def timerEvent(self, ev): # Fetch a frame from the video camera frame = highgui.cvQueryFrame(self.cap) img_orig = cv.cvCreateImage(cv.cvSize(frame.width,frame.height),cv.IPL_DEPTH_8U, frame.nChannels) if (frame.origin == cv.IPL_ORIGIN_TL): cv.cvCopy(frame, img_orig) else: cv.cvFlip(frame, img_orig, 0) # Create a grey frame to clarify data img_grey = cv.cvCreateImage(cv.cvSize(img_orig.width,img_orig.height), 8, 1) cv.cvCvtColor(img_orig, img_grey, cv.CV_BGR2GRAY) # Detect objects within the frame self.faces_storage = cv.cvCreateMemStorage(0) faces = self.detect_faces(img_grey) self.circles_storage = cv.cvCreateMemStorage(0) circles = self.detect_circles(img_grey) self.squares_storage = cv.cvCreateMemStorage(0) squares = self.detect_squares(img_grey, img_orig) self.lines_storage = cv.cvCreateMemStorage(0) lines = self.detect_lines(img_grey, img_orig) # Draw faces if faces: for face in faces: pt1, pt2 = self.face_points(face) cv.cvRectangle(img_orig, pt1, pt2, cv.CV_RGB(255,0,0), 3, 8, 0) # Draw lines if lines: for line in lines: cv.cvLine(img_orig, line[0], line[1], cv.CV_RGB(255,255,0), 3, 8) # Draw circles if circles: for circle in circles: cv.cvCircle(img_orig, cv.cvPoint(cv.cvRound(circle[0]),cv.cvRound(circle[1])),cv.cvRound(circle[2]),cv.CV_RGB(0,0,255),3,8,0) # Draw squares if squares: 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_orig, [pt], 1, cv.CV_RGB(0,255,0), 3, cv.CV_AA, 0) i += 4 # Resize the image to display properly within the window # CV_INTER_NN - nearest-neigbor interpolation, # CV_INTER_LINEAR - bilinear interpolation (used by default) # CV_INTER_AREA - resampling using pixel area relation. (preferred for image decimation) # CV_INTER_CUBIC - bicubic interpolation. img_display = cv.cvCreateImage(cv.cvSize(self.width(),self.height()), 8, 3) cv.cvResize(img_orig, img_display, cv.CV_INTER_NN) img_pil = adaptors.Ipl2PIL(img_display) s = StringIO() img_pil.save(s, "PNG") s.seek(0) q_img = QImage() q_img.loadFromData(s.read()) bitBlt(self, 0, 0, q_img)
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 timerEvent(self, ev):
# Fetch a frame from the video camera
frame = highgui.cvQueryFrame(self.cap)
img_orig = cv.cvCreateImage(cv.cvSize(frame.width, frame.height),
cv.IPL_DEPTH_8U, frame.nChannels)
if (frame.origin == cv.IPL_ORIGIN_TL):
cv.cvCopy(frame, img_orig)
else:
cv.cvFlip(frame, img_orig, 0)
# Create a grey frame to clarify data
img_grey = cv.cvCreateImage(cv.cvSize(img_orig.width, img_orig.height),
8, 1)
cv.cvCvtColor(img_orig, img_grey, cv.CV_BGR2GRAY)
# Detect objects within the frame
self.faces_storage = cv.cvCreateMemStorage(0)
faces = self.detect_faces(img_grey)
self.circles_storage = cv.cvCreateMemStorage(0)
circles = self.detect_circles(img_grey)
self.squares_storage = cv.cvCreateMemStorage(0)
squares = self.detect_squares(img_grey, img_orig)
self.lines_storage = cv.cvCreateMemStorage(0)
lines = self.detect_lines(img_grey, img_orig)
# Draw faces
if faces:
for face in faces:
pt1, pt2 = self.face_points(face)
cv.cvRectangle(img_orig, pt1, pt2, cv.CV_RGB(255, 0, 0), 3, 8,
0)
# Draw lines
if lines:
for line in lines:
cv.cvLine(img_orig, line[0], line[1], cv.CV_RGB(255, 255, 0),
3, 8)
# Draw circles
if circles:
for circle in circles:
cv.cvCircle(
img_orig,
cv.cvPoint(cv.cvRound(circle[0]), cv.cvRound(circle[1])),
cv.cvRound(circle[2]), cv.CV_RGB(0, 0, 255), 3, 8, 0)
# Draw squares
if squares:
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_orig, [pt], 1, cv.CV_RGB(0, 255, 0), 3,
cv.CV_AA, 0)
i += 4
# Resize the image to display properly within the window
# CV_INTER_NN - nearest-neigbor interpolation,
# CV_INTER_LINEAR - bilinear interpolation (used by default)
# CV_INTER_AREA - resampling using pixel area relation. (preferred for image decimation)
# CV_INTER_CUBIC - bicubic interpolation.
img_display = cv.cvCreateImage(cv.cvSize(self.width(), self.height()),
8, 3)
cv.cvResize(img_orig, img_display, cv.CV_INTER_NN)
img_pil = adaptors.Ipl2PIL(img_display)
s = StringIO()
img_pil.save(s, "PNG")
s.seek(0)
q_img = QImage()
q_img.loadFromData(s.read())
bitBlt(self, 0, 0, q_img)
# init the list of polylines
nb_polylines = 2
polylines_size = 3
pt = [0,] * nb_polylines
for a in range (nb_polylines):
pt [a] = [0,] * polylines_size
# draw some polylines
for i in range (number):
for a in range (nb_polylines):
for b in range (polylines_size):
pt [a][b] = cv.cvPoint (random.randrange (-width, 2 * width),
random.randrange (-height, 2 * height))
cv.cvPolyLine (image, pt, 1,
random_color (random),
random.randrange (1, 9),
line_type, 0)
highgui.cvShowImage (window_name, image)
highgui.cvWaitKey (delay)
# draw some filled polylines
for i in range (number):
for a in range (nb_polylines):
for b in range (polylines_size):
pt [a][b] = cv.cvPoint (random.randrange (-width, 2 * width),
random.randrange (-height, 2 * height))
cv.cvFillPoly (image, pt,
random_color (random),
line_type, 0)
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
# init the list of polylines
nb_polylines = 2
polylines_size = 3
pt = [0,] * nb_polylines
for a in range (nb_polylines):
pt [a] = [0,] * polylines_size
# draw some polylines
for i in range (number):
for a in range (nb_polylines):
for b in range (polylines_size):
pt [a][b] = cv.cvPoint (random.randrange (-width, 2 * width),
random.randrange (-height, 2 * height))
cv.cvPolyLine (image, pt, 1,
random_color (random),
random.randrange (1, 9),
line_type, 0)
highgui.cvShowImage (window_name, image)
highgui.cvWaitKey (delay)
# draw some filled polylines
for i in range (number):
for a in range (nb_polylines):
for b in range (polylines_size):
pt [a][b] = cv.cvPoint (random.randrange (-width, 2 * width),
random.randrange (-height, 2 * height))
cv.cvFillPoly (image, pt,
random_color (random),
line_type, 0)
