Question

我正在尝试创建一个过滤器，它会为所有照片创建一个棕褐色外观。但我在过滤后的图像中得到了一些蓝色和青色的斑点。我认为这可能是溢出但我试图解释它并没有给我我想要的影响。我不确定什么是错的。原始链接位于https://www.dropbox.com/s/pd0esc8tef3smae/Lampard.jpg，修改后变为https://www.dropbox.com/s/f9n9lrhoi1qqs53/Lampard_blue.jpg

def filter3(path, cv2):
cv2.destroyAllWindows()
cv2.namedWindow("Filter 3")
img = cv2.imread(path, cv2.IMREAD_UNCHANGED) 
newImg = img
(i,j,k) = img.shape
for x in xrange(i):
    for y in xrange(j):
        b = img[x,y,0]
        g = img[x,y,1]
        r = img[x,y,2]
        if(k == 4):
            newImg[x,y,0] = np.uint16(b * 0.272 + g * 0.534 + r * 0.131)
            newImg[x,y,1] = np.uint8(b * .349 + g * 0.686 + r * 0.168)
            newImg[x,y,2] = np.uint8(b * 0.393 + g * 0.769 + r * 0.189)
            # newB = np.uint16(b * 0.272 + g * 0.534 + r * 0.131)
            # newG = np.uint16(b * .349 + g * 0.686 + r * 0.168)
            # newR = np.uint16(b * 0.393 + g * 0.769 + r * 0.189)
            # if(newB > 255):
            #   newImg[x,y,0] = np.uint8(255)
            # else: 
            #   newImg[x,y,0] = np.uint8(newB)
            # if(newG > 255):
            #   newImg[x,y,1] = np.uint8(255) 
            # else: 
            #   newImg[x,y,1] = np.uint8(newB)
            # if(newR > 255):
            #   newImg[x,y,2] = np.uint8(255) 
            # else: 
            #   newImg[x,y,2] = np.uint8(newB)

            newImg[x,y,3] = img[x,y,3]
        else:
            newImg[x,y,0] = np.uint16(b * 0.272 + g * 0.534 + r * 0.131)
            newImg[x,y,1] = np.uint8(b * .349 + g * 0.686 + r * 0.168)
            newImg[x,y,2] = np.uint8(b * 0.393 + g * 0.769 + r * 0.189)
            # if(newB > 255):
            #   newImg[x,y,0] = np.uint8(0)
            # else: 
            #   newImg[x,y,0] = np.uint8(newB)
cv2.imshow("Filter 3", newImg)
return newImg

Answer 1

我不知道python但你可以参考下面的C ++代码对我来说很好。

Mat src=imread("Lampard.jpg",1);
Mat SepiaKernel = (Mat_<float>(4,4) << 0.189, 0.769, 0.393, 0,\
                                       0.168, 0.686, 0.349, 0,\
                                       0.131, 0.534, 0.272, 0,\
                                       0.000, 0.000, 0.000, 1);
Mat dst;
transform(src, dst, SepiaKernel);
imshow("src",src);
imshow("sepia",dst);
waitKey();

<强>结果： -

enter image description here

Answer 2

在我看来，问题在于类型。 Python认为在操作之后你想要整数。每个通道有8位。经过计算，当你得到值256（100000001）时，python将只读取前8位（00000001）。因为你有一点点奇怪的形象。在我的情况下，使用此代码：

import cv2
import numpy as np
from matplotlib import pyplot as plt

img = cv2.imread('lena.jpg')
newImage = cv2.imread('lena.jpg')
i, j, k = img.shape
for x in xrange(i):
    for y in xrange(j):
    R = img[x,y,2] * 0.393 + img[x,y,1] * 0.769 + img[x,y,0] * 0.189
    G = img[x,y,2] * 0.349 + img[x,y,1] * 0.686 + img[x,y,0] * 0.168
    B = img[x,y,2] * 0.272 + img[x,y,1] * 0.534 + img[x,y,0] * 0.131
    if R > 255:
        newImage[x,y,2] = 255
    else:
        newImage[x,y,2] = R
    if G > 255:
        newImage[x,y,1] = 255
    else:
        newImage[x,y,1] = G
    if B > 255:
        newImage[x,y,0] = 255
    else:
        newImage[x,y,0] = B

cv2.imshow('image',newImage)
cv2.waitKey(0)
cv2.destroyAllWindows()

也许它不是很好的代码，但我不熟悉Python。

Answer 3

您可以在应用棕褐色滤镜之前将图像阵列转换为浮点型。同样，使用cv2.transform比遍历所有像素要快得多。可以使用此代码

import cv2
import numpy as np

img = cv2.imread('messi.jpg')
original = img.copy()
img = np.array(img, dtype = np.float64)
img = cv2.transform(img, np.matrix([[ 0.393, 0.769, 0.189],
                                    [ 0.349, 0.686, 0.168],
                                    [ 0.272, 0.534, 0.131]]))
img[np.where(img>255)] = 255
img = np.array(img, dtype = np.uint8)
cv2.imshow("original", original)
cv2.imshow("Output", img)
cv2.waitKey(0)
cv2.destroyAllWindows()

棕褐色效果opencv python出错

3 个答案: