def EarthMovers_dist(s1, s2):
x = np.array(s1)
y = np.array(s2)
#print("x = ", x ," y = " , y)
a = np.zeros((len(x), 2))
b = np.zeros((len(y), 2))
for i in range(0, len(a)):
a[i][0] = x[i]
a[i][1] = i + 1.0
for i in range(0, len(b)):
b[i][0] = y[i]
b[i][1] = i + 1.0
#print("a = ", a ," b = " , b)
# Convert from numpy array to CV_32FC1 Mat
a64 = cv.fromarray(a)
a32 = cv.CreateMat(a64.rows, a64.cols, cv.CV_32FC1)
cv.Convert(a64, a32)
b64 = cv.fromarray(b)
b32 = cv.CreateMat(b64.rows, b64.cols, cv.CV_32FC1)
cv.Convert(b64, b32)
# Calculate Earth Mover's
dis = cv.CalcEMD2(
a32, b32, cv.CV_DIST_L1
) #CV_DIST_L2 -- Euclidean Distance, CV_DIST_L1 --- Manhattan Distance
return dis
def CalcEmd(histograms1, histograms2):
result = 0
for i in xrange(len(histograms1)):
data1 = ConvertForEmd(histograms1[i])
data2 = ConvertForEmd(histograms2[i])
distance = cv.CalcEMD2(data1, data2, cv.CV_DIST_L2) / HISTOGRAM_BIN_N
result = result + distance**2
return result**0.5
def histEMD(hist1, hist2, hist1weights, hist2weights):
a64 = cv.fromarray(np.hstack((hist1weights, hist1)).copy())
a32 = cv.CreateMat(a64.rows, a64.cols, cv.CV_32FC1)
cv.Convert(a64, a32)
b64 = cv.fromarray(np.hstack((hist2weights, hist2)).copy())
b32 = cv.CreateMat(b64.rows, b64.cols, cv.CV_32FC1)
cv.Convert(b64, b32)
return cv.CalcEMD2(a32, b32, cv.CV_DIST_L2)
def calcEM(hist1, hist2, h_bins, s_bins):
#Define number of rows
numRows = h_bins * s_bins
sig1 = cv.CreateMat(numRows, 3, cv.CV_32FC1)
sig2 = cv.CreateMat(numRows, 3, cv.CV_32FC1)
for h in range(h_bins):
for s in range(s_bins):
bin_val = cv.QueryHistValue_2D(hist1, h, s)
cv.Set2D(sig1, h * s_bins + s, 0, cv.Scalar(bin_val))
cv.Set2D(sig1, h * s_bins + s, 1, cv.Scalar(h))
cv.Set2D(sig1, h * s_bins + s, 2, cv.Scalar(s))
bin_val = cv.QueryHistValue_2D(hist2, h, s)
cv.Set2D(sig2, h * s_bins + s, 0, cv.Scalar(bin_val))
cv.Set2D(sig2, h * s_bins + s, 1, cv.Scalar(h))
cv.Set2D(sig2, h * s_bins + s, 2, cv.Scalar(s))
#This is the important line were the OpenCV EM algorithm is called
return cv.CalcEMD2(sig1, sig2, cv.CV_DIST_L2)
def calcEM(hist1, hist2, l_bins=16, u_bins=16, v_bins=16):
#Define number of rows
numRows = l_bins * u_bins * v_bins
sig1 = cv.CreateMat(numRows, 4, cv.CV_32FC1)
sig2 = cv.CreateMat(numRows, 4, cv.CV_32FC1)
eq_val = 1.0 / numRows
for l in range(l_bins):
for u in range(u_bins):
for v in range(v_bins):
bin_val = cv.QueryHistValue_3D(hist1, l, u, v)
cv.Set2D(sig1, l * u_bins * v_bins + u * v_bins + v, 0,
cv.Scalar(bin_val))
cv.Set2D(sig1, l * u_bins * v_bins + u * v_bins + v, 1,
cv.Scalar(l))
cv.Set2D(sig1, l * u_bins * v_bins + u * v_bins + v, 2,
cv.Scalar(u))
cv.Set2D(sig1, l * u_bins * v_bins + u * v_bins + v, 3,
cv.Scalar(v))
if hist2 == None:
bin_val = eq_val
else:
bin_val = cv.QueryHistValue_3D(hist2, l, u, v)
cv.Set2D(sig2, l * u_bins * v_bins + u * v_bins + v, 0,
cv.Scalar(bin_val))
cv.Set2D(sig2, l * u_bins * v_bins + u * v_bins + v, 1,
cv.Scalar(l))
cv.Set2D(sig2, l * u_bins * v_bins + u * v_bins + v, 2,
cv.Scalar(u))
cv.Set2D(sig2, l * u_bins * v_bins + u * v_bins + v, 3,
cv.Scalar(v))
#This is the important line were the OpenCV EM algorithm is called
return (cv.CalcEMD2(sig1, sig2, cv.CV_DIST_L2), np.asarray(sig1)[:, 0],
np.asarray(sig2)[:, 0])
import numpy as np
import scipy.stats
KL = scipy.stats.entropy(x, y)
print(KL)
# 编程实现
KL = 0.0
for i in range(len(px)):
KL += px[i] * np.log(px[i] / py[i])
print(KL)
import numpy as np
import cv
pp = cv.fromarray(p)
qq = cv.fromarray(q)
emd = cv.CalcEMD2(pp, qq, cv.CV_DIST_L2)