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HOG.py
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HOG.py
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# -*- coding: utf-8 -*-
from __future__ import print_function
from evaluate import evaluate_class
from DB import Database
from skimage.feature import hog
from skimage import color
from six.moves import cPickle
import numpy as np
import scipy.misc
import os
n_bin = 10
n_slice = 6
n_orient = 8
p_p_c = (2, 2)
c_p_b = (1, 1)
h_type = 'region'
d_type = 'd1'
depth = 5
''' MMAP
depth
depthNone, HOG-region-n_bin10-n_slice6-n_orient8-ppc(2, 2)-cpb(1, 1), distance=d1, MMAP 0.155887235348
depth100, HOG-region-n_bin10-n_slice6-n_orient8-ppc(2, 2)-cpb(1, 1), distance=d1, MMAP 0.261149622088
depth30, HOG-region-n_bin10-n_slice6-n_orient8-ppc(2, 2)-cpb(1, 1), distance=d1, MMAP 0.371054105819
depth10, HOG-region-n_bin10-n_slice6-n_orient8-ppc(2, 2)-cpb(1, 1), distance=d1, MMAP 0.449627835097
depth5, HOG-region-n_bin10-n_slice6-n_orient8-ppc(2, 2)-cpb(1, 1), distance=d1, MMAP 0.465333333333
depth3, HOG-region-n_bin10-n_slice6-n_orient8-ppc(2, 2)-cpb(1, 1), distance=d1, MMAP 0.463833333333
depth1, HOG-region-n_bin10-n_slice6-n_orient8-ppc(2, 2)-cpb(1, 1), distance=d1, MMAP 0.398
(exps below use depth=None)
ppc & cpb
HOG-global-n_bin10-n_orient8-ppc(2, 2)-cpb(1, 1), distance=d1, MMAP 0.105569494513
HOG-global-n_bin10-n_orient8-ppc(32, 32)-cpb(1, 1), distance=d1, MMAP 0.0945343258574
HOG-global-n_bin10-n_orient8-ppc(8, 8)-cpb(3, 3), distance=d1, MMAP 0.0782408187317
h_type
HOG-global-n_bin100-n_orient8-ppc(32, 32)-cpb(1, 1), distance=d1, MMAP 0.0990826443803
HOG-region-n_bin100-n_slice4-n_orient8-ppc(32, 32)-cpb(1, 1), distance=d1, MMAP 0.131164310773
n_orient
HOG-global-n_bin10-n_orient8-ppc(2, 2)-cpb(1, 1), distance=d1, MMAP 0.105569494513
HOG-region-n_bin10-n_slice4-n_orient18-ppc(2, 2)-cpb(1, 1), distance=d1, MMAP 0.14941454752
n_bin
HOG-region-n_bin5-n_slice4-n_orient8-ppc(32, 32)-cpb(1, 1), distance=d1, MMAP 0.140448910465
HOG-region-n_bin10-n_slice4-n_orient8-ppc(32, 32)-cpb(1, 1), distance=d1, MMAP 0.144675311048
HOG-region-n_bin20-n_slice4-n_orient8-ppc(32, 32)-cpb(1, 1), distance=d1, MMAP 0.1429074023
HOG-region-n_bin100-n_slice4-n_orient8-ppc(32, 32)-cpb(1, 1), distance=d1, MMAP 0.131164310773
n_slice
HOG-region-n_bin10-n_slice2-n_orient8-ppc(2, 2)-cpb(1, 1), distance=d1, MMAP 0.116513458785
HOG-region-n_bin10-n_slice4-n_orient8-ppc(2, 2)-cpb(1, 1), distance=d1, MMAP 0.151557545391
HOG-region-n_bin10-n_slice6-n_orient8-ppc(2, 2)-cpb(1, 1), distance=d1, MMAP 0.155887235348
HOG-region-n_bin10-n_slice8-n_orient8-ppc(2, 2)-cpb(1, 1), distance=d1, MMAP 0.15347983005
'''
# cache dir
cache_dir = 'cache'
if not os.path.exists(cache_dir):
os.makedirs(cache_dir)
class HOG(object):
def histogram(self, input, n_bin=n_bin, type=h_type, n_slice=n_slice, normalize=True):
''' count img histogram
arguments
input : a path to a image or a numpy.ndarray
n_bin : number of bins of histogram
type : 'global' means count the histogram for whole image
'region' means count the histogram for regions in images, then concatanate all of them
n_slice : work when type equals to 'region', height & width will equally sliced into N slices
normalize: normalize output histogram
return
type == 'global'
a numpy array with size n_bin
type == 'region'
a numpy array with size n_bin * n_slice * n_slice
'''
if isinstance(input, np.ndarray): # examinate input type
img = input.copy()
else:
img = scipy.misc.imread(input, mode='RGB')
height, width, channel = img.shape
if type == 'global':
hist = self._HOG(img, n_bin)
elif type == 'region':
hist = np.zeros((n_slice, n_slice, n_bin))
h_silce = np.around(np.linspace(0, height, n_slice+1, endpoint=True)).astype(int)
w_slice = np.around(np.linspace(0, width, n_slice+1, endpoint=True)).astype(int)
for hs in range(len(h_silce)-1):
for ws in range(len(w_slice)-1):
img_r = img[h_silce[hs]:h_silce[hs+1], w_slice[ws]:w_slice[ws+1]] # slice img to regions
hist[hs][ws] = self._HOG(img_r, n_bin)
if normalize:
hist /= np.sum(hist)
return hist.flatten()
def _HOG(self, img, n_bin, normalize=True):
image = color.rgb2gray(img)
fd = hog(image, orientations=n_orient, pixels_per_cell=p_p_c, cells_per_block=c_p_b)
bins = np.linspace(0, np.max(fd), n_bin+1, endpoint=True)
hist, _ = np.histogram(fd, bins=bins)
if normalize:
hist = np.array(hist) / np.sum(hist)
return hist
def make_samples(self, db, verbose=True):
if h_type == 'global':
sample_cache = "HOG-{}-n_bin{}-n_orient{}-ppc{}-cpb{}".format(h_type, n_bin, n_orient, p_p_c, c_p_b)
elif h_type == 'region':
sample_cache = "HOG-{}-n_bin{}-n_slice{}-n_orient{}-ppc{}-cpb{}".format(h_type, n_bin, n_slice, n_orient, p_p_c, c_p_b)
try:
samples = cPickle.load(open(os.path.join(cache_dir, sample_cache), "rb", True))
for sample in samples:
sample['hist'] /= np.sum(sample['hist']) # normalize
if verbose:
print("Using cache..., config=%s, distance=%s, depth=%s" % (sample_cache, d_type, depth))
except:
if verbose:
print("Counting histogram..., config=%s, distance=%s, depth=%s" % (sample_cache, d_type, depth))
samples = []
data = db.get_data()
for d in data.itertuples():
d_img, d_cls = getattr(d, "img"), getattr(d, "cls")
d_hist = self.histogram(d_img, type=h_type, n_slice=n_slice)
samples.append({
'img': d_img,
'cls': d_cls,
'hist': d_hist
})
cPickle.dump(samples, open(os.path.join(cache_dir, sample_cache), "wb", True))
return samples
if __name__ == "__main__":
db = Database()
# evaluate database
APs = evaluate_class(db, f_class=HOG, d_type=d_type, depth=depth)
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))