def __init__(self,
pd_path="models/palm_detection_6_shaves.blob",
pd_score_thresh=0.65,
pd_nms_thresh=0.3,
lm_path="models/hand_landmark_6_shaves.blob",
lm_score_threshold=0.5,
show_landmarks=True,
show_hand_box=True,
asl_path="models/hand_asl_6_shaves.blob",
asl_recognition=True,
show_asl=True):
self.pd_path = pd_path
self.pd_score_thresh = pd_score_thresh
self.pd_nms_thresh = pd_nms_thresh
self.lm_path = lm_path
self.lm_score_threshold = lm_score_threshold
self.asl_path = asl_path
self.show_landmarks = show_landmarks
self.show_hand_box = show_hand_box
self.asl_recognition = asl_recognition
self.show_asl = show_asl
anchor_options = mpu.SSDAnchorOptions(
num_layers=4,
min_scale=0.1484375,
max_scale=0.75,
input_size_height=128,
input_size_width=128,
anchor_offset_x=0.5,
anchor_offset_y=0.5,
strides=[8, 16, 16, 16],
aspect_ratios=[1.0],
reduce_boxes_in_lowest_layer=False,
interpolated_scale_aspect_ratio=1.0,
fixed_anchor_size=True)
self.anchors = mpu.generate_anchors(anchor_options)
self.nb_anchors = self.anchors.shape[0]
print(f"{self.nb_anchors} anchors have been created")
self.preview_width = 576
self.preview_height = 324
self.frame_size = None
self.ft = cv2.freetype.createFreeType2()
self.ft.loadFontData(fontFileName='HelveticaNeue.ttf', id=0)
self.right_char_queue = collections.deque(maxlen=5)
self.left_char_queue = collections.deque(maxlen=5)
self.previous_right_char = ""
self.right_sentence = ""
self.previous_right_update_time = time.time()
self.previous_left_char = ""
self.left_sentence = ""
self.previous_left_update_time = time.time()
def __init__(self,
input_src=None,
pd_path=POSE_DETECTION_MODEL,
pd_score_thresh=0.5,
pd_nms_thresh=0.3,
lm_path=FULL_BODY_LANDMARK_MODEL,
lm_score_threshold=0.7,
full_body=True,
use_gesture=False,
smoothing=True,
filter_window_size=5,
filter_velocity_scale=10,
show_3d=False,
crop=False,
multi_detection=False,
output=None,
internal_fps=15):
self.pd_path = pd_path
self.pd_score_thresh = pd_score_thresh
self.pd_nms_thresh = pd_nms_thresh
self.lm_path = lm_path
self.lm_score_threshold = lm_score_threshold
self.full_body = full_body
self.use_gesture = use_gesture
self.smoothing = smoothing
self.show_3d = show_3d
self.crop = crop
self.multi_detection = multi_detection
if self.multi_detection:
print("With multi-detection, smoothing filter is disabled.")
self.smoothing = False
self.internal_fps = internal_fps
if input_src == None:
self.input_type = "internal" # OAK* internal color camera
self.video_fps = internal_fps # Used when saving the output in a video file. Should be close to the real fps
video_height = video_width = 1080 # Depends on cam.setResolution() in create_pipeline()
elif input_src.endswith('.jpg') or input_src.endswith('.png'):
self.input_type = "image"
self.img = cv2.imread(input_src)
self.video_fps = 25
video_height, video_width = self.img.shape[:2]
else:
self.input_type = "video"
if input_src.isdigit():
input_type = "webcam"
input_src = int(input_src)
self.cap = cv2.VideoCapture(input_src)
self.video_fps = int(self.cap.get(cv2.CAP_PROP_FPS))
video_width = int(self.cap.get(cv2.CAP_PROP_FRAME_WIDTH))
video_height = int(self.cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
print("Video FPS:", self.video_fps)
self.nb_kps = 33 if self.full_body else 25
if self.smoothing:
self.filter = mpu.LandmarksSmoothingFilter(filter_window_size,
filter_velocity_scale,
(self.nb_kps, 3))
# Create SSD anchors
# https://github.com/google/mediapipe/blob/master/mediapipe/modules/pose_detection/pose_detection_cpu.pbtxt
anchor_options = mpu.SSDAnchorOptions(
num_layers=4,
min_scale=0.1484375,
max_scale=0.75,
input_size_height=128,
input_size_width=128,
anchor_offset_x=0.5,
anchor_offset_y=0.5,
strides=[8, 16, 16, 16],
aspect_ratios=[1.0],
reduce_boxes_in_lowest_layer=False,
interpolated_scale_aspect_ratio=1.0,
fixed_anchor_size=True)
self.anchors = mpu.generate_anchors(anchor_options)
