tensorflow：如何旋转图像进行数据扩充？

6 回答

• 1

  Update ：见@ astromme的答案如下 . Tensorflow现在支持原生旋转图像 .

  在tensorflow中没有本机方法时你可以做的是这样的：

  from PIL import Image
  sess = tf.InteractiveSession()
  
  # Pass image tensor object to a PIL image
  image = Image.fromarray(image.eval())
  
  # Use PIL or other library of the sort to rotate
  rotated = Image.Image.rotate(image, degrees)
  
  # Convert rotated image back to tensor
  rotated_tensor = tf.convert_to_tensor(np.array(rotated))
  

  回复于 2024-05-15T03:00:05+08:00
• 3

  这可以在tensorflow now中完成：

  tf.contrib.image.rotate(images, degrees * math.pi / 180, interpolation='BILINEAR')
  

  回复于 2024-05-15T03:00:05+08:00
• 0

  因为我希望能够旋转张量，所以我提出了以下代码，它将[高度，宽度，深度]张量旋转给定角度：

  def rotate_image_tensor(image, angle, mode='black'):
      """
      Rotates a 3D tensor (HWD), which represents an image by given radian angle.
  
      New image has the same size as the input image.
  
      mode controls what happens to border pixels.
      mode = 'black' results in black bars (value 0 in unknown areas)
      mode = 'white' results in value 255 in unknown areas
      mode = 'ones' results in value 1 in unknown areas
      mode = 'repeat' keeps repeating the closest pixel known
      """
      s = image.get_shape().as_list()
      assert len(s) == 3, "Input needs to be 3D."
      assert (mode == 'repeat') or (mode == 'black') or (mode == 'white') or (mode == 'ones'), "Unknown boundary mode."
      image_center = [np.floor(x/2) for x in s]
  
      # Coordinates of new image
      coord1 = tf.range(s[0])
      coord2 = tf.range(s[1])
  
      # Create vectors of those coordinates in order to vectorize the image
      coord1_vec = tf.tile(coord1, [s[1]])
  
      coord2_vec_unordered = tf.tile(coord2, [s[0]])
      coord2_vec_unordered = tf.reshape(coord2_vec_unordered, [s[0], s[1]])
      coord2_vec = tf.reshape(tf.transpose(coord2_vec_unordered, [1, 0]), [-1])
  
      # center coordinates since rotation center is supposed to be in the image center
      coord1_vec_centered = coord1_vec - image_center[0]
      coord2_vec_centered = coord2_vec - image_center[1]
  
      coord_new_centered = tf.cast(tf.pack([coord1_vec_centered, coord2_vec_centered]), tf.float32)
  
      # Perform backward transformation of the image coordinates
      rot_mat_inv = tf.dynamic_stitch([[0], [1], [2], [3]], [tf.cos(angle), tf.sin(angle), -tf.sin(angle), tf.cos(angle)])
      rot_mat_inv = tf.reshape(rot_mat_inv, shape=[2, 2])
      coord_old_centered = tf.matmul(rot_mat_inv, coord_new_centered)
  
      # Find nearest neighbor in old image
      coord1_old_nn = tf.cast(tf.round(coord_old_centered[0, :] + image_center[0]), tf.int32)
      coord2_old_nn = tf.cast(tf.round(coord_old_centered[1, :] + image_center[1]), tf.int32)
  
      # Clip values to stay inside image coordinates
      if mode == 'repeat':
          coord_old1_clipped = tf.minimum(tf.maximum(coord1_old_nn, 0), s[0]-1)
          coord_old2_clipped = tf.minimum(tf.maximum(coord2_old_nn, 0), s[1]-1)
      else:
          outside_ind1 = tf.logical_or(tf.greater(coord1_old_nn, s[0]-1), tf.less(coord1_old_nn, 0))
          outside_ind2 = tf.logical_or(tf.greater(coord2_old_nn, s[1]-1), tf.less(coord2_old_nn, 0))
          outside_ind = tf.logical_or(outside_ind1, outside_ind2)
  
          coord_old1_clipped = tf.boolean_mask(coord1_old_nn, tf.logical_not(outside_ind))
          coord_old2_clipped = tf.boolean_mask(coord2_old_nn, tf.logical_not(outside_ind))
  
          coord1_vec = tf.boolean_mask(coord1_vec, tf.logical_not(outside_ind))
          coord2_vec = tf.boolean_mask(coord2_vec, tf.logical_not(outside_ind))
  
      coord_old_clipped = tf.cast(tf.transpose(tf.pack([coord_old1_clipped, coord_old2_clipped]), [1, 0]), tf.int32)
  
