基于python+OpenCV的车牌号码识别
车牌识别行业已具备一定的市场规模，在电子警察、公路卡口、停车场、商业管理、汽修服务等领域已取得了部分应用。一个典型的车辆牌照识别系统一般包括以下4个部分：车辆图像获取、车牌定位、车牌字符分割和车牌字符识别
1、车牌定位的主要工作是从获取的车辆图像中找到汽车牌照所在位置，并把车牌从该区域中准确地分割出来
这里所采用的是利用车牌的颜色（黄色、蓝色、绿色） 来进行定位
#定位车牌
def color_position(img,output_path):
colors = [([26,43,46], [34,255,255]), # 黄色
([100,43,46], [124,255,255]), # 蓝色
([35, 43, 46], [77, 255, 255]) # 绿色
]
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
for (lower, upper) in colors:
lower = np.array(lower, dtype="uint8") # 颜色下限
upper = np.array(upper, dtype="uint8") # 颜色上限
# 根据阈值找到对应的颜色
mask = cv2.inRange(hsv, lowerb=lower, upperb=upper)
output = cv2.bitwise_and(img, img, mask=mask)
k = mark_zone_color(output,output_path)
if k==1:
return 1
# 展示图片
#cv2.imshow("image", img)
#cv2.imshow("image-color", output)
#cv2.waitKey(0)
return 0
2、将车牌提取出来
def mark_zone_color(src_img,output_img):
#根据颜色在原始图像上标记
#转灰度
gray = cv2.cvtColor(src_img,cv2.COLOR_BGR2GRAY)
#图像二值化
ret,binary = cv2.threshold(gray,0,255,cv2.THRESH_BINARY)
#轮廓检测
x,contours,hierarchy = cv2.findContours(binary,cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE)
#drawing = img
#cv2.drawContours(drawing, contours, -1, (0, 0, 255), 3) # 填充轮廓颜色
#cv2.imshow('drawing', drawing)
#cv2.waitKey(0)
#print(contours
)
temp_contours = [] # 存储合理的轮廓
car_plates=[]
if len(contours)>0:
for contour in contours:
if cv2.contourArea(contour) > Min_Area:
temp_contours.append(contour)
car_plates = []
for temp_contour in temp_contours:
rect_tupple = cv2.minAreaRect(temp_contour)
rect_width, rect_height = rect_tupple[1]
if rect_width < rect_height:
rect_width, rect_height = rect_height, rect_width
aspect_ratio = rect_width / rect_height
# 车牌正常情况下宽高比在2 - 5.5之间
if aspect_ratio > 2 and aspect_ratio < 5.5:
car_plates.append(temp_contour)
rect_vertices = cv2.boxPoints(rect_tupple)
rect_vertices = np.int0(rect_vertices)
if len(car_plates)==1:
oldimg = cv2.drawContours(img, [rect_vertices], -1, (0, 0, 255), 2)
#cv2.imshow("che pai ding wei", oldimg)
# print(rect_tupple)
break
#把车牌号截取出来
if len(car_plates)==1:
for car_plate in car_plates:
row_min,col_min = np.min(car_plate[:,0,:],axis=0)
row_max,col_max = np.max(car_plate[:,0,:],axis=0)
cv2.rectangle(img,(row_min,col_min),(row_max,col_max),(0,255,0),2)
card_img = img[col_min:col_max,row_min:row_max,:]
cv2.imshow("img",img)
cv2.imwrite(output_img + '/' + 'card_img' + '.jpg',card_img)
cv2.imshow("card_img.",card_img)
cv2.waitKey(0)
cv2.destroyAllWindows()
return 1
return 0
