def detect_faces(self, img_grey):
""" Detect faces within an image, then draw around them.
The default parameters (scale_factor=1.1, min_neighbors=3, flags=0) are tuned
for accurate yet slow object detection. For a faster operation on real video
images the settings are:
scale_factor=1.2, min_neighbors=2, flags=CV_HAAR_DO_CANNY_PRUNING,
min_size=<minimum possible face size
"""
min_size = cv.cvSize(20, 20)
self.image_scale = 1.3
haar_scale = 1.2
min_neighbors = 2
haar_flags = 0
# Create a small image for better performance
small_size = cv.cvSize(cv.cvRound(img_grey.width / self.image_scale),
cv.cvRound(img_grey.height / self.image_scale))
small_img = cv.cvCreateImage(small_size, 8, 1)
cv.cvResize(img_grey, small_img, cv.CV_INTER_LINEAR)
cv.cvEqualizeHist(small_img, small_img)
cv.cvClearMemStorage(self.faces_storage)
if (self.cascade):
t = cv.cvGetTickCount()
faces = cv.cvHaarDetectObjects(small_img, self.cascade,
self.faces_storage, haar_scale,
min_neighbors, haar_flags, min_size)
t = cv.cvGetTickCount() - t
cv.cvReleaseImage(small_img)
#print "detection time = %gms" % (t/(cvGetTickFrequency()*1000.));
return faces
def detect_faces(self, img_grey): """ Detect faces within an image, then draw around them. The default parameters (scale_factor=1.1, min_neighbors=3, flags=0) are tuned for accurate yet slow object detection. For a faster operation on real video images the settings are: scale_factor=1.2, min_neighbors=2, flags=CV_HAAR_DO_CANNY_PRUNING, min_size=<minimum possible face size """ min_size = cv.cvSize(20,20) self.image_scale = 1.3 haar_scale = 1.2 min_neighbors = 2 haar_flags = 0 # Create a small image for better performance small_size = cv.cvSize(cv.cvRound(img_grey.width/self.image_scale),cv.cvRound(img_grey.height/self.image_scale)) small_img = cv.cvCreateImage(small_size, 8, 1) cv.cvResize(img_grey, small_img, cv.CV_INTER_LINEAR) cv.cvEqualizeHist(small_img, small_img) cv.cvClearMemStorage(self.faces_storage) if(self.cascade): t = cv.cvGetTickCount(); faces = cv.cvHaarDetectObjects(small_img, self.cascade, self.faces_storage, haar_scale, min_neighbors, haar_flags, min_size) t = cv.cvGetTickCount() - t cv.cvReleaseImage(small_img) #print "detection time = %gms" % (t/(cvGetTickFrequency()*1000.)); return faces
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# the OpenCV API says this function is obsolete, but we can't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 resizeImage(self, image_location, ouput_location, size):
"""
resizes the image to a rectangle with the given size and saves it
"""
width = size
height = size
input_image = highgui.cvLoadImage(image_location, 1) # flag: >0 the loaded image is forced to be a 3-channel color image
output_image = cv.cvCreateImage(cv.cvSize(cv.cvRound(width), cv.cvRound(height)), 8, 3);
cv.cvResize(input_image, output_image, cv.CV_INTER_LINEAR);
highgui.cvSaveImage(ouput_location, output_image) # save the image to file
def __normImage(self, img, length):
#print "Generating norm image..."
width = length
height = length
gray = cv.cvCreateImage(cv.cvSize(img.width,img.height), 8, 1);
small_img = cv.cvCreateImage(cv.cvSize(cv.cvRound(width),
cv.cvRound(height)), 8, 1 );
# convert color input image to grayscale
cv.cvCvtColor(img, gray, cv.CV_BGR2GRAY);
# scale input image for faster processing
cv.cvResize(gray, small_img, cv.CV_INTER_LINEAR);
cv.cvEqualizeHist(small_img, small_img);
#cvClearMemStorage(self.storage);
norm_image = small_img # save the 'normalized image'
return norm_image
def detect_face(self, img): """ Detect faces within an image, then draw around them. The default parameters (scale_factor=1.1, min_neighbors=3, flags=0) are tuned for accurate yet slow object detection. For a faster operation on real video images the settings are: scale_factor=1.2, min_neighbors=2, flags=CV_HAAR_DO_CANNY_PRUNING, min_size=<minimum possible face size """ min_size = cv.cvSize(20,20) image_scale = 1.3 haar_scale = 1.2 min_neighbors = 2 haar_flags = 0 gray = cv.cvCreateImage(cv.cvSize(img.width,img.height), 8, 1) small_img = cv.cvCreateImage(cv.cvSize(cv.cvRound(img.width/image_scale), cv.cvRound(img.height/image_scale)), 8, 1) cv.cvCvtColor(img, gray, cv.CV_BGR2GRAY) cv.cvResize(gray, small_img, cv.CV_INTER_LINEAR) cv.cvEqualizeHist(small_img, small_img) cv.cvClearMemStorage(self.storage) if(self.cascade): t = cv.cvGetTickCount(); faces = cv.cvHaarDetectObjects(small_img, self.cascade, self.storage, haar_scale, min_neighbors, haar_flags, min_size) t = cv.cvGetTickCount() - t #print "detection time = %gms" % (t/(cvGetTickFrequency()*1000.)); if faces: for r in faces: pt1 = cv.cvPoint(int(r.x*image_scale), int(r.y*image_scale)) pt2 = cv.cvPoint(int((r.x+r.width)*image_scale), int((r.y+r.height)*image_scale)) cv.cvRectangle(img, pt1, pt2, cv.CV_RGB(255,0,0), 3, 8, 0) return img
def detect_face(self, img):
""" Detect faces within an image, then draw around them.
The default parameters (scale_factor=1.1, min_neighbors=3, flags=0) are tuned
for accurate yet slow object detection. For a faster operation on real video
images the settings are:
scale_factor=1.2, min_neighbors=2, flags=CV_HAAR_DO_CANNY_PRUNING,
min_size=<minimum possible face size
"""
min_size = cv.cvSize(20, 20)
image_scale = 1.3
haar_scale = 1.2
min_neighbors = 2
haar_flags = 0
gray = cv.cvCreateImage(cv.cvSize(img.width, img.height), 8, 1)
small_img = cv.cvCreateImage(
cv.cvSize(cv.cvRound(img.width / image_scale),
cv.cvRound(img.height / image_scale)), 8, 1)
cv.cvCvtColor(img, gray, cv.CV_BGR2GRAY)
cv.cvResize(gray, small_img, cv.CV_INTER_LINEAR)
cv.cvEqualizeHist(small_img, small_img)
cv.cvClearMemStorage(self.storage)
if (self.cascade):
t = cv.cvGetTickCount()
faces = cv.cvHaarDetectObjects(small_img, self.cascade,
self.storage, haar_scale,
min_neighbors, haar_flags, min_size)
t = cv.cvGetTickCount() - t
#print "detection time = %gms" % (t/(cvGetTickFrequency()*1000.));
if faces:
for r in faces:
pt1 = cv.cvPoint(int(r.x * image_scale),
int(r.y * image_scale))
pt2 = cv.cvPoint(int((r.x + r.width) * image_scale),
int((r.y + r.height) * image_scale))
cv.cvRectangle(img, pt1, pt2, cv.CV_RGB(255, 0, 0), 3, 8,
0)
return img
def returnEllipses(contours):
ellipses = []
for c in contours.hrange():
count = c.total;
if( count < 6 ):
continue;
PointArray = cv.cvCreateMat(1, count, cv.CV_32SC2)
PointArray2D32f= cv.cvCreateMat( 1, count, cv.CV_32FC2)
cv.cvCvtSeqToArray(c, PointArray, cv.cvSlice(0, cv.CV_WHOLE_SEQ_END_INDEX));
cv.cvConvert( PointArray, PointArray2D32f )
box = cv.CvBox2D()
box = cv.cvFitEllipse2(PointArray2D32f);
#cv.cvDrawContours(frame, c, cv.CV_RGB(255,255,255), cv.CV_RGB(255,255,255),0,1,8,cv.cvPoint(0,0));
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;
ellipses.append({'center':center, 'size':size, 'angle':box.angle})
return ellipses
def hsv2rgb(hue):
# convert the hue value to the corresponding rgb value
sector_data = [[0, 2, 1], [1, 2, 0], [1, 0, 2], [2, 0, 1], [2, 1, 0], [0, 1, 2]]
hue *= 0.1 / 3
sector = cv.cvFloor(hue)
p = cv.cvRound(255 * (hue - sector))
if sector & 1:
p ^= 255
rgb = {}
rgb[sector_data[sector][0]] = 255
rgb[sector_data[sector][1]] = 0
rgb[sector_data[sector][2]] = p
return cv.cvScalar(rgb[2], rgb[1], rgb[0], 0)
def hsv2rgb(hue):
# convert the hue value to the corresponding rgb value
sector_data = [[0, 2, 1], [1, 2, 0], [1, 0, 2], [2, 0, 1], [2, 1, 0],
[0, 1, 2]]
hue *= 0.1 / 3
sector = cv.cvFloor(hue)
p = cv.cvRound(255 * (hue - sector))
if sector & 1:
p ^= 255
rgb = {}
rgb[sector_data[sector][0]] = 255
rgb[sector_data[sector][1]] = 0
rgb[sector_data[sector][2]] = p
return cv.cvScalar(rgb[2], rgb[1], rgb[0], 0)
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)
scale = 0.
# scale the histograms
cv.cvConvertScale(hist.bins, hist.bins, scale, 0)
# clear the histogram image
cv.cvSetZero(histimg)
# compute the width for each bin do display
bin_w = histimg.width / hdims
for i in range(hdims):
# for all the bins
# get the value, and scale to the size of the hist image
val = cv.cvRound(
cv.cvGetReal1D(hist.bins, i) * histimg.height / 255)
# compute the color
color = hsv2rgb(i * 180. / hdims)
# draw the rectangle in the wanted color
cv.cvRectangle(histimg, cv.cvPoint(i * bin_w, histimg.height),
cv.cvPoint((i + 1) * bin_w, histimg.height - val),
color, -1, 8, 0)
# Make the sweet negative selection box
if mouse_select_object and mouse_selection.width > 0 and mouse_selection.height > 0:
a = cv.cvGetSubRect(frame, mouse_selection)
cv.cvXorS(a, cv.cvScalarAll(255),
a) # Take the negative of the image..
del a
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) )
#if v != 0.0:
#print v
#cv.cvSmooth(grayimage, grayimage,cv.CV_MEDIAN, 3, 3)
#Display image
highgui.cvShowImage ('GrayScale', grayimage)
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 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)
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 );
scale = 0.
# scale the histograms
cv.cvConvertScale (hist.bins, hist.bins, scale, 0)
# clear the histogram image
cv.cvSetZero (histimg)
# compute the width for each bin do display
bin_w = histimg.width / hdims
for i in range (hdims):
# for all the bins
# get the value, and scale to the size of the hist image
val = cv.cvRound (cv.cvGetReal1D (hist.bins, i)
* histimg.height / 255)
# compute the color
color = hsv2rgb (i * 180. / hdims)
# draw the rectangle in the wanted color
cv.cvRectangle (histimg,
cv.cvPoint (i * bin_w, histimg.height),
cv.cvPoint ((i + 1) * bin_w, histimg.height - val),
color, -1, 8, 0)
# we can now display the images
highgui.cvShowImage ('Camera', frame)
highgui.cvShowImage ('Histogram', histimg)
# handle events
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)
