From 608f46debea6b1e4ffb6c3ccea5771004693bd4c Mon Sep 17 00:00:00 2001 From: Nick Johnston Date: Fri, 9 Sep 2016 12:04:53 -0700 Subject: [PATCH 1/2] Initial commit of compression models. --- compression/README.md | 96 ++++++++++++++++++ compression/decoder.py | 124 +++++++++++++++++++++++ compression/encoder.py | 103 +++++++++++++++++++ compression/example.png | Bin 0 -> 3155141 bytes compression/msssim.py | 217 ++++++++++++++++++++++++++++++++++++++++ 5 files changed, 540 insertions(+) create mode 100644 compression/README.md create mode 100755 compression/decoder.py create mode 100755 compression/encoder.py create mode 100644 compression/example.png create mode 100755 compression/msssim.py diff --git a/compression/README.md b/compression/README.md new file mode 100644 index 00000000000..7415099a236 --- /dev/null +++ b/compression/README.md @@ -0,0 +1,96 @@ +# Image Compression with Neural Networks + +This is a [TensorFlow](http://www.tensorflow.org/) model for compressing and +decompressing images using an already trained Residual GRU model as descibed +in [Full Resolution Image Compression with Recurrent Neural Networks] +(https://arxiv.org/abs/1608.05148). Please consult the paper for more details +on the architecture and compression results. + +This code will allow you to perform the lossy compression on an model +already trained on compression. This code doesn't not currently contain the +Entropy Coding portions of our paper. + + +## Prerequisites +The only software requirements for running the encoder and decoder is having +Tensorflow installed. You will also need to [download] +(http://download.tensorflow.org/models/compression_residual_gru-2016-08-23.tar.gz) +and extract the model residual_gru.pb. + +If you want to generate the perceptual similarity under MS-SSIM, you will also +need to [Install SciPy](https://www.scipy.org/install.html). + +## Encoding +The Residual GRU network is fully convolutional, but requires the images +height and width in pixels by a multiple of 32. There is an image in this folder +called example.png that is 768x1024 if one is needed for testing. We also +rely on TensorFlow's built in decoding ops, which support only PNG and JPEG at +time of release. + +To encode an image, simply run the following command: + +`python encoder.py --input_image=/your/image/here.png +--output_codes=output_codes.npz --iteration=15 +--model=/path/to/model/residual_gru.pb +` + +The iteration parameter specifies the lossy-quality to target for compression. +The quality can be [0-15], where 0 corresponds to a target of 1/8 (bits per +pixel) bpp and every increment results in an additional 1/8 bpp. + +| Iteration | BPP | Compression Ratio | +|---: |---: |---: | +|0 | 0.125 | 192:1| +|1 | 0.250 | 96:1| +|2 | 0.375 | 64:1| +|3 | 0.500 | 48:1| +|4 | 0.625 | 38.4:1| +|5 | 0.750 | 32:1| +|6 | 0.875 | 27.4:1| +|7 | 1.000 | 24:1| +|8 | 1.125 | 21.3:1| +|9 | 1.250 | 19.2:1| +|10 | 1.375 | 17.4:1| +|11 | 1.500 | 16:1| +|12 | 1.625 | 14.7:1| +|13 | 1.750 | 13.7:1| +|14 | 1.875 | 12.8:1| +|15 | 2.000 | 12:1| + +The output_codes file contains the numpy shape and a flattened, bit-packed +array of the codes. These can be inspected in python by using numpy.load(). + + +## Decoding +After generating codes for an image, the lossy reconstructions for that image +can be done as follows: + +`python decoder.py --input_codes=codes.npz --output_directory=/tmp/decoded/ +--model=residual_gru.pb` + +The output_directory will contain images decoded at