Question

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：

“通过熵最小化的内在图像”，Finlayson等。人

我似乎无法与论文的结果相匹配。我的熵图与论文中的熵图不匹配，我得到了错误的最小熵。

有什么想法？（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：

Answer 1

用于影子分割的matlab代码并删除附件中附件中的附录部分。希望这可以帮到你。 reference doc

Answer 2

结果和讨论中的

Shadow Removal Using Illumination Invariant Image Formation (Ranaweera, Drew)注释由于JPEG压缩，JPEG图像和PNG图像的结果不同。因此，期望结果与“熵最小化的内在图像”（Finlayson等人）所显示的结果可能并不合理。

我还注意到你没有添加作者在其他论文中推荐的“额外光线”。

此外，在定义rg_chrom时，频道的顺序必须是BGR而不是您使用的RGB。

我正在努力实现论文，所以你的代码对我非常有用。谢谢你

Python OpenCV中的阴影删除

2 个答案: