added a class interface

standalone/README.md

@@ -2,3 +2,9 @@
 
 Use this code to extract image descriptor with pre-trained models.
 This folder is standalone.
+
+- demo.py : A simple demo 
+- WholeImageDescriptor : An easy to use class interface with pretrained models
+
+
+### Author: Manohar Kuse <mpkuse@connect.ust.hk> 

standalone/WholeImageDescriptor.py

@@ -0,0 +1,98 @@
+"""
+    A class interfce to netvlad based whole image descriptor. To use the
+    pre-trained network in your application use this code and unit-test
+
+    Author  : Manohar Kuse <mpkuse@connect.ust.hk>
+    Created : 20th Aug, 2018
+"""
+
+import cv2
+import numpy as np
+import os
+import time
+import code
+import argparse
+import sys
+
+import tensorflow as tf
+import tensorflow.contrib.slim as slim
+
+TF_MAJOR_VERSION = int(tf.__version__.split('.')[0])
+TF_MINOR_VERSION = int(tf.__version__.split('.')[1])
+
+
+from CartWheelFlow import VGGDescriptor
+from ColorLUT import ColorLUT
+
+import TerminalColors
+tcolor = TerminalColors.bcolors()
+
+class WholeImageDescriptor:
+    def __init__( self, NET_TYPE, PARAM_K, PARAM_model_restore ):
+        self.NET_TYPE = NET_TYPE
+        self.PARAM_K = PARAM_K
+        self.PARAM_model_restore = PARAM_model_restore
+
+        ## Create Network
+        tf_x = tf.placeholder( 'float', [1,240,320,3], name='x' ) #this has to be 3 if training with color images
+        is_training = tf.placeholder( tf.bool, [], name='is_training')
+        vgg_obj = VGGDescriptor(K=PARAM_K, D=256, N=60*80, b=1)
+        tf_vlad_word = vgg_obj.network(tf_x, is_training, net_type=NET_TYPE )
+
+        ## Restore Model
+        sess = tf.Session()
+
+        print tcolor.OKGREEN,'Restore model from : ', PARAM_model_restore, tcolor.ENDC
+        tensorflow_saver = tf.train.Saver()
+        tensorflow_saver.restore( sess, PARAM_model_restore )
+
+        self.tf_x = tf_x
+        self.tf_vlad_word = tf_vlad_word
+        self.is_training = is_training
+        self.vgg_obj = vgg_obj
+        self.sess = sess
+
+
+    def get_descriptor( self, im ):
+        """ im: 1x240x320x3 """
+        assert( len(im.shape) == 4 )
+        feed_dict = {self.tf_x : im,\
+                     self.is_training:True,\
+                     self.vgg_obj.initial_t: 0
+                    }
+
+        tff_vlad_word, tff_sm = self.sess.run( [self.tf_vlad_word, self.vgg_obj.nl_sm], feed_dict=feed_dict)
+        Assgn_matrix = np.reshape( tff_sm, [1,60,80,-1] ).argmax( axis=-1 ) #assuming batch size = 1
+
+        return tff_vlad_word, Assgn_matrix
+
+
+if __name__=='__main__':
+    ## Network Params
+    NET_TYPE = "resnet6"
+    PARAM_K = 16
+    PARAM_model_restore = './tfmodels/B_vgg/model-8000'
+    PARAM_model_restore = './tfmodels/D/model-8000'
+
+    WID_net = WholeImageDescriptor( NET_TYPE, PARAM_K, PARAM_model_restore )
+
+
+    ## Load Image
+    INPUT_FILE_NAME = 'sample_images/a0.jpg'
+    print 'Load Image : ', INPUT_FILE_NAME
+    IM = cv2.resize( cv2.imread( INPUT_FILE_NAME), (320, 240) )
+    im_batch = np.expand_dims( IM.astype('float32'), 0 )
+
+
+    ## descriptor and association map
+    ##      tff_vlad_word : 1x4096
+    ##      Assgn_matrix  : 1x60x80
+    tff_vlad_word, Assgn_matrix = WID_net.get_descriptor( im_batch )
+
+
+    ## Visualize Assgn_matrix - as a false color map
+    colorLUT = ColorLUT()
+    lut = colorLUT.lut( Assgn_matrix[0,:,:] )
+    cv2.imshow( 'IM', IM )
+    cv2.imshow( 'Assgn_matrix', cv2.resize( lut, (320,240) ) )
+    cv2.waitKey(0)