def read(self) :
frame=self.input.read()
cv_rs = cv.cvCreateImage(cv.cvSize(frame.width,frame.height),frame.depth,1)
cv.cvCvtColor(frame,cv_rs,cv.CV_RGB2GRAY)
frame = cv_rs
if self.enabled :
# I think these functions are too specialized for transforms
cv.cvSmooth(frame,frame,cv.CV_GAUSSIAN,3, 0, 0, 0 )
cv.cvErode(frame, frame, None, 1)
cv.cvDilate(frame, frame, None, 1)
num_contours,contours=cv.cvFindContours(frame,self.storage,cv.sizeof_CvContour,cv.CV_RETR_LIST,cv.CV_CHAIN_APPROX_NONE,cv.cvPoint(0,0))
if contours is None :
return []
else :
contours = cv.cvApproxPoly( contours, cv.sizeof_CvContour, self.storage, cv.CV_POLY_APPROX_DP, 3, 1 );
if contours is None :
return []
else :
final_contours = []
for c in contours.hrange() :
area = abs(cv.cvContourArea(c))
#self.debug_print('Polygon Area: %f'%area)
if area >= self.min_area :
lst = []
for pt in c :
lst.append((pt.x,pt.y))
final_contours.append(lst)
contours = contours.h_next
return final_contours
return []
def getData(): frame = highgui.cvQueryFrame(capture) if frame is None: return None cv.cvSplit(frame, b_img, g_img, r_img, None) cv.cvInRangeS(r_img, 150, 255, r_img) cv.cvInRangeS(g_img, 0, 100, g_img) cv.cvInRangeS(b_img, 0, 100, b_img) cv.cvAnd(r_img, g_img, laser_img) cv.cvAnd(laser_img, b_img, laser_img) cv.cvErode(laser_img,laser_img) #,0,2) cv.cvDilate(laser_img,laser_img) c_count, contours = cv.cvFindContours (laser_img, storage, cv.sizeof_CvContour, cv.CV_RETR_LIST, cv.CV_CHAIN_APPROX_NONE, cv.cvPoint (0,0)) if c_count: return returnEllipses(contours) else: return None
def read(self) :
frame=self.input.read()
if self.debug :
raw_frame = cv.cvCreateImage(cv.cvSize(frame.width,frame.height),frame.depth,frame.nChannels)
cv.cvCopy(frame,raw_frame,None)
self.raw_frame_surface=pygame.image.frombuffer(frame.imageData,(frame.width,frame.height),'RGB')
if self.enabled :
cv_rs = cv.cvCreateImage(cv.cvSize(frame.width,frame.height),frame.depth,1)
# convert color
cv.cvCvtColor(frame,cv_rs,cv.CV_BGR2GRAY)
# invert the image
cv.cvSubRS(cv_rs, 255, cv_rs, None);
# threshold the image
frame = cv.cvCreateImage(cv.cvSize(frame.width,frame.height),frame.depth,1)
cv.cvThreshold(cv_rs, frame, self.threshold, 255, cv.CV_THRESH_BINARY)
if self.debug :
thresh_frame = cv.cvCreateImage(cv.cvSize(frame.width,frame.height),frame.depth,3)
cv.cvCvtColor(frame,thresh_frame,cv.CV_GRAY2RGB)
self.thresh_frame_surface=pygame.image.frombuffer(thresh_frame.imageData,(frame.width,frame.height),'RGB')
# I think these functions are too specialized for transforms
cv.cvSmooth(frame,frame,cv.CV_GAUSSIAN,3, 0, 0, 0 )
cv.cvErode(frame, frame, None, 1)
cv.cvDilate(frame, frame, None, 1)
num_contours,contours=cv.cvFindContours(frame,self.storage,cv.sizeof_CvContour,cv.CV_RETR_LIST,cv.CV_CHAIN_APPROX_NONE,cv.cvPoint(0,0))
if contours is None :
return []
else :
contours = cv.cvApproxPoly( contours, cv.sizeof_CvContour, self.storage, cv.CV_POLY_APPROX_DP, 3, 1 );
if contours is None :
return []
else :
final_contours = []
for c in contours.hrange() :
area = abs(cv.cvContourArea(c))
#self.debug_print('Polygon Area: %f'%area)
if area >= self.min_area :
lst = []
for pt in c :
lst.append((pt.x,pt.y))
final_contours.append(lst)
contours = contours.h_next
return final_contours
return []
