def VLPR(self, car_pic):
result = {}
start = time.time()
# 初始化模型
self.model = SVM(C=1, gamma=0.5)
if os.path.exists("svm.dat"):
self.model.load("svm.dat")
else:
raise FileNotFoundError('svm.dat')
self.modelchinese = SVM(C=1, gamma=0.5)
if os.path.exists("svmchinese.dat"):
self.modelchinese.load("svmchinese.dat")
else:
raise FileNotFoundError('svmchinese.dat')
card_imgs, colors = self.__preTreatment(car_pic)
if card_imgs is []:
return
else:
predict_result, roi, card_color = self.__identification(card_imgs, colors,self.model,self.modelchinese)
if predict_result != []:
result['UseTime'] = round((time.time() - start), 2)
result['InputTime'] = time.strftime("%Y-%m-%d %H:%M:%S")
result['Type'] = self.cardtype[card_color]
result['List'] = predict_result
result['Number'] = ''.join(predict_result[:2]) + '·' + ''.join(predict_result[2:])
result['Picture'] = roi
try:
result['From'] = ''.join(self.Prefecture[result['List'][0]][result['List'][1]])
except:
result['From'] = '未知'
return result
else:
return None
def __init__(self):
self.SZ = args.Size # 训练图片长宽
self.MAX_WIDTH = args.MAX_WIDTH # 原始图片最大宽度
self.Min_Area = args.Min_Area # 车牌区域允许最大面积
self.PROVINCE_START = args.PROVINCE_START
self.provinces = args.provinces
self.cardType = args.cardType
self.Prefecture = args.Prefecture
self.config = args.Pic_config
# 训练模型 默认参数C=1，gamma=0.5
self.model = SVM()
self.cn_model = SVM()
class PlateRecognition:
"""
车牌识别类
"""
# 初始化相关属性
def __init__(self):
self.SZ = args.Size # 训练图片长宽
self.MAX_WIDTH = args.MAX_WIDTH # 原始图片最大宽度
self.Min_Area = args.Min_Area # 车牌区域允许最大面积
self.PROVINCE_START = args.PROVINCE_START
self.provinces = args.provinces
self.cardType = args.cardType
self.Prefecture = args.Prefecture
self.config = args.Pic_config
# 训练模型 默认参数C=1，gamma=0.5
self.model = SVM()
self.cn_model = SVM()
def __accurate_place(self, card_img_hsv, limit1, limit2, color):
row_num, col_num = card_img_hsv.shape[:2]
xl = col_num
xr = 0
yh = 0
yl = row_num
# col_num_limit = self.cfg["col_num_limit"]
row_num_limit = self.config["row_num_limit"]
col_num_limit = col_num * 0.8 if color != "green" else col_num * 0.5 # 绿色有渐变
for i in range(row_num):
count = 0
for j in range(col_num):
H = card_img_hsv.item(i, j, 0)
S = card_img_hsv.item(i, j, 1)
V = card_img_hsv.item(i, j, 2)
if limit1 < H <= limit2 and 34 < S and 46 < V:
count += 1
if count > col_num_limit:
if yl > i:
yl = i
if yh < i:
yh = i
for j in range(col_num):
count = 0
for i in range(row_num):
H = card_img_hsv.item(i, j, 0)
S = card_img_hsv.item(i, j, 1)
V = card_img_hsv.item(i, j, 2)
if limit1 < H <= limit2 and 34 < S and 46 < V:
count += 1
if count > row_num - row_num_limit:
if xl > j:
xl = j
if xr < j:
xr = j
return xl, xr, yh, yl
# 确定车牌区域
def __find_location(self, src):
if type(src) == type(""):
img = imread(src)
else:
img = src
pic_height, pic_width = img.shape[:2]
if pic_width > self.MAX_WIDTH:
resize_rate = self.MAX_WIDTH / pic_width
img = cv2.resize(img,
(self.MAX_WIDTH, int(pic_height * resize_rate)),
interpolation=cv2.INTER_AREA) # 图片分辨率调整
