Python OpenCV图像指定区域裁剪的实现

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 cv2import osimport sysimport timedef 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_pathsdef 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 multiprocessingimport cv2import osimport timedef 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_pathsdef 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_faileddef 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)