def detect(self, image):
# image size is needed by underlying opencv lib to allocate memory
image_size = opencv.cvGetSize(image)
# the algorithm works with grayscale images
grayscale = opencv.cvCreateImage(image_size, 8, 1)
opencv.cvCvtColor(image, grayscale, opencv.CV_BGR2GRAY)
# more underlying c lib memory allocation
storage = opencv.cvCreateMemStorage(0)
opencv.cvClearMemStorage(storage)
# equalize histogram
opencv.cvEqualizeHist(grayscale, grayscale)
# detect faces using haar cascade, the used file is trained to
# detect frontal faces
cascade = opencv.cvLoadHaarClassifierCascade(
'haarcascade_frontalface_alt.xml', opencv.cvSize(1, 1))
faces = opencv.cvHaarDetectObjects(grayscale, cascade, storage, 1.2, 2,
opencv.CV_HAAR_DO_CANNY_PRUNING,
opencv.cvSize(100, 100))
# draw rectangles around faces
for face in faces:
opencv.cvRectangle(
image, opencv.cvPoint(int(face.x), int(face.y)),
opencv.cvPoint(int(face.x + face.width),
int(face.y + face.height)),
opencv.CV_RGB(127, 255, 0), 2)
# return faces casted to list here, otherwise some obscure bug
# in opencv will make it segfault if the casting happens later
return image, list(faces)
def detect(self, image):
# image size is needed by underlying opencv lib to allocate memory
image_size = opencv.cvGetSize(image)
# the algorithm works with grayscale images
grayscale = opencv.cvCreateImage(image_size, 8, 1)
opencv.cvCvtColor(image, grayscale, opencv.CV_BGR2GRAY)
# more underlying c lib memory allocation
storage = opencv.cvCreateMemStorage(0)
opencv.cvClearMemStorage(storage)
# equalize histogram
opencv.cvEqualizeHist(grayscale, grayscale)
# detect faces using haar cascade, the used file is trained to
# detect frontal faces
cascade = opencv.cvLoadHaarClassifierCascade(
'haarcascade_frontalface_alt.xml', opencv.cvSize(1, 1))
faces = opencv.cvHaarDetectObjects(
grayscale, cascade, storage, 1.2, 2,
opencv.CV_HAAR_DO_CANNY_PRUNING, opencv.cvSize(100, 100))
# draw rectangles around faces
for face in faces:
opencv.cvRectangle(
image, opencv.cvPoint(
int(face.x), int(face.y)),
opencv.cvPoint(int(face.x + face.width),
int(face.y + face.height)), opencv.CV_RGB(127, 255, 0), 2)
# return faces casted to list here, otherwise some obscure bug
# in opencv will make it segfault if the casting happens later
return image, list(faces)
def get_frame(self, face_rec = False): image = highgui.cvQueryFrame(self.device) face_matches = False if face_rec: grayscale = cv.cvCreateImage(cv.cvSize(640, 480), 8, 1) cv.cvCvtColor(image, grayscale, cv.CV_BGR2GRAY) storage = cv.cvCreateMemStorage(0) cv.cvClearMemStorage(storage) cv.cvEqualizeHist(grayscale, grayscale) for cascade in self.haarfiles: matches = cv.cvHaarDetectObjects(grayscale, cascade, storage, 1.2, 2, cv.CV_HAAR_DO_CANNY_PRUNING, cv.cvSize(100,100)) if matches: face_matches = True for i in matches: cv.cvRectangle(image, cv.cvPoint( int(i.x), int(i.y)), cv.cvPoint(int(i.x+i.width), int(i.y+i.height)), cv.CV_RGB(0,0,255), 2, 5, 0) image = cv.cvGetMat(image) return (image, face_matches)
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 detect(image):
# Find out how large the file is, as the underlying C-based code
# needs to allocate memory in the following steps
image_size = opencv.cvGetSize(image)