kernel = np.array([[0, -1, 0], [-1, 5, -1], [0, -1, 0]],
np.float32) # 定义一个核
img = cv2.filter2D(img, -1, kernel=kernel) # 锐化
blur = self.config["blur"]
# 高斯去噪
if blur > 0:
img = cv2.GaussianBlur(img, (blur, blur), 0)
oldimg = img
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# cv2.imshow('GaussianBlur', img)
kernel = np.ones((20, 20), np.uint8)
img_opening = cv2.morphologyEx(img, cv2.MORPH_OPEN, kernel) # 开运算
img_opening = cv2.addWeighted(img, 1, img_opening, -1,
0) # 与上一次开运算结果融合
# cv2.imshow('img_opening', img_opening)
# 找到图像边缘
ret, img_thresh = cv2.threshold(
img_opening, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU) # 二值化
img_edge = cv2.Canny(img_thresh, 100, 200)
# cv2.imshow('img_edge', img_edge)
# 使用开运算和闭运算让图像边缘成为一个整体
kernel = np.ones(
(self.config["morphologyr"], self.config["morphologyc"]), np.uint8)
img_edge1 = cv2.morphologyEx(img_edge, cv2.MORPH_CLOSE, kernel) # 闭运算
img_edge2 = cv2.morphologyEx(img_edge1, cv2.MORPH_OPEN, kernel) # 开运算
# cv2.imshow('img_edge2', img_edge2)
# 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
try:
# 由于不同版本的opencv返回值数量不同，因此用try catch进行处理
image, contours, hierarchy = cv2.findContours(
img_edge2, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
except ValueError:
# ValueError: not enough values to unpack (expected 3, got 2)
# cv2.findContours方法在高版本OpenCV中只返回两个参数
contours, hierarchy = cv2.findContours(img_edge2, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
contours = [
cnt for cnt in contours if cv2.contourArea(cnt) > self.Min_Area
]
# 排除不是车牌的矩形区域
car_contours = []
for cnt in contours:
# 框选 生成最小外接矩形 返回值（中心(x,y), (宽,高), 旋转角度）
rect = cv2.minAreaRect(cnt)
# print('宽高:',rect[1])
area_width, area_height = rect[1]
# 选择宽大于高的区域
if area_width < area_height:
area_width, area_height = area_height, area_width
wh_ratio = area_width / area_height
# print('宽高比：',wh_ratio)
# 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
if 2 < wh_ratio < 5.5:
car_contours.append(rect)
# 框出所有可能的矩形
# oldimg = cv2.drawContours(oldimg, contours, -1, (0, 0, 255), 2)
# 矩形区域可能是倾斜的矩形，需要矫正，以便使用颜色定位
card_imgs = []
for rect in car_contours:
# 创造角度，使得左、高、右、低拿到正确的值
if -1 < rect[2] < 1:
angle = 1
else:
angle = rect[2]
# 扩大范围，避免车牌边缘被排除
rect = (rect[0], (rect[1][0] + 5, rect[1][1] + 5), angle)
box = cv2.boxPoints(rect)
height_point = right_point = [0, 0]
left_point = low_point = [pic_width, pic_height]
for point in box:
if left_point[0] > point[0]:
left_point = point
if low_point[1] > point[1]:
low_point = point
if height_point[1] < point[1]:
height_point = point
if right_point[0] < point[0]:
right_point = point
# 正角度
if left_point[1] <= right_point[1]:
new_right_point = [right_point[0], height_point[1]]
# 字符只是高度需要改变
pts2 = np.float32([left_point, height_point, new_right_point])
pts1 = np.float32([left_point, height_point, right_point])
M = cv2.getAffineTransform(pts1, pts2)
dst = cv2.warpAffine(oldimg, M, (pic_width, pic_height))
point_limit(new_right_point)
point_limit(height_point)
point_limit(left_point)
