这期继续分享SVM实践项目：车牌检测与识别，同时也介绍一些干货
回顾一下，上期介绍了OpenCv的SVM模型训练，这期继续介绍一下识别过程。
这幅流程图还是很经典，直观的。
我们先分享一下上期说的：
OpenCv的中文显示方法
我使用的是PIL的显示方法，下面简介一下教程：
1： 字体simhei.ttf需要下载，然后在font = ImageFont.truetype("./simhei.ttf", 20, encoding=“utf-8”)指定simhei.ttf的路径即可 ，同样的需要把这个字体放在的路径找到或者放在运行代码同级，都行。
2： 中文编码为utf-8。否则中文会显示为矩形。str1 = str1.decode(‘utf-8’)
3：上代码：
from PIL import Image, ImageDraw, ImageFont
import cv2
import numpy as np
# cv2读取图片
img = cv2.imread(r'C:\Users\acer\Desktop\black.jpg') # 名称不能有汉字
cv2img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # cv2和PIL中颜色的hex码的储存顺序不同
pilimg = Image.fromarray(cv2img)
# PIL图片上打印汉字
draw = ImageDraw.Draw(pilimg) # 图片上打印
font = ImageFont.truetype("simhei.ttf", 20, encoding="utf-8") # 参数1：字体文件路径，参数2：字体大小
draw.text((0, 0), "Hi", 1.8, (255, 0, 0), font=font) # 参数1：打印坐标，参数2：文本，参数3：字体颜色，参数4：字体
# PIL图片转cv2 图片
cv2charimg = cv2.cvtColor(np.array(pilimg), cv2.COLOR_RGB2BGR)
# cv2.imshow("图片", cv2charimg) # 汉字窗口标题显示乱码
cv2.imshow("photo", cv2charimg)
cv2.waitKey(0)
cv2.destroyAllWindows()
值得注意的是：
1）opencv读取图像后图像颜色通道是BGR排列的，而PIL读取的图像是RGB排列的。要注意图像颜色通道排列的转化cv2.cvtColor(img, cv2.COLOR_BGR2RGB)。
2）opencv读取完图像存储格式是numpy。PIL是自己定义的格式。要调用PIL的方法需要先将numpy转为自己的格式。pilimg = Image.fromarray(cv2img)。相反，PIL处理完后，调用opencv方法要将格式转回numpy。
cv2charimg = cv2.cvtColor(np.array(pilimg), cv2.COLOR_RGB2BGR)。
不转的话会报错。TypeError: Expected cv::UMat for argument ‘src’
还有一种常用的：freetype方式：
同样的先下载字体：比如上面的simhei.ttf，同样的还有msyh.ttf(这些百度就行，很多):
#-*- coding: utf-8 -*-
import cv2
import ft2
img = cv2.imread('pic_url.jpg')
line = '你好'
color = (0, 255, 0) # Green
pos = (3, 3)
text_size = 24
# ft = put_chinese_text('wqy-zenhei.ttc')
ft = ft2.put_chinese_text('msyh.ttf')
image = ft.draw_text(img, pos, line, text_size, color)
name = u'图片展示'
cv2.imshow(name, image)
cv2.waitKey(0)
个人推荐第一种！
接下来继续车牌检测~
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（这也是程序or算法的不足之处不过，并不影响结果）
try:
contours, hierarchy = cv2.findContours(img_edge2, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
except ValueError:
image, contours, hierarchy = cv2.findContours(img_edge2, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
contours = [cnt for cnt in contours if cv2.contourArea(cnt) > Min_Area]
需要注意的是cv2.findContours()函数接受的参数为二值图，即黑白的（不是灰度图），所以读取的图像要先转成灰度的，再转成二值图！
结果筛选（原因是上述的多可能性情况）：
car_contours = []
for cnt in contours:
rect = cv2.minAreaRect(cnt)
area_width, area_height = rect[1]
if area_width < area_height:
area_width, area_height = area_height, area_width
wh_ratio = area_width / area_height
#print(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 wh_ratio > 2 and wh_ratio < 5.5:
car_contours.append(rect)
box = cv2.boxPoints(rect)
box = np.int0(box)
接下来：
矩形区域可能是倾斜的矩形，需要矫正，以便使用颜色定位
for rect in car_contours:
if rect[2] > -1 and rect[2] < 1:#创造角度，使得左、高、右、低拿到正确的值
angle = 1
else:
angle = rect[2]
rect = (rect[0], (rect[1][0]+5, rect[1][1]+5), angle)#扩大范围，避免车牌边缘被排除
box = cv2.boxPoints(rect)
heigth_point = right_point = [0, 0]
left_point = low_point = [pic_width, pic_hight]
for point in box:
if left_point[0] > point[0]:
left_point = point
if low_point[1] > point[1]:
low_point = point
if heigth_point[1] < point[1]:
heigth_point = point
if right_point[0] < point[0]:
right_point = point
if left_point[1] <= right_point[1]:#正角度
new_right_point = [right_point[0], heigth_point[1]]
pts2 = np.float32([left_point, heigth_point, new_right_point])#字符只是高度需要改变
