# python实现车牌定位及分割

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

median = cv2.medianblur(gaussian, 5)

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)

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 region

2、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]

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