def __findContour(self, filename): #find the contour of images, and save all points in self.vKeyPoints
self.img = highgui.cvLoadImage (filename)
self.grayimg = cv.cvCreateImage(cv.cvSize(self.img.width, self.img.height), 8,1)
self.drawimg = cv.cvCreateImage(cv.cvSize(self.img.width, self.img.height), 8,3)
cv.cvCvtColor (self.img, self.grayimg, cv.CV_BGR2GRAY)
cv.cvSmooth(self.grayimg, self.grayimg, cv.CV_BLUR, 9)
cv.cvSmooth(self.grayimg, self.grayimg, cv.CV_BLUR, 9)
cv.cvSmooth(self.grayimg, self.grayimg, cv.CV_BLUR, 9)
cv.cvThreshold( self.grayimg, self.grayimg, self.threshold, self.threshold +100, cv.CV_THRESH_BINARY )
cv.cvZero(self.drawimg)
storage = cv.cvCreateMemStorage(0)
nb_contours, cont = cv.cvFindContours (self.grayimg,
storage,
cv.sizeof_CvContour,
cv.CV_RETR_LIST,
cv.CV_CHAIN_APPROX_NONE,
cv.cvPoint (0,0))
cv.cvDrawContours (self.drawimg, cont, cv.cvScalar(255,255,255,0), cv.cvScalar(255,255,255,0), 1, 1, cv.CV_AA, cv.cvPoint (0, 0))
self.allcurve = []
idx = 0
for c in cont.hrange():
PointArray = cv.cvCreateMat(1, c.total , cv.CV_32SC2)
PointArray2D32f= cv.cvCreateMat( 1, c.total , cv.CV_32FC2)
cv.cvCvtSeqToArray(c, PointArray, cv.cvSlice(0, cv.CV_WHOLE_SEQ_END_INDEX))
fpoints = []
for i in range(c.total):
kp = myPoint()
kp.x = cv.cvGet2D(PointArray,0, i)[0]
kp.y = cv.cvGet2D(PointArray,0, i)[1]
kp.index = idx
idx += 1
fpoints.append(kp)
self.allcurve.append(fpoints)
self.curvelength = idx
def read(self): frame=self.input.read() if self.enabled: cv_rs = [None]*4 cv_thresh = [0]*4 cv_max = [255]*4 for i in self.channels : cv_rs[i] = cv.cvCreateImage(cv.cvSize(frame.width,frame.height),frame.depth,1) cv_thresh[i] = self.thresholds[i] cv_max[i] = self.max_thresholds[i] # extract the color channel cv.cvSplit(frame,cv_rs[0],cv_rs[1],cv_rs[2],cv_rs[3]) #self.debug_print(cv_rs) for i in self.channels : cv.cvThreshold(cv_rs[i],cv_rs[i],cv_thresh[i],cv_max[i],self.type) #cv_thresh = cv.cvCreateImage(cv.cvSize(frame.width,frame.height),frame.depth,3) cv.cvZero(frame) cv.cvMerge(cv_rs[0],cv_rs[1],cv_rs[2],cv_rs[3],frame) #frame = cv_thresh return frame
def depthmatch(x,y,leftimage,rightimage,roi=80,buf=50,baseline=2.7,focal_length=80):
"""depthmatch function
x,y : (int) pixel position of target in left image
leftimage, rightimage : (IplImage) stereo images
roi: (int) region of interest around x,y to use in matching
buf: (int) buffer outside of a straight horizontal search for a match
"""
#print "Match",x,y
info = cv.cvGetSize(leftimage)
width = info.width
height = info.height
centerx = width/2
centery = height/2
(y1,x1,y2,x2) = (y-roi,x-roi,y+roi,x+roi)
if y1<0: y1 = 0
if x1<0: x1 = 0
if y2>height: y2 = height
if x2>width: x2 = width
# copy subregion roi x roi
template_rect = cv.cvRect(x1,y1,(x2-x1),(y2-y1))
template = cv.cvGetSubRect(leftimage, template_rect)
