jblitzar/github-python · Datasets at Hugging Face

text stringlengths 0 93.6k

img1 = cv2.resize(img, (input_shape_det[_W], input_shape_det[_H]))
img1 = img1.transpose((2, 0, 1)) # Change data layout from HWC to CHW
img1 = img1.reshape(input_shape_det)
res_det = exec_net_det.infer(inputs={input_name_det: img1}) # Detect faces

gaze_lines = []
for obj in res_det[out_name_det][0][0]: # obj = [ image_id, label, conf, xmin, ymin, xmax, ymax ]
if obj[2] > 0.75: # Confidence > 75%
xmin = abs(int(obj[3] * img.shape[1]))
ymin = abs(int(obj[4] * img.shape[0]))
xmax = abs(int(obj[5] * img.shape[1]))
ymax = abs(int(obj[6] * img.shape[0]))
class_id = int(obj[1])
face=img[ymin:ymax,xmin:xmax] # Crop the face image
if boundary_box_flag == True:
cv2.rectangle(out_img, (xmin, ymin), (xmax, ymax), (255,255,0), 2)

# Find facial landmarks (to find eyes)
face1=cv2.resize(face, (input_shape_lm[_W], input_shape_lm[_H]))
face1=face1.transpose((2,0,1))
face1=face1.reshape(input_shape_lm)
res_lm = exec_net_lm.infer(inputs={input_name_lm: face1}) # Run landmark detection
lm=res_lm[out_name_lm][0][:8].reshape(4,2) # [[left0x, left0y], [left1x, left1y], [right0x, right0y], [right1x, right1y] ]

# Estimate head orientation (yaw=Y, pitch=X, role=Z)
res_hp = exec_net_hp.infer(inputs={input_name_hp: face1}) # Run head pose estimation
yaw = res_hp['angle_y_fc'][0][0]
pitch = res_hp['angle_p_fc'][0][0]
roll = res_hp['angle_r_fc'][0][0]

_X=0
_Y=1
# Landmark position memo... lm[1] (eye) lm[0] (nose) lm[2] (eye) lm[3]
eye_sizes = [ abs(int((lm[0][_X]-lm[1][_X]) * face.shape[1])), abs(int((lm[3][_X]-lm[2][_X]) * face.shape[1])) ] # eye size in the cropped face image
eye_centers = [ [ int(((lm[0][_X]+lm[1][_X])/2 * face.shape[1])), int(((lm[0][_Y]+lm[1][_Y])/2 * face.shape[0])) ],
[ int(((lm[3][_X]+lm[2][_X])/2 * face.shape[1])), int(((lm[3][_Y]+lm[2][_Y])/2 * face.shape[0])) ] ] # eye center coordinate in the cropped face image
if eye_sizes[0]<4 or eye_sizes[1]<4:
continue

ratio = 0.7
eyes = []
for i in range(2):
# Crop eye images
x1 = int(eye_centers[i][_X]-eye_sizes[i]*ratio)
x2 = int(eye_centers[i][_X]+eye_sizes[i]*ratio)
y1 = int(eye_centers[i][_Y]-eye_sizes[i]*ratio)
y2 = int(eye_centers[i][_Y]+eye_sizes[i]*ratio)
eyes.append(cv2.resize(face[y1:y2, x1:x2].copy(), (input_shape_gaze[_W], input_shape_gaze[_H]))) # crop and resize

# Draw eye boundary boxes
if boundary_box_flag == True:
cv2.rectangle(out_img, (x1+xmin,y1+ymin), (x2+xmin,y2+ymin), (0,255,0), 2)

