def on_trackbar (position):
# create the image for putting in it the founded contours
contours_image = cv.cvCreateImage (cv.cvSize (_SIZE, _SIZE), 8, 3)
# compute the real level of display, given the current position
levels = position - 3
# initialisation
_contours = contours
if levels <= 0:
# zero or negative value
# => get to the nearest face to make it look more funny
_contours = contours.h_next.h_next.h_next
# first, clear the image where we will draw contours
cv.cvSetZero (contours_image)
# draw contours in red and green
cv.cvDrawContours (contours_image, _contours,
_red, _green,
levels, 3, cv.CV_AA,
cv.cvPoint (0, 0))
# finally, show the image
highgui.cvShowImage ("contours", contours_image)
def on_trackbar(position):
# create the image for putting in it the founded contours
contours_image = cv.cvCreateImage(cv.cvSize(_SIZE, _SIZE), 8, 3)
# compute the real level of display, given the current position
levels = position - 3
# initialisation
_contours = contours
if levels <= 0:
# zero or negative value
# => get to the nearest face to make it look more funny
_contours = contours.h_next.h_next.h_next
# first, clear the image where we will draw contours
cv.cvSetZero(contours_image)
# draw contours in red and green
cv.cvDrawContours(contours_image, _contours, _red, _green, levels, 3,
cv.CV_AA, cv.cvPoint(0, 0))
# finally, show the image
highgui.cvShowImage("contours", contours_image)
def findEdges(original, out, threshold1 = 100, threshold2 = None):
"""Return a new edge detected image with a specified threshold"""
warnings.warn("Use findBWEdges instead unless you really need colored edges.", DeprecationWarning)
#Define threshold2
if threshold2 == None:
threshold2 = threshold1 * 3
# Create two pictures with only one channel for a b/w copy
# and one for storring the edges found in the b/w picture
gray = cv.cvCreateImage(cv.cvGetSize(original), 8, 1)
edge = cv.cvCreateImage(cv.cvGetSize(original), 8, 1)
# Create the b/w copy of the original
cv.cvCvtColor(original, gray, cv.CV_BGR2GRAY)
# Blur the b/w copy, but put the result into edge pic
cv.cvSmooth(gray, edge, cv.CV_BLUR, 3, 3, 0)
# Negate the b/w copy of original with newly blurred
# b/w copy. This will make egdes stand out
cv.cvNot(gray, edge)
# Run an edge-finding algorithm called 'Canny'
# It will analyse the first argument and store the
# resulting picture in the second argument
cv.cvCanny(gray, edge, threshold1, threshold2)
# We initialize our out-image to black
cv.cvSetZero(out)
# Finally, we use the found edges, which are b/w, as
# a mask for copying the colored edges from the original
# to the out-image
cv.cvCopy(original, out, edge)
def getFilter(frameWidht, frameHeight):
cvNamedWindow("Filtred")
cvCreateTrackbar("hmax", "Filtred", getHlsFilter('hmax'), 180, trackBarChangeHmax)
cvCreateTrackbar("hmin", "Filtred", getHlsFilter('hmin'), 180, trackBarChangeHmin)