#(y3,x3,y4,x4) = (y-roi-buf,x-roi-buf,y+roi+buf,width) # +/- 20 pixels in vertical direction, -20 to the right edge
(y3,x3,y4,x4) = (y-roi-buf,0,y+roi+buf,x+roi+buf) # +/- buf pixels in vertical direction, +buf to the left edge
if x3<0: x3 = 0
if y3<0: y3 = 0
if x4>=width: x4 = width-1
if y4>height: y4 = height
#cv.cvSetImageROI(rightimage, (y3,x3,y4,x4))
rightsub_rect = cv.cvRect(x3,y3,(x4-x3),(y4-y3))
rightsub = cv.cvGetSubRect(rightimage, rightsub_rect)
# result matrix should be (W - w + 1) x (H - h + 1) where WxH are template dimensions, wxh are rightsub dimensions
W = x4-x3
H = y4-y3
w = x2-x1
h = y2-y1
resy = (y4-y3)-(y2-y1)+1
resx = (x4-x3)-(x2-x1)+1
resultmat = cv.cvCreateImage((resx, resy), 32, 1)
cv.cvZero(resultmat)
# match template image in a subportion of rightimage
cv.cvMatchTemplate(rightsub, template, resultmat, cv.CV_TM_SQDIFF)
min_val, max_val, min_point, max_point = cv.cvMinMaxLoc(resultmat)
cv.cvNormalize(resultmat, resultmat, 1, 0, cv.CV_MINMAX)
depth = plane2point(x-centerx, y-centery, x3+min_point.x+roi-centerx, y3+min_point.y+roi-centery, baseline, focal_length)
#print "Found match at", min_point.x+x3, min_point.y+y3
return (depth, (x,y), (x3+min_point.x+roi, y3+min_point.y+roi))
def getBackground(frameWidht, frameHeight):
cvNamedWindow("Background")
text = cvCreateImage(cvSize(frameWidth, frameHeight), IPL_DEPTH_8U, 3)
frame = cvCreateImage(cvSize(frameWidth, frameHeight), IPL_DEPTH_8U, 3)
background = cvCreateImage(cvSize(frameWidth, frameHeight), IPL_DEPTH_8U, 3)
font = cvInitFont(CV_FONT_HERSHEY_COMPLEX, 1.0, 1.0, 0.0, 2)
pt1 = cvPoint(50, 100)
pt2 = cvPoint(50, 150)
center = cvPoint(frameWidth/2, frameHeight/2)
cvPutText(text, "Press enter, run away and wait", pt1, font, CV_RGB(150, 100, 150))
cvPutText(text, str(delayS) + " seconds to capture background", pt2, font, CV_RGB(150, 100, 150))
cvShowImage("Background", text)
key = -1
while key == -1:
key = cvWaitKey(10)
like = False
while not like:
for i in range(delayS):
cvZero(text)
cvPutText(text, str(delayS-i), center, font, CV_RGB(150, 100, 150))
cvShowImage("Background", text)
cvWaitKey(1000)
csut = camStartUpTime
while (csut): # Stats capturing frames in order to give time to the cam to auto-adjust colors
if not cvGrabFrame(CAM):
print "Could not grab a frame"
exit
cvWaitKey(10)
csut -= 1
frame = cvQueryFrame(CAM)
cvCopy(frame, background)
cvCopy(frame, text)
cvPutText(text, "Is correct? [y/n]", center, font, CV_RGB(150, 100, 150))
cvShowImage("Background", text)
key = -1
while key != 'n' and key != 'y':
key = cvWaitKey(10)
if key == 'y':
like = True
return background
cvDestroyWindow("Background")
def process_image(slider_pos):
"""
Define trackbar callback functon. This function find contours,
draw it and approximate it by ellipses.
