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python: 'cv2.' -> 'cv.' via 'import cv2 as cv'

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Alexander Alekhin
2017-12-11 12:55:03 +03:00
parent 9665dde678
commit 5560db73bf
162 changed files with 2083 additions and 2084 deletions
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doc/py_tutorials/py_imgproc/py_grabcut/py_grabcut.markdown
+10 -10
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@@ -64,24 +64,24 @@ It is illustrated in below image (Image Courtesy: <http://www.cs.ru.ac.za/resear
Demo
----
Now we go for grabcut algorithm with OpenCV. OpenCV has the function, **cv2.grabCut()** for this. We
Now we go for grabcut algorithm with OpenCV. OpenCV has the function, **cv.grabCut()** for this. We
will see its arguments first:
- *img* - Input image
- *mask* - It is a mask image where we specify which areas are background, foreground or
probable background/foreground etc. It is done by the following flags, **cv2.GC_BGD,
cv2.GC_FGD, cv2.GC_PR_BGD, cv2.GC_PR_FGD**, or simply pass 0,1,2,3 to image.
probable background/foreground etc. It is done by the following flags, **cv.GC_BGD,
cv.GC_FGD, cv.GC_PR_BGD, cv.GC_PR_FGD**, or simply pass 0,1,2,3 to image.
- *rect* - It is the coordinates of a rectangle which includes the foreground object in the
format (x,y,w,h)
- *bdgModel*, *fgdModel* - These are arrays used by the algorithm internally. You just create
two np.float64 type zero arrays of size (1,65).
- *iterCount* - Number of iterations the algorithm should run.
- *mode* - It should be **cv2.GC_INIT_WITH_RECT** or **cv2.GC_INIT_WITH_MASK** or combined
- *mode* - It should be **cv.GC_INIT_WITH_RECT** or **cv.GC_INIT_WITH_MASK** or combined
which decides whether we are drawing rectangle or final touchup strokes.
First let's see with rectangular mode. We load the image, create a similar mask image. We create
*fgdModel* and *bgdModel*. We give the rectangle parameters. It's all straight-forward. Let the
algorithm run for 5 iterations. Mode should be *cv2.GC_INIT_WITH_RECT* since we are using
algorithm run for 5 iterations. Mode should be *cv.GC_INIT_WITH_RECT* since we are using
rectangle. Then run the grabcut. It modifies the mask image. In the new mask image, pixels will be
marked with four flags denoting background/foreground as specified above. So we modify the mask such
that all 0-pixels and 2-pixels are put to 0 (ie background) and all 1-pixels and 3-pixels are put to
@@ -89,17 +89,17 @@ that all 0-pixels and 2-pixels are put to 0 (ie background) and all 1-pixels and
segmented image.
@code{.py}
import numpy as np
import cv2
import cv2 as cv
from matplotlib import pyplot as plt
img = cv2.imread('messi5.jpg')
img = cv.imread('messi5.jpg')
mask = np.zeros(img.shape[:2],np.uint8)
bgdModel = np.zeros((1,65),np.float64)
fgdModel = np.zeros((1,65),np.float64)
rect = (50,50,450,290)
cv2.grabCut(img,mask,rect,bgdModel,fgdModel,5,cv2.GC_INIT_WITH_RECT)
cv.grabCut(img,mask,rect,bgdModel,fgdModel,5,cv.GC_INIT_WITH_RECT)
mask2 = np.where((mask==2)|(mask==0),0,1).astype('uint8')
img = img*mask2[:,:,np.newaxis]
@@ -122,14 +122,14 @@ remaining background with gray. Then loaded that mask image in OpenCV, edited or
got with corresponding values in newly added mask image. Check the code below:*
@code{.py}
# newmask is the mask image I manually labelled
newmask = cv2.imread('newmask.png',0)
newmask = cv.imread('newmask.png',0)
# whereever it is marked white (sure foreground), change mask=1
# whereever it is marked black (sure background), change mask=0
mask[newmask == 0] = 0
mask[newmask == 255] = 1
mask, bgdModel, fgdModel = cv2.grabCut(img,mask,None,bgdModel,fgdModel,5,cv2.GC_INIT_WITH_MASK)
mask, bgdModel, fgdModel = cv.grabCut(img,mask,None,bgdModel,fgdModel,5,cv.GC_INIT_WITH_MASK)
mask = np.where((mask==2)|(mask==0),0,1).astype('uint8')
img = img*mask[:,:,np.newaxis]