# rotate eyes around Z axis to keep them level
if roll != 0.:
rotMat = cv2.getRotationMatrix2D((int(input_shape_gaze[_W]/2), int(input_shape_gaze[_H]/2)), roll, 1.0)
eyes[i] = cv2.warpAffine(eyes[i], rotMat, (input_shape_gaze[_W], input_shape_gaze[_H]), flags=cv2.INTER_LINEAR)
eyes[i] = eyes[i].transpose((2, 0, 1)) # Change data layout from HWC to CHW
eyes[i] = eyes[i].reshape((1,3,60,60))

hp_angle = [ yaw, pitch, 0 ] # head pose angle in degree
res_gaze = exec_net_gaze.infer(inputs={'left_eye_image' : eyes[0],
'right_eye_image' : eyes[1],
'head_pose_angles': hp_angle}) # gaze estimation
gaze_vec = res_gaze['gaze_vector'][0] # result is in orthogonal coordinate system (x,y,z. not yaw,pitch,roll)and not normalized
gaze_vec_norm = gaze_vec / np.linalg.norm(gaze_vec) # normalize the gaze vector

vcos = math.cos(math.radians(roll))
vsin = math.sin(math.radians(roll))
tmpx = gaze_vec_norm[0]vcos + gaze_vec_norm[1]vsin
tmpy = -gaze_vec_norm[0]vsin + gaze_vec_norm[1]vcos
gaze_vec_norm = [tmpx, tmpy]

# Store gaze line coordinations
for i in range(2):
coord1 = (eye_centers[i][_X]+xmin, eye_centers[i][_Y]+ymin)
coord2 = (eye_centers[i][_X]+xmin+int((gaze_vec_norm[0]+0.)3000), eye_centers[i][_Y]+ymin-int((gaze_vec_norm[1]+0.)3000))
gaze_lines.append([coord1, coord2, False]) # line(coord1, coord2); False=spark flag


# Gaze lines intersection check (for sparking)
if spark_flag == True:
for g1 in range(len(gaze_lines)):
for g2 in range(g1+1, len(gaze_lines)):
if gaze_lines[g1][2]==True or gaze_lines[g2][2]==True:
continue # Skip if either line has already marked as crossed
x1 = gaze_lines[g1][0]
y1 = gaze_lines[g1][1]
x2 = gaze_lines[g2][0]
y2 = gaze_lines[g2][1]
if intersection_check(x1, y1, x2, y2) == True:
l1 = line(x1, y1)
l2 = line(x2, y2)
x, y = intersection( l1, l2 ) # calculate crossing coordinate
gaze_lines[g1][1] = [int(x), int(y)]
gaze_lines[g1][2] = True
gaze_lines[g2][1] = [int(x), int(y)]
gaze_lines[g2][2] = True

img1 = cv2.resize(img, (input_shape_det[_W], input_shape_det[_H]))

img1 = img1.transpose((2, 0, 1)) # Change data layout from HWC to CHW

img1 = img1.reshape(input_shape_det)

res_det = exec_net_det.infer(inputs={input_name_det: img1}) # Detect faces

gaze_lines = []

for obj in res_det[out_name_det][0][0]: # obj = [ image_id, label, conf, xmin, ymin, xmax, ymax ]

if obj[2] > 0.75: # Confidence > 75%

xmin = abs(int(obj[3] * img.shape[1]))

ymin = abs(int(obj[4] * img.shape[0]))

xmax = abs(int(obj[5] * img.shape[1]))

ymax = abs(int(obj[6] * img.shape[0]))

class_id = int(obj[1])

face=img[ymin:ymax,xmin:xmax] # Crop the face image

if boundary_box_flag == True:

cv2.rectangle(out_img, (xmin, ymin), (xmax, ymax), (255,255,0), 2)

# Find facial landmarks (to find eyes)

face1=cv2.resize(face, (input_shape_lm[_W], input_shape_lm[_H]))

face1=face1.transpose((2,0,1))

face1=face1.reshape(input_shape_lm)

res_lm = exec_net_lm.infer(inputs={input_name_lm: face1}) # Run landmark detection

lm=res_lm[out_name_lm][0][:8].reshape(4,2) # [[left0x, left0y], [left1x, left1y], [right0x, right0y], [right1x, right1y] ]

# Estimate head orientation (yaw=Y, pitch=X, role=Z)

res_hp = exec_net_hp.infer(inputs={input_name_hp: face1}) # Run head pose estimation

yaw = res_hp['angle_y_fc'][0][0]

pitch = res_hp['angle_p_fc'][0][0]

roll = res_hp['angle_r_fc'][0][0]

