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# coding=utf-8
# get_segment.pyで生成したセグメントを読み込み, 輪郭点の数と結合重みを振って認識率を確かめる実験を行うスクリプト
import numpy as np
import cv2
from matplotlib import pyplot as plt
from collections import Counter
def read_csv(filename):
return map(lambda line: line.split(','), list(open(filename, 'r')))
def normalize(v):
m = max(v)
for i in range(len(v)):
v[i] = v[i] / m
return v
def np_hist_to_cv(np_hist):
counts, bin_edges = np_hist
return counts.ravel().astype('float32')
# hist_r, hist_tが与えられた時, d_histsのどれに属すかをd_labelsから選んで返す.
def recognize(d_hists_r, d_hists_t, d_labels, hist_r, hist_t, w=0.5, k=5):
dists = []
# len(dists) == len(d_hists_r)
for i in range(len(d_hists_r)):
dist_r = cv2.compareHist(d_hists_r[i], hist_r, cv2.HISTCMP_BHATTACHARYYA)
dist_t = cv2.compareHist(d_hists_t[i], hist_t, cv2.HISTCMP_BHATTACHARYYA)
dists.append(w * dist_r + (1 - w) * dist_t)
k_labels = []
for l in range(k):
k_labels.append(d_labels[dists.index(sorted(dists)[l])])
ans = [ite for ite, it in Counter(k_labels).most_common(1)]
return ans[0]
def get_hists_from_csv(filepath):
data = read_csv(filepath)
hists_r = []
hists_t = []
for i, d in enumerate(data):
ar = map(lambda x: float(x), d)
vr = []
vt = []
for k, x in enumerate(ar):
if k % 2 == 1:
vt.append(x)
else:
vr.append(x)
hists_r.append(np_hist_to_cv(np.histogram(normalize(vr), bins=30)))
hists_t.append(np_hist_to_cv(np.histogram(vt, bins=30, range=(0, 180))))
return hists_r, hists_t
def recognize_images(filenames, images_label):
data = read_csv("./train_feature.csv")
labels = list("cpgcgppccgppcpgcgppccgppcpgcgppccgppcpgcgppccgpp")
answered = 0
hists_r = []
hists_t = []
for i, d in enumerate(data):
ar = map(lambda x: float(x), d)
vr = []
vt = []
for k, x in enumerate(ar):
if k % 2 == 1:
vt.append(x)
else:
vr.append(x)
hists_r.append(np_hist_to_cv(np.histogram(normalize(vr), bins=30)))
hists_t.append(np_hist_to_cv(np.histogram(vt, bins=30, range=(0, 180))))
for i, filename in enumerate(filenames):
recog_data = get_feature(filename)
vr = []
vt = []
for k, x in enumerate(recog_data):
vr.append(x[0])
vt.append(x[1])
t_hist_r = np_hist_to_cv(np.histogram(normalize(vr), bins=30))
t_hist_t = np_hist_to_cv(np.histogram(vt, bins=30, range=(0, 180)))
ans = recognize(hists_r, hists_t, labels, t_hist_r, t_hist_t)
if ans == images_label[i]:
answered += 1
# print answered, len(images_label)
print answered / float(len(images_label))
def recognize_feature(hists_r, hists_t, test_hists_r, test_hists_t, w=0.35, k=5):
labels = list("cpgcgppccgppcpgcgppccgppcpgcgppccgppcpgcgppccgpp")
#labels = list("pgcpppcgcppccpppcgg")
answered = 0
anss = []
for i in range(len(test_hists_r)):
ans = recognize(hists_r, hists_t, labels, test_hists_r[i], test_hists_t[i], w, k)
anss.append(ans)
if ans == images_label[i]:
answered += 1
# print answered, len(images_label)
return answered/ float(len(images_label))
#print images_label
#print anss
def get_contour_sets(edge, dd):
sets = []
center = get_centroid(edge)
r_array = []
r_array_norms = []
for i in range(len(edge)):
r_array.append(edge[i] - center)
r_array_norms.append(np.linalg.norm(r_array[i]))
r_ave = np.array(r_array_norms).mean()
d = int(r_ave / dd)
for i in range(len(edge) - d):
t = edge[i + d] - edge[i]
sets.append(np.array([r_array_norms[i], get_degree(r_array[i], t)]))
return sets
def get_degree(v1, v2):
return (360 / (2 * np.pi)) * np.arccos(np.dot(v1, v2) / (np.linalg.norm(v1) * np.linalg.norm(v2)))
def dimension_change_smoothing(edge, d):
l = len(edge)
a = [l / d] * d
m = l % d
if m != 0:
c = 0
while m > 0:
a[c] += 1
m -= 1
c += 1
if c > d:
c = 0
c = 0
ret = []
for i in range(l):
if i == sum(a[0:c]):
if 0 < i < l - 1:
ret.append((edge[i - 1] + edge[i] + edge[i + 1]) / 3.0)
else:
ret.append(edge[i])
c += 1
return ret
def get_centroid(edge):
s = np.array([0, 0])
for v in edge:
s += v.astype(int)
return s / len(edge)
def get_hists_from_segment(filepath, d):
data = read_csv(filepath)
features = []
for edge in data:
e = map(lambda x: int(x), edge)
ee = []
for i in range(len(e) - 1):
if i % 2 == 0:
ee.append(np.array([e[i], e[i + 1]]))
features.append(dimension_change_smoothing(get_contour_sets(ee, 5), d))
hists_r = []
hists_t = []
for i, feature in enumerate(features):
vr = []
vt = []
for x in feature:
vr.append(x[0])
vt.append(x[1])
hists_r.append(np_hist_to_cv(np.histogram(normalize(vr), bins=30)))
hists_t.append(np_hist_to_cv(np.histogram(vt, bins=30, range=(0, 180))))
return hists_r, hists_t
if __name__ == '__main__':
images_label = list("pgcpppcgcppccpppcgg")
#images_label = list("cpgcgppccgppcpgcgppccgppcpgcgppccgppcpgcgppccgpp")
for d in range(1, 16):
test_hists_r, test_hists_t = get_hists_from_segment("./test_segments.csv", d*20)
hists_r, hists_t = get_hists_from_segment("./train_segments.csv", d*20)
re = []
for i in range(21):
re.append((recognize_feature(hists_r, hists_t, test_hists_r, test_hists_t, w=i/20.0))),
print np.average(re), np.var(re)
print re
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