Python OpenCV实现图像指定区域裁剪与批量处理

import os  
import cv2 
 
# 遍历指定目录，显示目录下的所有文件名
def CropImage4File(filepath,destpath):
  pathDir = os.listdir(filepath)  # 列出文件路径中的所有路径或文件
  for allDir in pathDir:
    child = os.path.join(filepath, allDir)
    dest = os.path.join(destpath,allDir)
    if os.path.isfile(child):
     image = cv2.imread(child) 
      sp = image.shape      #获取图像形状：返回【行数值，列数值】列表
      sz1 = sp[0]         #图像的高度（行 范围）
      sz2 = sp[1]         #图像的宽度（列 范围）
      #sz3 = sp[2]        #像素值由【RGB】三原色组成
      
      #你想对文件的操作
      a=int(sz1/2-64) # x start
      b=int(sz1/2+64) # x end
      c=int(sz2/2-64) # y start
      d=int(sz2/2+64) # y end
      cropImg = image[a:b,c:d]  #裁剪图像
      cv2.imwrite(dest,cropImg) #写入图像路径
      
if __name__ == '__main__':
  filepath ='F:\\\maomi'       #源图像
  destpath='F:\\maomi_resize'    # resized images saved here
  CropImage4File(filepath,destpath)

"""
处理数据集 和 标签数据集的代码：（主要是对原始数据集裁剪）
  处理方式：分别处理
  注意修改 输入 输出目录 和 生成的文件名
  output_dir = "./label_temp"
  input_dir = "./label"
"""
import cv2
import os
import sys
import time


def get_img(input_dir):
  img_paths = []
  for (path,dirname,filenames) in os.walk(input_dir):
    for filename in filenames:
      img_paths.append(path+'/'+filename)
  print("img_paths:",img_paths)
  return img_paths


def cut_img(img_paths,output_dir):
  scale = len(img_paths)
  for i,img_path in enumerate(img_paths):
    a = "#"* int(i/1000)
    b = "."*(int(scale/1000)-int(i/1000))
    c = (i/scale)*100
    time.sleep(0.2)
    print('正在处理图像： %s' % img_path.split('/')[-1])
    img = cv2.imread(img_path)
    weight = img.shape[1]
    if weight>1600:             # 正常发票
      cropImg = img[50:200, 700:1500]  # 裁剪【y1,y2：x1,x2】
      #cropImg = cv2.resize(cropImg, None, fx=0.5, fy=0.5,
                 #interpolation=cv2.INTER_CUBIC) #缩小图像
      cv2.imwrite(output_dir + '/' + img_path.split('/')[-1], cropImg)
    else:                    # 卷帘发票
      cropImg_01 = img[30:150, 50:600]
      cv2.imwrite(output_dir + '/'+img_path.split('/')[-1], cropImg_01)
    print('{:^3.3f}%[{}>>{}]'.format(c,a,b))

if __name__ == '__main__':
  output_dir = "../img_cut"      # 保存截取的图像目录
  input_dir = "../img"        # 读取图片目录表
  img_paths = get_img(input_dir)
  print('图片获取完成 。。。！')
  cut_img(img_paths,output_dir)

#coding: utf-8
"""
采用多进程加快处理。添加了在读取图片时捕获异常，OpenCV对大分辨率或者tif格式图片支持不好
处理数据集 和 标签数据集的代码：（主要是对原始数据集裁剪）
  处理方式：分别处理
  注意修改 输入 输出目录 和 生成的文件名
  output_dir = "./label_temp"
  input_dir = "./label"
"""
import multiprocessing
import cv2
import os
import time


def get_img(input_dir):
  img_paths = []
  for (path,dirname,filenames) in os.walk(input_dir):
    for filename in filenames:
      img_paths.append(path+'/'+filename)
  print("img_paths:",img_paths)
  return img_paths


def cut_img(img_paths,output_dir):
  imread_failed = []
  try:
    img = cv2.imread(img_paths)
    height, weight = img.shape[:2]
    if (1.0 * height / weight) < 1.3:    # 正常发票
      cropImg = img[50:200, 700:1500]   # 裁剪【y1,y2：x1,x2】
      cv2.imwrite(output_dir + '/' + img_paths.split('/')[-1], cropImg)
    else:                  # 卷帘发票
      cropImg_01 = img[30:150, 50:600]
      cv2.imwrite(output_dir + '/' + img_paths.split('/')[-1], cropImg_01)
  except:
    imread_failed.append(img_paths)
  return imread_failed


def main(input_dir,output_dir):
  img_paths = get_img(input_dir)
  scale = len(img_paths)

  results = []
  pool = multiprocessing.Pool(processes = 4)
  for i,img_path in enumerate(img_paths):
    a = "#"* int(i/10)
    b = "."*(int(scale/10)-int(i/10))
    c = (i/scale)*100
    results.append(pool.apply_async(cut_img, (img_path,output_dir )))
    print('{:^3.3f}%[{}>>{}]'.format(c, a, b)) # 进度条（可用tqdm）
  pool.close()            # 调用join之前，先调用close函数，否则会出错。
  pool.join()             # join函数等待所有子进程结束
  for result in results:
    print('image read failed!:', result.get())
  print ("All done.")



if __name__ == "__main__":
  input_dir = "D:/image_person"    # 读取图片目录表
  output_dir = "D:/image_person_02"  # 保存截取的图像目录
  main(input_dir, output_dir)