#cvCreateTrackbar("lmax", "Filtred", hlsFilter['lmax'], 255, trackBarChangeLmax)
#cvCreateTrackbar("lmin", "Filtred", hlsFilter['lmin'], 255, trackBarChangeLmin)
cvCreateTrackbar("smax", "Filtred", getHlsFilter('smax'), 255, trackBarChangeSmax)
cvCreateTrackbar("smin", "Filtred", getHlsFilter('smin'), 255, trackBarChangeSmin)
cvSetMouseCallback("Filtred", mouseClick, None)
frame = cvCreateImage(cvSize(frameWidth, frameHeight), IPL_DEPTH_8U, 3)
hlsFrame = cvCreateImage(cvSize(frameWidth, frameHeight), IPL_DEPTH_8U, 3)
filtredFrame = cvCreateImage(cvSize(frameWidth, frameHeight), IPL_DEPTH_8U, 3)
mask = cvCreateImage(cvSize(frameWidth, frameHeight), IPL_DEPTH_8U, 1)
hFrame = cvCreateImage(cvSize(frameWidth, frameHeight), IPL_DEPTH_8U, 1)
lFrame = cvCreateImage(cvSize(frameWidth, frameHeight), IPL_DEPTH_8U, 1)
sFrame = cvCreateImage(cvSize(frameWidth, frameHeight), IPL_DEPTH_8U, 1)
ThHFrame = cvCreateImage(cvSize(frameWidth, frameHeight), IPL_DEPTH_8U, 1)
ThLFrame = cvCreateImage(cvSize(frameWidth, frameHeight), IPL_DEPTH_8U, 1)
ThSFrame = cvCreateImage(cvSize(frameWidth, frameHeight), IPL_DEPTH_8U, 1)
key = -1
while key == -1:
if not cvGrabFrame(CAM):
print "Could not grab a frame"
exit
frame = cvQueryFrame(CAM)
cvCvtColor(frame, hlsFrame, CV_BGR2HLS)
cvSplit(hlsFrame, hFrame, lFrame, sFrame, None)
pixelInRange(hFrame, getHlsFilter('hmin'), getHlsFilter('hmax'), 0, 180, ThHFrame)
#pixelInRange(lFrame, getHlsFilter('lmin'), getHlsFilter('lmax'), 0, 255, ThLFrame)
pixelInRange(sFrame, getHlsFilter('smin'), getHlsFilter('smax'), 0, 255, ThSFrame)
cvSetZero(mask)
cvAnd(ThHFrame, ThSFrame, mask)
cvSetZero(filtredFrame)
cvCopy(frame, filtredFrame, mask)
cvShowImage("Filtred", filtredFrame)
key = cvWaitKey(10)
if key == 'r':
key = -1
resetHlsFilter()
cvDestroyWindow("Filtred")
def main(): # ctrl+c to end
global h,s,v,h2,v2,s2,d,e
highgui.cvNamedWindow("Camera 1", 1)
highgui.cvNamedWindow("Orig", 1)
highgui.cvCreateTrackbar("H", "Camera 1", h, 256, tb_h)
highgui.cvCreateTrackbar("S", "Camera 1", s, 256, tb_s)
highgui.cvCreateTrackbar("V", "Camera 1", v, 256, tb_v)
highgui.cvCreateTrackbar("H2", "Camera 1", h2, 256, tb_h2)
highgui.cvCreateTrackbar("S2", "Camera 1", s2, 256, tb_s2)
highgui.cvCreateTrackbar("V2", "Camera 1", v2, 256, tb_v2)
highgui.cvCreateTrackbar("Dilate", "Camera 1", d, 30, tb_d)
highgui.cvCreateTrackbar("Erode", "Camera 1", e, 30, tb_e)
cap = highgui.cvCreateCameraCapture(1)
highgui.cvSetCaptureProperty(cap, highgui.CV_CAP_PROP_FRAME_WIDTH, IMGW)
highgui.cvSetCaptureProperty(cap, highgui.CV_CAP_PROP_FRAME_HEIGHT, IMGH)
c = 0
t1 = tdraw = time.clock()
t = 1
font = cv.cvInitFont(cv.CV_FONT_HERSHEY_PLAIN, 1, 1)
while c != 0x27:
image = highgui.cvQueryFrame(cap)
if not image:
print "capture failed"
break
thresh = cv.cvCreateImage(cv.cvSize(IMGW,IMGH),8,1)
cv.cvSetZero(thresh)
cv.cvCvtColor(image,image,cv.CV_RGB2HSV)