self.nb_anchors = self.anchors.shape[0]
print(f"{self.nb_anchors} anchors have been created")
# Rendering flags
self.show_pd_box = False
self.show_pd_kps = False
self.show_rot_rect = False
self.show_landmarks = True
self.show_scores = False
self.show_gesture = self.use_gesture
self.show_fps = True
if self.show_3d:
self.vis3d = o3d.visualization.Visualizer()
self.vis3d.create_window()
opt = self.vis3d.get_render_option()
opt.background_color = np.asarray([0, 0, 0])
z = min(video_height, video_width) / 3
self.grid_floor = create_grid([0, video_height, -z],
[video_width, video_height, -z],
[video_width, video_height, z],
[0, video_height, z],
5,
2,
color=(1, 1, 1))
self.grid_wall = create_grid([0, 0, z], [video_width, 0, z],
[video_width, video_height, z],
[0, video_height, z],
5,
2,
color=(1, 1, 1))
self.vis3d.add_geometry(self.grid_floor)
self.vis3d.add_geometry(self.grid_wall)
view_control = self.vis3d.get_view_control()
view_control.set_up(np.array([0, -1, 0]))
view_control.set_front(np.array([0, 0, -1]))
if output is None:
self.output = None
else:
fourcc = cv2.VideoWriter_fourcc(*"MJPG")
self.output = cv2.VideoWriter(output, fourcc, self.video_fps,
(video_width, video_height))
def __init__(self,
input_file=None,
pd_path="models/palm_detection.blob",
pd_score_thresh=0.5,
pd_nms_thresh=0.3,
use_lm=True,
lm_path="models/hand_landmark.blob",
lm_score_threshold=0.5):
self.camera = input_file is None
self.pd_path = pd_path
self.pd_score_thresh = pd_score_thresh
self.pd_nms_thresh = pd_nms_thresh
self.use_lm = use_lm
self.lm_path = lm_path
self.lm_score_threshold = lm_score_threshold
self.regions = []
self.seq_num = None
if not self.camera:
if input_file == "direct":
self.image_mode = None
elif input_file.endswith('.jpg') or input_file.endswith('.png'):
self.image_mode = True
self.img = cv2.imread(input_file)
self.video_size = np.min(self.img.shape[:2])
else:
self.image_mode = False
self.cap = cv2.VideoCapture(input_file)
width = self.cap.get(cv2.CAP_PROP_FRAME_WIDTH) # float `width`
height = self.cap.get(
cv2.CAP_PROP_FRAME_HEIGHT) # float `height`
self.video_size = int(min(width, height))
# Create SSD anchors
# https://github.com/google/mediapipe/blob/master/mediapipe/modules/palm_detection/palm_detection_cpu.pbtxt
anchor_options = mpu.SSDAnchorOptions(
num_layers=4,
min_scale=0.1484375,
max_scale=0.75,
input_size_height=128,
input_size_width=128,
anchor_offset_x=0.5,
anchor_offset_y=0.5,
strides=[8, 16, 16, 16],
aspect_ratios=[1.0],
reduce_boxes_in_lowest_layer=False,
interpolated_scale_aspect_ratio=1.0,
fixed_anchor_size=True)
self.anchors = mpu.generate_anchors(anchor_options)
self.nb_anchors = self.anchors.shape[0]
print(f"{self.nb_anchors} anchors have been created")
# Rendering flags
if self.use_lm:
self.show_pd_box = False
self.show_pd_kps = False
self.show_rot_rect = False
self.show_handedness = False
self.show_landmarks = True
self.show_scores = False
else:
self.show_pd_box = True
self.show_pd_kps = False
self.show_rot_rect = False
self.show_scores = False
def __init__(self,
input_src=None,
pd_xml=POSE_DETECTION_MODEL,
pd_device="CPU",
pd_score_thresh=0.5,
pd_nms_thresh=0.3,
lm_xml=FULL_BODY_LANDMARK_MODEL,
lm_device="CPU",
lm_score_threshold=0.7,
full_body=True,
use_gesture=False,
smoothing=True,
filter_window_size=5,
filter_velocity_scale=10,
show_3d=False,
crop=False,
multi_detection=False,
force_detection=False,
output=None):
self.pd_score_thresh = pd_score_thresh
self.pd_nms_thresh = pd_nms_thresh
self.lm_score_threshold = lm_score_threshold
self.full_body = full_body
self.use_gesture = use_gesture
self.smoothing = smoothing
self.show_3d = show_3d
self.crop = crop
self.multi_detection = multi_detection
self.force_detection = force_detection
if self.multi_detection:
print(
"Warning: with multi-detection, smoothing filter is disabled and pose detection is forced on every frame."