each quality level. + + +## Comparing Similarity +One of our primary metrics for comparing how similar two images are +is MS-SSIM. + +To generate these metrics on your images you can run: +`python msssim.py --original_image=/path/to/your/image.png +--compared_image=/tmp/decoded/image_15.png` + + +## FAQ + +#### How do I train my own compression network? +We currently don't provide the code to build and train a compression +graph from scratch. + +#### I get an InvalidArgumentError: Incompatible shapes. +This is usually due to the fact that our network only supports images that are +both height and width divisible by 32 pixel. Try padding your images to 32 +pixel boundaries. + + +## Contact Info +Model repository maintained by Nick Johnston ([nickj-google](https://github.com/nickj-google)). diff --git a/compression/decoder.py b/compression/decoder.py new file mode 100755 index 00000000000..52727e6398d --- /dev/null +++ b/compression/decoder.py @@ -0,0 +1,124 @@ +#!/usr/bin/python +# +# Copyright 2016 The TensorFlow Authors. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +r"""Neural Network Image Compression Decoder. + +Decompress an image from the numpy's npz format generated by the encoder. + +Example usage: +python decoder.py --input_codes=output_codes.pkl --iteration=15 \ +--output_directory=/tmp/compression_output/ --model=residual_gru.pb +""" +import os + +import numpy as np +import tensorflow as tf + +tf.flags.DEFINE_string('input_codes', None, 'Location of binary code file.') +tf.flags.DEFINE_integer('iteration', -1, 'The max quality level of ' + 'the images to output. Use -1 to infer from loaded ' + ' codes.') +tf.flags.DEFINE_string('output_directory', None, 'Directory to save decoded ' + 'images.') +tf.flags.DEFINE_string('model', None, 'Location of compression model.') + +FLAGS = tf.flags.FLAGS + + +def get_input_tensor_names(): + name_list = ['GruBinarizer/SignBinarizer/Sign:0'] + for i in xrange(1, 16): + name_list.append('GruBinarizer/SignBinarizer/Sign_{}:0'.format(i)) + return name_list + + +def get_output_tensor_names(): + return ['loop_{0:02d}/add:0'.format(i) for i in xrange(0, 16)] + + +def main(_): + if (FLAGS.input_codes is None or FLAGS.output_directory is None or + FLAGS.model is None): + print ('\nUsage: python decoder.py --input_codes=output_codes.pkl ' + '--iteration=15 --output_directory=/tmp/compression_output/ ' + '--model=residual_gru.pb\n\n') + return + + if FLAGS.iteration < -1 or FLAGS.iteration > 15: + print ('\n--iteration must be between 0 and 15 inclusive, or -1 to infer ' + 'from file.\n') + return + iteration = FLAGS.iteration + + if not tf.gfile.Exists(FLAGS.output_directory): + tf.gfile.MkDir(FLAGS.output_directory) + + if not tf.gfile.Exists(FLAGS.input_codes): + print '\nInput codes not found.\n' + return + + with tf.gfile.FastGFile(FLAGS.input_codes, 'rb') as code_file: + loaded_codes = np.load(code_file) + assert ['codes', 'shape'] not in loaded_codes.files + loaded_shape = loaded_codes['shape'] + loaded_array = loaded_codes['codes'] + + # Unpack and recover code shapes. + unpacked_codes = np.reshape(np.unpackbits(loaded_array) + [:np.prod(loaded_shape)], + loaded_shape) + + numpy_int_codes = np.split(unpacked_codes, len(unpacked_codes)) + if iteration == -1: + iteration = len(unpacked_codes) - 1 + # Convert back to float and recover scale. + numpy_codes = [np.squeeze(x.astype(np.float32), 0) * 2 - 1 for x in + numpy_int_codes] + + with tf.Graph().as_default() as graph: + # Load the