cv.cvInRangeS(image, (h,s,v,0), (h2,s2,v2,0), thresh)
result = cv.cvCreateImage(cv.cvSize(IMGW,IMGH),8,3)
cv.cvSetZero(result)
cv.cvOr(image,image,result,thresh)
for i in range(1,e):
cv.cvErode(result,result)
for i in range(1,d):
cv.cvDilate(result,result)
# floodfill objects back in, allowing threshold differences outwards
t2 = time.clock()
if t2 > tdraw+0.3:
t = t2-t1
tdraw=t2
cv.cvPutText(result, "FPS: " + str(1 / (t)), (0,25), font, (255,255,255))
t1 = t2
highgui.cvShowImage("Orig", image)
highgui.cvShowImage("Camera 1", result)
c = highgui.cvWaitKey(10)
def on_trackbar (position):
cv.cvSmooth (gray, edge, cv.CV_BLUR, 3, 3, 0)
cv.cvNot (gray, edge)
# run the edge dector on gray scale
cv.cvCanny (gray, edge, position, position * 3, 3)
# reset
cv.cvSetZero (col_edge)
# copy edge points
cv.cvCopy (image, col_edge, edge)
# show the image
highgui.cvShowImage (win_name, col_edge)
def on_trackbar(position):
cv.cvSmooth(gray, edge, cv.CV_BLUR, 3, 3, 0)
cv.cvNot(gray, edge)
# run the edge dector on gray scale
cv.cvCanny(gray, edge, position, position * 3, 3)
# reset
cv.cvSetZero(col_edge)
# copy edge points
cv.cvCopy(image, col_edge, edge)
# show the image
highgui.cvShowImage(win_name, col_edge)
def on_trackbar (position):
#下面两句应该是没什么用的
cv.cvSmooth (gray, edge, cv.CV_BLUR, 3, 3, 0) #图像平滑
cv.cvNot (gray, edge) #计算数组元素的按位取反
# run the edge dector on gray scale
cv.cvCanny (gray, edge, position, position * 3, 3) #采用 Canny 算法做边缘检测
# reset
cv.cvSetZero (col_edge) #清空数组
# copy edge points
cv.cvCopy (image, col_edge, edge) #参数edge影响拷贝的结果
# show the image
highgui.cvShowImage (win_name, col_edge)
def threshold_image(image, n=[]):
"""Record the first 5 images to get a background, then diff current frame with the last saved frame.
"""
if len(n) < 5:
# n[4] will be our background
# First capture a few images
n.append(cv.cvCloneMat(image))
if len(n) == 5:
# last time here
# could do averaging here.
pass
return image
original = n[4]
differenceImage = cv.cvCloneMat(image)
cv.cvAbsDiff(image, original, differenceImage)
"""The threshold value determines the amount of "Change" required
before something will show up"""
thresholdValue = 50 # 32
cv.cvThreshold(differenceImage, differenceImage, thresholdValue, 255,
cv.CV_THRESH_BINARY)
# Convert to one channel
gray = cv.cvCreateImage(cv.cvGetSize(differenceImage), 8, 1)
cv.cvCvtColor(differenceImage, gray, cv.CV_BGR2GRAY)
# Use median filter to remove salt and pepper noise.
cv.cvSmooth(gray, gray, cv.CV_MEDIAN, 15)
# Dilate and the threshold image
# It adds a border to the object.
#cv.cvDilate(gray,gray, None, 9)
# Add a bit of Blur to the threshold mask
cv.cvSmooth(gray, gray, cv.CV_GAUSSIAN, 5)
result = cv.cvCloneMat(image)
cv.cvSetZero(result)
cv.cvAnd(image, image, result, gray)
return result
def threshold_image(image, n=[]):
"""Record the first 5 images to get a background, then diff current frame with the last saved frame.
"""
if len(n) < 5:
# n[4] will be our background
# First capture a few images
n.append(cv.cvCloneMat(image))
if len(n) == 5:
# last time here
# could do averaging here.
pass
return image
original = n[4]
differenceImage = cv.cvCloneMat( image )
cv.cvAbsDiff( image, original, differenceImage )
"""The threshold value determines the amount of "Change" required
before something will show up"""
thresholdValue = 50 # 32
cv.cvThreshold( differenceImage, differenceImage, thresholdValue, 255, cv.CV_THRESH_BINARY )
# Convert to one channel
gray = cv.cvCreateImage( cv.cvGetSize(differenceImage), 8, 1 )
cv.cvCvtColor( differenceImage, gray, cv.CV_BGR2GRAY )
# Use median filter to remove salt and pepper noise.
cv.cvSmooth(gray, gray, cv.CV_MEDIAN, 15)
# Dilate and the threshold image
# It adds a border to the object.
#cv.cvDilate(gray,gray, None, 9)
# Add a bit of Blur to the threshold mask
cv.cvSmooth(gray, gray, cv.CV_GAUSSIAN, 5)
result = cv.cvCloneMat( image)
cv.cvSetZero(result)
cv.cvAnd(image,image, result, gray)
return result
highgui.CV_CAP_PROP_FRAME_HEIGHT,240)
else:
# we have an argument on the command line,
# we can assume this is a file name, so open it
capture = highgui.cvCreateFileCapture (sys.argv [1])
# check that capture device is OK
if not capture:
print "Error opening capture device"
sys.exit (1)
# create an image to put in the histogram
histimg = cv.cvCreateImage (cv.cvSize (320,240), 8, 3)
# init the image of the histogram to black
cv.cvSetZero (histimg)
# capture the 1st frame to get some propertie on it
frame = highgui.cvQueryFrame (capture)
# get some properties of the frame
frame_size = cv.cvGetSize (frame)
# compute which selection of the frame we want to monitor
selection = cv.cvRect (0, 0, frame.width, frame.height)
# create some images usefull later
hue = cv.cvCreateImage (frame_size, 8, 1)
mask = cv.cvCreateImage (frame_size, 8, 1)
hsv = cv.cvCreateImage (frame_size, 8, 3 )
1200)
else:
# we have an argument on the command line,
# we can assume this is a file name, so open it
capture = highgui.cvCreateFileCapture(sys.argv[1])
# check that capture device is OK
if not capture:
print "Error opening capture device"
sys.exit(1)
# create an image to put in the histogram
histimg = cv.cvCreateImage(cv.cvSize(320, 240), 8, 3)
# init the image of the histogram to black
cv.cvSetZero(histimg)
# capture the 1st frame to get some propertie on it
frame = highgui.cvQueryFrame(capture)
# get some properties of the frame
frame_size = cv.cvGetSize(frame)
# compute which selection of the frame we want to monitor
selection = cv.cvRect(0, 0, frame.width, frame.height)
# create some images usefull later
hue = cv.cvCreateImage(frame_size, 8, 1)
mask = cv.cvCreateImage(frame_size, 8, 1)
hsv = cv.cvCreateImage(frame_size, 8, 3)
backproject = cv.cvCreateImage(frame_size, 8, 1)
grayimage = cv.cvCreateImage(cv.cvGetSize(image), 8, 1)
cannyedges = cv.cvCreateImage(cv.cvGetSize(image), 8, 1)
#convert to grayscale
cv.cvCvtColor(image, grayimage, cv.CV_BGR2GRAY)
#Canny
#Canny(image, edges, threshold1, threshold2, aperture_size=3) = None
#Implements the Canny algorithm for edge detection.
cv.cvCanny(grayimage, cannyedges, 150, 450 , 3)
#This is the line that throws the error
storage = cv.cvCreateMat(50, 1, cv.CV_32FC3)
cv.cvSetZero(storage)
#circles = cv.cvHoughCircles(grayimage, storage, cv.CV_HOUGH_GRADIENT, 2, grayimage.height/4, 150, 40, long(sys.argv[2]), long(sys.argv[3]))
#circles = cv.cvHoughCircles(grayimage, storage, cv.CV_HOUGH_GRADIENT, 1, grayimage.height, 200, 40, long(sys.argv[2]), long(sys.argv[3]))
circles = cv.cvHoughCircles(grayimage, storage, cv.CV_HOUGH_GRADIENT, 1, grayimage.width, 150, 40, long(sys.argv[2]), grayimage.width)
print storage
for i in storage:
print i[0], i[1], i[2]
center = cv.cvRound(i[0]), cv.cvRound(i[1])
radius = cv.cvRound(i[2])
cv.cvCircle(image, (center), radius, cv.CV_RGB(255, 0, 0), 1, cv.CV_AA, 0 )
cv.cvCircle(image, (center), 10, cv.CV_RGB(255, 0, 0), -1, cv.CV_AA, 0 )
cv.cvCircle(cannyedges, (center), radius, cv.CV_RGB(255, 255, 255), 1, cv.CV_AA, 0 )
# for c in range(0,3):
#v = cv.cvRound( cv.cvmGet(storage, i, c) )
def main():
print "OpenCV version: %s (%d, %d, %d)" % (cv.CV_VERSION,
cv.CV_MAJOR_VERSION,
cv.CV_MINOR_VERSION,
cv.CV_SUBMINOR_VERSION)
# create windows
create_and_position_window('Thresholded_HSV_Image', 10, 10)
create_and_position_window('RGB_VideoFrame', 10+cam_width, 10)
create_and_position_window('Hue', 10, 10+cam_height)
create_and_position_window('Saturation', 210, 10+cam_height)
create_and_position_window('Value', 410, 10+cam_height)
create_and_position_window('LaserPointer', 0,0)
capture = setup_camera_capture()
# create images for the different channels
h_img = cv.cvCreateImage (cv.cvSize (cam_width,cam_height), 8, 1)
s_img = cv.cvCreateImage (cv.cvSize (cam_width,cam_height), 8, 1)
v_img = cv.cvCreateImage (cv.cvSize (cam_width,cam_height), 8, 1)
laser_img = cv.cvCreateImage (cv.cvSize (cam_width,cam_height), 8, 1)
cv.cvSetZero(h_img)
cv.cvSetZero(s_img)
cv.cvSetZero(v_img)
cv.cvSetZero(laser_img)
while True:
# 1. capture the current image
frame = highgui.cvQueryFrame (capture)
if frame is None:
# no image captured... end the processing
break
hsv_image = cv.cvCloneImage(frame) # temporary copy of the frame
cv.cvCvtColor(frame, hsv_image, cv.CV_BGR2HSV) # convert to HSV
# split the video frame into color channels
cv.cvSplit(hsv_image, h_img, s_img, v_img, None)
# Threshold ranges of HSV components.
cv.cvInRangeS(h_img, hmin, hmax, h_img)
cv.cvInRangeS(s_img, smin, smax, s_img)
cv.cvInRangeS(v_img, vmin, vmax, v_img)
# Perform an AND on HSV components to identify the laser!
cv.cvAnd(h_img, v_img, laser_img)
# This actually Worked OK for me without using Saturation.
#cv.cvAnd(laser_img, s_img,laser_img)
# Merge the HSV components back together.
cv.cvMerge(h_img, s_img, v_img, None, hsv_image)