# create grayscale version - this is also the point where the allegation about
# facial recognition being racist might be most true. A caucasian face would have more
# definition on a webcam image than an African face when greyscaled.
# I would suggest that adding in a routine to overlay edge-detection enhancements may
# help, but you would also need to do this to the training images as well.
grayscale = opencv.cvCreateImage(image_size, 8, 1)
opencv.cvCvtColor(image, grayscale, opencv.CV_BGR2GRAY)
# create storage (It is C-based so you need to do this sort of thing)
storage = opencv.cvCreateMemStorage(0)
opencv.cvClearMemStorage(storage)
# equalize histogram
opencv.cvEqualizeHist(grayscale, grayscale)
# detect objects - Haar cascade step
# In this case, the code uses a frontal_face cascade - trained to spot faces that look directly
# at the camera. In reality, I found that no bearded or hairy person must have been in the training
# set of images, as the detection routine turned out to be beardist as well as a little racist!
cascade = opencv.cvLoadHaarClassifierCascade('haarcascade_frontalface_alt.xml', opencv.cvSize(1,1))
faces = opencv.cvHaarDetectObjects(grayscale, cascade, storage, 1.2, 2, opencv.CV_HAAR_DO_CANNY_PRUNING, opencv.cvSize(50, 50))
if faces:
for face in faces:
# Hmm should I do a min-size check?
# Draw a Chartreuse rectangle around the face - Chartruese rocks
opencv.cvRectangle(image, opencv.cvPoint( int(face.x), int(face.y)),
opencv.cvPoint(int(face.x + face.width), int(face.y + face.height)),
opencv.CV_RGB(127, 255, 0), 2) # RGB #7FFF00 width=2
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 detectObject(self, classifier):
self.grayscale = opencv.cvCreateImage(opencv.cvGetSize(self.iplimage), 8, 1)
opencv.cvCvtColor(self.iplimage, self.grayscale, opencv.CV_BGR2GRAY)
self.storage = opencv.cvCreateMemStorage(0)
opencv.cvClearMemStorage(self.storage)
opencv.cvEqualizeHist(self.grayscale, self.grayscale)
try:
self.cascade = opencv.cvLoadHaarClassifierCascade(os.path.join(os.path.dirname(__file__), classifier+".xml"),opencv.cvSize(1, 1))
except:
raise AttributeError("could not load classifier file")
self.objects = opencv.cvHaarDetectObjects(self.grayscale, self.cascade, self.storage, 1.2, 2, opencv.CV_HAAR_DO_CANNY_PRUNING, opencv.cvSize(50, 50))
return self.objects
def detectObject(self, classifier):
self.grayscale = opencv.cvCreateImage(opencv.cvGetSize(self.iplimage), 8, 1)
opencv.cvCvtColor(self.iplimage, self.grayscale, opencv.CV_BGR2GRAY)
self.storage = opencv.cvCreateMemStorage(0)
opencv.cvClearMemStorage(self.storage)
opencv.cvEqualizeHist(self.grayscale, self.grayscale)
try:
self.cascade = opencv.cvLoadHaarClassifierCascade(os.path.join(os.path.dirname(__file__), classifier+".xml"),opencv.cvSize(1,1))
except:
raise AttributeError, "could not load classifier file"
self.objects = opencv.cvHaarDetectObjects(self.grayscale, self.cascade, self.storage, 1.2, 2, opencv.CV_HAAR_DO_CANNY_PRUNING, opencv.cvSize(50,50))
return self.objects
def detectHaar(iplimage, classifier):
srcimage = opencv.cvCloneImage(iplimage)
grayscale = opencv.cvCreateImage(opencv.cvGetSize(srcimage), 8, 1)
opencv.cvCvtColor(srcimage, grayscale, opencv.CV_BGR2GRAY)