card_img = dst[int(left_point[1]):int(height_point[1]),
int(left_point[0]):int(new_right_point[0])]
card_imgs.append(card_img)
# 负角度
elif left_point[1] > right_point[1]:
new_left_point = [left_point[0], height_point[1]]
# 字符只是高度需要改变
pts2 = np.float32([new_left_point, height_point, right_point])
pts1 = np.float32([left_point, height_point, right_point])
M = cv2.getAffineTransform(pts1, pts2)
dst = cv2.warpAffine(oldimg, M, (pic_width, pic_height))
point_limit(right_point)
point_limit(height_point)
point_limit(new_left_point)
card_img = dst[int(right_point[1]):int(height_point[1]),
int(new_left_point[0]):int(right_point[0])]
card_imgs.append(card_img)
# cv2.imshow('angel card_img', card_imgs[0])
#____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
colors = []
for card_index, card_img in enumerate(card_imgs):
green = yellow = blue = black = white = 0
try:
# 有转换失败的可能，原因来自于上面矫正矩形出错
card_img_hsv = cv2.cvtColor(card_img, cv2.COLOR_BGR2HSV)
except:
card_img_hsv = None
if card_img_hsv is None:
continue
row_num, col_num = card_img_hsv.shape[:2]
card_img_count = row_num * col_num
# 确定车牌颜色
for i in range(row_num):
for j in range(col_num):
H = card_img_hsv.item(i, j, 0)
S = card_img_hsv.item(i, j, 1)
V = card_img_hsv.item(i, j, 2)
if 11 < H <= 34 and S > 34: # 图片分辨率调整
yellow += 1
elif 35 < H <= 99 and S > 34: # 图片分辨率调整
green += 1
elif 99 < H <= 124 and S > 34: # 图片分辨率调整
blue += 1
if 0 < H < 180 and 0 < S < 255 and 0 < V < 46:
black += 1
elif 0 < H < 180 and 0 < S < 43 and 221 < V < 225:
white += 1
color = "no"
# print('黄：{:<6}绿：{:<6}蓝：{:<6}'.format(yellow,green,blue))
limit1 = limit2 = 0
if yellow * 2 >= card_img_count:
color = "yellow"
limit1 = 11
limit2 = 34 # 有的图片有色偏偏绿
elif green * 2 >= card_img_count:
color = "green"
limit1 = 35
limit2 = 99
elif blue * 2 >= card_img_count:
color = "blue"
limit1 = 100
limit2 = 124 # 有的图片有色偏偏紫
elif black + white >= card_img_count * 0.7:
color = "bw"
# print(color)
colors.append(color)
# print(blue, green, yellow, black, white, card_img_count)
if limit1 == 0:
continue
# 根据车牌颜色再定位，缩小边缘非车牌边界
xl, xr, yh, yl = self.__accurate_place(card_img_hsv, limit1,
limit2, color)
if yl == yh and xl == xr:
continue
need_accurate = False
if yl >= yh:
yl = 0
yh = row_num
need_accurate = True
if xl >= xr:
xl = 0
xr = col_num
need_accurate = True
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]
if need_accurate: # 可能x或y方向未缩小，需要再试一次
card_img = card_imgs[card_index]
card_img_hsv = cv2.cvtColor(card_img, cv2.COLOR_BGR2HSV)
xl, xr, yh, yl = self.__accurate_place(card_img_hsv, limit1,
limit2, color)
if yl == yh and xl == xr:
continue
if yl >= yh:
yl = 0
yh = row_num
if xl >= xr:
xl = 0
xr = col_num
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]
cv2.imshow("result", card_imgs[0])
cv2.waitKey(0)
print('颜色识别结果：' + colors[0])
return card_imgs, colors
# 利用投影法，根据设定的阈值和图片直方图，找出波峰，用于分隔字符
def __find_waves(self, threshold, histogram):
up_point = -1 # 上升点
is_peak = False
if histogram[0] > threshold:
up_point = 0
is_peak = True
wave_peaks = []
for i, x in enumerate(histogram):