#-----------------------------------------------------
# NOTE: default color space in OpenCV is BGR!!
# we can now display the images
highgui.cvShowImage ('Thresholded_HSV_Image', hsv_image)
highgui.cvShowImage ('RGB_VideoFrame', frame)
highgui.cvShowImage ('Hue', h_img)
highgui.cvShowImage ('Saturation', s_img)
highgui.cvShowImage ('Value', v_img)
highgui.cvShowImage('LaserPointer', laser_img)
# handle events
k = highgui.cvWaitKey (10)
if k == '\x1b' or k == 'q':
# user has press the ESC key, so exit
break
tx = x
ty = y
print x, y
showoriginal = False
elif event == highgui.CV_EVENT_LBUTTONDOWN:
showoriginal = True
print "Orig"
highgui.cvSetMouseCallback("Blob", mousecb, 0)
while True:
if not fromfile:
img = highgui.cvQueryFrame(cap0)
cv.cvCvtColor(img,img,cv.CV_RGB2HSV)
(h,s,v) = (img[ty][tx][0],img[ty][tx][1],img[ty][tx][2])
thresh = cv.cvCreateImage(cv.cvSize(IMGW,IMGH),8,1)
cv.cvSetZero(thresh)
# dynamic floodfill
import queue
q = queue.Queue(0)
q.put({"x":tx,"y":ty})
thresh[ty][tx] = 255
while True:
# move left and right until boundary is hit
n = q.pop()
l = n.x - 1
r = n.x + 1
y = n.y
while True:
if h-20 < img[y][l][0] < h+20:
thresh[y][l] = 255
prev_grey = cv.cvCreateImage (cv.cvGetSize (frame), 8, 1)
pyramid = cv.cvCreateImage (cv.cvGetSize (frame), 8, 1)
prev_pyramid = cv.cvCreateImage (cv.cvGetSize (frame), 8, 1)
eig = cv.cvCreateImage (cv.cvGetSize (frame), cv.IPL_DEPTH_32F, 1)
temp = cv.cvCreateImage (cv.cvGetSize (frame), cv.IPL_DEPTH_32F, 1)
points = [[], []]
# copy the frame, so we can draw on it
cv.cvCopy (frame, image)
# create a grey version of the image
cv.cvCvtColor (image, grey, cv.CV_BGR2GRAY)
if night_mode:
# night mode: only display the points
cv.cvSetZero (image)
if need_to_init:
# we want to search all the good points
# create the wanted images
# search the good points
points [1] = cv.cvGoodFeaturesToTrack (
grey, eig, temp,
MAX_COUNT,
quality, min_distance, None, 3, 0, 0.04)
# refine the corner locations
cv.cvFindCornerSubPix (
grey,
points [1],
def on_trackbar1(position):
global pos1
global pos2
global pos3
global pos4
global pos5
global pos6
global pos7
global img
global gray
global edges
print
print position, pos2, pos3, pos4, pos5, pos6, pos7
temp = cv.cvCloneImage(img)
gray = cv.cvCreateImage(cv.cvGetSize(temp), 8, 1)
edges = cv.cvCreateImage(cv.cvGetSize(temp), 8, 1)
dst = cv.cvCreateImage( cv.cvSize(256,256), 8, 3 )
src = cv.cvCloneImage(img)
src2 = cv.cvCreateImage( cv.cvGetSize(src), 8, 3 );
cv.cvCvtColor(img, gray, cv.CV_BGR2GRAY)
cv.cvCanny(gray, edges, position, pos2, 3)
cv.cvSmooth(edges, edges, cv.CV_GAUSSIAN, 9, 9)
storage = cv.cvCreateMat(50, 1, cv.CV_32FC3)
cv.cvSetZero(storage)
try:
circles = cv.cvHoughCircles(gray, storage, cv.CV_HOUGH_GRADIENT, 1, float(pos3), float(pos2), float(pos4), long(pos5),long(pos6) )
#print storage
for i in storage:
print "Center: ", i[0], i[1], " Radius: ", i[2]
center = cv.cvRound(i[0]), cv.cvRound(i[1])
radius = cv.cvRound(i[2])
cv.cvCircle(temp, (center), radius, cv.CV_RGB(255, 0, 0), 1, cv.CV_AA, 0 )
