def normalise(self, i_ipl_image):
#Do the affine transform
if self.__rot_mat == None:
warped_image = i_ipl_image
else:
warped_image = cv.cvCreateImage(
cv.cvSize(i_ipl_image.width, i_ipl_image.height), 8, 1)
cv.cvWarpAffine(i_ipl_image, warped_image, self.__rot_mat)
#Crop
if self.__roi == None:
self.__cropped_image = warped_image
else:
self.crop(warped_image)
#Scale
if self.__resize_scale == 1:
scaled_image = self.__cropped_image
else:
w = int(round(self.__cropped_image.width * self.__resize_scale))
h = int(round(self.__cropped_image.height * self.__resize_scale))
scaled_image = cv.cvCreateImage(cv.cvSize(w, h), 8, 1)
cv.cvResize(self.__cropped_image, scaled_image, cv.CV_INTER_LINEAR)
#Histogram equalisation
if self.__equalise_hist:
cv.cvEqualizeHist(scaled_image, scaled_image)
#Blur
if self.__filter_size == 0:
smoothed_image = scaled_image
else:
smoothed_image = cv.cvCreateImage(
cv.cvSize(scaled_image.width, scaled_image.height), 8, 1)
cv.cvSmooth(scaled_image, smoothed_image, cv.CV_GAUSSIAN,
self.__filter_size)
return smoothed_image
def findFaces(self, img):
faces = []
self.detect_time = time.time()
rects = self.face_detector.detect(img)
self.detect_time = time.time() - self.detect_time
cvtile = opencv.cvCreateMat(128, 128, opencv.CV_8UC3)
bwtile = opencv.cvCreateMat(128, 128, opencv.CV_8U)
cvimg = img.asOpenCV()
self.eye_time = time.time()
for rect in rects:
faceim = opencv.cvGetSubRect(cvimg, rect.asOpenCV())
opencv.cvResize(faceim, cvtile)
affine = pv.AffineFromRect(rect, (128, 128))
opencv.cvCvtColor(cvtile, bwtile, cv.CV_BGR2GRAY)
leye, reye, lcp, rcp = self.fel.locateEyes(bwtile)
leye = pv.Point(leye)
reye = pv.Point(reye)
leye = affine.invertPoint(leye)
reye = affine.invertPoint(reye)
faces.append([rect, leye, reye])
self.eye_time = time.time() - self.eye_time
self.current_faces = faces
return faces
def normalise(self, i_ipl_image):
#Do the affine transform
if self.__rot_mat == None:
warped_image = i_ipl_image
else:
warped_image = cv.cvCreateImage(cv.cvSize(i_ipl_image.width,i_ipl_image.height), 8, 1)
cv.cvWarpAffine(i_ipl_image , warped_image, self.__rot_mat );
#Crop
if self.__roi == None:
self.__cropped_image = warped_image
else:
self.crop(warped_image)
#Scale
if self.__resize_scale == 1:
scaled_image = self.__cropped_image
else:
w = int(round( self.__cropped_image.width * self.__resize_scale))
h = int(round( self.__cropped_image.height * self.__resize_scale))
scaled_image = cv.cvCreateImage(cv.cvSize(w, h), 8, 1)
cv.cvResize( self.__cropped_image, scaled_image ,cv.CV_INTER_LINEAR)
#Histogram equalisation
if self.__equalise_hist:
cv.cvEqualizeHist(scaled_image,scaled_image)
#Blur
if self.__filter_size == 0:
smoothed_image = scaled_image
else:
smoothed_image = cv.cvCreateImage(cv.cvSize(scaled_image.width, scaled_image.height), 8, 1)
cv.cvSmooth(scaled_image, smoothed_image, cv.CV_GAUSSIAN, self.__filter_size)
return smoothed_image
def drawImage(image,h,w,psize):
"""
Draw the image as a continuous line on a surface h by w "pixels" where
each continuous line representation of a pixel in image is represented
on the output suface using psize by psize "pixels".
