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color.py
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# -*- coding: utf-8 -*-
from __future__ import print_function
from evaluate import distance, evaluate_class
from DB import Database
from six.moves import cPickle
import numpy as np
import scipy.misc
import itertools
import os
# configs for histogram
n_bin = 12 # histogram bins
n_slice = 3 # slice image
h_type = 'region' # global or region
d_type = 'd1' # distance type
depth = 3 # retrieved depth, set to None will count the ap for whole database
''' MMAP
depth
depthNone, region,bin12,slice3, distance=d1, MMAP 0.273745840034
depth100, region,bin12,slice3, distance=d1, MMAP 0.406007856783
depth30, region,bin12,slice3, distance=d1, MMAP 0.516738512679
depth10, region,bin12,slice3, distance=d1, MMAP 0.614047666604
depth5, region,bin12,slice3, distance=d1, MMAP 0.650125
depth3, region,bin12,slice3, distance=d1, MMAP 0.657166666667
depth1, region,bin12,slice3, distance=d1, MMAP 0.62
(exps below use depth=None)
d_type
global,bin6,d1,MMAP 0.242345913685
global,bin6,cosine,MMAP 0.184176505586
n_bin
region,bin10,slice4,d1,MMAP 0.269872790396
region,bin12,slice4,d1,MMAP 0.271520862017
region,bin6,slcie3,d1,MMAP 0.262819311357
region,bin12,slice3,d1,MMAP 0.273745840034
n_slice
region,bin12,slice2,d1,MMAP 0.266076627332
region,bin12,slice3,d1,MMAP 0.273745840034
region,bin12,slice4,d1,MMAP 0.271520862017
region,bin14,slice3,d1,MMAP 0.272386552594
region,bin14,slice5,d1,MMAP 0.266877181379
region,bin16,slice3,d1,MMAP 0.273716788003
region,bin16,slice4,d1,MMAP 0.272221031804
region,bin16,slice8,d1,MMAP 0.253823360098
h_type
region,bin4,slice2,d1,MMAP 0.23358615622
global,bin4,d1,MMAP 0.229125435746
'''
# cache dir
cache_dir = 'cache'
if not os.path.exists(cache_dir):
os.makedirs(cache_dir)
class Color(object):
def histogram(self, input, n_bin=n_bin, type=h_type, n_slice=n_slice, normalize=True):
''' count img color histogram
arguments
input : a path to a image or a numpy.ndarray
n_bin : number of bins for each channel
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 ** channel
type == 'region'
a numpy array with size n_slice * n_slice * (n_bin ** channel)
'''
if isinstance(input, np.ndarray): # examinate input type
img = input.copy()
else:
img = scipy.misc.imread(input, mode='RGB')
height, width, channel = img.shape
bins = np.linspace(0, 256, n_bin+1, endpoint=True) # slice bins equally for each channel
if type == 'global':
hist = self._count_hist(img, n_bin, bins, channel)
elif type == 'region':
hist = np.zeros((n_slice, n_slice, n_bin ** channel))
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._count_hist(img_r, n_bin, bins, channel)
if normalize:
hist /= np.sum(hist)
return hist.flatten()
def _count_hist(self, input, n_bin, bins, channel):
img = input.copy()
bins_idx = {key: idx for idx, key in enumerate(itertools.product(np.arange(n_bin), repeat=channel))} # permutation of bins
hist = np.zeros(n_bin ** channel)
# cluster every pixels
for idx in range(len(bins)-1):
img[(input >= bins[idx]) & (input < bins[idx+1])] = idx
# add pixels into bins
height, width, _ = img.shape
for h in range(height):
for w in range(width):
b_idx = bins_idx[tuple(img[h,w])]
hist[b_idx] += 1
return hist
def make_samples(self, db, verbose=True):
if h_type == 'global':
sample_cache = "histogram_cache-{}-n_bin{}".format(h_type, n_bin)
elif h_type == 'region':
sample_cache = "histogram_cache-{}-n_bin{}-n_slice{}".format(h_type, n_bin, n_slice)
try:
samples = cPickle.load(open(os.path.join(cache_dir, sample_cache), "rb", True))
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_bin=n_bin, 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()
data = db.get_data()
color = Color()
# test normalize
hist = color.histogram(data.ix[0,0], type='global')
assert hist.sum() - 1 < 1e-9, "normalize false"
# test histogram bins
def sigmoid(z):
a = 1.0 / (1.0 + np.exp(-1. * z))
return a
np.random.seed(0)
IMG = sigmoid(np.random.randn(2,2,3)) * 255
IMG = IMG.astype(int)
hist = color.histogram(IMG, type='global', n_bin=4)
assert np.equal(np.where(hist > 0)[0], np.array([37, 43, 58, 61])).all(), "global histogram implement failed"
hist = color.histogram(IMG, type='region', n_bin=4, n_slice=2)
assert np.equal(np.where(hist > 0)[0], np.array([58, 125, 165, 235])).all(), "region histogram implement failed"
# examinate distance
np.random.seed(1)
IMG = sigmoid(np.random.randn(4,4,3)) * 255
IMG = IMG.astype(int)
hist = color.histogram(IMG, type='region', n_bin=4, n_slice=2)
IMG2 = sigmoid(np.random.randn(4,4,3)) * 255
IMG2 = IMG2.astype(int)
hist2 = color.histogram(IMG2, type='region', n_bin=4, n_slice=2)
assert distance(hist, hist2, d_type='d1') == 2, "d1 implement failed"
assert distance(hist, hist2, d_type='d2-norm') == 2, "d2 implement failed"
# evaluate database
APs = evaluate_class(db, f_class=Color, 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))