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This article mainly explains "how to use python's opencv to remove the white edge of a picture". The content in the article is simple and clear, and it is easy to learn and understand. Please follow the editor's train of thought to study and learn "how to use python's opencv to remove the white edge of a picture".

This article example for you to share the python use opencv to cut the white edge of the picture of the specific code, can be crosscutting and vertical cutting, for your reference, the specific content is as follows

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From PIL import Imagefrom itertools import groupbyimport cv2import datetimeimport os # from core.rabbitmq import MessageQueue THRESHOLD_VALUE = 2302 threshold PRETREATMENT_FILE = 'hq' # temporary saved folder W = 540 # minimum width H = 960 # minimum height class Pretreatment (object): _ _ doc__ = "picture horizontal cutting" 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 ('/' ) 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 roomH2 [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：

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