def detectObject(image):
grayscale = cv.cvCreateImage(size, 8, 1)
cv.cvFlip(image, None, 1)
cv.cvCvtColor(image, grayscale, cv.CV_BGR2GRAY)
storage = cv.cvCreateMemStorage(0)
cv.cvClearMemStorage(storage)
cv.cvEqualizeHist(grayscale, grayscale)
cascade = cv.cvLoadHaarClassifierCascade(haar_file, cv.cvSize(1,1))
objects = cv.cvHaarDetectObjects(grayscale, cascade, storage, 1.2, 2,
cv.CV_HAAR_DO_CANNY_PRUNING,
cv.cvSize(100,100))
# Draw dots where hands are
if objects:
for i in objects:
#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(0,255,0), 3, 8, 0)
center = cv.cvPoint(int(i.x+i.width/2), int(i.y+i.height/2))
cv.cvCircle(image, center, 10, cv.CV_RGB(0,0,0), 5,8, 0)
# Left side check
if center.x > box_forward_left[0].x and center.x < box_backwards_left[1].x and center.y > box_forward_left[0].y and center.y < box_backwards_left[1].y:
set_speed('left', center)
# Right side check
if center.x > box_forward_right[0].x and center.x < box_backwards_right[1].x and center.y > box_forward_right[0].y and center.y < box_backwards_right[1].y:
set_speed('right', center)
def init(self): #Load the cascade classifier data self.cascade=cv.cvLoadHaarClassifierCascade(self.file_path(self.cascade_file),cv.cvSize(self.haar_face_size[0],self.haar_face_size[1])) #Allocate/init storage self.storage=cv.cvCreateMemStorage(0) cv.cvClearMemStorage(self.storage)
def get_cascade(self, cascade_name):
self._cached_cascades[
cascade_name] = opencv.cvLoadHaarClassifierCascade(
os.path.join(self.cascade_dir, cascade_name),
opencv.cvSize(1, 1))
#cascade_name, opencv.cvSize(1,1))
return self._cached_cascades[cascade_name]
def face_detect(file, closeafter=True):
"""Converts an image to grayscale and prints the locations of any faces found"""
if hasattr(file, 'read'):
_, filename = tempfile.mkstemp()
tmphandle = open(filename, 'w')
shutil.copyfileobj(file, tmphandle)
tmphandle.close()
if closeafter:
file.close()
deleteafter = True
else:
filename = file
deleteafter = False
image = cvLoadImage(filename)
grayscale = cvCreateImage(cvSize(image.width, image.height), 8, 1)
cvCvtColor(image, grayscale, CV_BGR2GRAY)
storage = cvCreateMemStorage(0)
cvClearMemStorage(storage)
cvEqualizeHist(grayscale, grayscale)
cascade = cvLoadHaarClassifierCascade(
'/usr/share/opencv/haarcascades/haarcascade_frontalface_default.xml',
cvSize(1,1))
faces = cvHaarDetectObjects(grayscale, cascade, storage, 1.2, 2,
CV_HAAR_DO_CANNY_PRUNING, cvSize(50,50))
if deleteafter:
os.unlink(filename)
return (image.width, image.height), faces
def detectObject(image):
grayscale = cv.cvCreateImage(size, 8, 1)
cv.cvFlip(image, None, 1)
cv.cvCvtColor(image, grayscale, cv.CV_BGR2GRAY)
storage = cv.cvCreateMemStorage(0)
cv.cvClearMemStorage(storage)
cv.cvEqualizeHist(grayscale, grayscale)
cascade = cv.cvLoadHaarClassifierCascade(haar_file, cv.cvSize(1, 1))
objects = cv.cvHaarDetectObjects(grayscale, cascade, storage, 1.2, 2,
cv.CV_HAAR_DO_CANNY_PRUNING,
cv.cvSize(100, 100))
# Draw dots where hands are
if objects:
for i in objects:
#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(0,255,0), 3, 8, 0)
center = cv.cvPoint(int(i.x + i.width / 2),
int(i.y + i.height / 2))
cv.cvCircle(image, center, 10, cv.CV_RGB(0, 0, 0), 5, 8, 0)
# Left side check
if center.x > box_forward_left[
0].x and center.x < box_backwards_left[
1].x and center.y > box_forward_left[
0].y and center.y < box_backwards_left[1].y:
set_speed('left', center)
