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fusion.py
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fusion.py
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# -*- coding: utf-8 -*-
from __future__ import print_function
import itertools
import os
import matplotlib.image as mping
import imageio
import numpy as np
from HOG import HOG
from MyDatabase import MyDatabase
from color import Color
from daisy import Daisy
from edge import Edge
from evaluate import evaluate_class, myevaluate
from gabor import Gabor
d_type = 'd1'
depth = 30
feat_pools = ['color', 'daisy', 'edge', 'gabor', 'hog', 'vgg', 'res']
# result dir
result_dir = 'result'
if not os.path.exists(result_dir):
os.makedirs(result_dir)
class FeatureFusion(object):
def __init__(self, features):
assert len(features) > 1, "need to fuse more than one feature!"
self.features = features
self.samples = None
def make_samples(self, db, verbose=False):
if verbose:
print("Use features {}".format(" & ".join(self.features)))
if self.samples == None:
feats = []
for f_class in self.features:
feats.append(self._get_feat(db, f_class))
samples = self._concat_feat(db, feats)
self.samples = samples # cache the result
return self.samples
def _get_feat(self, db, f_class):
if f_class == 'color':
f_c = Color()
elif f_class == 'daisy':
f_c = Daisy()
elif f_class == 'edge':
f_c = Edge()
elif f_class == 'gabor':
f_c = Gabor()
elif f_class == 'hog':
f_c = HOG()
return f_c.make_samples(db, verbose=False)
def _concat_feat(self, db, feats):
samples = feats[0]
delete_idx = []
for idx in range(len(samples)):
for feat in feats[1:]:
feat = self._to_dict(feat)
key = samples[idx]['img']
if key not in feat:
delete_idx.append(idx)
continue
assert feat[key]['cls'] == samples[idx]['cls']
samples[idx]['hist'] = np.append(samples[idx]['hist'], feat[key]['hist'])
for d_idx in sorted(set(delete_idx), reverse=True):
del samples[d_idx]
if delete_idx != []:
print("Ignore %d samples" % len(set(delete_idx)))
return samples
def _to_dict(self, feat):
ret = {}
for f in feat:
ret[f['img']] = {
'cls': f['cls'],
'hist': f['hist']
}
return ret
def evaluate_feats(db, N, feat_pools=feat_pools, d_type='d1', depths=[None, 300, 200, 100, 50, 30, 10, 5, 3, 1]):
result = open(os.path.join(result_dir, 'feature_fusion-{}-{}feats.csv'.format(d_type, N)), 'w')
for i in range(N):
result.write("feat{},".format(i))
result.write("depth,distance,MMAP")
combinations = itertools.combinations(feat_pools, N)
for combination in combinations:
fusion = FeatureFusion(features=list(combination))
for d in depths:
APs = evaluate_class(db, f_instance=fusion, d_type=d_type, depth=d)
cls_MAPs = []
for cls, cls_APs in APs.items():
MAP = np.mean(cls_APs)
cls_MAPs.append(MAP)
r = "{},{},{},{}".format(",".join(combination), d, d_type, np.mean(cls_MAPs))
print(r)
result.write('\n' + r)
print()
result.close()
if __name__ == "__main__":
DB_train_dir = '/content/Data/train'
DB_train_csv = '/content/train.csv'
db = MyDatabase(DB_train_dir, DB_train_csv)
# DB_test_dir = '../database/test'
# DB_test_csv = 'data_test.csv'
DB_test_dir = '/content/Data/test'
DB_test_csv = '/content/test.csv'
db2 = MyDatabase(DB_test_dir, DB_test_csv)
# evaluate database
fusion = FeatureFusion(features=['color', 'edge'])
APs,res = evaluate_class(db, db2, f_instance=fusion, depth=3, d_type=d_type)
cls_MAPs = []
for cls, cls_APs in APs.items():
MAP = np.mean(cls_APs)
print("Class {}, MAP {}".format(cls, MAP))
cls_MAPs.append(MAP)
print("MMAP", np.mean(cls_MAPs))
for i in range(len(db2)):
saveName = "/content/traitement_images/Data/result_fusion/" + res[i] +"/" + db2.data.img[i].split('/')[-1]
bid = imageio.imread(db2.data.img[i])
mping.imsave(saveName,bid/255)