"""
stor = cv.cvCreateMemStorage(0)
# Threshold the source image. This needful for cv.cvFindContours().
cv.cvThreshold(image03, image02, slider_pos, 255, cv.CV_THRESH_BINARY)
# Find all contours.
nb_contours, cont = cv.cvFindContours(image02, stor, cv.sizeof_CvContour,
cv.CV_RETR_LIST,
cv.CV_CHAIN_APPROX_NONE,
cv.cvPoint(0, 0))
# Clear images. IPL use.
cv.cvZero(image02)
cv.cvZero(image04)
# This cycle draw all contours and approximate it by ellipses.
for c in cont.hrange():
count = c.total
# This is number point in contour
# Number point must be more than or equal to 6 (for cv.cvFitEllipse_32f).
if (count < 6):
continue
# Alloc memory for contour point set.
PointArray = cv.cvCreateMat(1, count, cv.CV_32SC2)
PointArray2D32f = cv.cvCreateMat(1, count, cv.CV_32FC2)
# Get contour point set.
cv.cvCvtSeqToArray(c, PointArray,
cv.cvSlice(0, cv.CV_WHOLE_SEQ_END_INDEX))
# Convert CvPoint set to CvBox2D32f set.
cv.cvConvert(PointArray, PointArray2D32f)
box = cv.CvBox2D()
# Fits ellipse to current contour.
box = cv.cvFitEllipse2(PointArray2D32f)
# Draw current contour.
cv.cvDrawContours(image04, c, cv.CV_RGB(255, 255, 255),
cv.CV_RGB(255, 255, 255), 0, 1, 8, cv.cvPoint(0, 0))
# Convert ellipse data from float to integer representation.
center = cv.CvPoint()
size = cv.CvSize()
center.x = cv.cvRound(box.center.x)
center.y = cv.cvRound(box.center.y)
size.width = cv.cvRound(box.size.width * 0.5)
size.height = cv.cvRound(box.size.height * 0.5)
box.angle = -box.angle
# Draw ellipse.
cv.cvEllipse(image04, center, size, box.angle, 0, 360,
cv.CV_RGB(0, 0, 255), 1, cv.CV_AA, 0)
# Show image. HighGUI use.
highgui.cvShowImage("Result", image04)
def depthmatch(x,y,leftimage,rightimage,roi=20,buf=10,debug=False):
__doc__ = """depthmatch function
x,y : (int) pixel position of target in left image
leftimage, rightimage : (IplImage) stereo images
roi: (int) region of interest around x,y to use in matching
buf: (int) buffer outside of a straight horizontal search for a match
"""
info = cv.cvGetSize(leftimage)
width = info.width
height = info.height
(y1,x1,y2,x2) = (y-roi,x-roi,y+roi,x+roi)
#template = cv.cvCreateImage((roi*2,roi*2), 8, 3)
if y1<0: y1 = 0
if x1<0: x1 = 0
if y2>height: y2 = height
if x2>width: x2 = width
#cv.cvSetZero(template)
# copy subregion roi x roi
template_rect = cv.cvRect(x1,y1,(x2-x1),(y2-y1))
template = cv.cvGetSubRect(leftimage, template_rect)
(y3,x3,y4,x4) = (y-roi-buf,x-roi-buf,y+roi+buf,width) # +/- 20 pixels in vertical direction, -20 to the right edge
if x3<0: x3 = 0
if y3<0: y3 = 0
if x4>=width: x4 = width-1
if y4>height: y4 = height
#cv.cvSetImageROI(rightimage, (y3,x3,y4,x4))
rightsub_rect = cv.cvRect(x3,y3,(x4-x3),(y4-y3))
rightsub = cv.cvGetSubRect(rightimage, rightsub_rect)
# result matrix should be (W - w + 1) x (H - h + 1) where WxH are template dimensions, wxh are rightsub dimensions
W = x4-x3
H = y4-y3
w = x2-x1
h = y2-y1
resy = (y4-y3)-(y2-y1)+1
resx = (x4-x3)-(x2-x1)+1
resultmat = cv.cvCreateImage((resx, resy), 32, 1)
cv.cvZero(resultmat)
# match template image in a subportion of rightimage
cv.cvMatchTemplate(rightsub, template, resultmat, cv.CV_TM_SQDIFF)
min_val, max_val, min_point, max_point = cv.cvMinMaxLoc(resultmat)
cv.cvNormalize(resultmat, resultmat, 1, 0, cv.CV_MINMAX)
depth = stereo.depth(x, x3+min_point.x, max_pixels=width/2)
if debug:
print "Input image: %ix%i, target: (%i,%i)" % (width,height,x,y)
print "Template box: (%i,%i) to (%i,%i)" % (x1, y1, x2, y2)
print "Search area: (%i,%i) to (%i,%i)" % (x3, y3, x4, y4)
print "%ix%i, %ix%i" % (W,H,w,h)
print "Result matrix %ix%i" % (resx, resy)
print "stereo.depth(%i,%i,max_pixels=%i)" % (x, min_point.x+x3,width/2)
if depth[0]:
print "Depth: ", depth[0], "(cm)"
#cv.cvRectangle(rightimage, cv.cvPoint(x1,y1), cv.cvPoint(x2,y2), (255,0,0))
cv.cvRectangle(rightimage, cv.cvPoint(min_point.x+x3,min_point.y+y3), cv.cvPoint(min_point.x+x3+roi*2,min_point.y+y3+roi*2), (0,255,0))
cv.cvRectangle(rightimage, cv.cvPoint(x3,y3), cv.cvPoint(x4,y4), (0,0,255))
cv.cvRectangle(leftimage, cv.cvPoint(x1,y1), cv.cvPoint(x2,y2), (255,0,0))
#cv.cvRectangle(leftimage, cv.cvPoint(min_point.x+x3,min_point.y+y3), cv.cvPoint(min_point.x+x3+roi*2,min_point.y+y3+roi*2), (0,255,0))
cv.cvRectangle(leftimage, cv.cvPoint(x3,y3), cv.cvPoint(x4,y4), (0,0,255))
if depth[0]:
cv.cvPutText(leftimage, "%5f(cm)" % depth[0], (x1,y1), font, (255,255,255))
highgui.cvShowImage("depthmatch - template", template)
highgui.cvShowImage("depthmatch - match", resultmat)
highgui.cvShowImage("depthmatch - right", rightimage)
highgui.cvShowImage("depthmatch - left", leftimage)
def process_image( slider_pos ):
"""
Define trackbar callback functon. This function find contours,
draw it and approximate it by ellipses.
"""
stor = cv.cvCreateMemStorage(0);
# Threshold the source image. This needful for cv.cvFindContours().
cv.cvThreshold( image03, image02, slider_pos, 255, cv.CV_THRESH_BINARY );
# Find all contours.
nb_contours, cont = cv.cvFindContours (image02,
stor,
cv.sizeof_CvContour,
cv.CV_RETR_LIST,
cv.CV_CHAIN_APPROX_NONE,
cv.cvPoint (0,0))
# Clear images. IPL use.
cv.cvZero(image02);
cv.cvZero(image04);
# This cycle draw all contours and approximate it by ellipses.
for c in cont.hrange():
count = c.total; # This is number point in contour
# Number point must be more than or equal to 6 (for cv.cvFitEllipse_32f).
if( count < 6 ):
continue;
# Alloc memory for contour point set.
PointArray = cv.cvCreateMat(1, count, cv.CV_32SC2)
PointArray2D32f= cv.cvCreateMat( 1, count, cv.CV_32FC2)
# Get contour point set.
cv.cvCvtSeqToArray(c, PointArray, cv.cvSlice(0, cv.CV_WHOLE_SEQ_END_INDEX));
# Convert CvPoint set to CvBox2D32f set.
cv.cvConvert( PointArray, PointArray2D32f )
box = cv.CvBox2D()
# Fits ellipse to current contour.
box = cv.cvFitEllipse2(PointArray2D32f);
# Draw current contour.
cv.cvDrawContours(image04, c, cv.CV_RGB(255,255,255), cv.CV_RGB(255,255,255),0,1,8,cv.cvPoint(0,0));
# Convert ellipse data from float to integer representation.
center = cv.CvPoint()
size = cv.CvSize()
center.x = cv.cvRound(box.center.x);
center.y = cv.cvRound(box.center.y);
size.width = cv.cvRound(box.size.width*0.5);
size.height = cv.cvRound(box.size.height*0.5);
box.angle = -box.angle;
