def main():
text = "Facial Landmarks with Dlib"
try:
shape_predictor = "shape_predictor_68_face_landmarks.dat"
dlib_flm = facial_landmarks.Dlib_FLM(shape_predictor)
image_paths = sorted(list(edgeiq.list_images("images/")))
print("Images:\n{}\n".format(image_paths))
with edgeiq.Streamer(queue_depth=len(image_paths),
inter_msg_time=3) as streamer:
for image_path in image_paths:
image = cv2.imread(image_path)
image, gray_image = dlib_flm.image_preprocessor(image)
facial_coordinates, rectangles = dlib_flm.detect_faces_shapes(
gray_image)
# Loop to markup image
for (i, rectangle) in enumerate(rectangles):
(x, y, w,
h) = dlib_flm.dlib_rectangle_to_cv_bondingbox(rectangle)
cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0),
2)
cv2.putText(image, "Face #{}".format(i + 1),
(x - 10, y - 10), cv2.FONT_HERSHEY_SIMPLEX,
0.5, (0, 255, 0), 2)
for facial_coordinate in facial_coordinates:
for (x, y) in facial_coordinate:
cv2.circle(image, (x, y), 3, (255, 0, 0), -1)
streamer.send_data(image, text)
streamer.wait()
finally:
print("Program Ending")
def main():
text = "Facial Part Detection with Dlib"
try:
shape_predictor = "shape_predictor_68_face_landmarks.dat"
dlib_flm = facial_landmarks.Dlib_FLM(shape_predictor)
image_paths = sorted(list(edgeiq.list_images("images/")))
print("Images:\n{}\n".format(image_paths))
with edgeiq.Streamer(queue_depth=len(image_paths), inter_msg_time=3) as streamer:
for image_path in image_paths:
image = cv2.imread(image_path)
resized_image, gray_image = dlib_flm.image_preprocessor(image)
facial_coordinates, rectangles =dlib_flm.detect_faces_shapes(gray_image)
for facial_coordinate in facial_coordinates:
for (name, (i, j)) in FACIAL_LANDMARKS_IDXS.items():
print(name)
clone = resized_image.copy()
cv2.putText(clone, name, (10, 30), cv2.FONT_HERSHEY_SIMPLEX,
1.0, (255, 0, 0), 2)
for (x, y) in facial_coordinate[i:j]:
cv2.circle(clone, (x, y), 3, (255, 0, 0), -1)
streamer.send_data(clone, text)
streamer.wait()
finally:
print("Program Ending")
def main():
text = "Facial Overlays with Dlib"
fps = edgeiq.FPS()
shape_predictor = "shape_predictor_68_face_landmarks.dat"
dlib_flm = facial_landmarks.Dlib_FLM(shape_predictor)
try:
with edgeiq.WebcamVideoStream(cam=0) as webcam, \
edgeiq.Streamer() as streamer:
# Allow webcam to warm up
time.sleep(2.0)
fps.start()
# loop detection
while True:
frame = webcam.read()
resized_frame, gray_resized_frame = dlib_flm.image_preprocessor(frame)
facial_coordinates, rectangles = dlib_flm.detect_faces_shapes(gray_resized_frame)
left_eye = 0
right_eye = 0
for facial_coordinate in facial_coordinates:
for (name, (i, j)) in FACIAL_LANDMARKS_IDXS.items():
if name is 'left_eye':
left_eye = facial_coordinate[i:j]
#Uncoment if you want the patch on right eye as well.
#elif name is 'right_eye':
# right_eye = facial_coordinate[i:j]
leftEyeSize, leftEyeCenter = eye_size(left_eye)
#Uncoment if you want the patch on right eye as well.
#rightEyeSize, rightEyeCenter = eye_size(right_eye)
place_mustache(resized_frame, facial_coordinate)
#Uncoment if you want to place spectacles on the face.
#place_glasses(resized_frame, facial_coordinate)
place_eye_patch(resized_frame, leftEyeCenter, leftEyeSize)
#place_eye(resized_frame, rightEyeCenter, rightEyeSize)
streamer.send_data(resized_frame, text)
fps.update()
if streamer.check_exit():
break
finally:
# stop fps counter and display information
fps.stop()
print("[INFO] elapsed time: {:.2f}".format(fps.get_elapsed_seconds()))
print("[INFO] approx. FPS: {:.2f}".format(fps.compute_fps()))
print("Program Ending")
def main():
text = "Facial Landmarks with Dlib"
fps = edgeiq.FPS()
shape_predictor = "shape_predictor_68_face_landmarks.dat"
dlib_flm = facial_landmarks.Dlib_FLM(shape_predictor)
try:
with edgeiq.WebcamVideoStream(cam=0) as webcam, \
edgeiq.Streamer() as streamer:
# Allow webcam to warm up
time.sleep(2.0)
fps.start()
# loop detection
while True:
frame = webcam.read()
resized_frame, gray_resized_frame = dlib_flm.image_preprocessor(
frame)
facial_coordinates, rectangles = dlib_flm.detect_faces_shapes(
gray_resized_frame)
# Loop to markup resized_frame
for (i, rectangle) in enumerate(rectangles):
(x, y, w,
h) = dlib_flm.dlib_rectangle_to_cv_bondingbox(rectangle)
cv2.rectangle(resized_frame, (x, y), (x + w, y + h),
(0, 255, 0), 2)
cv2.putText(resized_frame, "Face #{}".format(i + 1),
(x - 10, y - 10), cv2.FONT_HERSHEY_SIMPLEX,
0.5, (0, 255, 0), 2)
for facial_coordinate in facial_coordinates:
for (x, y) in facial_coordinate:
cv2.circle(resized_frame, (x, y), 1, (255, 0, 0), -1)
streamer.send_data(resized_frame, text)
fps.update()
if streamer.check_exit():
break
finally:
# stop fps counter and display information
fps.stop()
print("[INFO] elapsed time: {:.2f}".format(fps.get_elapsed_seconds()))
print("[INFO] approx. FPS: {:.2f}".format(fps.compute_fps()))
print("Program Ending")