OpenCV python图章过滤器photoshop

Question

我不熟悉opencv。我有多张照片。左上角显示的示例图像之一。基本上，我想分离背景和前景，这样边缘是清晰的，我可以适当地检测轮廓。在

我尝试过很多过滤器，当然还有使用各种参数的阈值。在

最后，当我在photoshop filters gallery上查看时，我注意到了一个名为Stamp的过滤器，它给了我想要的结果（右上角）。它使边缘清晰，我想使用一些模糊的软角。在

我不确定如何使用pythoncv2获得和photoshop的stamp过滤器相同的操作？在

任何帮助或建议将不胜感激。在

原始原始图像

尝试1:--代码

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

input_img = cv2.imread('images/Tas/t3.bmp')
desired_img = cv2.imread('images/stamp.jpg')

# gray scale
gray = cv2.cvtColor(input_img, cv2.COLOR_BGR2GRAY)

kernel = np.ones((3,3),np.uint8)

thresh1 = cv2.threshold(input_img,80,255,cv2.THRESH_BINARY)[1]
erosion1 = cv2.erode(thresh1,kernel,iterations = 1)
dilation1 = cv2.dilate(erosion1,kernel,iterations = 1)

thresh2 = cv2.threshold(input_img,120,255,cv2.THRESH_BINARY)[1]
erosion2 = cv2.erode(thresh2,kernel,iterations = 1)
dilation2 = cv2.dilate(erosion2,kernel,iterations = 1)

titles = ['Original', 'Desired','thresh1', 'erosion1','dilation1','thresh2','erosion2','dilation2']
images = [input_img, desired_img, thresh1, erosion1,dilation1, thresh2,erosion2, dilation2]
for i in xrange(8):
  plt.subplot(2,4,i+1),plt.imshow(images[i])
  plt.title(titles[i])
  plt.xticks([]),plt.yticks([])

plt.show()

输出：

Answer 1

这可能会有助于为自己添加一些用于高斯模糊和阈值过滤的滑块，您可以获得相当不错的结果：

下面是我用来生成它的基本片段：

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

# slider callbacks
def printThreshold(x):
    print "threshold",x
def printGaussianBlur(x):
    print "gaussian blur kernel size",x
# make a window to add sliders/preview to
cv2.namedWindow('processed')
#make some sliders
cv2.createTrackbar('threshold','processed',60,255,printThreshold)
cv2.createTrackbar('gaussian blur','processed',3,10,printGaussianBlur)
# load image
img = cv2.imread('cQMgT.png',0)
# continously process for quick feedback
while 1:
    # exit on ESC key
    k = cv2.waitKey(1) & 0xFF
    if k == 27:
        break

    # Gaussian Blur ( x2 +1 = odd number for kernel size)
    kernelSize = ((cv2.getTrackbarPos('gaussian blur','processed') * 2) + 1)
    blur = cv2.GaussianBlur(img,(kernelSize,kernelSize),0)
    # Threshold
    ret,thresh = cv2.threshold(blur,cv2.getTrackbarPos('threshold','processed',),255,0)
    # show result
    cv2.imshow('processed ',thresh)

# exit
cv2.destroyAllWindows()

随意添加其他过滤器到混合和实验滑块。在