python图片处理

Question

改写别人项目，但是结果图片不尽人意，结果如下：

这是一个原图的一个分量，明显看到歪了，正确的应该是正的黑白图，原图：

以下是相关代码，我就贴一些相关的，不然代码太多太繁琐，最后cv.imshow(m)结果就是我的歪图：

def computeKernel(mat, nRowSamples, nColSamples, hx, hy):
    if nRowSamples > mat.shape[0] or nColSamples > mat.shape[1]:
        raise ValueError("Number of samples per row and col must be <= that of image.")

    nPixels = mat.size
    selected, rest = samplePixels(mat.shape[0], mat.shape[1], nRowSamples, nColSamples)
    nSamples = len(selected)
    Ka = np.zeros((nSamples, nSamples))
    Kab = np.zeros((nSamples, nPixels - nSamples))

    photometricWeight = 1.0 / (hy * hy)
    spatialWeight = 1.0 / (hx * hx)
    for i in range(nSamples):
        # Ka
        p1 = selected[i]
        for j in range(i, nSamples):
            val = negativeWeightedDistance(mat, p1, selected[j], spatialWeight, photometricWeight)
            Ka[i, j] = val
            Ka[j, i] = val
        # Kab
        for j in range(len(rest)):
            Kab[i, j] = negativeWeightedDistance(mat, p1, rest[j], spatialWeight, photometricWeight)

    Ka = np.exp(Ka)
    Kab = np.exp(Kab)

    if (Ka.transpose() == Ka).all():
        print("Ka is symmetric")
    else:
        print("Ka is NOT symmetric")

    P = np.zeros((nPixels,), dtype=np.int32)
    for i in range(nSamples):
        p = selected[i]
        P[i] = to1DIndex(p[0], p[1], mat.shape[1])
    for j in range(len(rest)):
        p = rest[j]
        P[j + nSamples] = to1DIndex(p[0], p[1], mat.shape[1])
    return P, Ka, Kab
    pass

def eigen2opencv(v, nrows, ncols):
    mat = np.array(v).reshape((nrows, ncols))
    return mat.copy()
    pass

def rescaleForVisualization(mat):
    mat = np.float32(mat)
    minVal, maxVal, _, _ = cv2.minMaxLoc(mat)
    rescaledMat = (mat - minVal) / (maxVal - minVal) * 255
    return rescaledMat.astype(np.uint8)
    pass

    def trainFilter(self, channel, nRowSamples, nColSamples, hx, hy, nSinkhornIter, nEigenVectors):
        # implementation of the filter training algorithm
        # ...
        print("Computing kernel")
        P, Ka, Kab = computeKernel(channel, nRowSamples, nColSamples, hx, hy)

        print("Nystrom approximation")
        eigvals, phi = nystromApproximation(Ka, Kab)

        print("Sinkhorn")
        Wa, Wab = sinkhorn(phi, eigvals, nSinkhornIter)

        print("Orthogonalize")
        eigvecs, eigvals = orthogonalize(Wa, Wab, nEigenVectors,EPS)

        # Permute values back into correct position
        #eigvecs = np.dot(P, eigvecs)
        #eigvecs2 = np.empty((0, eigvecs.shape[1]))
        #for i in range(P.size):
            #row_to_add = eigvecs[P[i], :]
            #eigvecs2 = np.vstack((eigvecs2, row_to_add))
        eigvecs2=np.take(eigvecs,P,axis=0)
        #eigvecs2=eigvecs
        for i in range(min(nEigenVectors, 5)):
            v = eigvecs2[:,i]
            print("Eigvec ", i, " eigval: ", eigvals[i], " minCoeff: ", np.min(v), " maxCoeff: ", np.max(v))
            m = eigen2opencv(v, channel.shape[0], channel.shape[1])
            m = rescaleForVisualization(m)
            m = cv2.convertScaleAbs(m)
            cv2.imshow("image" + str(i), m)

        return eigvecs2,eigvals
        pass

Answer 1

图像的手绘效果：
注：将第6行的打开图片改为你的原图名称，并放在同一文件夹中。

from PIL import Image
import numpy as np
vec_el = np.pi/2.2 # 光源的俯视角度，弧度值
vec_az = np.pi/4. # 光源的方位角度，弧度值
depth = 10. # (0-100)
im = Image.open('fcity.jpg').convert('L')
a = np.asarray(im).astype('float')
grad = np.gradient(a) #取图像灰度的梯度值
grad_x, grad_y = grad #分别取横纵图像梯度值
grad_x = grad_x*depth/100.
grad_y = grad_y*depth/100.
dx = np.cos(vec_el)*np.cos(vec_az) #光源对x 轴的影响
dy = np.cos(vec_el)*np.sin(vec_az) #光源对y 轴的影响
dz = np.sin(vec_el) #光源对z 轴的影响
A = np.sqrt(grad_x**2 + grad_y**2 + 1.)
uni_x = grad_x/A
uni_y = grad_y/A
uni_z = 1./A
a2 = 255*(dx*uni_x + dy*uni_y + dz*uni_z) #光源归一化
a2 = a2.clip(0,255)
im2 = Image.fromarray(a2.astype('uint8')) #重构图像
im2.save('fcityHandDraw.jpg')

或可下载资源：https://download.csdn.net/download/leyang0910/87699125
可以直接运行程序，图片我放在了文件夹里了。
如对您有帮助，请采纳此回答。谢谢！