我正在尝试使用 Finlayson 等的熵最小化方法在 python OpenCV 中实现阴影去除。人：
“熵最小化的内在图像”，Finlayson 等。人。
我似乎无法匹配论文中的结果。我的熵图与论文中的不匹配，并且我得到了错误的最小熵。
有什么想法吗？（根据要求，我有更多的源代码和论文）
#############
# LIBRARIES
#############
import numpy as np
import cv2
import os
import sys
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
from PIL import Image
import scipy
from scipy.optimize import leastsq
from scipy.stats.mstats import gmean
from scipy.signal import argrelextrema
from scipy.stats import entropy
from scipy.signal import savgol_filter
root = r'\path\to\my_folder'
fl = r'my_file.jpg'
#############
# PROGRAM
#############
if __name__ == '__main__':
#-----------------------------------
## 1. Create Chromaticity Vectors ##
#-----------------------------------
# Get Image
img = cv2.imread(os.path.join(root, fl))
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
h, w = img.shape[:2]
plt.imshow(img)
plt.title('Original')
plt.show()
img = cv2.GaussianBlur(img, (5,5), 0)
# Separate Channels
r, g, b = cv2.split(img)
im_sum = np.sum(img, axis=2)
im_mean = gmean(img, axis=2)
# Create "normalized", mean, and rg chromaticity vectors
# We use mean (works better than norm). rg Chromaticity is
# for visualization
n_r = np.ma.divide( 1.*r, g )
n_b = np.ma.divide( 1.*b, g )
mean_r = np.ma.divide(1.*r, im_mean)
mean_g = np.ma.divide(1.*g, im_mean)
mean_b = np.ma.divide(1.*b, im_mean)
rg_chrom_r = np.ma.divide(1.*r, im_sum)
rg_chrom_g = np.ma.divide(1.*g, im_sum)
rg_chrom_b = np.ma.divide(1.*b, im_sum)
# Visualize rg Chromaticity --> DEBUGGING
rg_chrom = np.zeros_like(img)
rg_chrom[:,:,0] = np.clip(np.uint8(rg_chrom_r*255), 0, 255)
rg_chrom[:,:,1] = np.clip(np.uint8(rg_chrom_g*255), 0, 255)
rg_chrom[:,:,2] = np.clip(np.uint8(rg_chrom_b*255), 0, 255)
plt.imshow(rg_chrom)
plt.title('rg Chromaticity')
plt.show()
#-----------------------
## 2. Take Logarithms ##
#-----------------------
l_rg = np.ma.log(n_r)
l_bg = np.ma.log(n_b)
log_r = np.ma.log(mean_r)
log_g = np.ma.log(mean_g)
log_b = np.ma.log(mean_b)
## rho = np.zeros_like(img, dtype=np.float64)
##
## rho[:,:,0] = log_r
## rho[:,:,1] = log_g
## rho[:,:,2] = log_b
rho = cv2.merge((log_r, log_g, log_b))
# Visualize Logarithms --> DEBUGGING
plt.scatter(l_rg, l_bg, s = 2)
plt.xlabel('Log(R/G)')
plt.ylabel('Log(B/G)')
plt.title('Log Chromaticities')
plt.show()
plt.scatter(log_r, log_b, s = 2)
plt.xlabel('Log( R / 3root(R*G*B) )')
plt.ylabel('Log( B / 3root(R*G*B) )')
plt.title('Geometric Mean Log Chromaticities')
plt.show()
#----------------------------
## 3. Rotate through Theta ##
#----------------------------
u = 1./np.sqrt(3)*np.array([[1,1,1]]).T
I = np.eye(3)
tol = 1e-15
P_u_norm = I - u.dot(u.T)
U_, s, V_ = np.linalg.svd(P_u_norm, full_matrices = False)
s[ np.where( s <= tol ) ] = 0.
U = np.dot(np.eye(3)*np.sqrt(s), V_)
U = U[ ~np.all( U == 0, axis = 1) ].T
# Columns are upside down and column 2 is negated...?
U = U[::-1,:]
U[:,1] *= -1.
## TRUE ARRAY:
##
## U = np.array([[ 0.70710678, 0.40824829],
## [-0.70710678, 0.40824829],
## [ 0. , -0.81649658]])
chi = rho.dot(U)
# Visualize chi --> DEBUGGING
plt.scatter(chi[:,:,0], chi[:,:,1], s = 2)
plt.xlabel('chi1')
plt.ylabel('chi2')
plt.title('2D Log Chromaticities')
plt.show()
e = np.array([[np.cos(np.radians(np.linspace(1, 180, 180))), \
np.sin(np.radians(np.linspace(1, 180, 180)))]])
gs = chi.dot(e)
prob = np.array([np.histogram(gs[...,i], bins='scott', density=True)[0]
for i in range(np.size(gs, axis=3))])
eta = np.array([entropy(p, base=2) for p in prob])
plt.plot(eta)
plt.xlabel('Angle (deg)')
plt.ylabel('Entropy, eta')
plt.title('Entropy Minimization')
plt.show()
theta_min = np.radians(np.argmin(eta))
print('Min Angle: ', np.degrees(theta_min))
e = np.array([[-1.*np.sin(theta_min)],
[np.cos(theta_min)]])
gs_approx = chi.dot(e)
# Visualize Grayscale Approximation --> DEBUGGING
plt.imshow(gs_approx.squeeze(), cmap='gray')
plt.title('Grayscale Approximation')
plt.show()
P_theta = np.ma.divide( np.dot(e, e.T), np.linalg.norm(e) )
chi_theta = chi.dot(P_theta)
rho_estim = chi_theta.dot(U.T)
mean_estim = np.ma.exp(rho_estim)
estim = np.zeros_like(mean_estim, dtype=np.float64)
estim[:,:,0] = np.divide(mean_estim[:,:,0], np.sum(mean_estim, axis=2))
estim[:,:,1] = np.divide(mean_estim[:,:,1], np.sum(mean_estim, axis=2))
estim[:,:,2] = np.divide(mean_estim[:,:,2], np.sum(mean_estim, axis=2))
plt.imshow(estim)
plt.title('Invariant rg Chromaticity')
plt.show()
输出：