# Draw ellipse.
cv.cvEllipse(image04, center, size,
box.angle, 0, 360,
cv.CV_RGB(0,0,255), 1, cv.CV_AA, 0);
# Show image. HighGUI use.
highgui.cvShowImage( "Result", image04 );
if (track_box.size.width > 10 or track_box.size.height > 10):
rotate = ( (image.width/2.0) - track_box.center.x) / (image.width/2.0)
translate = ((image.height/2.0) - track_box.center.y) / (image.height/2.0)
#print "rotate =", rotate, "translate =", translate
if go:
move(translate, rotate)
if select_object and selection.width > 0 and selection.height > 0:
subimg = cv.cvGetSubRect(image, selection)
cv.cvXorS( subimage, cv.cvScalarAll(255), subimage, 0 )
highgui.cvShowImage( "VisualJoystick", image )
c = highgui.cvWaitKey(10)
if c == '\x1b':
break
elif c == 'b':
backproject_mode ^= 1
elif c == 'c':
track_object = 0
cv.cvZero( histimg )
if go:
stop()
if go:
stop()
def compute_saliency(image):
global thresh
global scale
saliency_scale = int(math.pow(2,scale));
bw_im1 = cv.cvCreateImage(cv.cvGetSize(image), cv.IPL_DEPTH_8U,1)
cv.cvCvtColor(image, bw_im1, cv.CV_BGR2GRAY)
bw_im = cv.cvCreateImage(cv.cvSize(saliency_scale,saliency_scale), cv.IPL_DEPTH_8U,1)
cv.cvResize(bw_im1, bw_im)
highgui.cvShowImage("BW", bw_im)
realInput = cv.cvCreateImage( cv.cvGetSize(bw_im), cv.IPL_DEPTH_32F, 1);
imaginaryInput = cv.cvCreateImage( cv.cvGetSize(bw_im), cv.IPL_DEPTH_32F, 1);
complexInput = cv.cvCreateImage( cv.cvGetSize(bw_im), cv.IPL_DEPTH_32F, 2);
cv.cvScale(bw_im, realInput, 1.0, 0.0);
cv.cvZero(imaginaryInput);
cv.cvMerge(realInput, imaginaryInput, None, None, complexInput);
dft_M = saliency_scale #cv.cvGetOptimalDFTSize( bw_im.height - 1 );
dft_N = saliency_scale #cv.cvGetOptimalDFTSize( bw_im.width - 1 );
dft_A = cv.cvCreateMat( dft_M, dft_N, cv.CV_32FC2 );
image_Re = cv.cvCreateImage( cv.cvSize(dft_N, dft_M), cv.IPL_DEPTH_32F, 1);
image_Im = cv.cvCreateImage( cv.cvSize(dft_N, dft_M), cv.IPL_DEPTH_32F, 1);
# copy A to dft_A and pad dft_A with zeros
tmp = cv.cvGetSubRect( dft_A, cv.cvRect(0,0, bw_im.width, bw_im.height));
cv.cvCopy( complexInput, tmp, None );
if(dft_A.width > bw_im.width):
tmp = cv.cvGetSubRect( dft_A, cv.cvRect(bw_im.width,0, dft_N - bw_im.width, bw_im.height));
cv.cvZero( tmp );
cv.cvDFT( dft_A, dft_A, cv.CV_DXT_FORWARD, complexInput.height );
cv.cvSplit( dft_A, image_Re, image_Im, None, None );
# Compute the phase angle
image_Mag = cv.cvCreateImage(cv.cvSize(dft_N, dft_M), cv.IPL_DEPTH_32F, 1);
image_Phase = cv.cvCreateImage(cv.cvSize(dft_N, dft_M), cv.IPL_DEPTH_32F, 1);
#compute the phase of the spectrum
cv.cvCartToPolar(image_Re, image_Im, image_Mag, image_Phase, 0)
log_mag = cv.cvCreateImage(cv.cvSize(dft_N, dft_M), cv.IPL_DEPTH_32F, 1);