if is_peak and x < threshold:
if i - up_point > 2:
is_peak = False
wave_peaks.append((up_point, i))
elif not is_peak and x >= threshold:
is_peak = True
up_point = i
if is_peak and up_point != -1 and i - up_point > 4:
wave_peaks.append((up_point, i))
return wave_peaks
# 根据找出的波峰，分隔图片，从而得到逐个字符图片
def __seperate_card(img, waves):
part_cards = []
for wave in waves:
part_cards.append(img[:, wave[0]:wave[1]])
return part_cards
def __identify_plate(self, card_imgs, colors, model, cn_model):
# 识别车牌中的字符
result = {}
predict_result = []
roi = None
card_color = None
for i, color in enumerate(colors):
if color in ("blue", "yellow", "green"):
card_img = card_imgs[i]
# old_img = card_img
# 做一次锐化处理
kernel = np.array([[0, -1, 0], [-1, 5, -1], [0, -1, 0]],
np.float32) # 锐化
card_img = cv2.filter2D(card_img, -1, kernel=kernel)
# cv2.imshow("custom_blur", card_img)
# RGB转GARY
gray_img = cv2.cvtColor(card_img, cv2.COLOR_BGR2GRAY)
# cv2.imshow('gray_img', gray_img)
# 黄、绿车牌字符比背景暗、与蓝车牌刚好相反，所以黄、绿车牌需要反向
if color == "green" or color == "yellow":
gray_img = cv2.bitwise_not(gray_img)
# 二值化
ret, gray_img = cv2.threshold(
gray_img, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
# cv2.imshow('gray_img', gray_img)
# 查找水平直方图波峰
x_histogram = np.sum(gray_img, axis=1)
# 最小值
x_min = np.min(x_histogram)
# 均值
x_average = np.sum(x_histogram) / x_histogram.shape[0]
x_threshold = (x_min + x_average) / 2
wave_peaks = self.__find_waves(x_threshold, x_histogram)
if len(wave_peaks) == 0:
continue
# 认为水平方向，最大的波峰为车牌区域
wave = max(wave_peaks, key=lambda x: x[1] - x[0])
gray_img = gray_img[wave[0]:wave[1]]
# cv2.imshow('gray_img', gray_img)
# 查找垂直直方图波峰
row_num, col_num = gray_img.shape[:2]
# 去掉车牌上下边缘1个像素，避免白边影响阈值判断
gray_img = gray_img[1:row_num - 1]
# cv2.imshow('gray_img', gray_img)
y_histogram = np.sum(gray_img, axis=0)
y_min = np.min(y_histogram)
y_average = np.sum(y_histogram) / y_histogram.shape[0]
y_threshold = (y_min + y_average) / 5 # U和0要求阈值偏小，否则U和0会被分成两半
wave_peaks = self.__find_waves(y_threshold, y_histogram)
# print(wave_peaks)
# for wave in wave_peaks:
# cv2.line(card_img, pt1=(wave[0], 5), pt2=(wave[1], 5), color=(0, 0, 255), thickness=2)
# 车牌字符数应大于6
if len(wave_peaks) <= 6:
# print(wave_peaks)
continue
wave = max(wave_peaks, key=lambda x: x[1] - x[0])
max_wave_dis = wave[1] - wave[0]
# 判断是否是左侧车牌边缘
if wave_peaks[0][1] - wave_peaks[0][
0] < max_wave_dis / 3 and wave_peaks[0][0] == 0:
wave_peaks.pop(0)
# 组合分离汉字
cur_dis = 0
for i, wave in enumerate(wave_peaks):
if wave[1] - wave[0] + cur_dis > max_wave_dis * 0.6:
break
else:
cur_dis += wave[1] - wave[0]
if i > 0:
wave = (wave_peaks[0][0], wave_peaks[i][1])
wave_peaks = wave_peaks[i + 1:]
wave_peaks.insert(0, wave)
# 去除车牌上的分隔点
point = wave_peaks[2]
if point[1] - point[0] < max_wave_dis / 3:
point_img = gray_img[:, point[0]:point[1]]
if np.mean(point_img) < 255 / 5:
wave_peaks.pop(2)
if len(wave_peaks) <= 6:
# print("peak less 2:", wave_peaks)
continue
# print(wave_peaks)
# 分割牌照字符
part_cards = self.__seperate_card(gray_img, wave_peaks)