pts1 = np.float32([left_point, heigth_point, right_point])
M = cv2.getAffineTransform(pts1, pts2)
dst = cv2.warpAffine(oldimg, M, (pic_width, pic_hight))
point_limit(new_right_point)
point_limit(heigth_point)
point_limit(left_point)
card_img = dst[int(left_point[1]):int(heigth_point[1]), int(left_point[0]):int(new_right_point[0])]
card_imgs.append(card_img)
elif left_point[1] > right_point[1]:#负角度
new_left_point = [left_point[0], heigth_point[1]]
pts2 = np.float32([new_left_point, heigth_point, right_point])#字符只是高度需要改变
pts1 = np.float32([left_point, heigth_point, right_point])
M = cv2.getAffineTransform(pts1, pts2)
dst = cv2.warpAffine(oldimg, M, (pic_width, pic_hight))
point_limit(right_point)
point_limit(heigth_point)
point_limit(new_left_point)
card_img = dst[int(right_point[1]):int(heigth_point[1]), int(new_left_point[0]):int(right_point[0])]
card_imgs.append(card_img)
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 = yello = blue = black = white = 0
card_img_hsv = cv2.cvtColor(card_img, cv2.COLOR_BGR2HSV)
#有转换失败的可能，原因来自于上面矫正矩形出错
if card_img_hsv is None:
continue
row_num, col_num= card_img_hsv.shape[:2]
card_img_count = row_num * col_num
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
yello += 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"
limit1 = limit2 = 0
if yello*2 >= card_img_count:
color = "yello"
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:#TODO
color = "bw"
print(color)
colors.append(color)
print(blue, green, yello, black, white, card_img_count)
cv2.imshow("color", card_img)
cv2.waitKey(1110)
if limit1 == 0:
continue
#以上为确定车牌颜色
#以下为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)
print('size', xl,xr,yh,yl)
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]
上个表情防止兄弟看得睡着了
核心部分来了，详解一下：
predict_result = []
roi = None
card_color = None
for i, color in enumerate(colors):
if color in ("blue", "yello", "green"):
card_img = card_imgs[i]
gray_img = cv2.cvtColor(card_img, cv2.COLOR_BGR2GRAY)
#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 == "yello":
gray_img = cv2.bitwise_not(gray_img)
ret, gray_img = cv2.threshold(gray_img, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
#查找水平直方图波峰
x_histogram = np.sum(gray_img, axis=1)
x_min = np.min(x_histogram)
x_average = np.sum(x_histogram)/x_histogram.shape[0]
x_threshold = (x_min + x_average)/2
wave_peaks = find_waves(x_threshold, x_histogram)
if len(wave_peaks) == 0:
print("peak less 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]]
#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]
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 = find_waves(y_threshold, y_histogram)
#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("peak less 1:", len(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:", len(wave_peaks))
continue
part_cards = seperate_card(gray_img, wave_peaks)
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:
print("a point")
continue
part_card_old = part_card
w = abs(part_card.shape[1] - SZ)//2
part_card = cv2.copyMakeBorder(part_card, 0, 0, w, w, cv2.BORDER_CONSTANT, value = [0,0,0])
part_card = cv2.resize(part_card, (SZ, SZ), interpolation=cv2.INTER_AREA)
#part_card = deskew(part_card)
part_card = preprocess_hog([part_card])
if i == 0:
resp = self.modelchinese.predict(part_card)
charactor = provinces[int(resp[0]) - PROVINCE_START]
