computeFV.py

import os, sys, collections
import numpy as np
from yael import ynumpy
import IDT_feature
from tempfile import TemporaryFile

"""
Encodes a fisher vector.
"""

def create_fisher_vector(gmm_list, video_desc, fisher_path):
  """
  expects a single video_descriptors object. videos_desciptors objects are defined in IDT_feature.py
  fisher path is the full path to the fisher vector that is created.
  this single video_desc contains the (trajs, hogs, hofs, mbhxs, mbhys) np.ndarrays
  """
  vid_desc_list = []
  vid_desc_list.append(video_desc.traj)
  vid_desc_list.append(video_desc.hog)
  vid_desc_list.append(video_desc.hof)
  vid_desc_list.append(video_desc.mbhx)
  vid_desc_list.append(video_desc.mbhy)
  #For each video create and normalize a fisher vector for each of the descriptors. Then, concatenate the
  #fisher vectors together to get an extra long fisher vector.
  # Return a list of all of these long fisher vectors. The list should be the same length as the number
  # of input videos.
  fvs = []

  for descriptor,gmm_mean_pca in zip(vid_desc_list,gmm_list):
    gmm, mean, pca_transform = gmm_mean_pca
    # apply the PCA to the vid_trajectory descriptor
    #each image_desc is of size (X,TRAJ_DIM). Pca_tranform is of size (TRAJ_DIM,TRAJ_DIM/2)
    descrip = descriptor.astype('float32') - mean
    print type(gmm),type(mean),type(pca_transform)
    print len(gmm), len(mean), len(pca_transform)
    if pca_transform.all != None:
      descrip = np.dot(descriptor.astype('float32') - mean, pca_transform)
    # compute the Fisher vector, using the derivative w.r.t mu and sigma
    fv = ynumpy.fisher(gmm, descrip, include = ['mu', 'sigma'])
    # normalizations are done on each descriptors individually
    # power-normalization
    fv = np.sign(fv) * (np.abs(fv) ** 0.5)
    # L2 normalize
    #sum along the rows.
    norms = np.sqrt(np.sum(fv ** 2))
    # -1 allows reshape to infer the length. So it just solidifies the dimensions to (274,1)
    fv /= norms
    # handle images with 0 local descriptor (100 = far away from "normal" images)
    fv[np.isnan(fv)] = 100
      
    print "Performing fvs"
    fvs.append(fv.T)

  output_fv = np.hstack(fvs)

  #L2 normalize the entire fv.
  norm = np.sqrt(np.sum(output_fv ** 2))
  output_fv /= norm

  #example name:
  # 'v_Archery_g01_c01.fisher.npz'
  #subdirectory name
  np.savez(fisher_path, fish=output_fv)
  print fisher_path
  return output_fv