import argparse
import time
import numpy as np
import cv2 as cv
# ------------------------Service operations------------------------
def weight_path(model_path):
""" Get path of weights based on path to IR
Params:
model_path: the string contains path to IR file
Return:
Path to weights file
"""
assert model_path.endswith('.xml'), "Wrong topology path was provided"
return model_path[:-3] + 'bin'
def build_argparser():
""" Parse arguments from command line
Return:
Pack of arguments from command line
"""
parser = argparse.ArgumentParser(description='This is an OpenCV-based version of Gaze Estimation example')
parser.add_argument('--input',
help='Path to the input video file or camera device number')
parser.add_argument('--out',
help='Path to the output video file')
parser.add_argument('--facem',
default='face-detection-retail-0005.xml',
help='Path to OpenVINO face detection model (.xml)')
parser.add_argument('--faced',
default='CPU',
help='Target device for the face detection' +
'(e.g. CPU, GPU, VPU, ...)')
parser.add_argument('--headm',
default='head-pose-estimation-adas-0001.xml',
help='Path to OpenVINO head pose estimation model (.xml)')
parser.add_argument('--headd',
default='CPU',
help='Target device for the head pose estimation inference ' +
'(e.g. CPU, GPU, VPU, ...)')
parser.add_argument('--landm',
default='facial-landmarks-35-adas-0002.xml',
help='Path to OpenVINO landmarks detector model (.xml)')
parser.add_argument('--landd',
default='CPU',
help='Target device for the landmarks detector (e.g. CPU, GPU, VPU, ...)')
parser.add_argument('--gazem',
default='gaze-estimation-adas-0002.xml',
help='Path to OpenVINO gaze vector estimaiton model (.xml)')
parser.add_argument('--gazed',
default='CPU',
help='Target device for the gaze vector estimation inference ' +
'(e.g. CPU, GPU, VPU, ...)')
parser.add_argument('--eyem',
default='open-closed-eye-0001.xml',
help='Path to OpenVINO open closed eye model (.xml)')
parser.add_argument('--eyed',
default='CPU',
help='Target device for the eyes state inference (e.g. CPU, GPU, VPU, ...)')
return parser
# ------------------------Support functions for custom kernels------------------------
def intersection(surface, rect):
""" Remove zone of out of bound from ROI
Params:
surface: image bounds is rect representation (top left coordinates and width and height)
rect: region of interest is also has rect representation
Return:
Modified ROI with correct bounds
"""
l_x = max(surface[0], rect[0])
l_y = max(surface[1], rect[1])
width = min(surface[0] + surface[2], rect[0] + rect[2]) - l_x
height = min(surface[1] + surface[3], rect[1] + rect[3]) - l_y
if width < 0 or height < 0:
return (0, 0, 0, 0)
return (l_x, l_y, width, height)
def process_landmarks(r_x, r_y, r_w, r_h, landmarks):
""" Create points from result of inference of facial-landmarks network and size of input image
Params:
r_x: x coordinate of top left corner of input image
r_y: y coordinate of top left corner of input image
r_w: width of input image
r_h: height of input image
landmarks: result of inference of facial-landmarks network
Return:
Array of landmarks points for one face
"""
lmrks = landmarks[0]
raw_x = lmrks[::2] * r_w + r_x
raw_y = lmrks[1::2] * r_h + r_y
return np.array([[int(x), int(y)] for x, y in zip(raw_x, raw_y)])
