print("Estimating pose...")
person_pose_array, _ = pose_detector(img)
res_img = cv2.addWeighted(img, 0.6,
draw_person_pose(img, person_pose_array), 0.4, 0)
# each person detected
for person_pose in person_pose_array:
unit_length = pose_detector.get_unit_length(person_pose)
# face estimation
print("Estimating face keypoints...")
cropped_face_img, bbox = pose_detector.crop_face(
img, person_pose, unit_length)
if cropped_face_img is not None:
face_keypoints = face_detector(cropped_face_img)
res_img = draw_face_keypoints(res_img, face_keypoints,
(bbox[0], bbox[1]))
cv2.rectangle(res_img, (bbox[0], bbox[1]), (bbox[2], bbox[3]),
(255, 255, 255), 1)
# hands estimation
print("Estimating hands keypoints...")
hands = pose_detector.crop_hands(img, person_pose, unit_length)
if hands["left"] is not None:
hand_img = hands["left"]["img"]
bbox = hands["left"]["bbox"]
hand_keypoints = hand_detector(hand_img, hand_type="left")
res_img = draw_hand_keypoints(res_img, hand_keypoints,
(bbox[0], bbox[1]))
cv2.rectangle(res_img, (bbox[0], bbox[1]), (bbox[2], bbox[3]),
(255, 255, 255), 1)
# load model
face_detector = FaceDetector("facenet", "models/facenet.npz", device=args.gpu)
cascade = cv2.CascadeClassifier("models/haarcascade_frontalface_alt.xml")
cap = cv2.VideoCapture(0)
cap.set(cv2.CAP_PROP_FRAME_WIDTH, 640)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 480)
while True:
# get video frame
ret, img = cap.read()
if not ret:
print("Failed to capture image")
break
# crop face
gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
facerects = cascade.detectMultiScale(gray_img, scaleFactor=1.2, minNeighbors=3, minSize=(1, 1))
res_img = img.copy()
if len(facerects) > 0:
for facerect in facerects:
cv2.rectangle(res_img, (facerect[0], facerect[1]), (facerect[0] + facerect[2], facerect[1] + facerect[3]), (255, 255, 255), 1)
cropped_face, face_left_top = crop_face(img, facerect)
face_keypoints = face_detector(cropped_face)
res_img = draw_face_keypoints(res_img, face_keypoints, face_left_top)
cv2.imshow("result", res_img)
cv2.waitKey(1)
# load model
cam_id = args.camera
face_detector = FaceDetector("facenet", "models/facenet.npz", device=args.gpu)
cascade = cv2.CascadeClassifier("models/haarcascade_frontalface_alt.xml")
cap = cv2.VideoCapture(cam_id)
cap.set(cv2.CAP_PROP_FRAME_WIDTH, 640)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 480)
while True:
# get video frame
ret, img = cap.read()
if not ret:
print("Failed to capture image")
break
# crop face
gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
facerects = cascade.detectMultiScale(gray_img, scaleFactor=1.2, minNeighbors=3, minSize=(1, 1))
res_img = img.copy()
if len(facerects) > 0:
for facerect in facerects:
cv2.rectangle(res_img, (facerect[0], facerect[1]), (facerect[0] + facerect[2], facerect[1] + facerect[3]), (255, 255, 255), 1)
cropped_face, face_left_top = crop_face(img, facerect)
face_keypoints = face_detector(cropped_face)
res_img = draw_face_keypoints(res_img, face_keypoints, face_left_top)
cv2.imshow("result", res_img)
cv2.waitKey(1)
# inference
print("Estimating pose...")
person_pose_array, _ = pose_detector(img)
res_img = cv2.addWeighted(img, 0.6, draw_person_pose(img, person_pose_array), 0.4, 0)
# each person detected
for person_pose in person_pose_array:
unit_length = pose_detector.get_unit_length(person_pose)
# face estimation
print("Estimating face keypoints...")
cropped_face_img, bbox = pose_detector.crop_face(img, person_pose, unit_length)
if cropped_face_img is not None:
face_keypoints = face_detector(cropped_face_img)
res_img = draw_face_keypoints(res_img, face_keypoints, (bbox[0], bbox[1]))
cv2.rectangle(res_img, (bbox[0], bbox[1]), (bbox[2], bbox[3]), (255, 255, 255), 1)
# hands estimation
print("Estimating hands keypoints...")
hands = pose_detector.crop_hands(img, person_pose, unit_length)
if hands["left"] is not None:
hand_img = hands["left"]["img"]
bbox = hands["left"]["bbox"]
hand_keypoints = hand_detector(hand_img, hand_type="left")
res_img = draw_hand_keypoints(res_img, hand_keypoints, (bbox[0], bbox[1]))
cv2.rectangle(res_img, (bbox[0], bbox[1]), (bbox[2], bbox[3]), (255, 255, 255), 1)
if hands["right"] is not None:
hand_img = hands["right"]["img"]
bbox = hands["right"]["bbox"]