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 read(self): frame = self.input.read() if self.debug : raw_frame = cv.cvCreateImage(cv.cvSize(frame.width,frame.height),frame.depth,frame.nChannels) cv.cvCopy(frame,raw_frame,None) self.raw_frame_surface=pygame.image.frombuffer(frame.imageData,(frame.width,frame.height),'RGB') if self.enabled : cvt_red = cv.cvCreateImage(cv.cvSize(frame.width,frame.height),frame.depth,1) cv.cvSplit(frame,None,None,cvt_red,None) if self.debug : red_frame = cv.cvCreateImage(cv.cvSize(cvt_red.width,cvt_red.height),cvt_red.depth,3) cv.cvMerge(cvt_red,None,None,None,red_frame) self.red_frame_surface = pygame.image.frombuffer(red_frame.imageData,(cvt_red.width,cvt_red.height),'RGB') # I think these functions are too specialized for transforms cv.cvSmooth(cvt_red,cvt_red,cv.CV_GAUSSIAN,3, 0, 0, 0 ) cv.cvErode(cvt_red, cvt_red, None, 1) cv.cvDilate(cvt_red, cvt_red, None, 1) if self.debug : thresh_frame = cv.cvCreateImage(cv.cvSize(cvt_red.width,cvt_red.height),cvt_red.depth,3) cv.cvMerge(cvt_red,None,None,None,thresh_frame) self.thresh_frame_surface = pygame.image.frombuffer(cvt_red.imageData,(cvt_red.width,cvt_red.height),'RGB') cvpt_min = cv.cvPoint(0,0) cvpt_max = cv.cvPoint(0,0) t = cv.cvMinMaxLoc(cvt_red,cvpt_min,cvpt_max) print t if cvpt_max.x == 0 and cvpt_max.y == 0 : return [] return [(cvpt_max.x,cvpt_max.y)]
def detect_squares(self, img_grey, img_orig): """ Find squares within the video stream and draw them """ cv.cvClearMemStorage(self.faces_storage) N = 11 thresh = 5 sz = cv.cvSize(img_grey.width & -2, img_grey.height & -2) timg = cv.cvCloneImage(img_orig) pyr = cv.cvCreateImage(cv.cvSize(sz.width/2, sz.height/2), 8, 3) # create empty sequence that will contain points - # 4 points per square (the square's vertices) squares = cv.cvCreateSeq(0, cv.sizeof_CvSeq, cv.sizeof_CvPoint, self.squares_storage) squares = cv.CvSeq_CvPoint.cast(squares) # select the maximum ROI in the image # with the width and height divisible by 2 subimage = cv.cvGetSubRect(timg, cv.cvRect(0, 0, sz.width, sz.height)) cv.cvReleaseImage(timg) # down-scale and upscale the image to filter out the noise cv.cvPyrDown(subimage, pyr, 7) cv.cvPyrUp(pyr, subimage, 7) cv.cvReleaseImage(pyr) tgrey = cv.cvCreateImage(sz, 8, 1) # find squares in every color plane of the image for c in range(3): # extract the c-th color plane channels = [None, None, None] channels[c] = tgrey cv.cvSplit(subimage, channels[0], channels[1], channels[2], None) for l in range(N): # hack: use Canny instead of zero threshold level. # Canny helps to catch squares with gradient shading if(l == 0): # apply Canny. Take the upper threshold from slider # and set the lower to 0 (which forces edges merging) cv.cvCanny(tgrey, img_grey, 0, thresh, 5) # dilate canny output to remove potential # holes between edge segments cv.cvDilate(img_grey, img_grey, None, 1) else: # apply threshold if l!=0: # tgray(x,y) = gray(x,y) < (l+1)*255/N ? 255 : 0 cv.cvThreshold(tgrey, img_grey, (l+1)*255/N, 255, cv.CV_THRESH_BINARY) # find contours and store them all as a list count, contours = cv.cvFindContours(img_grey, self.squares_storage, cv.sizeof_CvContour, cv.CV_RETR_LIST, cv.CV_CHAIN_APPROX_SIMPLE, cv.cvPoint(0,0)) if not contours: continue # test each contour for contour in contours.hrange(): # approximate contour with accuracy proportional # to the contour perimeter result = cv.cvApproxPoly(contour, cv.sizeof_CvContour, self.squares_storage, cv.CV_POLY_APPROX_DP, cv.cvContourPerimeter(contours)*0.02, 0) # square contours should have 4 vertices after approximation # relatively large area (to filter out noisy contours) # and be convex. # Note: absolute value of an area is used because # area may be positive or negative - in accordance with the # contour orientation if(result.total == 4 and abs(cv.cvContourArea(result)) > 1000 and cv.cvCheckContourConvexity(result)): s = 0 for i in range(5): # find minimum angle between joint # edges (maximum of cosine) if(i >= 2): t = abs(self.squares_angle(result[i], result[i-2], result[i-1])) if s<t: s = t # if cosines of all angles are small # (all angles are ~90 degree) then write quandrange # vertices to resultant sequence if(s < 0.3): for i in range(4): squares.append(result[i]) cv.cvReleaseImage(tgrey) return squares