def eye_box(p_1, p_2, scale=1.8):
""" Get bounding box of eye
Params:
p_1: point of left edge of eye
p_2: point of right edge of eye
scale: change size of box with this value
Return:
Bounding box of eye and its midpoint
"""
size = np.linalg.norm(p_1 - p_2)
midpoint = (p_1 + p_2) / 2
width = scale * size
height = width
p_x = midpoint[0] - (width / 2)
p_y = midpoint[1] - (height / 2)
return (int(p_x), int(p_y), int(width), int(height)), list(map(int, midpoint))
# ------------------------Custom graph operations------------------------
@cv.gapi.op('custom.GProcessPoses',
in_types=[cv.GArray.GMat, cv.GArray.GMat, cv.GArray.GMat],
out_types=[cv.GArray.GMat])
class GProcessPoses:
@staticmethod
def outMeta(arr_desc0, arr_desc1, arr_desc2):
return cv.empty_array_desc()
@cv.gapi.op('custom.GParseEyes',
in_types=[cv.GArray.GMat, cv.GArray.Rect, cv.GOpaque.Size],
out_types=[cv.GArray.Rect, cv.GArray.Rect, cv.GArray.Point, cv.GArray.Point])
class GParseEyes:
@staticmethod
def outMeta(arr_desc0, arr_desc1, arr_desc2):
return cv.empty_array_desc(), cv.empty_array_desc(), \
cv.empty_array_desc(), cv.empty_array_desc()
@cv.gapi.op('custom.GGetStates',
in_types=[cv.GArray.GMat, cv.GArray.GMat],
out_types=[cv.GArray.Int, cv.GArray.Int])
class GGetStates:
@staticmethod
def outMeta(arr_desc0, arr_desc1):
return cv.empty_array_desc(), cv.empty_array_desc()
# ------------------------Custom kernels------------------------
@cv.gapi.kernel(GProcessPoses)
class GProcessPosesImpl:
""" Custom kernel. Processed poses of heads
"""
@staticmethod
def run(in_ys, in_ps, in_rs):
""" Сustom kernel executable code
Params:
in_ys: yaw angle of head
in_ps: pitch angle of head
in_rs: roll angle of head
Return:
Arrays with heads poses
"""
return [np.array([ys[0], ps[0], rs[0]]).T for ys, ps, rs in zip(in_ys, in_ps, in_rs)]
@cv.gapi.kernel(GParseEyes)
class GParseEyesImpl:
""" Custom kernel. Get information about eyes
"""
@staticmethod
def run(in_landm_per_face, in_face_rcs, frame_size):
""" Сustom kernel executable code
Params:
in_landm_per_face: landmarks from inference of facial-landmarks network for each face
in_face_rcs: bounding boxes for each face
frame_size: size of input image
Return:
Arrays of ROI for left and right eyes, array of midpoints and
array of landmarks points
"""
left_eyes = []
right_eyes = []
midpoints = []
lmarks = []
surface = (0, 0, *frame_size)
for landm_face, rect in zip(in_landm_per_face, in_face_rcs):
points = process_landmarks(*rect, landm_face)
lmarks.extend(points)
rect, midpoint_l = eye_box(points[0], points[1])
left_eyes.append(intersection(surface, rect))
rect, midpoint_r = eye_box(points[2], points[3])
right_eyes.append(intersection(surface, rect))
midpoints.append(midpoint_l)
midpoints.append(midpoint_r)
return left_eyes, right_eyes, midpoints, lmarks
@cv.gapi.kernel(GGetStates)
class GGetStatesImpl:
""" Custom kernel. Get state of eye - open or closed
"""
@staticmethod
def run(eyesl, eyesr):
""" Сustom kernel executable code
Params:
eyesl: result of inference of open-closed-eye network for left eye
eyesr: result of inference of open-closed-eye network for right eye
Return:
States of left eyes and states of right eyes
"""
out_l_st = [int(st) for eye_l in eyesl for st in (eye_l[:, 0] < eye_l[:, 1]).ravel()]