cv.cvCircle(edges, (center), radius, cv.CV_RGB(255, 255, 255), 1, cv.CV_AA, 0 )
if radius > 200:
print "Circle found over 200 Radius"
center_crop_topleft = (center[0]-(radius - pos7)), (center[1]-(radius - pos7))
center_crop_bottomright = (center[0]+(radius - pos7)), (center[1]+(radius - pos7))
print "crop top left: ", center_crop_topleft
print "crop bottom right: ", center_crop_bottomright
center_crop = cv.cvGetSubRect(src, (center_crop_topleft[0], center_crop_topleft[1] , (center_crop_bottomright[0] - center_crop_topleft[0]), (center_crop_bottomright[1] - center_crop_topleft[1]) ))
#center_crop = cv.cvGetSubRect(src, (50, 50, radius/2, radius/2))
cvShowImage( "center_crop", center_crop )
print "center_crop created"
#mark found circle's center with blue point and blue circle of pos 7 radius
cv.cvCircle(temp ,(center), 2, cv.CV_RGB(0, 0, 255), 3, cv.CV_AA, 0 )
cv.cvCircle(temp ,(center), (radius - pos7), cv.CV_RGB(0, 0, 255), 3, cv.CV_AA, 0 )
#cvLogPolar(src, dst, (center), 48, CV_INTER_LINEAR +CV_WARP_FILL_OUTLIERS )
#this will draw a smaller cirle outlining the center circle
#pos7 = int(pos7 /2.5)
#cv.cvCircle(dst ,(img_size.width-pos7, 0), 2, cv.CV_RGB(0, 0, 255), 3, cv.CV_AA, 0 )
#cv.cvLine(dst, (img_size.width-pos7-1, 0), (img_size.width-pos7-1, img_size.height), cv.CV_RGB(0, 0, 255),1,8,0)
#cvShowImage( "log-polar", dst )
#print radius, (radius-pos7)
#cropped = cv.cvCreateImage( (pos7, img_size.height), 8, 3)
#cropped2 = cv.cvCreateImage( (pos7, img_size.height), 8, 3)
#coin_edge_img = cv.cvGetSubRect(dst, (img_size.width-pos7, 0, pos7 ,img_size.height ))
#to create the center cropped part of coin
#img_size = cvGetSize(scr)
#cvCopy(coin_edge_img, cropped)
#cvSaveImage("temp.png", cropped)
#im = Image.open("temp.png").rotate(90)
#print "pil image size = ", im.size[0], im.size[1]
#im = im.resize((im.size[0]*2, im.size[1]*2))
#print "pil image size = ", im.size
#im.show()
#im.save("temp2.png")
cropped2 = highgui.cvLoadImage("temp2.png")
#cvShowImage( "cropped", cropped2)
except:
print "Exception:", sys.exc_info()[0]
print position, pos2, pos3, pos4, pos5, pos6, pos7
pass
highgui.cvShowImage("edges", edges)
#cvShowImage( "log-polar", dst )
cvShowImage(wname, temp)
image.origin = frame.origin
grey = cv.cvCreateImage(cv.cvGetSize(frame), 8, 1)
prev_grey = cv.cvCreateImage(cv.cvGetSize(frame), 8, 1)
pyramid = cv.cvCreateImage(cv.cvGetSize(frame), 8, 1)
prev_pyramid = cv.cvCreateImage(cv.cvGetSize(frame), 8, 1)
points = [[], []]
# copy the frame, so we can draw on it
cv.cvCopy(frame, image)
# create a grey version of the image
cv.cvCvtColor(image, grey, cv.CV_BGR2GRAY)
if night_mode:
# night mode: only display the points
cv.cvSetZero(image)
if need_to_init:
# we want to search all the good points
# create the wanted images
eig = cv.cvCreateImage(cv.cvGetSize(grey), 32, 1)
temp = cv.cvCreateImage(cv.cvGetSize(grey), 32, 1)
# the default parameters
quality = 0.01
min_distance = 10
# search the good points
points[1] = cv.cvGoodFeaturesToTrack(grey, eig, temp, MAX_COUNT,
quality, min_distance, None,