# Right side check
if center.x > box_forward_right[
0].x and center.x < box_backwards_right[
1].x and center.y > box_forward_right[
0].y and center.y < box_backwards_right[1].y:
set_speed('right', center)
def detect(image, cascade_file='haarcascade_data/haarcascade_frontalface_alt.xml'):
image_size = cv.cvGetSize(image)
# create grayscale version
grayscale = cv.cvCreateImage(image_size, 8, 1)
cv.cvCvtColor(image, grayscale, cv.CV_BGR2GRAY)
# create storage
storage = cv.cvCreateMemStorage(0)
cv.cvClearMemStorage(storage)
# equalize histogram
cv.cvEqualizeHist(grayscale, grayscale)
# detect objects
cascade = cv.cvLoadHaarClassifierCascade(cascade_file, cv.cvSize(1,1))
faces = cv.cvHaarDetectObjects(grayscale, cascade, storage, 1.2, 2, cv.CV_HAAR_DO_CANNY_PRUNING, cv.cvSize(50, 50))
positions = []
if faces:
for i in faces:
positions.append({'x': i.x, 'y': i.y, 'width': i.width, 'height':
i.height,})
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, 255, 0), 3, 8, 0)
return positions
def __init__(self, *args): apply(QWidget.__init__,(self, ) + args) self.cascade_name = 'haarcascades/haarcascade_frontalface_alt.xml' self.cascade = cv.cvLoadHaarClassifierCascade(self.cascade_name, cv.cvSize(20,20)) self.cap = highgui.cvCreateCameraCapture(0) self.q_buffer = QImage() self.q_buffer.create(self.width(),self.height(),8) self.timer = self.startTimer(1)
def __init__(self, *args):
apply(QWidget.__init__, (self, ) + args)
self.cascade_name = 'haarcascades/haarcascade_frontalface_alt.xml'
self.cascade = cv.cvLoadHaarClassifierCascade(self.cascade_name,
cv.cvSize(20, 20))
self.cap = highgui.cvCreateCameraCapture(0)
self.q_buffer = QImage()
self.q_buffer.create(self.width(), self.height(), 8)
self.timer = self.startTimer(1)
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 detect_faces_on(path):
faces = []
image = cvLoadImage(path)
# convert to grayscale for faster results
grayscale = cvCreateImage(cvSize(image.width, image.height), 8, 1)
cvCvtColor(image, grayscale, CV_BGR2GRAY)
# smooth picture for better results
cvSmooth(grayscale, grayscale, CV_GAUSSIAN, 3, 3)
storage = cvCreateMemStorage(0)
cvClearMemStorage(storage)
cvEqualizeHist(grayscale, grayscale)
cascade_files = [
# ('/usr/share/opencv/haarcascades/haarcascade_eye_tree_eyeglasses.xml', (50, 50)),
# ('/usr/share/opencv/haarcascades/haarcascade_frontalface_alt.xml', (50, 50)),
# ('/usr/share/opencv/haarcascades/haarcascade_lowerbody.xml', (50, 50)),
# ('/usr/share/opencv/haarcascades/haarcascade_mcs_mouth.xml', (50, 50)),
# ('/usr/share/opencv/haarcascades/haarcascade_profileface.xml', (50, 50)),
# ('/usr/share/opencv/haarcascades/haarcascade_eye.xml', (50, 50)),
# ('/usr/share/opencv/haarcascades/haarcascade_frontalface_default.xml', (50, 50)),
# ('/usr/share/opencv/haarcascades/haarcascade_mcs_eyepair_big.xml', (50, 50)),
# ('/usr/share/opencv/haarcascades/haarcascade_mcs_nose.xml', (50, 50)),
# ('/usr/share/opencv/haarcascades/haarcascade_righteye_2splits.xml', (50, 50)),
# ('/usr/share/opencv/haarcascades/haarcascade_frontalface_alt2.xml', (50, 50)),
# ('/usr/share/opencv/haarcascades/haarcascade_fullbody.xml', (50, 50)),
# ('/usr/share/opencv/haarcascades/haarcascade_mcs_eyepair_small.xml', (50, 50)),
# ('/usr/share/opencv/haarcascades/haarcascade_mcs_righteye.xml', (50, 50)),
# ('/usr/share/opencv/haarcascades/haarcascade_upperbody.xml', (50, 50)),
('/usr/share/opencv/haarcascades/haarcascade_frontalface_alt_tree.xml',
(50, 50)),
# ('/usr/share/opencv/haarcascades/haarcascade_lefteye_2splits.xml', (50, 50)),
# ('/usr/share/opencv/haarcascades/haarcascade_mcs_lefteye.xml', (50, 50)),
# ('/usr/share/opencv/haarcascades/haarcascade_mcs_upperbody.xml', (50, 50)),
# ('parojos_22_5.1.xml', (22, 5)),
# ('Mouth.xml', (22, 15)),
]
for cascade_file, cascade_sizes in cascade_files:
cascade = cvLoadHaarClassifierCascade(os.path.join(cascade_file),
cvSize(1, 1))
faces += cvHaarDetectObjects(grayscale, cascade, storage, HAAR_SCALE,
HAAR_NEIGHBORS, CV_HAAR_DO_CANNY_PRUNING,
cvSize(*cascade_sizes))
return [{'x': f.x, 'y': f.y, 'w': f.width, 'h': f.height} for f in faces]
def detect_faces(image):
"""Converts an image to grayscale and prints the locations of any
faces found"""
grayscale = cvCreateImage(cvSize(image.width, image.height), 8, 1)
cvCvtColor(image, grayscale, CV_BGR2GRAY)
storage = cvCreateMemStorage(0)
cvClearMemStorage(storage)
cvEqualizeHist(grayscale, grayscale)
# The default parameters (scale_factor=1.1, min_neighbors=3,
# flags=0) are tuned for accurate yet slow face detection. For
# faster face detection on real video images the better settings are
# (scale_factor=1.2, min_neighbors=2, flags=CV_HAAR_DO_CANNY_PRUNING).
# --- http://www710.univ-lyon1.fr/~bouakaz/OpenCV-0.9.5/docs/ref/OpenCVRef_Experimental.htm#decl_cvHaarDetectObjects
# The size box is of the *minimum* detectable object size. Smaller box = more processing time. - http://cell.fixstars.com/opencv/index.php/Facedetect
minsize = (int(MINFACEWIDTH_PERCENT * image.width + 0.5),
int(MINFACEHEIGHT_PERCENT * image.height))
print >> sys.stderr, "Min size of face: %s" % ` minsize `
faces = []
for cascadefile in [
'/usr/share/opencv/haarcascades/haarcascade_frontalface_default.xml'
]:
# for cascadefile in ['/usr/share/opencv/haarcascades/haarcascade_frontalface_default.xml', '/usr/share/opencv/haarcascades/haarcascade_profileface.xml']:
cascade = cvLoadHaarClassifierCascade(cascadefile, cvSize(1, 1))
# faces += cvHaarDetectObjects(grayscale, cascade, storage, 1.2, 2, CV_HAAR_DO_CANNY_PRUNING, cvSize(50,50))
# faces += cvHaarDetectObjects(grayscale, cascade, storage, 1.1, 3, 0, cvSize(MINFACEWIDTH,MINFACEHEIGHT))
# faces += cvHaarDetectObjects(grayscale, cascade, storage, 1.1, 3, 0, cvSize(MINFACEWIDTH,MINFACEHEIGHT))
# faces += cvHaarDetectObjects(grayscale, cascade, storage, 1.1, 3, CV_HAAR_DO_CANNY_PRUNING, cvSize(*minsize))
faces += cvHaarDetectObjects(grayscale, cascade, storage, 1.1,
4, CV_HAAR_DO_CANNY_PRUNING,
cvSize(*minsize))
# faces += cvHaarDetectObjects(grayscale, cascade, storage, scale_factor=1.1, min_neighbors=3, flags=0, cvSize(50,50))
# print dir(faces)
bboxes = []
if faces:
for f in faces:
print >> sys.stderr, "\tFace at [(%d,%d) -> (%d,%d)]" % (
f.x, f.y, f.x + f.width, f.y + f.height)
bboxes = [Face(f.x, f.y, f.x + f.width, f.y + f.height) for f in faces]
return bboxes
def detectObjects(image):
"""Converts an image to grayscale and prints the locations of any
faces found"""
size = cv.cvSize(image.width, image.height)
grayscale = cv.cvCreateImage(size, 8, 1)
cv.cvCvtColor(image, grayscale, cv.CV_BGR2GRAY)
storage = cv.cvCreateMemStorage(0)
cv.cvClearMemStorage(storage)
cv.cvEqualizeHist(grayscale, grayscale)
cascade = cv.cvLoadHaarClassifierCascade(PATH, cv.cvSize(1,1))
faces = cv.cvHaarDetectObjects(grayscale, cascade, storage, 1.2, 2, cv.CV_HAAR_DO_CANNY_PRUNING, cv.cvSize(30,30))