out_r_st = [int(st) for eye_r in eyesr for st in (eye_r[:, 0] < eye_r[:, 1]).ravel()]
return out_l_st, out_r_st
if __name__ == '__main__':
ARGUMENTS = build_argparser().parse_args()
# ------------------------Demo's graph------------------------
g_in = cv.GMat()
# Detect faces
face_inputs = cv.GInferInputs()
face_inputs.setInput('data', g_in)
face_outputs = cv.gapi.infer('face-detection', face_inputs)
faces = face_outputs.at('detection_out')
# Parse faces
sz = cv.gapi.streaming.size(g_in)
faces_rc = cv.gapi.parseSSD(faces, sz, 0.5, False, False)
# Detect poses
head_inputs = cv.GInferInputs()
head_inputs.setInput('data', g_in)
face_outputs = cv.gapi.infer('head-pose', faces_rc, head_inputs)
angles_y = face_outputs.at('angle_y_fc')
angles_p = face_outputs.at('angle_p_fc')
angles_r = face_outputs.at('angle_r_fc')
# Parse poses
heads_pos = GProcessPoses.on(angles_y, angles_p, angles_r)
# Detect landmarks
landmark_inputs = cv.GInferInputs()
landmark_inputs.setInput('data', g_in)
landmark_outputs = cv.gapi.infer('facial-landmarks', faces_rc,
landmark_inputs)
landmark = landmark_outputs.at('align_fc3')
# Parse landmarks
left_eyes, right_eyes, mids, lmarks = GParseEyes.on(landmark, faces_rc, sz)
# Detect eyes
eyes_inputs = cv.GInferInputs()
eyes_inputs.setInput('input.1', g_in)
eyesl_outputs = cv.gapi.infer('open-closed-eye', left_eyes, eyes_inputs)
eyesr_outputs = cv.gapi.infer('open-closed-eye', right_eyes, eyes_inputs)
eyesl = eyesl_outputs.at('19')
eyesr = eyesr_outputs.at('19')
# Process eyes states
l_eye_st, r_eye_st = GGetStates.on(eyesl, eyesr)
# Gaze estimation
gaze_inputs = cv.GInferListInputs()
gaze_inputs.setInput('left_eye_image', left_eyes)
gaze_inputs.setInput('right_eye_image', right_eyes)
gaze_inputs.setInput('head_pose_angles', heads_pos)
gaze_outputs = cv.gapi.infer2('gaze-estimation', g_in, gaze_inputs)
gaze_vectors = gaze_outputs.at('gaze_vector')
out = cv.gapi.copy(g_in)
# ------------------------End of graph------------------------
comp = cv.GComputation(cv.GIn(g_in), cv.GOut(out,
faces_rc,
left_eyes,
right_eyes,
gaze_vectors,
angles_y,
angles_p,
angles_r,
l_eye_st,
r_eye_st,
mids,
lmarks))
# Networks
face_net = cv.gapi.ie.params('face-detection', ARGUMENTS.facem,
weight_path(ARGUMENTS.facem), ARGUMENTS.faced)
head_pose_net = cv.gapi.ie.params('head-pose', ARGUMENTS.headm,
weight_path(ARGUMENTS.headm), ARGUMENTS.headd)
landmarks_net = cv.gapi.ie.params('facial-landmarks', ARGUMENTS.landm,
weight_path(ARGUMENTS.landm), ARGUMENTS.landd)
gaze_net = cv.gapi.ie.params('gaze-estimation', ARGUMENTS.gazem,
weight_path(ARGUMENTS.gazem), ARGUMENTS.gazed)
eye_net = cv.gapi.ie.params('open-closed-eye', ARGUMENTS.eyem,
weight_path(ARGUMENTS.eyem), ARGUMENTS.eyed)
nets = cv.gapi.networks(face_net, head_pose_net, landmarks_net, gaze_net, eye_net)
# Kernels pack
kernels = cv.gapi.kernels(GParseEyesImpl, GProcessPosesImpl, GGetStatesImpl)
# ------------------------Execution part------------------------
ccomp = comp.compileStreaming(args=cv.gapi.compile_args(kernels, nets))
if ARGUMENTS.input.isdigit():
source = cv.gapi.wip.make_capture_src(int(ARGUMENTS.input))
else:
source = cv.gapi.wip.make_capture_src(ARGUMENTS.input)
ccomp.setSource(cv.gin(source))
