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daisy.py
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daisy.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 daisy
from skimage import color
from six.moves import cPickle
import numpy as np
import scipy.misc
import math
import os
n_slice = 2
n_orient = 8
step = 10
radius = 30
rings = 2
histograms = 6
h_type = 'region'
d_type = 'd1'
depth = 3
R = (rings * histograms + 1) * n_orient
''' MMAP
depth
depthNone, daisy-region-n_slice2-n_orient8-step10-radius30-rings2-histograms6, distance=d1, MMAP 0.162806083971
depth100, daisy-region-n_slice2-n_orient8-step10-radius30-rings2-histograms6, distance=d1, MMAP 0.269333190731
depth30, daisy-region-n_slice2-n_orient8-step10-radius30-rings2-histograms6, distance=d1, MMAP 0.388199474789
depth10, daisy-region-n_slice2-n_orient8-step10-radius30-rings2-histograms6, distance=d1, MMAP 0.468182738095
depth5, daisy-region-n_slice2-n_orient8-step10-radius30-rings2-histograms6, distance=d1, MMAP 0.497688888889
depth3, daisy-region-n_slice2-n_orient8-step10-radius30-rings2-histograms6, distance=d1, MMAP 0.499833333333
depth1, daisy-region-n_slice2-n_orient8-step10-radius30-rings2-histograms6, distance=d1, MMAP 0.448
(exps below use depth=None)
d_type
daisy-global-n_orient8-step180-radius58-rings2-histograms6, distance=d1, MMAP 0.101883969577
daisy-global-n_orient8-step180-radius58-rings2-histograms6, distance=cosine, MMAP 0.104779921854
h_type
daisy-global-n_orient8-step10-radius30-rings2-histograms6, distance=d1, MMAP 0.157738278588
daisy-region-n_slice2-n_orient8-step10-radius30-rings2-histograms6, distance=d1, MMAP 0.162806083971
'''
# cache dir
cache_dir = 'cache'
if not os.path.exists(cache_dir):
os.makedirs(cache_dir)
class Daisy(object):
def histogram(self, input, type=h_type, n_slice=n_slice, normalize=True):
''' count img histogram
arguments
input : a path to a image or a numpy.ndarray
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 R
type == 'region'
a numpy array with size n_slice * n_slice * R
#R = (rings * histograms + 1) * n_orient#
'''
if isinstance(input, np.ndarray): # examinate input type
img = input.copy()
else:
img = scipy.misc.imread(input, mode='RGB')
height, width, channel = img.shape
P = math.ceil((height - radius*2) / step)
Q = math.ceil((width - radius*2) / step)
assert P > 0 and Q > 0, "input image size need to pass this check"
if type == 'global':
hist = self._daisy(img)
elif type == 'region':
hist = np.zeros((n_slice, n_slice, R))
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._daisy(img_r)
if normalize:
hist /= np.sum(hist)
return hist.flatten()
def _daisy(self, img, normalize=True):
image = color.rgb2gray(img)
descs = daisy(image, step=step, radius=radius, rings=rings, histograms=histograms, orientations=n_orient)
descs = descs.reshape(-1, R) # shape=(N, R)
hist = np.mean(descs, axis=0) # shape=(R,)
if normalize:
hist = np.array(hist) / np.sum(hist)
return hist
def make_samples(self, db, verbose=True):
if h_type == 'global':
sample_cache = "daisy-{}-n_orient{}-step{}-radius{}-rings{}-histograms{}".format(h_type, n_orient, step, radius, rings, histograms)
elif h_type == 'region':
sample_cache = "daisy-{}-n_slice{}-n_orient{}-step{}-radius{}-rings{}-histograms{}".format(h_type, n_slice, n_orient, step, radius, rings, histograms)
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=Daisy, 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))