, element2, iterations = 1) # 腐蚀一次，去掉细节 erosion = cv2.erode(dilation, element1, iterations = 1) # 再次膨胀，让轮廓明显一些 dilation2 = cv2.dilate(erosion, element2,iterations = 3)
对形态学变换后的图像进行轮廓查找，根据车牌的长宽比提取车牌
1、查找车牌区域
def findPlateNumberRegion(img):
region = []
# 查找轮廓
contours,hierarchy = cv2.findContours(img, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
# 筛选面积小的
for i in range(len(contours)):
cnt = contours[i]
# 计算该轮廓的面积
area = cv2.contourArea(cnt)
# 面积小的都筛选掉
if (area < 2000):
continue
# 轮廓近似，作用很小
epsilon = 0.001 * cv2.arcLength(cnt,True)
approx = cv2.approxPolyDP(cnt, epsilon, True)
# 找到最小的矩形，该矩形可能有方向
rect = cv2.minAreaRect(cnt)
print "rect is: "
print rect
# box是四个点的坐标
box = cv2.cv.BoxPoints(rect)
box = np.int0(box)
# 计算高和宽
height = abs(box[0][1] - box[2][1])
width = abs(box[0][0] - box[2][0])
# 车牌正常情况下长高比在2.7-5之间
ratio =float(width) / float(height)
if (ratio > 5 or ratio < 2):
continue
region.append(box)
return region2、用绿线绘出车牌区域和切割车牌
# 用绿线画出这些找到的轮廓
for box in region:
cv2.drawContours(img, [box], 0, (0, 255, 0), 2)
ys = [box[0, 1], box[1, 1], box[2, 1], box[3, 1]]
xs = [box[0, 0], box[1, 0], box[2, 0], box[3, 0]]
ys_sorted_index = np.argsort(ys)
xs_sorted_index = np.argsort(xs)
x1 = box[xs_sorted_index[0], 0]
x2 = box[xs_sorted_index[3], 0]
y1 = box[ys_sorted_index[0], 1]
y2 = box[ys_sorted_index[3], 1]
img_org2 = img.copy()
img_plate = img_org2[y1:y2, x1:x2]