def detect_squares(self, img):
""" Find squares within the video stream and draw them """
N = 11
thresh = 5
sz = cv.cvSize(img.width & -2, img.height & -2)
timg = cv.cvCloneImage(img)
gray = cv.cvCreateImage(sz, 8, 1)
pyr = cv.cvCreateImage(cv.cvSize(sz.width / 2, sz.height / 2), 8, 3)
# create empty sequence that will contain points -
# 4 points per square (the square's vertices)
squares = cv.cvCreateSeq(0, cv.sizeof_CvSeq, cv.sizeof_CvPoint,
self.storage)
squares = cv.CvSeq_CvPoint.cast(squares)
# select the maximum ROI in the image
# with the width and height divisible by 2
subimage = cv.cvGetSubRect(timg, cv.cvRect(0, 0, sz.width, sz.height))
# down-scale and upscale the image to filter out the noise
cv.cvPyrDown(subimage, pyr, 7)
cv.cvPyrUp(pyr, subimage, 7)
tgray = cv.cvCreateImage(sz, 8, 1)
# find squares in every color plane of the image
for c in range(3):
# extract the c-th color plane
channels = [None, None, None]
channels[c] = tgray
cv.cvSplit(subimage, channels[0], channels[1], channels[2], None)
for l in range(N):
# hack: use Canny instead of zero threshold level.
# Canny helps to catch squares with gradient shading
if (l == 0):
# apply Canny. Take the upper threshold from slider
# and set the lower to 0 (which forces edges merging)
cv.cvCanny(tgray, gray, 0, thresh, 5)
# dilate canny output to remove potential
# holes between edge segments
cv.cvDilate(gray, gray, None, 1)
else:
# apply threshold if l!=0:
# tgray(x,y) = gray(x,y) < (l+1)*255/N ? 255 : 0
cv.cvThreshold(tgray, gray, (l + 1) * 255 / N, 255,
cv.CV_THRESH_BINARY)
# find contours and store them all as a list
count, contours = cv.cvFindContours(gray, self.storage,
cv.sizeof_CvContour,
cv.CV_RETR_LIST,
cv.CV_CHAIN_APPROX_SIMPLE,
cv.cvPoint(0, 0))
if not contours:
continue
# test each contour
for contour in contours.hrange():
# approximate contour with accuracy proportional
# to the contour perimeter
result = cv.cvApproxPoly(
contour, cv.sizeof_CvContour, self.storage,
cv.CV_POLY_APPROX_DP,
cv.cvContourPerimeter(contours) * 0.02, 0)
# square contours should have 4 vertices after approximation
# relatively large area (to filter out noisy contours)
# and be convex.
# Note: absolute value of an area is used because
# area may be positive or negative - in accordance with the
# contour orientation
if (result.total == 4
and abs(cv.cvContourArea(result)) > 1000
and cv.cvCheckContourConvexity(result)):
s = 0
for i in range(5):
# find minimum angle between joint
# edges (maximum of cosine)
if (i >= 2):
t = abs(
self.squares_angle(result[i],
result[i - 2],
result[i - 1]))
if s < t:
s = t
# if cosines of all angles are small
# (all angles are ~90 degree) then write quandrange
# vertices to resultant sequence
if (s < 0.3):
for i in range(4):
squares.append(result[i])
i = 0
while i < squares.total:
pt = []
# read 4 vertices
pt.append(squares[i])
pt.append(squares[i + 1])
pt.append(squares[i + 2])
pt.append(squares[i + 3])
# draw the square as a closed polyline
cv.cvPolyLine(img, [pt], 1, cv.CV_RGB(0, 255, 0), 3, cv.CV_AA, 0)
i += 4
return img
def get_smoothed(self,image): cv.cvSmooth(image,image,cv.CV_GAUSSIAN,3,0,0,0) cv.cvErode(image,image,None,1) cv.cvDilate(image,image,None,1) return image
def dilateImage(image):
cv.cvDilate(image, image, None, 5)
return image