      # Coordinates of the new image
      coord_new = tf.transpose(tf.cast(tf.pack([coord1_vec, coord2_vec]), tf.int32), [1, 0])
  
      image_channel_list = tf.split(2, s[2], image)
  
      image_rotated_channel_list = list()
      for image_channel in image_channel_list:
          image_chan_new_values = tf.gather_nd(tf.squeeze(image_channel), coord_old_clipped)
  
          if (mode == 'black') or (mode == 'repeat'):
              background_color = 0
          elif mode == 'ones':
              background_color = 1
          elif mode == 'white':
              background_color = 255
  
          image_rotated_channel_list.append(tf.sparse_to_dense(coord_new, [s[0], s[1]], image_chan_new_values,
                                                               background_color, validate_indices=False))
  
      image_rotated = tf.transpose(tf.pack(image_rotated_channel_list), [1, 2, 0])
  
      return image_rotated
  

  回复于 2024-05-15T03:00:05+08:00
• 21

  TensorFlow中的旋转和裁剪

  我个人需要在TensorFlow中进行图像旋转和裁剪黑色边框功能，如下所示 .
  
  我可以实现如下功能 .

  def _rotate_and_crop(image, output_height, output_width, rotation_degree, do_crop):
      """Rotate the given image with the given rotation degree and crop for the black edges if necessary
      Args:
          image: A `Tensor` representing an image of arbitrary size.
          output_height: The height of the image after preprocessing.
          output_width: The width of the image after preprocessing.
          rotation_degree: The degree of rotation on the image.
          do_crop: Do cropping if it is True.
      Returns:
          A rotated image.
      """
  
      # Rotate the given image with the given rotation degree
      if rotation_degree != 0:
          image = tf.contrib.image.rotate(image, math.radians(rotation_degree), interpolation='BILINEAR')
  
          # Center crop to ommit black noise on the edges
          if do_crop == True:
              lrr_width, lrr_height = _largest_rotated_rect(output_height, output_width, math.radians(rotation_degree))
              resized_image = tf.image.central_crop(image, float(lrr_height)/output_height)    
              image = tf.image.resize_images(resized_image, [output_height, output_width], method=tf.image.ResizeMethod.BILINEAR, align_corners=False)
  
      return image
  
  def _largest_rotated_rect(w, h, angle):
      """
      Given a rectangle of size wxh that has been rotated by 'angle' (in
      radians), computes the width and height of the largest possible
      axis-aligned rectangle within the rotated rectangle.
      Original JS code by 'Andri' and Magnus Hoff from Stack Overflow
      Converted to Python by Aaron Snoswell
      Source: http://stackoverflow.com/questions/16702966/rotate-image-and-crop-out-black-borders
      """
  
      quadrant = int(math.floor(angle / (math.pi / 2))) & 3
      sign_alpha = angle if ((quadrant & 1) == 0) else math.pi - angle
      alpha = (sign_alpha % math.pi + math.pi) % math.pi
  
      bb_w = w * math.cos(alpha) + h * math.sin(alpha)
      bb_h = w * math.sin(alpha) + h * math.cos(alpha)
  
      gamma = math.atan2(bb_w, bb_w) if (w < h) else math.atan2(bb_w, bb_w)
  
      delta = math.pi - alpha - gamma
  
      length = h if (w < h) else w
  
      d = length * math.cos(alpha)
      a = d * math.sin(alpha) / math.sin(delta)
  
      y = a * math.cos(gamma)
      x = y * math.tan(gamma)
  
      return (
          bb_w - 2 * x,
          bb_h - 2 * y
      )
  

  如果需要在TensorFlow中进一步实现示例和可视化，可以使用this repository . 我希望这可以对其他人有所帮助 .

  回复于 2024-05-15T03:00:05+08:00
• 9

  这是更新为Tensorflow v0.12的@zimmermc答案

  变化：

  • pack() 现在 stack()

  • split 参数的顺序颠倒了

  def rotate_image_tensor(image, angle, mode='white'):
      """
      Rotates a 3D tensor (HWD), which represents an image by given radian angle.
  
