import shutil
import requests
from loguru import logger
for i in range(100):
 url = 'http://xxxx/create/validate/image'
 response = requests.get(url, stream=True)
 with open(f'./imgs/{i}.png', 'wb') as out_file:
  response.raw.decode_content = True
  shutil.copyfileobj(response.raw, out_file)
  logger.info(f"download {i}.png successfully.")
 del response
 
 

from PIL import Image
import tesserocr
img = Image.open("./imgs/98.png")
img.show()
img_l = img.convert("L")# 灰階圖
img_l.show()
verify_code1 = tesserocr.image_to_text(img)
verify_code2 = tesserocr.image_to_text(img_l)
print(f"verify_code1:{verify_code1}")
print(f"verify_code2:{verify_code2}")
 

from PIL import Image
# https://www.cnblogs.com/jhao/p/10345853.html Python圖片驗證碼降噪 — 8鄰域降噪
 
def noise_remove_pil(image_name, k):
 """
 8鄰域降噪
 Args:
  image_name: 圖片文件命名
  k: 判斷閾值
 Returns:
 """
 def calculate_noise_count(img_obj, w, h):
  """
  計算鄰域非白色的個數(shù)
  Args:
img_obj: img obj
w: width
h: height
  Returns:
count (int)
  """
  count = 0
  width, height = img_obj.size
  for _w_ in [w - 1, w, w + 1]:
for _h_ in [h - 1, h, h + 1]:
 if _w_ > width - 1:
  continue
 if _h_ > height - 1:
  continue
 if _w_ == w and _h_ == h:
  continue
 if img_obj.getpixel((_w_, _h_)) < 230:  # 這里因為是灰度圖像，設(shè)置小于230為非白色
  count += 1
  return count
 img = Image.open(image_name)
 # 灰度
 gray_img = img.convert('L')
 w, h = gray_img.size
 for _w in range(w):
  for _h in range(h):
if _w == 0 or _h == 0:
 gray_img.putpixel((_w, _h), 255)
 continue
# 計算鄰域非白色的個數(shù)
pixel = gray_img.getpixel((_w, _h))
if pixel == 255:
 continue
if calculate_noise_count(gray_img, _w, _h) < k:
 gray_img.putpixel((_w, _h), 255)
 return gray_img

if __name__ == '__main__':
 image = noise_remove_pil("./imgs/1.png", 4)
 image.show()
 

from PIL import Image
import tesserocr
img = Image.open("./imgs/98.png")
img.show()
# 嘗試去掉紅像素點
w, h = img.size
for _w in range(w):
 for _h in range(h):
  o_pixel = img.getpixel((_w, _h))
  if o_pixel == (255, 0, 0):
img.putpixel((_w, _h), (255, 255, 255))
img.show()
img_l = img.convert("L")
# img_l.show()
verify_code1 = tesserocr.image_to_text(img)
verify_code2 = tesserocr.image_to_text(img_l)
print(f"verify_code1:{verify_code1}")
print(f"verify_code2:{verify_code2}")

from PIL import Image
import tesserocr
from loguru import logger

class VerfyCodeOCR():
 def __init__(self) -> None:
  pass
 def ocr(self, img):
  """ 驗證碼OCR
  Args:
img (img): imgObject/imgPath
  Returns:
[string]: 識別結(jié)果
  """
  img_obj = Image.open(img) if type(img) == str else img
  self._remove_pil(img_obj)
  verify_code = tesserocr.image_to_text(img_obj)
  return verify_code.replace("\n", "").strip()
 def _get_p_black_count(self, img: Image, _w: int, _h: int):
  """ 獲取當(dāng)前位置周圍像素點中黑色元素的個數(shù)
  Args:
img (img): 圖像信息
_w (int): w坐標(biāo)
_h (int): h坐標(biāo)
  Returns:
int: 個數(shù)
  """
  w, h = img.size
  p_round_items = []
  # 超過了橫縱坐標(biāo)
  if _w == 0 or _w == w-1 or 0 == _h or _h == h-1:
return 0
  p_round_items = [img.getpixel(
(_w, _h-1)), img.getpixel((_w, _h+1)), img.getpixel((_w-1, _h)), img.getpixel((_w+1, _h))]
  p_black_count = 0
  for p_item in p_round_items:
if p_item == (0, 0, 0):
 p_black_count = p_black_count+1
  return p_black_count
 def _remove_pil(self, img: Image):
  """清理干擾識別的線條和噪點
  Args:
img (img): 圖像對象
  Returns:
[img]: 被清理過的圖像對象
  """
  w, h = img.size
  for _w in range(w):
for _h in range(h):
 o_pixel = img.getpixel((_w, _h))
 # 當(dāng)前像素點是紅色(線段) 或者 綠色（噪點）
 if o_pixel == (255, 0, 0) or o_pixel == (0, 0, 255):
  # 周圍黑色數(shù)量大于2，則把當(dāng)前像素點填成黑色；否則用白色覆蓋
  p_black_count = self._get_p_black_count(img, _w, _h)
  if p_black_count >= 2:img.putpixel((_w, _h), (0, 0, 0))
  else:img.putpixel((_w, _h), (255, 255, 255))
  logger.info(f"_remove_pil finish.")
  # img.show()
  return img

if __name__ == '__main__':
 verfyCodeOCR = VerfyCodeOCR()
 img_path = "./imgs/51.png"
 img= Image.open(img_path)
 img.show()
 ocr_result = verfyCodeOCR.ocr(img)
 img.show()
 logger.info(ocr_result)