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 analyzeCut(scaleImage, edgeImage, cut): """Extract the interesting features respecting the cut""" # Set up constraints constraints = regionSelector.Constraints(cv.cvGetSize(scaleImage), cut, margin, superMargin, 0.002, 0.25) # Create temporary images blurImage = cv.cvCreateImage(cv.cvGetSize(scaleImage), 8, 3) workImage = cv.cvCreateImage(cv.cvGetSize(scaleImage), 8, 3) # Create a blurred copy of the original cv.cvSmooth(scaleImage, blurImage, cv.CV_BLUR, 3, 3, 0) # Superimpose the edges onto the blured image cv.cvNot(edgeImage, edgeImage) cv.cvCopy(blurImage, workImage, edgeImage) # Get the edges back to white cv.cvNot(edgeImage, edgeImage) # We're done with the blurred image now cv.cvReleaseImage(blurImage) # Retrive the regions touching the cut component_dictionary = featureDetector.ribbonFloodFill(scaleImage, edgeImage, workImage, cut, margin, lo, up) # Clean up cv.cvReleaseImage(workImage) # Prune components newComponents = regionSelector.pruneRegions(component_dictionary, constraints) # Return the dictionary of accepted components #transformer.translateBoundingBoxes(newComponents, 1) return newComponents
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 _detect(image):
""" Detects faces on `image`
Parameters:
@image: image file path
Returns:
[((x1, y1), (x2, y2)), ...] List of coordenates for top-left
and bottom-right corner
"""
# the OpenCV API says this function is obsolete, but we can't
# cast the output of cvLoad to a HaarClassifierCascade, so use
# this anyways the size parameter is ignored
capture = cvCreateFileCapture(image)
if not capture:
return []
frame = cvQueryFrame(capture)
if not frame:
return []
img = cvCreateImage(cvSize(frame.width, frame.height), IPL_DEPTH_8U,
frame.nChannels)
cvCopy(frame, img)
# allocate temporary images
gray = cvCreateImage((img.width, img.height), COPY_DEPTH, COPY_CHANNELS)
width, height = (cvRound(img.width / IMAGE_SCALE),
cvRound(img.height / IMAGE_SCALE))
small_img = cvCreateImage((width, height), COPY_DEPTH, COPY_CHANNELS)
# convert color input image to grayscale
cvCvtColor(img, gray, CV_BGR2GRAY)
# scale input image for faster processing
cvResize(gray, small_img, CV_INTER_LINEAR)
cvEqualizeHist(small_img, small_img)
cvClearMemStorage(STORAGE)
coords = []
for haar_file in CASCADES:
cascade = cvLoadHaarClassifierCascade(haar_file, cvSize(1, 1))
if cascade:
faces = cvHaarDetectObjects(small_img, cascade, STORAGE,
HAAR_SCALE, MIN_NEIGHBORS, HAAR_FLAGS,
MIN_SIZE) or []
for face_rect in faces:
# the input to cvHaarDetectObjects was resized, so scale the
# bounding box of each face and convert it to two CvPoints
x, y = face_rect.x, face_rect.y
pt1 = (int(x * IMAGE_SCALE), int(y * IMAGE_SCALE))
pt2 = (int((x + face_rect.width) * IMAGE_SCALE),
int((y + face_rect.height) * IMAGE_SCALE))
coords.append((pt1, pt2))
return coords
def _detect(image):
""" Detects faces on `image`
Parameters:
@image: image file path
Returns:
[((x1, y1), (x2, y2)), ...] List of coordenates for top-left
and bottom-right corner
"""
# the OpenCV API says this function is obsolete, but we can't
# cast the output of cvLoad to a HaarClassifierCascade, so use
# this anyways the size parameter is ignored
capture = cvCreateFileCapture(image)
if not capture:
return []
frame = cvQueryFrame(capture)
if not frame:
return []
img = cvCreateImage(cvSize(frame.width, frame.height),
IPL_DEPTH_8U, frame.nChannels)
cvCopy(frame, img)
# allocate temporary images
gray = cvCreateImage((img.width, img.height),