@param image an opencv image with at least 3 channels
@param h integer representing the hight of the output surface
@param w integer representing the width of the output surface
@param psize ammount that each pixel in the input image is scaled up
"""
h = (h/psize)-2
w = (w/psize)-2
size = opencv.cvSize(w,h)
scaled = opencv.cvCreateImage(size,8,3)
red = opencv.cvCreateImage(size,8,1)
blue = opencv.cvCreateImage(size,8,1)
green = opencv.cvCreateImage(size,8,1)
opencv.cvSplit(scaled,blue,green,red,None)
opencv.cvEqualizeHist(red,red)
opencv.cvEqualizeHist(green,green)
opencv.cvEqualizeHist(blue,blue)
opencv.cvMerge(red,green,blue,None,scaled)
opencv.cvResize(image,scaled,opencv.CV_INTER_LINEAR)
opencv.cvNot(scaled,scaled)
# Draw each pixel in the image
xr = range(scaled.width)
whitespace = 0
for y in range(scaled.height):
for x in xr:
s = opencv.cvGet2D(scaled,y,x)
s = [s[j] for j in range(3)]
if (sum(s)/710.0 < 1.0/psize):
whitespace = whitespace+psize
else:
if whitespace is not 0:
line(whitespace,6,(xr[0]>0))
whitespace = 0
drawPixel([j/255.0 for j in s],psize,(xr[0]>0))
if whitespace is not 0:
line(whitespace,6,(xr[0]>0))
whitespace = 0
line(psize,2)
xr.reverse()
displayImage(output)
events = pygame.event.get()
for event in events:
if event.type == QUIT:
exit()
def get_image():
im = highgui.cvQueryFrame(camera)
# Add the line below if you need it (Ubuntu 8.04+)
im = opencv.cvGetMat(im)
# Resize image if needed
resized_im = opencv.cvCreateImage(opencv.cvSize(XSCALE, YSCALE), 8, 3)
opencv.cvResize(im, resized_im, opencv.CV_INTER_CUBIC)
#convert Ipl image to PIL image
pil_img = opencv.adaptors.Ipl2PIL(resized_im)
enhancer = ImageEnhance.Brightness(pil_img)
pil_img = enhancer.enhance(BRIGHTNESS)
pil_img = ImageOps.mirror(pil_img)
return pil_img
def get_image():
im = highgui.cvQueryFrame(camera)
# Add the line below if you need it (Ubuntu 8.04+)
im = opencv.cvGetMat(im)
# Resize image if needed
resized_im=opencv.cvCreateImage(opencv.cvSize(XSCALE,YSCALE),8,3)
opencv.cvResize( im, resized_im, opencv.CV_INTER_CUBIC);
#convert Ipl image to PIL image
pil_img = opencv.adaptors.Ipl2PIL(resized_im)
enhancer=ImageEnhance.Brightness(pil_img)
pil_img=enhancer.enhance(BRIGHTNESS)
pil_img=ImageOps.mirror(pil_img)
return pil_img
def scale_image(img, scale):
new_img = cv.cvCreateImage(
cv.cvSize(img.width * scale, img.height * scale), img.depth,
img.nChannels)
cv.cvResize(img, new_img, cv.CV_INTER_NN)
return new_img
def scale(image, s):
scaled = cv.cvCreateImage(
cv.cvSize(int(image.width * s), int(image.height * s)), image.depth,
image.nChannels)
cv.cvResize(image, scaled, cv.CV_INTER_AREA)
return scaled
def compute(self, i_data, i_ipl=False):
frame = 0
self.__frame = -1
max_dist = 0
if i_ipl:
nframes = len(i_data)
else:
nframes = i_data.shape[2]
list_of_roi = []
list_of_frames = []
list_of_sizes = []
for frame in range(0, nframes):
if i_ipl:
ipl_image = i_data[frame]
else:
ipl_image = cv.NumPy2Ipl(i_data[:,:,frame])
if self.__scale < 1.0:
w = numpy.int(numpy.round( float( ipl_image.width ) * self.__scale ))
h = numpy.int(numpy.round( float( ipl_image.height ) * self.__scale ))
small_image = cv.cvCreateImage( cv.cvSize( w, h ) , ipl_image.depth, ipl_image.nChannels )
cv.cvResize( ipl_image , small_image )
vj_box = viola_jones_opencv(small_image)
else:
vj_box = viola_jones_opencv(ipl_image)
if vj_box is not None:
(min_row, min_col, max_row, max_col) = vj_box
w = max_col - min_col
h = max_row - min_row
dist = w*w + h*h
list_of_roi.append(vj_box)
list_of_frames.append(frame)
list_of_sizes.append(dist)
self.__n_rows = ipl_image.height
self.__n_cols = ipl_image.width
#Choose a percentile of the sorted list
nboxes = len(list_of_sizes)
if nboxes == 0:
#print "Viola-Jones failed on all images"
return
list_of_sizes = numpy.array(list_of_sizes)
idx = numpy.argsort(list_of_sizes)
percentile = 0.8
arg_idx = numpy.int(numpy.round(percentile * nboxes))
if arg_idx >= nboxes:
arg_idx = nboxes - 1
if arg_idx < 0:
arg_idx = 0
#print "n boxes: ", nboxes, " chosen arg: ", arg_idx
self.__frame = frame = list_of_frames[idx[arg_idx]]
best_roi = list_of_roi[idx[arg_idx]]
(min_row, min_col, max_row, max_col) = best_roi
if self.__scale < 1.0:
#print "best roi width = ", max_col - min_col, " best roi height = ", max_row-min_row
max_col = numpy.int(numpy.round( float(max_col) / self.__scale ))
max_row = numpy.int(numpy.round( float(max_row) / self.__scale ))
min_col = numpy.int(numpy.round( float(min_col) / self.__scale ))
min_row = numpy.int(numpy.round( float(min_row) / self.__scale ))
vj_box = (min_row, min_col, max_row, max_col)
self.setRoi(vj_box)
return self.getRoi()
def IplResize(i_image, i_width, i_height):
"""Convert RGB to Gray scale and resize to input width and height"""
small_image = cv.cvCreateImage( cv.cvSize( i_width, i_height ) , i_image.depth, i_image.nChannels )
cv.cvResize( i_image , small_image )
return small_image
def scale(image, s):
scaled = cv.cvCreateImage(cv.cvSize(int(image.width * s), int(image.height * s)), image.depth, image.nChannels)
cv.cvResize(image, scaled, cv.CV_INTER_AREA)
return scaled
def scale_image(img, scale):
new_img = cv.cvCreateImage(cv.cvSize(img.width*scale, img.height*scale),
img.depth, img.nChannels)
cv.cvResize(img, new_img, cv.CV_INTER_NN)
return new_img
def IplResize(i_image, i_width, i_height):
"""Convert RGB to Gray scale and resize to input width and height"""
small_image = cv.cvCreateImage(cv.cvSize(i_width, i_height), i_image.depth,
i_image.nChannels)
cv.cvResize(i_image, small_image)
return small_image