怎么利用python的opencv去除图片的白边

这篇文章主要讲解了"怎么利用python的opencv去除图片的白边"，文中的讲解内容简单清晰，易于学习与理解，下面请大家跟着小编的思路慢慢深入，一起来研究和学习"怎么利用python的opencv去除图片的白边"吧！

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 Imagefrom itertools import groupbyimport cv2import datetimeimport 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, thresh2 = 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 thresh2[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, thresh2 = 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 thresh2[:, 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：

感谢各位的阅读，以上就是"怎么利用python的opencv去除图片的白边"的内容了，经过本文的学习后，相信大家对怎么利用python的opencv去除图片的白边这一问题有了更深刻的体会，具体使用情况还需要大家实践验证。这里是，小编将为大家推送更多相关知识点的文章，欢迎关注！