COPY_DEPTH, COPY_CHANNELS)
width, height = (cvRound(img.width / IMAGE_SCALE),
cvRound(img.height / IMAGE_SCALE))
small_img = cvCreateImage((width, height), COPY_DEPTH, COPY_CHANNELS)
# convert color input image to grayscale
cvCvtColor(img, gray, CV_BGR2GRAY)
# scale input image for faster processing
cvResize(gray, small_img, CV_INTER_LINEAR)
cvEqualizeHist(small_img, small_img)
cvClearMemStorage(STORAGE)
coords = []
for haar_file in CASCADES:
cascade = cvLoadHaarClassifierCascade(haar_file, cvSize(1, 1))
if cascade:
faces = cvHaarDetectObjects(small_img, cascade, STORAGE, HAAR_SCALE,
MIN_NEIGHBORS, HAAR_FLAGS, MIN_SIZE) or []
for face_rect in faces:
# the input to cvHaarDetectObjects was resized, so scale the
# bounding box of each face and convert it to two CvPoints
x, y = face_rect.x, face_rect.y
pt1 = (int(x * IMAGE_SCALE), int(y * IMAGE_SCALE))
pt2 = (int((x + face_rect.width) * IMAGE_SCALE),
int((y + face_rect.height) * IMAGE_SCALE))
coords.append((pt1, pt2))
return coords
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 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 analyzeCut(original, edgeImage, cut, settings, showBlobs=False): """Extract the interesting features in the vicinity of a given cut""" # Get all data from the settings lo = settings.lo up = settings.up # Set up the margin with respect to the cut margin = marginCalculator.getPixels(original, cut, settings.marginPercentage) superMargin = 0 # ^^ We don't use superMargin # Set up constraints constraints = regionSelector.Constraints(cv.cvGetSize(original), cut, margin, superMargin, 0.002, 0.25) # Create temporary images blurImage = cv.cvCreateImage(cv.cvGetSize(original), 8, 3) workImage = cv.cvCreateImage(cv.cvGetSize(original), 8, 3) # Create a blurred copy of the original cv.cvSmooth(original, blurImage, cv.CV_BLUR, 3, 3, 0) # Superimpose the edges onto the blured image cv.cvNot(edgeImage, edgeImage) cv.cvCopy(blurImage, workImage, edgeImage) # We're done with the blurred image now cv.cvReleaseImage(blurImage) # Get the edges back to white cv.cvNot(edgeImage, edgeImage) # Retrive the regions touching the cut component_dictionary = featureDetector.ribbonFloodFill(original, edgeImage, workImage, cut, margin, lo, up) #start expanded # Prune components BEFORE we delete the workImage tmpnewComponents = regionSelector.pruneExpandedRegions(component_dictionary, constraints) newComponents = regionSelector.pruneExpandedRagionsto(tmpnewComponents, constraints, cut, workImage) # Clean up only if we do not return the image if not showBlobs: cv.cvReleaseImage(workImage) # Return the dictionary of accepted components or both if not showBlobs: return newComponents else: return (workImage, newComponents)
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 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 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 = self.detect_face(frame_copy) img = self.detect_squares(frame_copy) img_pil = adaptors.Ipl2PIL(img) 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 startChroma(background, frameWidht, frameHeight):
#cvNamedWindow("Original")
cvNamedWindow("Chroma")
hlsFrame = cvCreateImage(cvSize(frameWidth, frameHeight), IPL_DEPTH_8U, 3)
transparency = 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)
cvAnd(ThHFrame, ThSFrame, mask)
cvCopy(background, frame, mask)
cvShowImage("Chroma", frame)
key = cvWaitKey(10)
cvDestroyWindow("Chroma")
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 = self.detect_face(frame_copy)