cv.cvLog(image_Mag, log_mag)
#Box filter the magnitude, then take the difference
image_Mag_Filt = cv.cvCreateImage(cv.cvSize(dft_N, dft_M), cv.IPL_DEPTH_32F, 1);
filt = cv.cvCreateMat(3,3, cv.CV_32FC1);
cv.cvSet(filt,cv.cvScalarAll(-1.0/9.0))
cv.cvFilter2D(log_mag, image_Mag_Filt, filt, cv.cvPoint(-1,-1))
cv.cvAdd(log_mag, image_Mag_Filt, log_mag, None)
cv.cvExp(log_mag, log_mag)
cv.cvPolarToCart(log_mag, image_Phase, image_Re, image_Im,0);
cv.cvMerge(image_Re, image_Im, None, None, dft_A)
cv.cvDFT( dft_A, dft_A, cv.CV_DXT_INVERSE, complexInput.height)
tmp = cv.cvGetSubRect( dft_A, cv.cvRect(0,0, bw_im.width, bw_im.height));
cv.cvCopy( tmp, complexInput, None );
cv.cvSplit(complexInput, realInput, imaginaryInput, None, None)
min, max = cv.cvMinMaxLoc(realInput);
#cv.cvScale(realInput, realInput, 1.0/(max-min), 1.0*(-min)/(max-min));
cv.cvSmooth(realInput, realInput);
threshold = thresh/100.0*cv.cvAvg(realInput)[0]
cv.cvThreshold(realInput, realInput, threshold, 1.0, cv.CV_THRESH_BINARY)
tmp_img = cv.cvCreateImage(cv.cvGetSize(bw_im1),cv.IPL_DEPTH_32F, 1)
cv.cvResize(realInput,tmp_img)
cv.cvScale(tmp_img, bw_im1, 255,0)
return bw_im1
rotate = ((image.width / 2.0) -
track_box.center.x) / (image.width / 2.0)
translate = ((image.height / 2.0) -
track_box.center.y) / (image.height / 2.0)
#print "rotate =", rotate, "translate =", translate
if go:
move(translate, rotate)
if select_object and selection.width > 0 and selection.height > 0:
subimg = cv.cvGetSubRect(image, selection)
cv.cvXorS(subimage, cv.cvScalarAll(255), subimage, 0)
highgui.cvShowImage("VisualJoystick", image)
c = highgui.cvWaitKey(10)
if c == '\x1b':
break
elif c == 'b':
backproject_mode ^= 1
elif c == 'c':
track_object = 0
cv.cvZero(histimg)
if go:
stop()
if go:
stop()
rgb[sector_data[sector][2]] = p
return cv.cvScalar(rgb[2], rgb[1], rgb[0], 0)
img_h = cv.cvCreateImage(size, 8, 1)
img_s = cv.cvCreateImage(size, 8, 1)
img_v = cv.cvCreateImage(size, 8, 1)
thresh_mask = cv.cvCreateImage(size, 8, 1)
hist_hue_img = cv.cvCreateImage((int(h_bins * scalewidth), scaleheight), 8, 3)
hist_val_img = cv.cvCreateImage((int(v_bins * scalewidth), scaleheight), 8, 3)
output_mask = cv.cvCreateImage(size, 8, 1)
while True:
img = highgui.cvQueryFrame(cap)
cv.cvZero(img_h)
cv.cvZero(img_s)
cv.cvZero(img_v)
cv.cvZero(thresh_mask)
highgui.cvShowImage("Input", img)
# 5x5 Gaussian Blur
cv.cvSmooth(img, img, cv.CV_GAUSSIAN, 5, 5)
# convert to HSV
cv.cvCvtColor(img, img, cv.CV_BGR2HSV)
# threshold bad values
cv.cvInRangeS(img, hsv_min, hsv_max, thresh_mask)
cv.cvAnd(thresh_mask, mask_bw, thresh_mask)
# Hue(0,180), Saturation(0,255), Value(0,255)
cv.cvSplit(img, img_h, img_s, img_v, 0)
