display.EraseAll()

ais_boxshp = build_shape(display)

ax1 = gp_Ax1(gp_Pnt(25, 25, 25), gp_Dir(0., 0., 1.))

aCubeTrsf = gp_Trsf()

angle = 0.0

tA = time.time()

use_raw_image = True #don't do any processing on the image from the webcam before sending it to the face recog algo

#cascPathLeft = '/Applications/opencv-2.4.9/data/haarcascades/haarcascade_lefteye_2splits.xml'

#cascPathLeft = '/Applications/opencv-2.4.9/data/haarcascades/haarcascade_frontalface_default.xml'

cascPathFace = '/Applications/opencv-2.4.9/data/haarcascades/haarcascade_frontalface_default.xml'

cascPathLeft = '/Applications/opencv-2.4.9/data/haarcascades/haarcascade_mcs_lefteye.xml'

cascPathRight = '/Applications/opencv-2.4.9/data/haarcascades/haarcascade_mcs_righteye.xml'

cascPath = [cascPathFace]

cascade =[]

for i in xrange(len(cascPath)):

cascade += [cv2.CascadeClassifier(cascPath[i])]

video_capture = cv2.VideoCapture(0)

angle = 0.0

while True:

# Capture frame-by-frame

if use_raw_image:

ret, frame = video_capture.read()

gray2 = frame

else:

ret, frame = video_capture.read()

gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

#gray2 = cv2.equalizeHist(gray)

#remove saturated regions from the image... this helps if the subject is underlit

#a = np.array(np.random.randint(200,245, (gray.shape[0], gray.shape[1])),dtype=np.uint8)

gray = gray*(gray<200) #+ a*(gray>=245)

#gray2 = gray*(gray<150) #+ a*(gray>=245)

gray2 = cv2.equalizeHist(gray)

all_faces = []

for i in xrange(len(cascade)):

'''

faces = cascade[i].detectMultiScale(

gray2,

scaleFactor=4.0,

minNeighbors=5,

minSize=(20, 20),

flags=cv2.cv.CV_HAAR_SCALE_IMAGE

)

'''

faces = cascade[i].detectMultiScale(

gray2,

scaleFactor=1.1,

minNeighbors=5,

minSize=(30, 30),

flags=cv2.cv.CV_HAAR_SCALE_IMAGE

)

all_faces += list(faces)

#print all_faces, len(all_faces)

print all_faces

size_largest =0

for f in all_faces:

if f[2]*f[3] > size_largest:

size_largest = f[2]*f[3]

face = f

'''

# Draw a rectangle around the faces

for (x, y, w, h) in faces:

cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)

# Display the resulting frame

cv2.imshow('Video', gray2)

'''

aCubeTrsf.SetRotation(ax1, angle)

aCubeToploc = TopLoc_Location(aCubeTrsf)

display.Context.SetLocation(ais_boxshp, aCubeToploc)

display.Context.UpdateCurrentViewer()

try:

angle -= (face[0] - all_faces_prev[0])/300.0

except:

None

all_faces_prev = face

# Stop video capture

# in the opencv window (ie NOT in the terminal) hold down a key - you might have to hold it down a bit.

#print 'click in the opencv window and hold down a key to quit'

if cv2.waitKey(1) != -1:

break

# When everything is done, release the capture

video_capture.release()

cv2.destroyAllWindows()

use_raw_image = True #don't do any processing on the image from the webcam before sending it to the face recog algo

#cascPathLeft = '/Applications/opencv-2.4.9/data/haarcascades/haarcascade_lefteye_2splits.xml'

#cascPathLeft = '/Applications/opencv-2.4.9/data/haarcascades/haarcascade_frontalface_default.xml'

cascPathFace = '/Applications/opencv-2.4.9/data/haarcascades/haarcascade_frontalface_default.xml'

cascPathLeft = '/Applications/opencv-2.4.9/data/haarcascades/haarcascade_mcs_lefteye.xml'

cascPathRight = '/Applications/opencv-2.4.9/data/haarcascades/haarcascade_mcs_righteye.xml'

cascPath = [cascPathFace]

cascade =[]

for i in xrange(len(cascPath)):

cascade += [cv2.CascadeClassifier(cascPath[i])]

video_capture = cv2.VideoCapture(0)

while True:

# Capture frame-by-frame

if use_raw_image:

ret, frame = video_capture.read()

gray2 = frame

else:

ret, frame = video_capture.read()

gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

#gray2 = cv2.equalizeHist(gray)

#remove saturated regions from the image... this helps if the subject is underlit

#a = np.array(np.random.randint(200,245, (gray.shape[0], gray.shape[1])),dtype=np.uint8)

gray = gray*(gray<200) #+ a*(gray>=245)

#gray2 = gray*(gray<150) #+ a*(gray>=245)

gray2 = cv2.equalizeHist(gray)

all_faces = []

for i in xrange(len(cascade)):

'''

faces = cascade[i].detectMultiScale(

gray2,

scaleFactor=4.0,

minNeighbors=5,

minSize=(20, 20),

flags=cv2.cv.CV_HAAR_SCALE_IMAGE

)

'''

faces = cascade[i].detectMultiScale(

gray2,

scaleFactor=1.1,

minNeighbors=5,

minSize=(30, 30),

flags=cv2.cv.CV_HAAR_SCALE_IMAGE

)

all_faces += list(faces)

print all_faces, len(all_faces)

# Draw a rectangle around the faces

for (x, y, w, h) in faces:

cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)

# Display the resulting frame

cv2.imshow('Video', gray2)

# Stop video capture

# in the opencv window (ie NOT in the terminal) hold down a key - you might have to hold it down a bit.

print 'click in the opencv window and hold down a key to quit'

if cv2.waitKey(1) != -1:

break

# When everything is done, release the capture

video_capture.release()

cv2.destroyAllWindows()

light = LightSource( [2,4,-3], 'color', [1,1,1] )

sphere1 = Sphere( [0,1,2], 2, Texture( Pigment( 'color', [1,0,1] )))

sphere2 = Sphere( [0,1,6], 2, Texture( Pigment( 'color', [1,0,0] )))

i = 0

while i <30:

#camera = Camera( 'location', [0,2,-3], 'look_at', [0,1,2] )

camera = Camera( 'location', [0,2+8.0*i/29,-3-4.0*i/29], 'look_at', [0,1,2] )

scene = Scene( camera, objects= [light, sphere1, sphere2])

print dir(scene)

cv2.imshow('Video', scene.render(width=800, height=600))

print i, camera

i += 1

if cv2.waitKey(1) & 0xFF == ord('q'):

break

'''

display, start_display, add_menu, add_function_to_menu = init_display()

my_box = BRepPrimAPI_MakeBox(10., 20., 30.).Shape()

display.DisplayShape(my_box, update=True)

#start_display()