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Images

Note: Functions taking Tensor arguments can also take anything accepted by @{tf.convert_to_tensor}.

[TOC]

Encoding and Decoding

TensorFlow provides Ops to decode and encode JPEG and PNG formats. Encoded images are represented by scalar string Tensors, decoded images by 3-D uint8 tensors of shape [height, width, channels]. (PNG also supports uint16.)

The encode and decode Ops apply to one image at a time. Their input and output are all of variable size. If you need fixed size images, pass the output of the decode Ops to one of the cropping and resizing Ops.

Note: The PNG encode and decode Ops support RGBA, but the conversions Ops presently only support RGB, HSV, and GrayScale. Presently, the alpha channel has to be stripped from the image and re-attached using slicing ops.

  • @{tf.image.decode_gif}
  • @{tf.image.decode_jpeg}
  • @{tf.image.encode_jpeg}
  • @{tf.image.decode_png}
  • @{tf.image.encode_png}
  • @{tf.image.decode_image}

Resizing

The resizing Ops accept input images as tensors of several types. They always output resized images as float32 tensors.

The convenience function @{tf.image.resize_images} supports both 4-D and 3-D tensors as input and output. 4-D tensors are for batches of images, 3-D tensors for individual images.

Other resizing Ops only support 4-D batches of images as input: @{tf.image.resize_area}, @{tf.image.resize_bicubic}, @{tf.image.resize_bilinear}, @{tf.image.resize_nearest_neighbor}.

Example:

# Decode a JPG image and resize it to 299 by 299 using default method.
image = tf.image.decode_jpeg(...)
resized_image = tf.image.resize_images(image, [299, 299])
  • @{tf.image.resize_images}
  • @{tf.image.resize_area}
  • @{tf.image.resize_bicubic}
  • @{tf.image.resize_bilinear}
  • @{tf.image.resize_nearest_neighbor}

Cropping

  • @{tf.image.resize_image_with_crop_or_pad}
  • @{tf.image.central_crop}
  • @{tf.image.pad_to_bounding_box}
  • @{tf.image.crop_to_bounding_box}
  • @{tf.image.extract_glimpse}
  • @{tf.image.crop_and_resize}

Flipping, Rotating and Transposing

  • @{tf.image.flip_up_down}
  • @{tf.image.random_flip_up_down}
  • @{tf.image.flip_left_right}
  • @{tf.image.random_flip_left_right}
  • @{tf.image.transpose_image}
  • @{tf.image.rot90}

Converting Between Colorspaces

Image ops work either on individual images or on batches of images, depending on the shape of their input Tensor.

If 3-D, the shape is [height, width, channels], and the Tensor represents one image. If 4-D, the shape is [batch_size, height, width, channels], and the Tensor represents batch_size images.

Currently, channels can usefully be 1, 2, 3, or 4. Single-channel images are grayscale, images with 3 channels are encoded as either RGB or HSV. Images with 2 or 4 channels include an alpha channel, which has to be stripped from the image before passing the image to most image processing functions (and can be re-attached later).

Internally, images are either stored in as one float32 per channel per pixel (implicitly, values are assumed to lie in [0,1)) or one uint8 per channel per pixel (values are assumed to lie in [0,255]).

TensorFlow can convert between images in RGB or HSV. The conversion functions work only on float images, so you need to convert images in other formats using @{tf.image.convert_image_dtype}.

Example:

# Decode an image and convert it to HSV.
rgb_image = tf.image.decode_png(...,  channels=3)
rgb_image_float = tf.image.convert_image_dtype(rgb_image, tf.float32)
hsv_image = tf.image.rgb_to_hsv(rgb_image)
  • @{tf.image.rgb_to_grayscale}
  • @{tf.image.grayscale_to_rgb}
  • @{tf.image.hsv_to_rgb}
  • @{tf.image.rgb_to_hsv}
  • @{tf.image.convert_image_dtype}

Image Adjustments

TensorFlow provides functions to adjust images in various ways: brightness, contrast, hue, and saturation. Each adjustment can be done with predefined parameters or with random parameters picked from predefined intervals. Random adjustments are often useful to expand a training set and reduce overfitting.

If several adjustments are chained it is advisable to minimize the number of redundant conversions by first converting the images to the most natural data type and representation (RGB or HSV).

  • @{tf.image.adjust_brightness}
  • @{tf.image.random_brightness}
  • @{tf.image.adjust_contrast}
  • @{tf.image.random_contrast}
  • @{tf.image.adjust_hue}
  • @{tf.image.random_hue}
  • @{tf.image.adjust_gamma}
  • @{tf.image.adjust_saturation}
  • @{tf.image.random_saturation}
  • @{tf.image.per_image_standardization}

Working with Bounding Boxes

  • @{tf.image.draw_bounding_boxes}
  • @{tf.image.non_max_suppression}
  • @{tf.image.sample_distorted_bounding_box}

Denoising

  • @{tf.image.total_variation}