python使用opencv切割图片白边

本文实例为大家分享了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：

来源：https://blog.csdn.net/qq_37049050/article/details/108116304