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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_watershed/py_watershed.markdown
+13 -13
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@@ -6,7 +6,7 @@ Goal
In this chapter,
- We will learn to use marker-based image segmentation using watershed algorithm
- We will see: **cv2.watershed()**
- We will see: **cv.watershed()**
Theory
------
@@ -45,12 +45,12 @@ We start with finding an approximate estimate of the coins. For that, we can use
binarization.
@code{.py}
import numpy as np
import cv2
import cv2 as cv
from matplotlib import pyplot as plt
img = cv2.imread('coins.png')
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(gray,0,255,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)
img = cv.imread('coins.png')
gray = cv.cvtColor(img,cv.COLOR_BGR2GRAY)
ret, thresh = cv.threshold(gray,0,255,cv.THRESH_BINARY_INV+cv.THRESH_OTSU)
@endcode
Result:
@@ -78,18 +78,18 @@ obtained from subtracting sure_fg area from sure_bg area.
@code{.py}
# noise removal
kernel = np.ones((3,3),np.uint8)
opening = cv2.morphologyEx(thresh,cv2.MORPH_OPEN,kernel, iterations = 2)
opening = cv.morphologyEx(thresh,cv.MORPH_OPEN,kernel, iterations = 2)
# sure background area
sure_bg = cv2.dilate(opening,kernel,iterations=3)
sure_bg = cv.dilate(opening,kernel,iterations=3)
# Finding sure foreground area
dist_transform = cv2.distanceTransform(opening,cv2.DIST_L2,5)
ret, sure_fg = cv2.threshold(dist_transform,0.7*dist_transform.max(),255,0)
dist_transform = cv.distanceTransform(opening,cv.DIST_L2,5)
ret, sure_fg = cv.threshold(dist_transform,0.7*dist_transform.max(),255,0)
# Finding unknown region
sure_fg = np.uint8(sure_fg)
unknown = cv2.subtract(sure_bg,sure_fg)
unknown = cv.subtract(sure_bg,sure_fg)
@endcode
See the result. In the thresholded image, we get some regions of coins which we are sure of coins
and they are detached now. (In some cases, you may be interested in only foreground segmentation,
@@ -103,7 +103,7 @@ Now we know for sure which are region of coins, which are background and all. So
(it is an array of same size as that of original image, but with int32 datatype) and label the
regions inside it. The regions we know for sure (whether foreground or background) are labelled with
any positive integers, but different integers, and the area we don't know for sure are just left as
zero. For this we use **cv2.connectedComponents()**. It labels background of the image with 0, then
zero. For this we use **cv.connectedComponents()**. It labels background of the image with 0, then
other objects are labelled with integers starting from 1.
But we know that if background is marked with 0, watershed will consider it as unknown area. So we
@@ -111,7 +111,7 @@ want to mark it with different integer. Instead, we will mark unknown region, de
with 0.
@code{.py}
# Marker labelling
ret, markers = cv2.connectedComponents(sure_fg)
ret, markers = cv.connectedComponents(sure_fg)
# Add one to all labels so that sure background is not 0, but 1
markers = markers+1
@@ -128,7 +128,7 @@ compared to unknown region.
Now our marker is ready. It is time for final step, apply watershed. Then marker image will be
modified. The boundary region will be marked with -1.
@code{.py}
markers = cv2.watershed(img,markers)
markers = cv.watershed(img,markers)
img[markers == -1] = [255,0,0]
@endcode
See the result below. For some coins, the region where they touch are segmented properly and for