Python+OpenCV实现车牌字符分割和识别

import cv2 
 
# 1、读取图像，并把图像转换为灰度图像并显示 
img = cv2.imread("chepai/6.png") # 读取图片 
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)  # 转换了灰度化 
cv2.imshow('gray', img_gray) # 显示图片 
cv2.waitKey(0) 
 
# 2、将灰度图像二值化，设定阈值是100 
img_thre = img_gray 
cv2.threshold(img_gray, 100, 255, cv2.THRESH_BINARY_INV, img_thre) 
cv2.imshow('threshold', img_thre) 
cv2.waitKey(0) 
 
# 3、保存黑白图片 
cv2.imwrite('thre_res.png', img_thre) 
 
# 4、分割字符 
white = [] # 记录每一列的白色像素总和 
black = [] # ..........黑色....... 
height = img_thre.shape[0] 
width = img_thre.shape[1] 
white_max = 0 
black_max = 0 
# 计算每一列的黑白色像素总和 
for i in range(width): 
  s = 0 # 这一列白色总数 
  t = 0 # 这一列黑色总数 
  for j in range(height): 
    if img_thre[j][i] == 255: 
      s += 1 
    if img_thre[j][i] == 0: 
      t += 1 
  white_max = max(white_max, s) 
  black_max = max(black_max, t) 
  white.append(s) 
  black.append(t) 
  print(s) 
  print(t) 
 
arg = False # False表示白底黑字；True表示黑底白字 
if black_max > white_max: 
  arg = True 
 
# 分割图像 
def find_end(start_): 
  end_ = start_+1 
  for m in range(start_+1, width-1): 
    if (black[m] if arg else white[m]) > (0.95 * black_max if arg else 0.95 * white_max): # 0.95这个参数请多调整，对应下面的0.05 
      end_ = m 
      break 
  return end_ 
 
n = 1 
start = 1 
end = 2 
while n < width-2: 
  n += 1 
  if (white[n] if arg else black[n]) > (0.05 * white_max if arg else 0.05 * black_max): 
    # 上面这些判断用来辨别是白底黑字还是黑底白字 
    # 0.05这个参数请多调整，对应上面的0.95 
    start = n 
    end = find_end(start) 
    n = end 
    if end-start > 5: 
      cj = img_thre[1:height, start:end] 
      cv2.imshow('caijian', cj) 
      cv2.waitKey(0)