      New image has the same size as the input image.
  
      mode controls what happens to border pixels.
      mode = 'black' results in black bars (value 0 in unknown areas)
      mode = 'white' results in value 255 in unknown areas
      mode = 'ones' results in value 1 in unknown areas
      mode = 'repeat' keeps repeating the closest pixel known
      """
      s = image.get_shape().as_list()
      assert len(s) == 3, "Input needs to be 3D."
      assert (mode == 'repeat') or (mode == 'black') or (mode == 'white') or (mode == 'ones'), "Unknown boundary mode."
      image_center = [np.floor(x/2) for x in s]
  
      # Coordinates of new image
      coord1 = tf.range(s[0])
      coord2 = tf.range(s[1])
  
      # Create vectors of those coordinates in order to vectorize the image
      coord1_vec = tf.tile(coord1, [s[1]])
  
      coord2_vec_unordered = tf.tile(coord2, [s[0]])
      coord2_vec_unordered = tf.reshape(coord2_vec_unordered, [s[0], s[1]])
      coord2_vec = tf.reshape(tf.transpose(coord2_vec_unordered, [1, 0]), [-1])
  
      # center coordinates since rotation center is supposed to be in the image center
      coord1_vec_centered = coord1_vec - image_center[0]
      coord2_vec_centered = coord2_vec - image_center[1]
  
      coord_new_centered = tf.cast(tf.stack([coord1_vec_centered, coord2_vec_centered]), tf.float32)
  
      # Perform backward transformation of the image coordinates
      rot_mat_inv = tf.dynamic_stitch([[0], [1], [2], [3]], [tf.cos(angle), tf.sin(angle), -tf.sin(angle), tf.cos(angle)])
      rot_mat_inv = tf.reshape(rot_mat_inv, shape=[2, 2])
      coord_old_centered = tf.matmul(rot_mat_inv, coord_new_centered)
  
      # Find nearest neighbor in old image
      coord1_old_nn = tf.cast(tf.round(coord_old_centered[0, :] + image_center[0]), tf.int32)
      coord2_old_nn = tf.cast(tf.round(coord_old_centered[1, :] + image_center[1]), tf.int32)
  
      # Clip values to stay inside image coordinates
      if mode == 'repeat':
          coord_old1_clipped = tf.minimum(tf.maximum(coord1_old_nn, 0), s[0]-1)
          coord_old2_clipped = tf.minimum(tf.maximum(coord2_old_nn, 0), s[1]-1)
      else:
          outside_ind1 = tf.logical_or(tf.greater(coord1_old_nn, s[0]-1), tf.less(coord1_old_nn, 0))
          outside_ind2 = tf.logical_or(tf.greater(coord2_old_nn, s[1]-1), tf.less(coord2_old_nn, 0))
          outside_ind = tf.logical_or(outside_ind1, outside_ind2)
  
          coord_old1_clipped = tf.boolean_mask(coord1_old_nn, tf.logical_not(outside_ind))
          coord_old2_clipped = tf.boolean_mask(coord2_old_nn, tf.logical_not(outside_ind))
  
          coord1_vec = tf.boolean_mask(coord1_vec, tf.logical_not(outside_ind))
          coord2_vec = tf.boolean_mask(coord2_vec, tf.logical_not(outside_ind))
  
      coord_old_clipped = tf.cast(tf.transpose(tf.stack([coord_old1_clipped, coord_old2_clipped]), [1, 0]), tf.int32)
  
      # Coordinates of the new image
      coord_new = tf.transpose(tf.cast(tf.stack([coord1_vec, coord2_vec]), tf.int32), [1, 0])
  
      image_channel_list = tf.split(image, s[2], 2)
  
      image_rotated_channel_list = list()
      for image_channel in image_channel_list:
          image_chan_new_values = tf.gather_nd(tf.squeeze(image_channel), coord_old_clipped)
  
          if (mode == 'black') or (mode == 'repeat'):
              background_color = 0
          elif mode == 'ones':
              background_color = 1
          elif mode == 'white':
              background_color = 255
  
          image_rotated_channel_list.append(tf.sparse_to_dense(coord_new, [s[0], s[1]], image_chan_new_values,
                                                               background_color, validate_indices=False))
  
      image_rotated = tf.transpose(tf.stack(image_rotated_channel_list), [1, 2, 0])
  
      return image_rotated
  

  回复于 2024-05-15T03:00:05+08:00
• 3

  要将图像或一批图像逆时针旋转90度的倍数，可以使用 tf.image.rot90(image,k=1,name=None) .

  k 表示您要制作的90度旋转数 .

  如果是单个图像， image 是 3-D Tensor of shape [height, width, channels] ，如果是一批图像， image 是 4-D Tensor of shape [batch, height, width, channels]

  回复于 2024-05-15T03:00:05+08:00