def processVideo(vid,IDT_DIR,FV_DIR,gmm_list):
"""
Extracts the IDTFs, constructs a Fisher Vector, and saves the Fisher Vector at FV_DIR
output_file: the full path to the newly constructed fisher vector.
gmm_list: a list of gmms
"""
input_file = os.path.join(IDT_DIR, vid.split('.')[0]+'.bin')
output_file = os.path.join(FV_DIR, vid.split('.')[0]+'.fv')
if not os.path.exists(input_file):
print '%s IDT Feature does not exist!' % vid
return False
if os.path.exists(output_file+'.mat'):
print '%s Fisher Vector exists, skip!' % vid
return False
video_desc = IDT_feature.vid_descriptors(IDT_feature.read_IDTF_file(input_file))
computeFV.create_fisher_vector(gmm_list, video_desc, output_file)
return True
def processVideo(vid, IDT_DIR, FV_DIR, gmm_list):
"""
Extracts the IDTFs, constructs a Fisher Vector, and saves the Fisher Vector at FV_DIR
output_file: the full path to the newly constructed fisher vector.
gmm_list: a list of gmms
"""
input_file = os.path.join(IDT_DIR, vid.split('.')[0] + '.bin')
output_file = os.path.join(FV_DIR, vid.split('.')[0] + '.fv')
if not os.path.exists(input_file):
print '%s IDT Feature does not exist!' % vid
return False
if os.path.exists(output_file + '.mat'):
print '%s Fisher Vector exists, skip!' % vid
return False
video_desc = IDT_feature.vid_descriptors(
IDT_feature.read_IDTF_file(input_file))
computeFV.create_fisher_vector(gmm_list, video_desc, output_file)
return True
else:
svm = classify_library.load_model('../data/models/svm_nopca.sav')
gmm_list = np.load(gmm_list + ".npz")['gmm_list']
index_class = np.load(class_index)['index_class']
index_class = index_class[()]
points = [] # a list of IDT features.
frame_lim = frame_step
for line in sys.stdin:
if line[0] != '[': # avoid getting info message as data
frame = int(line.split()[0])
if frame_lim <= frame:
frame_lim = frame_lim + frame_step
# print frame_lim<=frame
if points != []:
video_desc = IDT_feature.vid_descriptors(points)
fish = computeFV.create_fisher_vector_unsaved(
gmm_list, video_desc)
fish = np.array(fish).reshape(1, -1)
if args.no_pca:
result = svm.predict(fish)
else:
fish_pca = pca.transform(fish)
result = svm.predict(fish_pca)
print '\n' + 'RESULT: ' + OKGREEN + BOLD + index_class[
result[0]] + ENDC + '\n'
points = []
points.append(IDT_feature.IDTFeature(line))
import numpy as np
from yael import ynumpy
import IDT_feature
from tempfile import TemporaryFile
import argparse
import computeFV
"""
computes a Fisher vector given an input stream of IDTFs
Usage:
stream_of_IDTFs | python computeFVstream.py fisher_path gmm_list
./DenseTrackStab video_file | python computeFVstream.py fisher_path gmm_list
"""
#The input is a stream of IDTFs associated with a single video.
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("fisher_path", help="File to save the output Fisher Vector", type=str)
parser.add_argument("gmm_list", help="File of saved list of GMMs", type=str)
args = parser.parse_args()
gmm_list = np.load(args.gmm_list+".npz")['gmm_list']
points = [] # a list of IDT features.
for line in sys.stdin:
points.append(IDT_feature.IDTFeature(line))
video_desc = IDT_feature.vid_descriptors(points)
computeFV.create_fisher_vector(gmm_list, video_desc, args.fisher_path)
