python captcha recognition tutorial of the use of the drip algorithm to segment the picture

from itertools import groupby

def binarizing(img,threshold):
 """Pass in image object for grayscale, binary processing"""
 img = ("L") # To grayscale
 pixdata = ()
 w, h = 
 # Iterate over all pixels, those greater than the threshold are black
 for y in range(h):
  for x in range(w):
   if pixdata[x, y] < threshold:
    pixdata[x, y] = 0
   else:
    pixdata[x, y] = 255
 return img

def vertical(img):
 """Pass in binarized image for vertical projection."""
 pixdata = ()
 w,h = 
 result = []
 for x in range(w):
  black = 0
  for y in range(h):
   if pixdata[x,y] == 0:
    black += 1
  (black)
 return result

def get_start_x(hist_width):
 """Determine the starting point based on the vertical projection of the image.
  hist_width middle value 4 values before and after, and then the minimum value within this range.
 """
 mid = len(hist_width) // 2 # Note that py3 division is different from py2 #
 temp = hist_width[mid-4:mid+5]
 return mid - 4 + (min(temp))

def get_nearby_pix_value(img_pix,x,y,j):
 """Getting 5 points of pixel data in close proximity."""
 if j == 1:
  return 0 if img_pix[x-1,y+1] == 0 else 1
 elif j ==2:
  return 0 if img_pix[x,y+1] == 0 else 1
 elif j ==3:
  return 0 if img_pix[x+1,y+1] == 0 else 1
 elif j ==4:
  return 0 if img_pix[x+1,y] == 0 else 1
 elif j ==5:
  return 0 if img_pix[x-1,y] == 0 else 1
 else:
  raise Exception("get_nearby_pix_value error")


def get_end_route(img,start_x,height):
 """Getting the drip path"""
 left_limit = 0
 right_limit = [0] - 1
 end_route = []
 cur_p = (start_x,0)
 last_p = cur_p
 end_route.append(cur_p)

 while cur_p[1] < (height-1):
  sum_n = 0
  max_w = 0
  next_x = cur_p[0]
  next_y = cur_p[1]
  pix_img = ()
  for i in range(1,6):
   cur_w = get_nearby_pix_value(pix_img,cur_p[0],cur_p[1],i) * (6-i)
   sum_n += cur_w
   if max_w < cur_w:
    max_w = cur_w
  if sum_n == 0:
   # If it's all black then look at the inertia
   max_w = 4
  if sum_n == 15:
   max_w = 6

  if max_w == 1:
   next_x = cur_p[0] - 1
   next_y = cur_p[1]
  elif max_w == 2:
   next_x = cur_p[0] + 1
   next_y = cur_p[1]
  elif max_w == 3:
   next_x = cur_p[0] + 1
   next_y = cur_p[1] + 1
  elif max_w == 5:
   next_x = cur_p[0] - 1
   next_y = cur_p[1] + 1
  elif max_w == 6:
   next_x = cur_p[0]
   next_y = cur_p[1] + 1
  elif max_w == 4:
   if next_x > cur_p[0]:
    # To the right
    next_x = cur_p[0] + 1
    next_y = cur_p[1] + 1
   if next_x < cur_p[0]:
    next_x = cur_p[0]
    next_y = cur_p[1] + 1
   if sum_n == 0:
    next_x = cur_p[0]
    next_y = cur_p[1] + 1
  else:
   raise Exception("get end route error")

  if last_p[0] == next_x and last_p[1] == next_y:
   if next_x < cur_p[0]:
    max_w = 5
    next_x = cur_p[0] + 1
    next_y = cur_p[1] + 1
   else:
    max_w = 3
    next_x = cur_p[0] - 1
    next_y = cur_p[1] + 1
  last_p = cur_p

  if next_x > right_limit:
   next_x = right_limit
   next_y = cur_p[1] + 1
  if next_x < left_limit:
   next_x = left_limit
   next_y = cur_p[1] + 1
  cur_p = (next_x,next_y)
  end_route.append(cur_p)
 return end_route

def get_split_seq(projection_x):
 split_seq = []
 start_x = 0
 length = 0
 for pos_x, val in enumerate(projection_x):
  if val == 0 and length == 0:
   continue
  elif val == 0 and length != 0:
   split_seq.append([start_x, length])
   length = 0
  elif val == 1:
   if length == 0:
    start_x = pos_x
   length += 1
  else:
   raise Exception('generating split sequence occurs error')
 # If length is not 0 at the end of the loop, there is still a part to append.
 if length != 0:
  split_seq.append([start_x, length])
 return split_seq


def do_split(source_image, starts, filter_ends):
 """
 Cutting in practice
 : param starts: tuple of list
 : param ends: the end of each line
 """
 left = starts[0][0]
 top = starts[0][1]
 right = filter_ends[0][0]
 bottom = filter_ends[0][1]
 pixdata = source_image.load()
 for i in range(len(starts)):
  left = min(starts[i][0], left)
  top = min(starts[i][1], top)
  right = max(filter_ends[i][0], right)
  bottom = max(filter_ends[i][1], bottom)
 width = right - left + 1
 height = bottom - top + 1
 image = ('RGB', (width, height), (255,255,255))
 for i in range(height):
  start = starts[i]
  end = filter_ends[i]
  for x in range(start[0], end[0]+1):
   if pixdata[x,start[1]] == 0:
    ((x - left, start[1] - top), (0,0,0))
 return image

def drop_fall(img):
 """Drip splitting."""
 width,height = 
 # 1 Binarization
 b_img = binarizing(img,200)
 # 2 Vertical projection
 hist_width = vertical(b_img)
 # 3 Getting Started
 start_x = get_start_x(hist_width)

 # 4 Start the drip algorithm
 start_route = []
 for y in range(height):
  start_route.append((0,y))

 end_route = get_end_route(img,start_x,height)
 filter_end_route = [max(list(k)) for _,k in groupby(end_route,lambda x:x[1])] # Note the groupby here
 img1 = do_split(img,start_route,filter_end_route)
 ('')

 start_route = list(map(lambda x : (x[0]+1,x[1]),filter_end_route)) # python3 map does not return list need to convert yourself
 end_route = []
 for y in range(height):
  end_route.append((width-1,y))
 img2 = do_split(img,start_route,end_route)
 ('')

if __name__ == '__main__':
 p = ("")
 drop_fall(p)