inference model for decoding. + with tf.gfile.FastGFile(FLAGS.model, 'rb') as model_file: + graph_def = tf.GraphDef() + graph_def.ParseFromString(model_file.read()) + _ = tf.import_graph_def(graph_def, name='') + + # For encoding the tensors into PNGs. + input_image = tf.placeholder(tf.uint8) + encoded_image = tf.image.encode_png(input_image) + + input_tensors = [graph.get_tensor_by_name(name) for name in + get_input_tensor_names()][0:iteration+1] + outputs = [graph.get_tensor_by_name(name) for name in + get_output_tensor_names()][0:iteration+1] + + feed_dict = {key: value for (key, value) in zip(input_tensors, + numpy_codes)} + + with tf.Session(graph=graph) as sess: + results = sess.run(outputs, feed_dict=feed_dict) + + for index, result in enumerate(results): + img = np.uint8(np.clip(result + 0.5, 0, 255)) + img = img.squeeze() + png_img = sess.run(encoded_image, feed_dict={input_image: img}) + + with tf.gfile.FastGFile(os.path.join(FLAGS.output_directory, + 'image_{0:02d}.png'.format(index)), + 'w') as output_image: + output_image.write(png_img) + + +if __name__ == '__main__': + tf.app.run() diff --git a/compression/encoder.py b/compression/encoder.py new file mode 100755 index 00000000000..31e794d0684 --- /dev/null +++ b/compression/encoder.py @@ -0,0 +1,103 @@ +#!/usr/bin/python +# +# Copyright 2016 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +r"""Neural Network Image Compression Encoder. + +Compresses an image to a binarized numpy array. The image must be padded to a +multiple of 32 pixels in height and width. + +Example usage: +python encoder.py --input_image=/your/image/here.png \ +--output_codes=output_codes.pkl --iteration=15 --model=residual_gru.pb +""" +import os + +import numpy as np +import tensorflow as tf + +tf.flags.DEFINE_string('input_image', None, 'Location of input image. We rely ' + 'on tf.image to decode the image, so only PNG and JPEG ' + 'formats are currently supported.') +tf.flags.DEFINE_integer('iteration', 15, 'Quality level for encoding image. ' + 'Must be between 0 and 15 inclusive.') +tf.flags.DEFINE_string('output_codes', None, 'Directory to save decoded ' + 'images.') +tf.flags.DEFINE_string('model', None, 'Location of compression model.') + +FLAGS = tf.flags.FLAGS + + +def get_output_tensor_names(): + name_list = ['GruBinarizer/SignBinarizer/Sign:0'] + for i in xrange(1, 16): + name_list.append('GruBinarizer/SignBinarizer/Sign_{}:0'.format(i)) + return name_list + + +def main(_): + if (FLAGS.input_image is None or FLAGS.output_codes is None or + FLAGS.model is None): + print ('\nUsage: python encoder.py --input_image=/your/image/here.png ' + '--output_codes=output_codes.pkl --iteration=15 ' + '--model=residual_gru.pb\n\n') + return + + if FLAGS.iteration < 0 or FLAGS.iteration > 15: + print '\n--iteration must be between 0 and 15 inclusive.\n' + return + + with tf.gfile.FastGFile(FLAGS.input_image) as input_image: + input_image_str = input_image.read() + + with tf.Graph().as_default() as graph: + # Load the inference model for encoding. + with tf.gfile.FastGFile(FLAGS.model, 'rb') as model_file: + graph_def = tf.GraphDef() + graph_def.ParseFromString(model_file.read()) + _ = tf.import_graph_def(graph_def, name='') + + input_tensor = graph.get_tensor_by_name('Placeholder:0') + outputs = [graph.get_tensor_by_name(name) for name in + get_output_tensor_names()] + + input_image = tf.placeholder(tf.string) + _, ext = os.path.splitext(FLAGS.input_image) + if ext == '.png': + decoded_image = tf.image.decode_png(input_image, channels=3) + elif ext == '.jpeg' or ext == '.jpg': + decoded_image = tf.image.decode_jpeg(input_image, channels=3) + else: + assert False, 'Unsupported file format {}'.format(ext) + decoded_image = tf.expand_dims(decoded_image, 0) + + with tf.Session(graph=graph) as sess: + img_array = sess.run(decoded_image, feed_dict={input_image: + input_image_str}) + results = sess.run(outputs, feed_dict={input_tensor: img_array}) + + results = results[0:FLAGS.iteration + 1] + int_codes = np.asarray([x.astype(np.int8) for x in results]) + + # Convert int codes to binary. + int_codes = (int_codes + 1)/2 + export = np.packbits(int_codes.reshape(-1)) + + with tf.gfile.FastGFile(FLAGS.output_codes, 'wb') as code_file: + np.savez_compressed(code_file, shape=int_codes.shape, codes=export) + + +if __name__ == '__main__': + tf.app.run() diff --git a/compression/example.png b/compression/example.png new file mode 100644 index 0000000000000000000000000000000000000000..d3409b01a557fe8c3058fad21ed969f8af28cb97 GIT binary patch literal 3155141 zcmb@P2b>*OdG_C#xxH-P)voHQyBizaG1$0cz{VI;0s$inTi}MVF)g$>B$NaK#G!>2 zLQPCUf(;JUKwL2HRkmccmA3ERy}i!&f6izvg#^C*@_kpjyLac#oH^$`Z+YHw&V1y; zmz|T(EX|l%{=9R~yvQuzW6V+seqFNR1^mptamyKJTzK9YXN+EZ-8FCCa^+^TcY5Br zx!a0-ckWBJTz!q-V|LZm*I#$hIWHQ0^=n=`n!L|^OIyN@F}rlr+pm4;E6%={hU~nv z&KP|=C+YKnSLHGD^w+~jo_pcw=z-(^qffn?uD$MhGw(vqe(q(P-@b{@H}Uz(8?V1s z$LIMx@TRxwvmevv>*ye#hxPgL=<^Zooapmu`h3|{S6{|C>hraiU3Hm0|BBCl@s1ld 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All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== + +"""Python implementation of MS-SSIM. + +Usage: + +python msssim.py --original_image=original.png --compared_image=distorted.png +""" +import numpy as np +from scipy import signal +from scipy.ndimage.filters import convolve +import tensorflow as tf + + +tf.flags.DEFINE_string('original_image', None, 'Path to PNG image.') +tf.flags.DEFINE_string('compared_image', None, 'Path to PNG image.') +FLAGS = tf.flags.FLAGS + + +def _FSpecialGauss(size, sigma): + """Function to mimic the 'fspecial' gaussian MATLAB function.""" + radius = size // 2 + offset = 0.0 + start, stop = -radius, radius + 1 + if size % 2 == 0: + offset = 0.5 + stop -= 1 + x, y = np.mgrid[offset + start:stop, offset + start:stop] + assert len(x) == size + g = np.exp(-((x**2 + y**2)/(2.0 * sigma**2))) + return g / g.sum() + + +def _SSIMForMultiScale(img1, img2, max_val=255, filter_size=11, + filter_sigma=1.5, k1=0.01, k2=0.03): + """Return the Structural Similarity Map between `img1` and `img2`. + + This function attempts to match the functionality of ssim_index_new.m by + Zhou Wang: http://www.cns.nyu.edu/~lcv/ssim/msssim.zip + + Arguments: + img1: Numpy array holding the first RGB image batch. + img2: Numpy array holding the second RGB image batch. + max_val: the dynamic range of the images (i.e., the difference between the + maximum the and minimum allowed values). + filter_size: Size of blur kernel to use (will be reduced for small images). + filter_sigma: Standard deviation for Gaussian blur kernel (will be reduced + for small images). + k1: Constant used to maintain stability in the SSIM calculation (0.01 in + the original paper). + k2: Constant used to maintain stability in the SSIM calculation (0.03 in + the original paper). + + Returns: + Pair containing the mean SSIM and contrast sensitivity between `img1` and + `img2`. + + Raises: + RuntimeError: If input images don't have the same shape or don't have four + dimensions: [batch_size, height, width, depth]. + """ + if img1.shape != img2.shape: + raise RuntimeError('Input images must have the same shape (%s vs. %s).', + img1.shape, img2.shape) + if img1.ndim != 4: + raise RuntimeError('Input images must have four dimensions, not %d', + img1.ndim) + + img1 = img1.astype(np.float64) + img2 = img2.astype(np.float64) + _, height, width, _ = img1.shape + + # Filter size can't be larger than height or width of images. + size = min(filter_size, height, width) + + # Scale down sigma if a smaller filter size is used. + sigma = size * filter_sigma / filter_size if filter_size else 0 + + if filter_size: + window = np.reshape(_FSpecialGauss(size, sigma), (1, size, size, 1)) + mu1 = signal.fftconvolve(img1, window, mode='valid') + mu2 = signal.fftconvolve(img2, window, mode='valid') + sigma11 = signal.fftconvolve(img1 * img1, window, mode='valid') + sigma22 = signal.fftconvolve(img2 * img2, window, mode='valid') + sigma12 = signal.fftconvolve(img1 * img2, window, mode='valid') + else: + # Empty blur kernel so no need to convolve. + mu1, mu2 = img1, img2 + sigma11 = img1 * img1 + sigma22 = img2 * img2 + sigma12 = img1 * img2 + + mu11 = mu1 * mu1 + mu22 = mu2 * mu2 + mu12 = mu1 * mu2 + sigma11 -= mu11 + sigma22 -= mu22 + sigma12 -= mu12 + + # Calculate intermediate values used by both ssim and cs_map. + c1 = (k1 * max_val) ** 2 + c2 = (k2 * max_val) ** 2 + v1 = 2.0 * sigma12 + c2 + v2 = sigma11 + sigma22 + c2 + ssim = np.mean((((2.0 * mu12 + c1) * v1) / ((mu11 + mu22 + c1) * v2))) + cs = np.mean(v1 / v2) + return ssim, cs + + +def MultiScaleSSIM(img1, img2, max_val=255, filter_size=11, filter_sigma=1.5, + k1=0.01, k2=0.03, weights=None): + """Return the MS-SSIM score between `img1` and `img2`. + + This function implements Multi-Scale Structural Similarity (MS-SSIM) Image + Quality Assessment according to Zhou Wang's paper, "Multi-scale structural + similarity for image quality assessment" (2003). + Link: https://ece.uwaterloo.ca/~z70wang/publications/msssim.pdf + + Author's MATLAB implementation: + http://www.cns.nyu.edu/~lcv/ssim/msssim.zip + + Arguments: + img1: Numpy array holding the first RGB image batch. + img2: Numpy array holding the second RGB image batch. + max_val: the dynamic range of the images (i.e., the difference between the + maximum the and minimum allowed values). + filter_size: Size of blur kernel to use (will be reduced for small images). + filter_sigma: Standard deviation for Gaussian blur kernel (will be reduced + for small images). + k1: Constant used to maintain stability in the SSIM calculation (0.01 in + the original paper). + k2: Constant used to maintain stability in the SSIM calculation (0.03 in + the original paper). + weights: List of weights for each level; if none, use five levels and the + weights from the original paper. + + Returns: + MS-SSIM score between `img1` and `img2`. + + Raises: + RuntimeError: If input images don't have the same shape or don't have four + dimensions: [batch_size, height, width, depth]. + """ + if img1.shape != img2.shape: + raise RuntimeError('Input images must have the same shape (%s vs. %s).', + img1.shape, img2.shape) + if img1.ndim != 4: + raise RuntimeError('Input images must have four dimensions, not %d', + img1.ndim) + + # Note: default weights don't sum to 1.0 but do match the paper / matlab code. + weights = np.array(weights if weights else + [0.0448, 0.2856, 0.3001, 0.2363, 0.1333]) + levels = weights.size + downsample_filter = np.ones((1, 