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

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This commit is contained in:
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_video/py_meanshift/py_meanshift.markdown
+32 -32
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@@ -39,12 +39,12 @@ algorithm moves our window to the new location with maximum density.
To use meanshift in OpenCV, first we need to setup the target, find its histogram so that we can
backproject the target on each frame for calculation of meanshift. We also need to provide initial
location of window. For histogram, only Hue is considered here. Also, to avoid false values due to
low light, low light values are discarded using **cv2.inRange()** function.
low light, low light values are discarded using **cv.inRange()** function.
@code{.py}
import numpy as np
import cv2
import cv2 as cv
cap = cv2.VideoCapture('slow.flv')
cap = cv.VideoCapture('slow.flv')
# take first frame of the video
ret,frame = cap.read()
@@ -55,39 +55,39 @@ track_window = (c,r,w,h)
# set up the ROI for tracking
roi = frame[r:r+h, c:c+w]
hsv_roi = cv2.cvtColor(roi, cv2.COLOR_BGR2HSV)
mask = cv2.inRange(hsv_roi, np.array((0., 60.,32.)), np.array((180.,255.,255.)))
roi_hist = cv2.calcHist([hsv_roi],[0],mask,[180],[0,180])
cv2.normalize(roi_hist,roi_hist,0,255,cv2.NORM_MINMAX)
hsv_roi = cv.cvtColor(roi, cv.COLOR_BGR2HSV)
mask = cv.inRange(hsv_roi, np.array((0., 60.,32.)), np.array((180.,255.,255.)))
roi_hist = cv.calcHist([hsv_roi],[0],mask,[180],[0,180])
cv.normalize(roi_hist,roi_hist,0,255,cv.NORM_MINMAX)
# Setup the termination criteria, either 10 iteration or move by atleast 1 pt
term_crit = ( cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 1 )
term_crit = ( cv.TERM_CRITERIA_EPS | cv.TERM_CRITERIA_COUNT, 10, 1 )
while(1):
ret ,frame = cap.read()
if ret == True:
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
dst = cv2.calcBackProject([hsv],[0],roi_hist,[0,180],1)
hsv = cv.cvtColor(frame, cv.COLOR_BGR2HSV)
dst = cv.calcBackProject([hsv],[0],roi_hist,[0,180],1)
# apply meanshift to get the new location
ret, track_window = cv2.meanShift(dst, track_window, term_crit)
ret, track_window = cv.meanShift(dst, track_window, term_crit)
# Draw it on image
x,y,w,h = track_window
img2 = cv2.rectangle(frame, (x,y), (x+w,y+h), 255,2)
cv2.imshow('img2',img2)
img2 = cv.rectangle(frame, (x,y), (x+w,y+h), 255,2)
cv.imshow('img2',img2)
k = cv2.waitKey(60) & 0xff
k = cv.waitKey(60) & 0xff
if k == 27:
break
else:
cv2.imwrite(chr(k)+".jpg",img2)
cv.imwrite(chr(k)+".jpg",img2)
else:
break
cv2.destroyAllWindows()
cv.destroyAllWindows()
cap.release()
@endcode
Three frames in a video I used is given below:
@@ -116,9 +116,9 @@ It is almost same as meanshift, but it returns a rotated rectangle (that is our
parameters (used to be passed as search window in next iteration). See the code below:
@code{.py}
import numpy as np
import cv2
import cv2 as cv
cap = cv2.VideoCapture('slow.flv')
cap = cv.VideoCapture('slow.flv')
# take first frame of the video
ret,frame = cap.read()
@@ -129,40 +129,40 @@ track_window = (c,r,w,h)
# set up the ROI for tracking
roi = frame[r:r+h, c:c+w]
hsv_roi = cv2.cvtColor(roi, cv2.COLOR_BGR2HSV)
mask = cv2.inRange(hsv_roi, np.array((0., 60.,32.)), np.array((180.,255.,255.)))
roi_hist = cv2.calcHist([hsv_roi],[0],mask,[180],[0,180])
cv2.normalize(roi_hist,roi_hist,0,255,cv2.NORM_MINMAX)
hsv_roi = cv.cvtColor(roi, cv.COLOR_BGR2HSV)
mask = cv.inRange(hsv_roi, np.array((0., 60.,32.)), np.array((180.,255.,255.)))
roi_hist = cv.calcHist([hsv_roi],[0],mask,[180],[0,180])
cv.normalize(roi_hist,roi_hist,0,255,cv.NORM_MINMAX)
# Setup the termination criteria, either 10 iteration or move by atleast 1 pt
term_crit = ( cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 1 )
term_crit = ( cv.TERM_CRITERIA_EPS | cv.TERM_CRITERIA_COUNT, 10, 1 )
while(1):
ret ,frame = cap.read()
if ret == True:
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
dst = cv2.calcBackProject([hsv],[0],roi_hist,[0,180],1)
hsv = cv.cvtColor(frame, cv.COLOR_BGR2HSV)
dst = cv.calcBackProject([hsv],[0],roi_hist,[0,180],1)
# apply meanshift to get the new location
ret, track_window = cv2.CamShift(dst, track_window, term_crit)
ret, track_window = cv.CamShift(dst, track_window, term_crit)
# Draw it on image
pts = cv2.boxPoints(ret)
pts = cv.boxPoints(ret)
pts = np.int0(pts)
img2 = cv2.polylines(frame,[pts],True, 255,2)
cv2.imshow('img2',img2)
img2 = cv.polylines(frame,[pts],True, 255,2)
cv.imshow('img2',img2)
k = cv2.waitKey(60) & 0xff
k = cv.waitKey(60) & 0xff
if k == 27:
break
else:
cv2.imwrite(chr(k)+".jpg",img2)
cv.imwrite(chr(k)+".jpg",img2)
else:
break
cv2.destroyAllWindows()
cv.destroyAllWindows()
cap.release()
@endcode
Three frames of the result is shown below: