def backProject(self):
work = self.work1[0]
#cv.Smooth(self.hsvImage,self.hsvImage,CVtypes.CV_GAUSSIAN,7,7);
cv.InRangeS(self.hsvImage, cv.Scalar(0, 30, 200, 0),
cv.Scalar(180, 256, 256, 0), self.maskImage)
cv.InRangeS(self.hsvImage, cv.Scalar(0, 30, 100, 0),
cv.Scalar(180, 256, 256, 0), work)
if self.queHistSnap:
cv.CalcHist([self.hsvPlanes[0]], self.histogram, 0, self.maskImage)
values = cv.GetMinMaxHistValue(self.histogram)
self.histSnapTaken = True
self.queHistSnap = False
if self.histSnapTaken:
cv.CalcBackProject([self.hsvPlanes[0]], self.backprojectImage,
self.histogram)
cv.And(self.backprojectImage, work, self.backprojectImage, 0)
if self.displayType == 'color':
cv.Copy(self.image, self.modifiedImage)
elif self.displayType == 'back':
cv.CvtColor(self.backprojectImage, self.modifiedImage,
CVtypes.CV_GRAY2RGB)
elif self.displayType == 'gray':
cv.CvtColor(self.grayImage, self.modifiedImage,
CVtypes.CV_GRAY2RGB)
elif self.displayType == 'mask':
cv.CvtColor(self.maskImage, self.modifiedImage,
CVtypes.CV_GRAY2RGB)
self.getContours(self.backprojectImage)
def detect(image):
image_size = cv.GetSize(image)
# create grayscale version
grayscale = cv.CreateImage(image_size, 8, 1)
cv.CvtColor(image, grayscale, cv.BGR2GRAY)
# create storage
storage = cv.CreateMemStorage(0)
cv.ClearMemStorage(storage)
# equalize histogram
cv.EqualizeHist(grayscale, grayscale)
# detect objects
cascade = cv.LoadHaarClassifierCascade('haarcascade_frontalface_alt.xml',
cv.Size(1, 1))
faces = cv.HaarDetectObjects(grayscale, cascade, storage, 1.2, 2,
cv.HAAR_DO_CANNY_PRUNING, cv.Size(50, 50))
if faces:
print 'face detected!'
for i in faces:
cv.Rectangle(image, cv.Point(int(i.x), int(i.y)),
cv.Point(int(i.x + i.width), int(i.y + i.height)),
cv.RGB(0, 255, 0), 3, 8, 0)
def preRender(self):
frame = self.getFrame()
capture = self.getCapture()
#print _AR.GetMultiMV('data/19_20_21_22_23_24.cfg')
#_AR.GetMultiMV('data/13_14_15_16_17_18.cfg')
#if 'darwin' in os.uname()[0].lower():
if 0:
settings = [
cv.GetCaptureProperty(capture, CVtypes.CV_CAP_PROP_FOURCC),
cv.GetCaptureProperty(capture, CVtypes.CV_CAP_PROP_BRIGHTNESS),
cv.GetCaptureProperty(capture, CVtypes.CV_CAP_PROP_CONTRAST),
cv.GetCaptureProperty(capture, CVtypes.CV_CAP_PROP_SATURATION),
cv.GetCaptureProperty(capture, CVtypes.CV_CAP_PROP_HUE),
]
print settings
size = cv.GetSize(frame)
fsize = size.width, size.height
size = cv.GetSize(self.image)
isize = size.width, size.height
if fsize == isize:
cv.Copy(frame, self.image)
else:
cv.Resize(
frame,
self.image,
CVtypes.CV_INTER_LINEAR,
#CVtypes.CV_INTER_CUBIC,
)
cv.CvtColor(self.image, self.hsvImage, CVtypes.CV_RGB2HSV)
cv.Split(self.hsvImage, self.hsvPlanes[0], self.hsvPlanes[1],
self.hsvPlanes[2], 0)
cv.CvtColor(self.image, self.grayImage, CVtypes.CV_RGB2GRAY)
if 1:
#cv.EqualizeHist(self.hsvPlanes[0],self.hsvPlanes[0])
#cv.EqualizeHist(self.hsvPlanes[2],self.hsvPlanes[2])
#cv.Merge(self.hsvPlanes[0],self.hsvPlanes[1],
# self.hsvPlanes[2],0, self.hsvImage)
#cv.CvtColor(self.hsvImage,self.image,CVtypes.CV_HSV2RGB)
cv.EqualizeHist(self.grayImage, self.grayImage)
cv.Split(self.image, self.rgbPlanes[0], self.rgbPlanes[1],
self.rgbPlanes[2], 0)
cv.Copy(self.image, self.modifiedImage)
def detect(self, image):
size = cv.GetSize(image)
# create grayscale version
grayscale = cv.CreateImage(size, 8, 1)
cv.CvtColor(image, grayscale, cv.BGR2GRAY)
# create and clear storage
storage = cv.CreateMemStorage(0)
cv.ClearMemStorage(storage)
# equalize histogram
cv.EqualizeHist(grayscale, grayscale)
# detect faces
faces = cv.HaarDetectObjects(grayscale, self.face_cascade, storage,
1.2, 2, cv.HAAR_DO_CANNY_PRUNING,
self.face_size)
if faces:
# faces detected
for i in faces:
cv.Rectangle(image, cv.Point(int(i.x), int(i.y)),
cv.Point(int(i.x + i.width), int(i.y + i.height)),
cv.RGB(0, 255, 0), 3, 8, 0)
detected = True
is_face = True
else:
# detect body
bodies = cv.HaarDetectObjects(grayscale, self.body_cascade,
storage, 1.1, 3, 0, self.body_size)
if bodies:
# body detected
for i in bodies:
cv.Rectangle(
image, cv.Point(int(i.x), int(i.y)),
cv.Point(int(i.x + i.width), int(i.y + i.height)),
cv.RGB(0, 255, 0), 3, 8, 0)
detected = True
is_face = False
else:
detected = False
is_face = False
# release resources we don't need any more
cv.ReleaseImage(grayscale)
cv.ReleaseMemStorage(storage)
return (detected, is_face)
def detect_and_draw(img ,cascade):
global age
global trackedFaces
global plotpoints
t = cv.GetTickCount() ## start counter
cv.CvtColor( img, gray, cv.BGR2GRAY )
cv.Resize( gray, small_img, cv.INTER_LINEAR )
cv.ClearMemStorage( storage )
#Ages all trackedFaces
for f in trackedFaces:
f.updateLife()
#Remove expired faces
for f in trackedFaces:
if (f.isTooOld()):
trackedFaces.remove(f)
faces = cv.HaarDetectObjects( small_img, cascade, storage, haar_scale, min_neighbors, haar_flags, min_size )
drawline = 0
if faces:
#found a face
for r in faces:
matchedFace = False;
pt1 = cv.Point( int(r.x*image_scale), int(r.y*image_scale))
pt2 = cv.Point( int((r.x+r.width)*image_scale), int((r.y+r.height)*image_scale) )
#check if there are trackedFaces
if (len(trackedFaces) > 0):
#each face being tracked
for f in trackedFaces:
#the face is found (small movement)
if ((abs(f.xpt - pt1.x) < FACE_MAX_MOVEMENT) and (abs(f.ypt - pt1.y) < FACE_MAX_MOVEMENT)):
matchedFace = True;
f.updateFace(int(r.width*image_scale), int(r.height*image_scale), pt1.x, pt1.y);
#f.updateFace(r.width*image_scale, r.height*image_scale, pt1.x, pt1.y);
mf = f;
break;
#if face not found, add a new face
if (matchedFace == False):
f = Face(0,int(r.width*image_scale), int(r.height*image_scale), pt1.x, pt1.y,0);
trackedFaces.append(f);
mf = f;
#No tracked faces: adding one
else:
f = Face(0,int (r.width*image_scale), int (r.height*image_scale), pt1.x, pt1.y,0);
trackedFaces.append(f);
mf = f;
#where to draw face and properties
if (mf.age > 5):
#draw attention line
lnpt1 = cv.Point (int (mf.xpt*scale), int(mf.ypt*scale-5)-5)
if (mf.age > mf.width):
lnpt2 = cv.Point (int (mf.xpt*scale+mf.width), int(mf.ypt*scale-5))
else:
lnpt2 = cv.Point (int (mf.xpt*scale+mf.age), int(mf.ypt*scale-5))
#cv.Line(img, lnpt1, lnpt2, RED, 2, 8, 0) ## drawing attention line
cv.Rectangle(img, lnpt1, lnpt2, RED, 4, 8, 0) ## drawing bolded attention line
### draw eyes
cv.Rectangle(img, mf.eyeLeft1, mf.eyeLeft2, MAGENTA, 3,8,0)
cv.Rectangle(img, mf.eyeRight1, mf.eyeRight2, MAGENTA, 3,8,0)
#
### draw mouth
cv.Rectangle(img, mf.mouthTopLeft, mf.mouthBotRight, ORANGE, 3, 8, 0)
#
### draw face
cv.Rectangle( img, pt1, pt2, getColor(mf), 3, 8, 0 )
drawline = mf.age
if(CAPTURING): saveAsJPG(img)
if (osName == "nt"): cv.Flip(img, img, 0)
cv.ShowImage ('Camera', img)
t = cv.GetTickCount() - t ## counter for FPS
print "%i fps." % (cv.GetTickFrequency()*1000000./t) ## print FPS
def detect(image):
image_size = cv.GetSize(image)
# create grayscale version
grayscale = cv.CreateImage(image_size, 8, 1)
cv.CvtColor(image, grayscale, cv.BGR2GRAY)
# create storage
storage = cv.CreateMemStorage(0)
cv.ClearMemStorage(storage)
# equalize histogram
cv.EqualizeHist(grayscale, grayscale)
# detect objects
cascade = cv.LoadHaarClassifierCascade('haarcascade_frontalface_alt.xml',
cv.Size(1, 1))
faces = cv.HaarDetectObjects(grayscale, cascade, storage, 1.2, 2,
cv.HAAR_DO_CANNY_PRUNING, cv.Size(50, 50))
if faces:
print 'face detected!'
for i in faces:
cv.Rectangle(image, cv.Point(int(i.x), int(i.y)),
cv.Point(int(i.x + i.width), int(i.y + i.height)),
cv.RGB(0, 255, 0), 3, 8, 0)
# create windows
cv.NamedWindow('Camera', cv.WINDOW_AUTOSIZE)
# create capture device
device = 0 # assume we want first device
capture = cv.CreateCameraCapture(0)
cv.SetCaptureProperty(capture, cv.CAP_PROP_FRAME_WIDTH, 640)
cv.SetCaptureProperty(capture, cv.CAP_PROP_FRAME_HEIGHT, 480)
# check if capture device is OK
if not capture:
print "Error opening capture device"
sys.exit(1)
while 1:
# do forever
# capture the current frame
frame = cv.QueryFrame(capture)
if frame is None:
break
# mirror
cv.Flip(frame, None, 1)
# face detection
detect(frame)
# display webcam image
cv.ShowImage('Camera', frame)
# handle events
k = cv.WaitKey(10)
if k == 0x1b: # ESC
print 'ESC pressed. Exiting ...'
break
hist = cv.CreateHist(1, [hdims], cv.HIST_ARRAY, hranges, 1)
while 1:
# do forever
# 1. capture the current image
frame = cv.QueryFrame(capture)
if frame is None:
# no image captured... end the processing
break
# mirror the captured image
cv.Flip(frame, None, 1)
# compute the hsv version of the image
cv.CvtColor(frame, hsv, cv.BGR2HSV)
# compute which pixels are in the wanted range
cv.InRangeS(hsv, hsv_min, hsv_max, mask)
# extract the hue from the hsv array
cv.Split(hsv, hue, None, None, None)
# select the rectangle of interest in the hue/mask arrays
cv.SetImageROI(hue, selection)
cv.SetImageROI(mask, selection)
# it's time to compute the histogram
cv.CalcHist([hue], hist, 0, mask)
# clear the ROIs