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jblitzar
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github-python

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def intersection_check(p1, p2, p3, p4):
tc1 = (p1[0] - p2[0]) * (p3[1] - p1[1]) + (p1[1] - p2[1]) * (p1[0] - p3[0])
tc2 = (p1[0] - p2[0]) * (p4[1] - p1[1]) + (p1[1] - p2[1]) * (p1[0] - p4[0])
td1 = (p3[0] - p4[0]) * (p1[1] - p3[1]) + (p3[1] - p4[1]) * (p3[0] - p1[0])
td2 = (p3[0] - p4[0]) * (p2[1] - p3[1]) + (p3[1] - p4[1]) * (p3[0] - p2[0])
return tc1*tc2<0 and td1*td2<0
def draw_gaze_line(img, coord1, coord2, laser_flag):
if laser_flag == False:
# simple line
cv2.line(img, coord1, coord2, (0, 0, 255),2)
else:
# Laser mode :-)
beam_img = np.zeros(img.shape, np.uint8)
for t in range(20)[::-2]:
cv2.line(beam_img, coord1, coord2, (0, 0, 255-t*10), t*2)
img |= beam_img
def draw_spark(img, coord):
for i in range(20):
angle = random.random()*2*math.pi
dia = random.randrange(10,60)
x = coord[0] + int(math.cos(angle)*dia - math.sin(angle)*dia)
y = coord[1] + int(math.sin(angle)*dia + math.cos(angle)*dia)
cv2.line(img, coord, (x, y), (0, 255, 255), 2)
def usage():
print("""
Gaze estimation demo
'f': Flip image
'l': Laser mode on/off
's': Spark mode on/off
'b': Boundary box on/off
""")
def main():
usage()
boundary_box_flag = True
# Prep for face detection
ie = IECore()
net_det = ie.read_network(model=model_det+'.xml', weights=model_det+'.bin')
input_name_det = next(iter(net_det.input_info)) # Input blob name "data"
input_shape_det = net_det.input_info[input_name_det].tensor_desc.dims # [1,3,384,672]
out_name_det = next(iter(net_det.outputs)) # Output blob name "detection_out"
exec_net_det = ie.load_network(network=net_det, device_name='CPU', num_requests=1)
del net_det
# Preparation for landmark detection
net_lm = ie.read_network(model=model_lm+'.xml', weights=model_lm+'.bin')
input_name_lm = next(iter(net_lm.input_info)) # Input blob name
input_shape_lm = net_lm.input_info[input_name_lm].tensor_desc.dims # [1,3,60,60]
out_name_lm = next(iter(net_lm.outputs)) # Output blob name "embd/dim_red/conv"
out_shape_lm = net_lm.outputs[out_name_lm].shape # 3x [1,1]
exec_net_lm = ie.load_network(network=net_lm, device_name='CPU', num_requests=1)
del net_lm
# Preparation for headpose detection
net_hp = ie.read_network(model=model_hp+'.xml', weights=model_hp+'.bin')
input_name_hp = next(iter(net_hp.input_info)) # Input blob name
input_shape_hp = net_hp.input_info[input_name_hp].tensor_desc.dims # [1,3,60,60]
out_name_hp = next(iter(net_hp.outputs)) # Output blob name
out_shape_hp = net_hp.outputs[out_name_hp].shape # [1,70]
exec_net_hp = ie.load_network(network=net_hp, device_name='CPU', num_requests=1)
del net_hp
# Preparation for gaze estimation
net_gaze = ie.read_network(model=model_gaze+'.xml', weights=model_gaze+'.bin')
input_shape_gaze = [1, 3, 60, 60]
exec_net_gaze = ie.load_network(network=net_gaze, device_name='CPU')
del net_gaze
# Open USB webcams
cam = cv2.VideoCapture(0)
camx, camy = [(1920, 1080), (1280, 720), (800, 600), (480, 480)][1] # Set camera resolution [1]=1280,720
cam.set(cv2.CAP_PROP_FRAME_WIDTH , camx)
cam.set(cv2.CAP_PROP_FRAME_HEIGHT, camy)
laser_flag=True
flip_flag =True
spark_flag=True
while True:
ret,img = cam.read() # img won't be modified
if ret==False:
break
if flip_flag == True:
img = cv2.flip(img, 1) # flip image
out_img = img.copy() # out_img will be drawn and modified to make an display image