img = self.detect_squares(frame_copy)
img_pil = adaptors.Ipl2PIL(img)
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 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)]
variable_focal=v
def cb_base(v):
global variable_base
variable_base = v/10.0
xmatch = size.width / 2 + 1
ymatch = size.height / 2 + 1
highgui.cvSetMouseCallback("depthmatch - left", mousecb)
highgui.cvCreateTrackbar("ROI", "depthmatch - left", variable_roi, size.width, cb_roi)
highgui.cvCreateTrackbar("Buffer", "depthmatch - left", variable_buf, size.width, cb_buf)
highgui.cvCreateTrackbar("Focal Length", "depthmatch - left", variable_focal, 1000, cb_focal)
highgui.cvCreateTrackbar("Baseline/10", "depthmatch - left", variable_base, 1000, cb_base)
leftdraw = cv.cvCreateImage(size, 8, 3)
rightdraw = cv.cvCreateImage(size, 8, 3)
while 1:
depth = depthmatch(xmatch, ymatch, left, right, roi=variable_roi, buf=variable_buf,baseline=variable_base, focal_length=variable_focal)
cv.cvCopy(left, leftdraw)
cv.cvCopy(right, rightdraw)
cv.cvLine(leftdraw, depth[1], depth[2], (0,255,0), 2)
cv.cvPutText(leftdraw, "%2f(m) at (%2f,%2f)" % (depth[0][2],depth[0][0],depth[0][1]), (xmatch,ymatch), font, (0,0,255))
cv.cvLine(rightdraw, depth[2], depth[2], (0,0,255), 5)
highgui.cvShowImage("depthmatch - left", leftdraw)
highgui.cvShowImage("depthmatch - right", rightdraw)
print depth
highgui.cvWaitKey(10)
if __name__ == "__main__" and test_number == 2:
left = highgui.cvLoadImage(str(sys.argv[1]))
right = highgui.cvLoadImage(str(sys.argv[2]))
highgui.cvNamedWindow("Depth")
depth = depthmatrix(left, right, 4)
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)
# no image captured... end the processing
break
if image is None:
# create the images we need
image = cv.cvCreateImage (cv.cvGetSize (frame), 8, 3)
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)
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,
print "Finding the golden means in the picture"
lines = lib.findGoldenMeans(cv.cvGetSize(image))
#lines = lib.findMeans(cv.cvGetSize(image), lib.PHI)
print "Test plot and line scanner methods"
points = lineScanner.naiveBWLineScanner(edges, lines[0])
#cv.cvSmooth(out, out, cv.CV_MEDIAN, 7, 7, 0)
cv.cvSmooth(image, blurImage, cv.CV_BLUR, 3, 3, 0)
#cv.cvSmooth(out, out, cv.CV_GAUSSIAN, 7, 7, 0)
#out = blurImage
# Superimpose the edges onto the blured image
cv.cvNot(edges, edges)
cv.cvCopy(blurImage, out, edges)
# We're done with the blurred image now
#cv.cvReleaseImage(blurImage)
#print points[:0]
cut = lines[1]
margin = marginCalculator.getPixels(image, cut, 0.024)
component_dictionary = featureDetector.ribbonFloodFill(image, edges, out, cut, margin, lo, up)
#featureDetector.floodFillLine(image, out, points, cut, lo, up, {})
#flags = cv.CV_FLOODFILL_FIXED_RANGE
#flags = 4
color = lib.getRandomColor()
comp = cv.CvConnectedComp()
#cv.cvFloodFill(out, cv.cvPoint(x,y), color, cv.CV_RGB(lo,lo,lo), cv.CV_RGB(up,up,up),comp ,flags)#, None);
#lib.plot(out, cv.cvPoint(x,y), 3, lib.COL_RED)
