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 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 removeMinimum(img, x, y, w, h):
"""Utility function for removing the global minimum from a cvArray.
This finds the global maximum of the same array and then blits a rectangle of the same color
over the area designated by x,y and w,h.
This way to find the next _local_ minimum, we can just remove the current global
minimum and search for the global one again using cvMinMaxLoc - should be faster than looking
for all local minima with python."""
minmax = cvMinMaxLoc(img)
cvRectangle(img, cvPoint(x-int(w/2), y-int(h/2)), cvPoint(x+int(w/2), y+int(h/2)), cvScalar(minmax[1], 0, 0, 0), CV_FILLED)
def draw_boxes(self,input_data,bboxmin,bboxmax):
"""
Draws three boxes arond the returned location.
"""
opencv.cvRectangle(self.input_data, opencv.cvPoint(np.int(bboxmin[0]),np.int(bboxmin[1])),opencv.cvPoint(np.int(bboxmax[0])
,np.int(bboxmax[1])),0,1)
opencv.cvRectangle(self.input_data, opencv.cvPoint(np.int(bboxmin[0])-1,np.int(bboxmin[1])-1),opencv.cvPoint(np.int(bboxmax[0])
+1,np.int(bboxmax[1])+1),0,1)
opencv.cvRectangle(self.input_data, opencv.cvPoint(np.int(bboxmin[0])-2,np.int(bboxmin[1])-2),opencv.cvPoint(np.int(bboxmax[0])
+2,np.int(bboxmax[1])+2),1,1)
def draw_boxes(self,input_data,bboxmin,bboxmax):
"""
Draws three boxes arond the returned location.
"""
opencv.cvRectangle(self.input_data, opencv.cvPoint(np.int(bboxmin[0]),np.int(bboxmin[1])),opencv.cvPoint(np.int(bboxmax[0])
,np.int(bboxmax[1])),0,1)
opencv.cvRectangle(self.input_data, opencv.cvPoint(np.int(bboxmin[0])-1,np.int(bboxmin[1])-1),opencv.cvPoint(np.int(bboxmax[0])
+1,np.int(bboxmax[1])+1),0,1)
opencv.cvRectangle(self.input_data, opencv.cvPoint(np.int(bboxmin[0])-2,np.int(bboxmin[1])-2),opencv.cvPoint(np.int(bboxmax[0])
+2,np.int(bboxmax[1])+2),1,1)