if faces:
for f in faces:
print("[(%d,%d) -> (%d,%d)]" % (f.x, f.y, f.x+f.width, f.y+f.height))
def detect_faces_on(path):
faces = []
image = cvLoadImage(path)
# convert to grayscale for faster results
grayscale = cvCreateImage(cvSize(image.width, image.height), 8, 1)
cvCvtColor(image, grayscale, CV_BGR2GRAY)
# smooth picture for better results
cvSmooth(grayscale, grayscale, CV_GAUSSIAN, 3, 3)
storage = cvCreateMemStorage(0)
cvClearMemStorage(storage)
cvEqualizeHist(grayscale, grayscale)
cascade_files = [
# ('/usr/share/opencv/haarcascades/haarcascade_eye_tree_eyeglasses.xml', (50, 50)),
# ('/usr/share/opencv/haarcascades/haarcascade_frontalface_alt.xml', (50, 50)),
# ('/usr/share/opencv/haarcascades/haarcascade_lowerbody.xml', (50, 50)),
# ('/usr/share/opencv/haarcascades/haarcascade_mcs_mouth.xml', (50, 50)),
# ('/usr/share/opencv/haarcascades/haarcascade_profileface.xml', (50, 50)),
# ('/usr/share/opencv/haarcascades/haarcascade_eye.xml', (50, 50)),
# ('/usr/share/opencv/haarcascades/haarcascade_frontalface_default.xml', (50, 50)),
# ('/usr/share/opencv/haarcascades/haarcascade_mcs_eyepair_big.xml', (50, 50)),
# ('/usr/share/opencv/haarcascades/haarcascade_mcs_nose.xml', (50, 50)),
# ('/usr/share/opencv/haarcascades/haarcascade_righteye_2splits.xml', (50, 50)),
# ('/usr/share/opencv/haarcascades/haarcascade_frontalface_alt2.xml', (50, 50)),
# ('/usr/share/opencv/haarcascades/haarcascade_fullbody.xml', (50, 50)),
# ('/usr/share/opencv/haarcascades/haarcascade_mcs_eyepair_small.xml', (50, 50)),
# ('/usr/share/opencv/haarcascades/haarcascade_mcs_righteye.xml', (50, 50)),
# ('/usr/share/opencv/haarcascades/haarcascade_upperbody.xml', (50, 50)),
('/usr/share/opencv/haarcascades/haarcascade_frontalface_alt_tree.xml', (50, 50)),
# ('/usr/share/opencv/haarcascades/haarcascade_lefteye_2splits.xml', (50, 50)),
# ('/usr/share/opencv/haarcascades/haarcascade_mcs_lefteye.xml', (50, 50)),
# ('/usr/share/opencv/haarcascades/haarcascade_mcs_upperbody.xml', (50, 50)),
# ('parojos_22_5.1.xml', (22, 5)),
# ('Mouth.xml', (22, 15)),
]
for cascade_file, cascade_sizes in cascade_files:
cascade = cvLoadHaarClassifierCascade(os.path.join(cascade_file), cvSize(1, 1))
faces += cvHaarDetectObjects(grayscale, cascade, storage, HAAR_SCALE, HAAR_NEIGHBORS, CV_HAAR_DO_CANNY_PRUNING, cvSize(*cascade_sizes))
return [{'x': f.x, 'y': f.y, 'w': f.width, 'h': f.height} for f in faces]
def detect(image):
image_size = cv.cvGetSize(image)
# create grayscale version
grayscale = cv.cvCreateImage(image_size, 8, 1)
cv.cvCvtColor(image, grayscale, opencv.CV_BGR2GRAY)
# create storage
storage = cv.cvCreateMemStorage(0)
cv.cvClearMemStorage(storage)
# equalize histogram
cv.cvEqualizeHist(grayscale, grayscale)
# detect objects
cascade = cv.cvLoadHaarClassifierCascade('haarcascade_frontalface_alt.xml', cv.cvSize(1,1))
faces = cv.cvHaarDetectObjects(grayscale, cascade, storage, 1.2, 2, opencv.CV_HAAR_DO_CANNY_PRUNING, cv.cvSize(100, 100))
if faces:
for i in faces:
r = image[int(i.y):int(i.y+i.height),int(i.x):int(i.x+i.width)]
cv.cvSmooth(r,r,cv.CV_BLUR,51,51)
def detect_faces(image):
"""Converts an image to grayscale and prints the locations of any
faces found"""
grayscale = cvCreateImage(cvSize(image.width, image.height), 8, 1)
cvCvtColor(image, grayscale, CV_BGR2GRAY)
storage = cvCreateMemStorage(0)
cvClearMemStorage(storage)
cvEqualizeHist(grayscale, grayscale)