threshold2 = 70; out = cv.cvCreateImage(cv.cvGetSize(image), 8, 3) edgeDetector.findEdges(image, out, threshold1, threshold2) print "Finding the golden means in the picture" lines = lib.findMeans(cv.cvGetSize(image)) print "Test plot and line scanner methods" points = lineScanner.naiveLineScanner(out, image, lines[0]) out = highgui.cvLoadImage (filename) outcopy = highgui.cvLoadImage (filename) (out,areaOfBlobs) = featureDetector.floodFillLine(out, points, lines[0], lo, up) copy = cv.cvCopy(out,outcopy) #print "Finding the golden means in the picture" #lines = lib.findMeans(cv.cvGetSize(out)) #print "Drawing the means" #lib.drawLines(lines, out) #out = out[:,points[0].x :] listOverSizeOfBlobs = statestik.splitOpBlob(out,outcopy,points,lines[0]) #print listOverSizeOfBlobs #startpoint = lines[0].getPoints()[0] #points.append(lines[0].getPoints()[1]) #for point in points: # out = floofill.floofill(out, lowerThres, upperThres, startpoint, point, 1) # startpoint = point
if not image: print "Error loading image '%s'" % filename print "" sys.exit(-1) print "Finding edges using Canny" bwe = cv.cvCreateImage(cv.cvGetSize(image), 8, 1) out = cv.cvCreateImage(cv.cvGetSize(image), 8, 3) #edgeDetector.findEdges(image, out, threshold1, threshold2) edgeDetector.findBWEdges(image, bwe, threshold1, threshold2) #set if you need the image and the Edges togeter #cv.cvNot(bwe, bwe) cv.cvCopy(image, out, bwe) outname = "edgeDetectorTest" orgname = "Original" nystr = str(str(threshold1)+'-'+str(threshold2)+'.png') print nystr highgui.cvNamedWindow (outname, highgui.CV_WINDOW_AUTOSIZE) highgui.cvNamedWindow (orgname, highgui.CV_WINDOW_AUTOSIZE) highgui.cvSaveImage(nystr, out) while True: highgui.cvShowImage (orgname, image) highgui.cvShowImage (outname, out) c = highgui.cvWaitKey(0)
threshold2 = 2.6 * 70; # Set up the needed images out = cv.cvCreateImage(cv.cvGetSize(image), 8, 3) blur = cv.cvCreateImage(cv.cvGetSize(image), 8, 3) edges = cv.cvCreateImage(cv.cvGetSize(image), 8, 1) # Blur the image cv.cvSmooth(image, blur, cv.CV_BLUR, 3, 3, 0) # Find edges in the original image edgeDetector.findBWEdges(image, edges, threshold1, threshold2) # Superimpose the edges onto the blured image cv.cvNot(edges, edges) cv.cvCopy(blur, out, edges) # Get the edges back to white cv.cvNot(edges, edges) print "Finding the golden means in the picture" lines = lib.findGoldenMeans(cv.cvGetSize(image)) # Define cut cut = lines[0] # Set margin margin = 15 components = featureDetector.ribbonFloodFill(image, edges, out, cut, margin, lo, up)
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 mask_image(image_to_mask, mask):
ret_image = cv.cvCreateImage(cv.cvGetSize(image_to_mask), cv.IPL_DEPTH_8U,3)
cv.cvScale(image_to_mask, ret_image, 0.5, 0.0);
cv.cvCopy(image_to_mask, ret_image, mask)
return ret_image
if frame is None:
# no image captured... end the processing
break
if image is None:
# create the images we need
image = cv.cvCreateImage(cv.cvGetSize(frame), 8, 3)
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)
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
highgui.cvNamedWindow (win_name, highgui.CV_WINDOW_AUTOSIZE)
# create the trackbar
highgui.cvCreateTrackbar (trackbar_name, win_name, 1, 100, on_trackbar)
# show the image
on_trackbar (0)
frame_copy = None
while True:
image = highgui.cvQueryFrame( capture );
if( not image ):
break;
if( not frame_copy ):
frame_copy = cv.cvCreateImage( cv.cvSize(image.width,image.height),
cv.IPL_DEPTH_8U, image.nChannels );
if( image.origin == cv.IPL_ORIGIN_TL ):
cv.cvCopy( image, frame_copy );
else:
cv.cvFlip( image, frame_copy, 0 );
gray = cv.cvCreateImage (cv.cvSize (image.width, image.height), 8, 1)
edge = cv.cvCreateImage (cv.cvSize (image.width, image.height), 8, 1)
cv.cvCvtColor (image, gray, cv.CV_BGR2GRAY)
highgui.cvShowImage (win_name, col_edge)
cv.cvFlip( image, frame_copy, 0 );
if( highgui.cvWaitKey( 10 ) >= 0 ):
break;
def harrisResponse(frame):
"""pyvision/point/DetectorHarris.py
Runs at 10.5 fps...