# 分割输出
#for i, part_card in enumerate(part_cards):
# cv2.imshow(str(i), part_card)
# 识别
for i, part_card in enumerate(part_cards):
# 可能是固定车牌的铆钉
if np.mean(part_card) < 255 / 5:
continue
part_card_old = part_card
w = abs(part_card.shape[1] - self.SZ) // 2
# 边缘填充
part_card = cv2.copyMakeBorder(part_card,
0,
0,
w,
w,
cv2.BORDER_CONSTANT,
value=[0, 0, 0])
# cv2.imshow('part_card', part_card)
# 图片缩放（缩小）
part_card = cv2.resize(part_card, (self.SZ, self.SZ),
interpolation=cv2.INTER_AREA)
# cv2.imshow('part_card', part_card)
part_card = SVM_Train.preprocess_hog([part_card])
if i == 0: # 识别汉字
resp = self.cn_model.predict(part_card) # 匹配样本
character = self.provinces[int(resp[0]) -
self.PROVINCE_START]
print(character)
else: # 识别字母
resp = self.model.predict(part_card) # 匹配样本
character = chr(resp[0])
print(character)
# 判断最后一个数是否是车牌边缘，假设车牌边缘被认为是1
if character == "1" and i == len(part_cards) - 1:
if color == 'blue' and len(part_cards) > 7:
if part_card_old.shape[0] / part_card_old.shape[
1] >= 7: # 1太细，认为是边缘
continue
elif color == 'blue' and len(part_cards) > 7:
if part_card_old.shape[0] / part_card_old.shape[
1] >= 7: # 1太细，认为是边缘
continue
elif color == 'green' and len(part_cards) > 8:
if part_card_old.shape[0] / part_card_old.shape[
1] >= 7: # 1太细，认为是边缘
continue
predict_result.append(character)
roi = card_img # old_img
card_color = color
break
return predict_result, roi, card_color # 识别到的字符、定位的车牌图像、车牌颜色
def __start_recognize__(self, src):
result = {}
start = time.time()
if os.path.exists("svm.dat"):
self.model.load("svm.dat")
else:
raise FileNotFoundError("svm.dat")
if os.path.exists("svmchinese.dat"):
self.cn_model.load("svmchinese.dat")
else:
raise FileNotFoundError("svmchinese.dat")
card_imgs, colors = self.__find_location(src)
if card_imgs is []:
print('车牌区域提取为空（card_imgs）')
return
else:
predict_result, roi, card_color = self.__identify_plate(
card_imgs, colors, self.model, self.cn_model)
if predict_result:
result['UseTime'] = round((time.time() - start), 2)
result['InputTime'] = time.strftime("%Y-%m-%d %H:%M:%S")
result['Type'] = self.cardType[card_color]
result['List'] = predict_result
result['Number'] = ''.join(predict_result[:2]) + '·' + ''.join(
predict_result[2:])
result['Picture'] = roi
try:
result['From'] = ''.join(
self.Prefecture[result['List'][0]][result['List'][1]])
except:
result['From'] = '未知'
return result
else:
return None
import time
from SVM_Train import SVM
import SVM_Train
from args import args
global SZ, MAX_WIDTH, Min_Area, PROVINCE_START, provinces, cardType, Prefecture, config, model, cn_model
SZ = args.Size # 训练图片长宽
MAX_WIDTH = args.MAX_WIDTH # 原始图片最大宽度
Min_Area = args.Min_Area # 车牌区域允许最大面积
PROVINCE_START = args.PROVINCE_START
provinces = args.provinces
cardType = args.cardType
Prefecture = args.Prefecture
config = args.Pic_config
# 训练模型 默认参数C=1，gamma=0.5
model = SVM()
cn_model = SVM()
def imread(filename):
"""
图片读取
:param filename: 文件路径
:return:
"""
return cv2.imdecode(np.fromfile(filename, dtype=np.uint8),
cv2.IMREAD_COLOR)
def point_limit(point):
"""