run.py 文件源码

def apply_median(k):
  ''' Apply the given kernel to images

  This function searches through the images/source subfolder, and
  uses your convolution funciton implemented in part0 to apply the given kernel
  to each image found inside. It will then save the resulting images to the 
  images/filtered subfolder, appending their names with kernel_name.
  '''
  print 'applying median filter to images'

  sourcefolder = os.path.abspath(os.path.join(os.curdir, 'images', 'source'))
  outfolder = os.path.abspath(os.path.join(os.curdir, 'images', 'filtered'))

  print 'Searching for images in {} folder'.format(sourcefolder)

  exts = ['.bmp', '.pbm', '.pgm', '.ppm', '.sr', '.ras', '.jpeg', '.jpg', 
    '.jpe', '.jp2', '.tiff', '.tif', '.png']

  for dirname, dirnames, filenames in os.walk(sourcefolder):
    for filename in filenames:
      name, ext = os.path.splitext(filename)
      if ext in exts:
        print "Reading image {}.".format(filename)
        img = cv2.imread(os.path.join(dirname, filename))

        print "Applying filter."
        if len(img.shape) == 2:
          outimg = part3.filter_median(img, k)
        else:
          outimg = [] 
          for channel in range(img.shape[2]):
            outimg.append(part3.filter_median(img[:,:,channel], k))
          outimg = cv2.merge(outimg)
        outpath = os.path.join(outfolder, name + 'median' + str(k) + ext)

        print "Writing image {}.\n\n".format(outpath)
        cv2.imwrite(outpath, outimg)