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diff --git a/tensorflow/docs_src/api_guides/python/image.md b/tensorflow/docs_src/api_guides/python/image.md deleted file mode 100644 index c51b92db05..0000000000 --- a/tensorflow/docs_src/api_guides/python/image.md +++ /dev/null @@ -1,144 +0,0 @@ -# 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_bmp` -* `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: - -```python -# 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: - -```python -# 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` |