├── BlockDCT.py ├── LICENSE ├── Readme.md └── cameraman.png /BlockDCT.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -* 2 | ''' Block Discrete Cosine Transform (block DCT) using TensorFlow''' 3 | # Copyright 2018 Takamichi Miyata 4 | # 5 | # Licensed under the Apache License, Version 2.0 (the "License"); 6 | # you may not use this file except in compliance with the License. 7 | # You may obtain a copy of the License at 8 | # 9 | # http://www.apache.org/licenses/LICENSE-2.0 10 | # 11 | # Unless required by applicable law or agreed to in writing, software 12 | # distributed under the License is distributed on an "AS IS" BASIS, 13 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 14 | # See the License for the specific language governing permissions and 15 | # limitations under the License. 16 | 17 | import numpy as np 18 | import cv2 19 | import tensorflow as tf 20 | 21 | #Definition of functions 22 | def conv2d_block(x, W): 23 | with tf.name_scope("conv2d_block"): 24 | # strideを[8,8]に設定 25 | return tf.nn.conv2d(x, W, strides=[1, 8, 8, 1], padding='VALID') 26 | 27 | def conv2d_deblock(x, W): 28 | with tf.name_scope("conv2d_deblock"): 29 | r=8 30 | X = tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='VALID') 31 | s = tf.shape(X) 32 | X = tf.reshape(X, (1, s[1], s[2], 8,8)) 33 | X = tf.transpose(X, [0, 1, 3, 2, 4]) # bsize, a, b, 1, 1 34 | return tf.reshape(X, (s[0],s[1]*8, s[2]*8, 1)) 35 | 36 | def make_DCT_filter(): 37 | with tf.name_scope("DCT"): 38 | # Initialize DCT filters 39 | DCT_filter_n = np.zeros([8, 8, 1, 64]) 40 | # Definition of 8x8 mesh grid 41 | XX, YY = np.meshgrid(range(8), range(8)) 42 | # DCT basis as filters 43 | C=np.ones(8) 44 | C[0]=1/np.sqrt(2) 45 | for v in range(8): 46 | for u in range(8): 47 | DCT_filter_n[:, :, 0, u+v*8]=(2*C[v]*C[u]/8)*np.cos((2*YY+1)*v*np.pi/(16))*np.cos((2*XX+1)*u*np.pi/(16)) 48 | 49 | DCT_filter=tf.constant(DCT_filter_n.astype(np.float32)) 50 | 51 | return DCT_filter 52 | 53 | def make_IDCT_filter(): 54 | with tf.name_scope("IDCT"): 55 | # Initialize inverse DCT (IDCT) filters 56 | IDCT_filter_n = np.zeros([1, 1, 64, 64]) 57 | # IDCT basis as filters 58 | C=np.ones(8) 59 | C[0]=1/np.sqrt(2) 60 | for v in range(8): 61 | for u in range(8): 62 | for j in range(8): 63 | for i in range(8): 64 | IDCT_filter_n[0,0,u+v*8,i+j*8]=(2*C[v]*C[u]/8)*np.cos((2*j+1)*v*np.pi/(16))*np.cos((2*i+1)*u*np.pi/(16)) 65 | 66 | IDCT_filter=tf.constant(IDCT_filter_n.astype(np.float32)) 67 | 68 | return IDCT_filter 69 | 70 | def main(): 71 | # Preparation DCT filter coefficients 72 | DCT_filter = make_DCT_filter() 73 | IDCT_filter = make_IDCT_filter() 74 | 75 | # Placeholder for the input image 76 | inp_im_p = tf.placeholder(tf.float32, shape=[None, None]) 77 | inp_im_b = tf.expand_dims(tf.expand_dims(inp_im_p, 0), 3) 78 | # OPs 79 | out_coef = conv2d_block(inp_im_b, DCT_filter) 80 | out_im = conv2d_deblock(out_coef, IDCT_filter) 81 | 82 | 83 | # Reading the input image 84 | img = cv2.imread('./cameraman.png', cv2.IMREAD_GRAYSCALE) 85 | inp_im_n = img.astype(np.float32) 86 | 87 | # Defining summary for TensorBoard 88 | _ = tf.summary.image('input', inp_im_b, max_outputs=1, collections=None) 89 | _ = tf.summary.image('DCT_basis', tf.transpose(DCT_filter, [3,0,1,2]), max_outputs=64, collections=None) 90 | _ = tf.summary.image('DCT_coef', tf.transpose(out_coef, [3,1,2,0]), max_outputs=64, collections=None) 91 | _ = tf.summary.image('ouput', out_im, max_outputs=1, collections=None) 92 | 93 | merged_summary = tf.summary.merge_all() 94 | 95 | # Preparation for TensorBoard 96 | log_dir = './log' 97 | 98 | # Starting the TF session 99 | with tf.Session() as sess: 100 | summary = sess.run(merged_summary, feed_dict={inp_im_p: inp_im_n}) 101 | 102 | # Output for TensorBoard 103 | summary_writer = tf.summary.FileWriter(log_dir, sess.graph) 104 | summary_writer.add_summary(summary) 105 | summary_writer.close() 106 | 107 | if __name__ == '__main__': 108 | main() 109 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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