ccomp.start()
frames = 0
fps = 0
print('Processing')
START_TIME = time.time()
while True:
start_time_cycle = time.time()
has_frame, (oimg,
outr,
l_eyes,
r_eyes,
outg,
out_y,
out_p,
out_r,
out_st_l,
out_st_r,
out_mids,
outl) = ccomp.pull()
if not has_frame:
break
# Draw
GREEN = (0, 255, 0)
RED = (0, 0, 255)
WHITE = (255, 255, 255)
BLUE = (255, 0, 0)
PINK = (255, 0, 255)
YELLOW = (0, 255, 255)
M_PI_180 = np.pi / 180
M_PI_2 = np.pi / 2
M_PI = np.pi
FACES_SIZE = len(outr)
for i, out_rect in enumerate(outr):
# Face box
cv.rectangle(oimg, out_rect, WHITE, 1)
rx, ry, rwidth, rheight = out_rect
# Landmarks
lm_radius = int(0.01 * rwidth + 1)
lmsize = int(len(outl) / FACES_SIZE)
for j in range(lmsize):
cv.circle(oimg, outl[j + i * lmsize], lm_radius, YELLOW, -1)
# Headposes
yaw = out_y[i]
pitch = out_p[i]
roll = out_r[i]
sin_y = np.sin(yaw[:] * M_PI_180)
sin_p = np.sin(pitch[:] * M_PI_180)
sin_r = np.sin(roll[:] * M_PI_180)
cos_y = np.cos(yaw[:] * M_PI_180)
cos_p = np.cos(pitch[:] * M_PI_180)
cos_r = np.cos(roll[:] * M_PI_180)
axis_length = 0.4 * rwidth
x_center = int(rx + rwidth / 2)
y_center = int(ry + rheight / 2)
# center to right
cv.line(oimg, [x_center, y_center],
[int(x_center + axis_length * (cos_r * cos_y + sin_y * sin_p * sin_r)),
int(y_center + axis_length * cos_p * sin_r)],
RED, 2)
# center to top
cv.line(oimg, [x_center, y_center],
[int(x_center + axis_length * (cos_r * sin_y * sin_p + cos_y * sin_r)),
int(y_center - axis_length * cos_p * cos_r)],
GREEN, 2)
# center to forward
cv.line(oimg, [x_center, y_center],
[int(x_center + axis_length * sin_y * cos_p),
int(y_center + axis_length * sin_p)],
PINK, 2)
scale_box = 0.002 * rwidth
cv.putText(oimg, "head pose: (y=%0.0f, p=%0.0f, r=%0.0f)" %
(np.round(yaw), np.round(pitch), np.round(roll)),
[int(rx), int(ry + rheight + 5 * rwidth / 100)],
cv.FONT_HERSHEY_PLAIN, scale_box * 2, WHITE, 1)
# Eyes boxes
color_l = GREEN if out_st_l[i] else RED
cv.rectangle(oimg, l_eyes[i], color_l, 1)
color_r = GREEN if out_st_r[i] else RED
cv.rectangle(oimg, r_eyes[i], color_r, 1)
# Gaze vectors
norm_gazes = np.linalg.norm(outg[i][0])
gaze_vector = outg[i][0] / norm_gazes
arrow_length = 0.4 * rwidth
gaze_arrow = [arrow_length * gaze_vector[0], -arrow_length * gaze_vector[1]]
left_arrow = [int(a+b) for a, b in zip(out_mids[0 + i * 2], gaze_arrow)]
right_arrow = [int(a+b) for a, b in zip(out_mids[1 + i * 2], gaze_arrow)]
if out_st_l[i]:
cv.arrowedLine(oimg, out_mids[0 + i * 2], left_arrow, BLUE, 2)
if out_st_r[i]:
cv.arrowedLine(oimg, out_mids[1 + i * 2], right_arrow, BLUE, 2)
v0, v1, v2 = outg[i][0]
gaze_angles = [180 / M_PI * (M_PI_2 + np.arctan2(v2, v0)),
180 / M_PI * (M_PI_2 - np.arccos(v1 / norm_gazes))]
cv.putText(oimg, "gaze angles: (h=%0.0f, v=%0.0f)" %
(np.round(gaze_angles[0]), np.round(gaze_angles[1])),
[int(rx), int(ry + rheight + 12 * rwidth / 100)],
cv.FONT_HERSHEY_PLAIN, scale_box * 2, WHITE, 1)
# Add FPS value to frame
cv.putText(oimg, "FPS: %0i" % (fps), [int(20), int(40)],
cv.FONT_HERSHEY_PLAIN, 2, RED, 2)
# Show result
cv.imshow('Gaze Estimation', oimg)
cv.waitKey(1)
fps = int(1. / (time.time() - start_time_cycle))
frames += 1
EXECUTION_TIME = time.time() - START_TIME
print('Execution successful')
print('Mean FPS is ', int(frames / EXECUTION_TIME))