python使用opencv切割图片白边

本文为大家分享了python使用opencv切割图片白边的具体代码，可以横切和竖切，供大家参考，具体内容如下

废话不多说直接上码，分享使人进步：

from PIL import Image
from itertools import groupby
import cv2
import datetime
import os

# from core.rabbitmq import MessageQueue

THRESHOLD_VALUE = 230  # 二值化时的阈值
PRETREATMENT_FILE = 'hq'  # 横切时临时保存的文件夹
W = 540  # 最小宽度
H = 960  # 最小高度


class Pretreatment(object):
  __doc__ = "图片横向切割"

  def __init__(self, path, save_path, min_size=960):
      self.x = 0
      self.y = 0
      self.img_section = []
      self.continuity_position = []
      self.path = path
      self.save_path = save_path
      self.img_obj = None
      self.min_size = min_size
      self.mkdir(self.save_path)
      self.file_name = self.path.split('/')[-1]

  def get_continuity_position_new(self):
      img = cv2.imread(self.path)
      gray_image = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
      ret, thresh1 = cv2.threshold(gray_image, THRESHOLD_VALUE, 255, cv2.THRESH_BINARY)

      width = img.shape[1]
      height = img.shape[0]
      self.x = width
      self.y = height
      for i in range(0, height):
          if thresh1[i].sum() != 255 * width:
              self.continuity_position.append(i)

  def filter_rule(self):
      if self.y < self.min_size:
          return True

  def mkdir(self, path):
      if not os.path.exists(path):
          os.makedirs(path)

  def get_section(self):
      # 获取区间
      for k, g in groupby(enumerate(self.continuity_position), lambda x: x[1] - x[0]):
          l1 = [j for i, j in g]  # 连续数字的列表
          if len(l1) > 1:
              self.img_section.append([min(l1), max(l1)])

  def split_img(self):
      print(self.img_section)
      for k, s in enumerate(self.img_section):
          if s:
              if not self.img_obj:
                  self.img_obj = Image.open(self.path)

              if self.x < W:
                  return
              if s[1] - s[0] < H:
                  return
              cropped = self.img_obj.crop((0, s[0], self.x, s[1]))  # (left, upper, right, lower)
              self.mkdir(os.path.join(self.save_path, PRETREATMENT_FILE))
              cropped.save(os.path.join(self.save_path, PRETREATMENT_FILE, f"hq_{k}_{self.file_name}"))

  def remove_raw_data(self):
      os.remove(self.path)

  def main(self):
      # v2
      try:
          self.get_continuity_position_new()
          self.filter_rule()
          self.get_section()
          self.split_img()
      except Exception as e:
          print(self.file_name)
          print(e)
      finally:
          if self.img_obj:
              self.img_obj.close()


class Longitudinal(Pretreatment):
  def get_continuity_position_new(self):
      print(self.path)
      img = cv2.imread(self.path)
      gray_image = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
      ret, thresh1 = cv2.threshold(gray_image, THRESHOLD_VALUE, 255, cv2.THRESH_BINARY)

      width = img.shape[1]
      height = img.shape[0]
      print(width, height)
      self.x = width
      self.y = height
      for i in range(0, width):
          if thresh1[:, i].sum() != 255 * height:
              self.continuity_position.append(i)

  def split_img(self):
      print(self.img_section)
      for k, s in enumerate(self.img_section):
          if s:
              if not self.img_obj:
                  self.img_obj = Image.open(self.path)
              if self.y < H:
                  return
              if s[1] - s[0] < W:
                  return
              cropped = self.img_obj.crop((s[0], 0, s[1], self.y))  # (left, upper, right, lower)
              cropped.save(os.path.join(self.save_path, f"{k}_{self.file_name}"))


def main(path, save_path):
  starttime = datetime.datetime.now()
  a = Pretreatment(path=path, save_path=save_path)
  a.main()
  for root, dirs, files in os.walk(os.path.join(save_path, PRETREATMENT_FILE)):
      for i in files:
          b = Longitudinal(path=os.path.join(save_path, PRETREATMENT_FILE, i), save_path=save_path)
          b.main()
          os.remove(os.path.join(save_path, PRETREATMENT_FILE, i))
  endtime = datetime.datetime.now()
  print(f'耗时:{(endtime - starttime)}')


if __name__ == '__main__':
  path = '你图片存放的路径'
  save_path = '要保存的路径'
  for _, _, files in os.walk(path):
      for i in files:
          main(path=os.path.join(path, i), save_path=save_path)
  os.rmdir(os.path.join(save_path, PRETREATMENT_FILE))

原始图片：

结果：

以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持服务器之家。

原文链接：https://blog.csdn.net/qq_37049050/article/details/108116304