_X=0

_Y=1

# Landmark position memo... lm[1] (eye) lm[0] (nose) lm[2] (eye) lm[3]

eye_sizes = [ abs(int((lm[0][_X]-lm[1][_X]) * face.shape[1])), abs(int((lm[3][_X]-lm[2][_X]) * face.shape[1])) ] # eye size in the cropped face image

eye_centers = [ [ int(((lm[0][_X]+lm[1][_X])/2 * face.shape[1])), int(((lm[0][_Y]+lm[1][_Y])/2 * face.shape[0])) ],

[ int(((lm[3][_X]+lm[2][_X])/2 * face.shape[1])), int(((lm[3][_Y]+lm[2][_Y])/2 * face.shape[0])) ] ] # eye center coordinate in the cropped face image

if eye_sizes[0]<4 or eye_sizes[1]<4:

continue

ratio = 0.7

eyes = []

for i in range(2):

# Crop eye images

x1 = int(eye_centers[i][_X]-eye_sizes[i]*ratio)

x2 = int(eye_centers[i][_X]+eye_sizes[i]*ratio)

y1 = int(eye_centers[i][_Y]-eye_sizes[i]*ratio)

y2 = int(eye_centers[i][_Y]+eye_sizes[i]*ratio)

eyes.append(cv2.resize(face[y1:y2, x1:x2].copy(), (input_shape_gaze[_W], input_shape_gaze[_H]))) # crop and resize

# Draw eye boundary boxes

if boundary_box_flag == True:

cv2.rectangle(out_img, (x1+xmin,y1+ymin), (x2+xmin,y2+ymin), (0,255,0), 2)

# rotate eyes around Z axis to keep them level

if roll != 0.:

rotMat = cv2.getRotationMatrix2D((int(input_shape_gaze[_W]/2), int(input_shape_gaze[_H]/2)), roll, 1.0)

eyes[i] = cv2.warpAffine(eyes[i], rotMat, (input_shape_gaze[_W], input_shape_gaze[_H]), flags=cv2.INTER_LINEAR)

eyes[i] = eyes[i].transpose((2, 0, 1)) # Change data layout from HWC to CHW

eyes[i] = eyes[i].reshape((1,3,60,60))

hp_angle = [ yaw, pitch, 0 ] # head pose angle in degree

res_gaze = exec_net_gaze.infer(inputs={'left_eye_image' : eyes[0],

'right_eye_image' : eyes[1],

'head_pose_angles': hp_angle}) # gaze estimation

gaze_vec = res_gaze['gaze_vector'][0] # result is in orthogonal coordinate system (x,y,z. not yaw,pitch,roll)and not normalized

gaze_vec_norm = gaze_vec / np.linalg.norm(gaze_vec) # normalize the gaze vector

vcos = math.cos(math.radians(roll))

vsin = math.sin(math.radians(roll))

tmpx = gaze_vec_norm[0]*vcos + gaze_vec_norm[1]*vsin

tmpy = -gaze_vec_norm[0]*vsin + gaze_vec_norm[1]*vcos

gaze_vec_norm = [tmpx, tmpy]

# Store gaze line coordinations

for i in range(2):

coord1 = (eye_centers[i][_X]+xmin, eye_centers[i][_Y]+ymin)

coord2 = (eye_centers[i][_X]+xmin+int((gaze_vec_norm[0]+0.)*3000), eye_centers[i][_Y]+ymin-int((gaze_vec_norm[1]+0.)*3000))

gaze_lines.append([coord1, coord2, False]) # line(coord1, coord2); False=spark flag

# Gaze lines intersection check (for sparking)

if spark_flag == True:

for g1 in range(len(gaze_lines)):

for g2 in range(g1+1, len(gaze_lines)):

if gaze_lines[g1][2]==True or gaze_lines[g2][2]==True:

continue # Skip if either line has already marked as crossed

x1 = gaze_lines[g1][0]

y1 = gaze_lines[g1][1]

x2 = gaze_lines[g2][0]

y2 = gaze_lines[g2][1]

if intersection_check(x1, y1, x2, y2) == True:

l1 = line(x1, y1)

l2 = line(x2, y2)

x, y = intersection( l1, l2 ) # calculate crossing coordinate

gaze_lines[g1][1] = [int(x), int(y)]

gaze_lines[g1][2] = True

gaze_lines[g2][1] = [int(x), int(y)]

gaze_lines[g2][2] = True