# The default parameters (scale_factor=1.1, min_neighbors=3,
# flags=0) are tuned for accurate yet slow face detection. For
# faster face detection on real video images the better settings are
# (scale_factor=1.2, min_neighbors=2, flags=CV_HAAR_DO_CANNY_PRUNING).
# --- http://www710.univ-lyon1.fr/~bouakaz/OpenCV-0.9.5/docs/ref/OpenCVRef_Experimental.htm#decl_cvHaarDetectObjects
# The size box is of the *minimum* detectable object size. Smaller box = more processing time. - http://cell.fixstars.com/opencv/index.php/Facedetect
minsize = (int(MINFACEWIDTH_PERCENT*image.width+0.5),int(MINFACEHEIGHT_PERCENT*image.height))
print >> sys.stderr, "Min size of face: %s" % `minsize`
faces = []
for cascadefile in ['/usr/share/opencv/haarcascades/haarcascade_frontalface_default.xml']:
# for cascadefile in ['/usr/share/opencv/haarcascades/haarcascade_frontalface_default.xml', '/usr/share/opencv/haarcascades/haarcascade_profileface.xml']:
cascade = cvLoadHaarClassifierCascade(cascadefile, cvSize(1,1))
# faces += cvHaarDetectObjects(grayscale, cascade, storage, 1.2, 2, CV_HAAR_DO_CANNY_PRUNING, cvSize(50,50))
# faces += cvHaarDetectObjects(grayscale, cascade, storage, 1.1, 3, 0, cvSize(MINFACEWIDTH,MINFACEHEIGHT))
# faces += cvHaarDetectObjects(grayscale, cascade, storage, 1.1, 3, 0, cvSize(MINFACEWIDTH,MINFACEHEIGHT))
# faces += cvHaarDetectObjects(grayscale, cascade, storage, 1.1, 3, CV_HAAR_DO_CANNY_PRUNING, cvSize(*minsize))
faces += cvHaarDetectObjects(grayscale, cascade, storage, 1.1, 4, CV_HAAR_DO_CANNY_PRUNING, cvSize(*minsize))
# faces += cvHaarDetectObjects(grayscale, cascade, storage, scale_factor=1.1, min_neighbors=3, flags=0, cvSize(50,50))
# print dir(faces)
bboxes = []
if faces:
for f in faces:
print >> sys.stderr, "\tFace at [(%d,%d) -> (%d,%d)]" % (f.x, f.y, f.x+f.width, f.y+f.height)
bboxes = [Face(f.x, f.y, f.x+f.width, f.y+f.height) for f in faces]
return bboxes
def get_cascade(self, cascade_name):
self._cached_cascades[cascade_name] = opencv.cvLoadHaarClassifierCascade(
os.path.join(self.cascade_dir, cascade_name), opencv.cvSize(1,1))
#cascade_name, opencv.cvSize(1,1))
return self._cached_cascades[cascade_name]
import pygame
import Image
from pygame.locals import *
import sys
from opencv import cv as opencv
# Magic class with a method for grabbing images
from opencv import highgui
from opencv import Ipl2PIL
camera = highgui.cvCreateCameraCapture(-1)
cascade = opencv.cvLoadHaarClassifierCascade('haarcascade_hand_fist.xml', opencv.cvSize(1,1))
eye_cascade = opencv.cvLoadHaarClassifierCascade('/usr/local/share/opencv/haarcascades/haarcascade_mcs_mouth.xml', opencv.cvSize(1,1))
def get_image():
im = highgui.cvQueryFrame(camera)
detect(im)
#convert Ipl image to PIL image
return Ipl2PIL(im)
def draw_bounding_boxes(cascade_list, img, r,g,b, width):
if cascade_list:
for rect in cascade_list:
opencv.cvRectangle(img, opencv.cvPoint( int(rect.x), int(rect.y)),
opencv.cvPoint(int(rect.x + rect.width), int(rect.y + rect.height)),
opencv.CV_RGB(r,g,b), width)
def detect(image):
image_size = opencv.cvGetSize(image)