storage = opencv.cvCreateMemStorage(0)
opencv.cvClearMemStorage(storage)
opencv.cvEqualizeHist(grayscale, grayscale)
try:
cascade = opencv.cvLoadHaarClassifierCascade(os.path.join(os.path.dirname(__file__), classifier + ".xml"), opencv.cvSize(1, 1))
except:
raise AttributeError("could not load classifier file")
objs = opencv.cvHaarDetectObjects(grayscale, cascade, storage, 1.2, 2, opencv.CV_HAAR_DO_CANNY_PRUNING, opencv.cvSize(50, 50))
objects = []
for obj in objs:
objects.append(Haarobj(obj))
opencv.cvReleaseImage(srcimage)
opencv.cvReleaseImage(grayscale)
opencv.cvReleaseMemStorage(storage)
return objects
def detectHaar(iplimage, classifier):
srcimage = opencv.cvCloneImage(iplimage)
grayscale = opencv.cvCreateImage(opencv.cvGetSize(srcimage), 8, 1)
opencv.cvCvtColor(srcimage, grayscale, opencv.CV_BGR2GRAY)
storage = opencv.cvCreateMemStorage(0)
opencv.cvClearMemStorage(storage)
opencv.cvEqualizeHist(grayscale, grayscale)
try:
cascade = opencv.cvLoadHaarClassifierCascade(os.path.join(os.path.dirname(__file__), classifier+".xml"),opencv.cvSize(1,1))
except:
raise AttributeError, "could not load classifier file"
objs = opencv.cvHaarDetectObjects(grayscale, cascade, storage, 1.2, 2, opencv.CV_HAAR_DO_CANNY_PRUNING, opencv.cvSize(50,50))
objects = []
for obj in objs:
objects.append(Haarobj(obj))
opencv.cvReleaseImage(srcimage)
opencv.cvReleaseImage(grayscale)
opencv.cvReleaseMemStorage(storage)
return objects
def detect(self, pil_image, cascade_name, recogn_w = 50, recogn_h = 50):
# Get cascade:
cascade = self.get_cascade(cascade_name)
image = opencv.PIL2Ipl(pil_image)
image_size = opencv.cvGetSize(image)
grayscale = image
if pil_image.mode == "RGB":
# create grayscale version
grayscale = opencv.cvCreateImage(image_size, 8, 1)
# Change to RGB2Gray - I dont think itll affect the conversion
opencv.cvCvtColor(image, grayscale, opencv.CV_BGR2GRAY)
# create storage
storage = opencv.cvCreateMemStorage(0)
opencv.cvClearMemStorage(storage)
# equalize histogram
opencv.cvEqualizeHist(grayscale, grayscale)
# detect objects
return opencv.cvHaarDetectObjects(grayscale, cascade, storage, 1.2, 2, opencv.CV_HAAR_DO_CANNY_PRUNING, opencv.cvSize(recogn_w, recogn_h))
def detect(self, pil_image, cascade_name, recogn_w=50, recogn_h=50):
# Get cascade:
cascade = self.get_cascade(cascade_name)
image = opencv.PIL2Ipl(pil_image)
image_size = opencv.cvGetSize(image)
grayscale = image
if pil_image.mode == "RGB":
# create grayscale version
grayscale = opencv.cvCreateImage(image_size, 8, 1)
# Change to RGB2Gray - I dont think itll affect the conversion
opencv.cvCvtColor(image, grayscale, opencv.CV_BGR2GRAY)
# create storage
storage = opencv.cvCreateMemStorage(0)
opencv.cvClearMemStorage(storage)
# equalize histogram
opencv.cvEqualizeHist(grayscale, grayscale)
# detect objects
return opencv.cvHaarDetectObjects(grayscale, cascade, storage, 1.2, 2,
opencv.CV_HAAR_DO_CANNY_PRUNING,
opencv.cvSize(recogn_w, recogn_h))
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 toGrayscale(self): grayscale = opencv.cvCreateImage(self.details.size(), 8, 1) opencv.cvCvtColor(self.image, grayscale, 6) opencv.cvEqualizeHist(grayscale, grayscale) return grayscale