"""
#gray = cv.cvCreateImage( cv.cvGetSize(image), 8, 1 )
#corners = cv.cvCreateImage( cv.cvGetSize(image), 32, 1 )
#cv.cvCvtColor( image, gray, cv.CV_BGR2GRAY )
#cv.cvCornerHarris(gray,corners,15)
# This could be done in a persistant way
# create the images we need
image = cv.cvCreateImage(cv.cvGetSize(frame), 8, 3)
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)
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)
# 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], cv.cvSize(win_size, win_size), cv.cvSize(-1, -1),
cv.cvTermCriteria(cv.CV_TERMCRIT_ITER | cv.CV_TERMCRIT_EPS, 20, 0.03))
if len(points[0]) > 0:
# we have points, so display them
# calculate the optical flow
[points[1], status], something = cv.cvCalcOpticalFlowPyrLK(
prev_grey, grey, prev_pyramid, pyramid, points[0], len(points[0]),
(win_size, win_size), 3, len(points[0]), None,
cv.cvTermCriteria(cv.CV_TERMCRIT_ITER | cv.CV_TERMCRIT_EPS, 20,
0.03), flags)
# initializations
point_counter = -1
new_points = []
for the_point in points[1]:
# go trough all the points
# increment the counter
point_counter += 1
if add_remove_pt:
# we have a point to add, so see if it is close to
# another one. If yes, don't use it
dx = pt.x - the_point.x
dy = pt.y - the_point.y
if dx * dx + dy * dy <= 25:
# too close
add_remove_pt = 0
continue
if not status[point_counter]:
# we will disable this point
continue
# this point is a correct point
new_points.append(the_point)
# draw the current point
cv.cvCircle(image, cv.cvPointFrom32f(the_point), 3,
cv.cvScalar(0, 255, 0, 0), -1, 8, 0)
# set back the points we keep
points[1] = new_points
# swapping
prev_grey, grey = grey, prev_grey
prev_pyramid, pyramid = pyramid, prev_pyramid
points[0], points[1] = points[1], points[0]
return image
def harrisResponse(frame):
"""pyvision/point/DetectorHarris.py
Runs at 10.5 fps...
"""
#gray = cv.cvCreateImage( cv.cvGetSize(image), 8, 1 )
#corners = cv.cvCreateImage( cv.cvGetSize(image), 32, 1 )
#cv.cvCvtColor( image, gray, cv.CV_BGR2GRAY )
#cv.cvCornerHarris(gray,corners,15)
# This could be done in a persistant way
# create the images we need
image = cv.cvCreateImage (cv.cvGetSize (frame), 8, 3)
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)
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)
# 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],
cv.cvSize (win_size, win_size), cv.cvSize (-1, -1),
cv.cvTermCriteria (cv.CV_TERMCRIT_ITER | cv.CV_TERMCRIT_EPS,
20, 0.03))
if len (points [0]) > 0:
# we have points, so display them
# calculate the optical flow
[points [1], status], something = cv.cvCalcOpticalFlowPyrLK (
prev_grey, grey, prev_pyramid, pyramid,
points [0], len (points [0]),
(win_size, win_size), 3,
len (points [0]),
None,
cv.cvTermCriteria (cv.CV_TERMCRIT_ITER|cv.CV_TERMCRIT_EPS,
20, 0.03),
flags)
# initializations
point_counter = -1
new_points = []
for the_point in points [1]:
# go trough all the points
# increment the counter
point_counter += 1
if add_remove_pt:
# we have a point to add, so see if it is close to
# another one. If yes, don't use it
dx = pt.x - the_point.x
dy = pt.y - the_point.y
if dx * dx + dy * dy <= 25:
# too close
add_remove_pt = 0
continue
if not status [point_counter]:
# we will disable this point
continue
# this point is a correct point
new_points.append (the_point)
# draw the current point
cv.cvCircle (image,
cv.cvPointFrom32f(the_point),
3, cv.cvScalar (0, 255, 0, 0),
-1, 8, 0)
# set back the points we keep
points [1] = new_points
# swapping
prev_grey, grey = grey, prev_grey
prev_pyramid, pyramid = pyramid, prev_pyramid
points [0], points [1] = points [1], points [0]
return image