else:
resp = self.model.predict(part_card)
charactor = chr(resp[0])
#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 charactor == "1" and i == len(part_cards)-1:
if part_card_old.shape[0]/part_card_old.shape[1] >= 7:#1太细，认为是边缘
continue
predict_result.append(charactor)
roi = card_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
部分代码有注释，大致说说：
这是识别车牌中的字符
gray_img = cv2.bitwise_not(gray_img)
这个是掩膜方法，我们后续再统一介绍吧， 大致思路就是把原图中要放logo的区域抠出来，再把logo放进去就行了。
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(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 deskew(img):
m = cv2.moments(img)
if abs(m['mu02']) < 1e-2:
return img.copy()
skew = m['mu11']/m['mu02']
M = np.float32([[1, skew, -0.5*SZ*skew], [0, 1, 0]])
img = cv2.warpAffine(img, M, (SZ, SZ), flags=cv2.WARP_INVERSE_MAP | cv2.INTER_LINEAR)
return img
其中：
m = cv2.moments(img)
是 矩 计算 下期介绍
最后，结果筛选：
#车牌字符数应大于6
if len(wave_peaks) <= 6:
print("peak less 1:", len(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:", len(wave_peaks))
continue
part_cards = seperate_card(gray_img, wave_peaks)
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:
print("a point")
continue
part_card_old = part_card
w = abs(part_card.shape[1] - SZ)//2
part_card = cv2.copyMakeBorder(part_card, 0, 0, w, w, cv2.BORDER_CONSTANT, value = [0,0,0])
part_card = cv2.resize(part_card, (SZ, SZ), interpolation=cv2.INTER_AREA)
#part_card = deskew(part_card)
part_card = preprocess_hog([part_card])
if i == 0:
resp = self.modelchinese.predict(part_card)
charactor = provinces[int(resp[0]) - PROVINCE_START]
else:
resp = self.model.predict(part_card)
charactor = chr(resp[0])
#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 charactor == "1" and i == len(part_cards)-1:
if part_card_old.shape[0]/part_card_old.shape[1] >= 7:#1太细，认为是边缘
continue
predict_result.append(charactor)
roi = card_img
card_color = color
break
return predict_result, roi, card_color
返回识别到的字符、定位的车牌图像、车牌颜色
main函数：
if __name__ == '__main__':
c = CardPredictor()
c.train_svm()
r, roi, color = c.predict("test//car7.jpg")
print(r, roi.shape[0],roi.shape[1],roi.shape[2])
img = cv2.imread("test//car7.jpg")
img = cv2.resize(img,(480,640),interpolation=cv2.INTER_LINEAR)
r = ','.join(r)
r = r.replace(',', '')
print(r)
from PIL import Image, ImageDraw, ImageFont
cv2img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # cv2和PIL中颜色的hex码的储存顺序不同
pilimg = Image.fromarray(cv2img)
# PIL图片上打印汉字
draw = ImageDraw.Draw(pilimg) # 图片上打印
font = ImageFont.truetype("simhei.ttf", 30, encoding="utf-8") # 参数1：字体文件路径，参数2：字体大小
draw.text((0, 0), r, (255, 0, 0), font=font) # 参数1：打印坐标，参数2：文本，参数3：字体颜色，参数4：字体
# PIL图片转cv2 图片
cv2charimg = cv2.cvtColor(np.array(pilimg), cv2.COLOR_RGB2BGR)
# cv2.imshow("图片", cv2charimg) # 汉字窗口标题显示乱码
cv2.imshow("photo", cv2charimg)
cv2.waitKey(0)
cv2.destroyAllWindows()
最后在此说明：代码非本人原创，来自朋友毕设，过段时间会开源请关注一下博主，谢谢
小结一下：
OPENCV的SVM的SVC训练模型——>OpenCv进行图像采集/控制摄像头——>图像预处理（二值化操作，边缘计算等）——>定位车牌位置，并正放置处理——>确定车牌颜色——>根据车牌颜色再定位，缩小边缘非车牌边界——>以下为识别车牌中的字符——>返回结果——>最后ptrdict返回识别到的字符、定位的车牌图像、车牌颜色——>结果显示，并使用PIL方法显示中文
最后我想说明的是，根据我找bug的能力，已经发现一堆bug，但是无可否认，这个机器学习项目已经写的很棒了，至少我短期不能达到这个效果，不过写出来还是没有太大困难，逻辑在，做就完了！另外，程序基于机器学习的SVM算法问题，以及在数据预处理上的优化问题 ，还是很欠缺的，最大的问题就是准确率问题，以及欠拟合问题，这两者是我这个项目的问题，换成深度学习会好很多！
上图，介绍下期内容：初识CVLIB 最后别忘了给博主一个赞和关注~，码字不易，一起进步！
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上海第二工业大学智能科学与技术大二 周小夏（CV调包侠）