2, 2, 1)) / 4.0 + im1, im2 = [x.astype(np.float64) for x in [img1, img2]] + mssim = np.array([]) + mcs = np.array([]) + for _ in xrange(levels): + ssim, cs = _SSIMForMultiScale( + im1, im2, max_val=max_val, filter_size=filter_size, + filter_sigma=filter_sigma, k1=k1, k2=k2) + mssim = np.append(mssim, ssim) + mcs = np.append(mcs, cs) + filtered = [convolve(im, downsample_filter, mode='reflect') + for im in [im1, im2]] + im1, im2 = [x[:, ::2, ::2, :] for x in filtered] + return (np.prod(mcs[0:levels-1] ** weights[0:levels-1]) * + (mssim[levels-1] ** weights[levels-1])) + + +def main(_): + if FLAGS.original_image is None or FLAGS.compared_image is None: + print ('\nUsage: python msssim.py --original_image=original.png ' + '--compared_image=distorted.png\n\n') + return + + if not tf.gfile.Exists(FLAGS.original_image): + print '\nCannot find --original_image.\n' + return + + if not tf.gfile.Exists(FLAGS.compared_image): + print '\nCannot find --compared_image.\n' + return + + with tf.gfile.FastGFile(FLAGS.original_image) as image_file: + img1_str = image_file.read() + with tf.gfile.FastGFile(FLAGS.compared_image) as image_file: + img2_str = image_file.read() + + input_img = tf.placeholder(tf.string) + decoded_image = tf.expand_dims(tf.image.decode_png(input_img, channels=3), 0) + + with tf.Session() as sess: + img1 = sess.run(decoded_image, feed_dict={input_img: img1_str}) + img2 = sess.run(decoded_image, feed_dict={input_img: img2_str}) + + print MultiScaleSSIM(img1, img2, max_val=255) + + +if __name__ == '__main__': + tf.app.run() From f0de6d562e31dbac430c9360bf11b6610f73a327 Mon Sep 17 00:00:00 2001 From: Nick Johnston Date: Thu, 6 Oct 2016 14:52:39 -0700 Subject: [PATCH 2/2] Fix GFile issue with numpy by using io library. --- compression/decoder.py | 7 +++++-- compression/encoder.py | 7 +++++-- 2 files changed, 10 insertions(+), 4 deletions(-) diff --git a/compression/decoder.py b/compression/decoder.py index 52727e6398d..b1ee8c0a5e1 100755 --- a/compression/decoder.py +++ b/compression/decoder.py @@ -21,6 +21,7 @@ python decoder.py --input_codes=output_codes.pkl --iteration=15 \ --output_directory=/tmp/compression_output/ --model=residual_gru.pb """ +import io import os import numpy as np @@ -69,8 +70,10 @@ def main(_): print '\nInput codes not found.\n' return - with tf.gfile.FastGFile(FLAGS.input_codes, 'rb') as code_file: - loaded_codes = np.load(code_file) + contents = '' + with tf.gfile.FastGFile(FLAGS.input_codes, 'r') as code_file: + contents = code_file.read() + loaded_codes = np.load(io.BytesIO(contents)) assert ['codes', 'shape'] not in loaded_codes.files loaded_shape = loaded_codes['shape'] loaded_array = loaded_codes['codes'] diff --git a/compression/encoder.py b/compression/encoder.py index 31e794d0684..483962c1fa6 100755 --- a/compression/encoder.py +++ b/compression/encoder.py @@ -23,6 +23,7 @@ python encoder.py --input_image=/your/image/here.png \ --output_codes=output_codes.pkl --iteration=15 --model=residual_gru.pb """ +import io import os import numpy as np @@ -95,8 +96,10 @@ def main(_): int_codes = (int_codes + 1)/2 export = np.packbits(int_codes.reshape(-1)) - with tf.gfile.FastGFile(FLAGS.output_codes, 'wb') as code_file: - np.savez_compressed(code_file, shape=int_codes.shape, codes=export) + output = io.BytesIO() + np.savez_compressed(output, shape=int_codes.shape, codes=export) + with tf.gfile.FastGFile(FLAGS.output_codes, 'w') as code_file: + code_file.write(output.getvalue()) if __name__ == '__main__':