)
self.smoothing = False
self.force_detection = True
if input_src.endswith('.jpg') or input_src.endswith('.png'):
self.input_type = "image"
self.img = cv2.imread(input_src)
self.video_fps = 25
video_height, video_width = self.img.shape[:2]
else:
self.input_type = "video"
if input_src.isdigit():
input_type = "webcam"
input_src = int(input_src)
self.cap = cv2.VideoCapture(input_src)
self.video_fps = int(self.cap.get(cv2.CAP_PROP_FPS))
video_width = int(self.cap.get(cv2.CAP_PROP_FRAME_WIDTH))
video_height = int(self.cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
print("Video FPS:", self.video_fps)
# The full body landmark model predict 39 landmarks.
# We are interested in the first 35 landmarks
# from 1 to 33 correspond to the well documented body parts,
# 34th (mid hips) and 35th (a point above the head) are used to predict ROI of next frame
# Same for upper body model but with 8 less landmarks
self.nb_lms = 35 if self.full_body else 27
if self.smoothing:
self.filter = mpu.LandmarksSmoothingFilter(filter_window_size,
filter_velocity_scale,
(self.nb_lms - 2, 3))
# Create SSD anchors
# https://github.com/google/mediapipe/blob/master/mediapipe/modules/pose_detection/pose_detection_cpu.pbtxt
anchor_options = mpu.SSDAnchorOptions(
num_layers=4,
min_scale=0.1484375,
max_scale=0.75,
input_size_height=128,
input_size_width=128,
anchor_offset_x=0.5,
anchor_offset_y=0.5,
strides=[8, 16, 16, 16],
aspect_ratios=[1.0],
reduce_boxes_in_lowest_layer=False,
interpolated_scale_aspect_ratio=1.0,
fixed_anchor_size=True)
self.anchors = mpu.generate_anchors(anchor_options)
self.nb_anchors = self.anchors.shape[0]
print(f"{self.nb_anchors} anchors have been created")
# Load Openvino models
self.load_models(pd_xml, pd_device, lm_xml, lm_device)
# Rendering flags
self.show_pd_box = False
self.show_pd_kps = False
self.show_rot_rect = False
self.show_landmarks = True
self.show_scores = False
self.show_gesture = self.use_gesture
self.show_fps = True
self.show_segmentation = False
if self.show_3d:
self.vis3d = o3d.visualization.Visualizer()
self.vis3d.create_window()
opt = self.vis3d.get_render_option()
opt.background_color = np.asarray([0, 0, 0])
z = min(video_height, video_width) / 3
self.grid_floor = create_grid([0, video_height, -z],
[video_width, video_height, -z],
[video_width, video_height, z],
[0, video_height, z],
5,
2,
color=(1, 1, 1))
self.grid_wall = create_grid([0, 0, z], [video_width, 0, z],
[video_width, video_height, z],
[0, video_height, z],
5,
2,
color=(1, 1, 1))
self.vis3d.add_geometry(self.grid_floor)
self.vis3d.add_geometry(self.grid_wall)
view_control = self.vis3d.get_view_control()
view_control.set_up(np.array([0, -1, 0]))
view_control.set_front(np.array([0, 0, -1]))
if output is None:
self.output = None
else:
fourcc = cv2.VideoWriter_fourcc(*"MJPG")
self.output = cv2.VideoWriter(output, fourcc, self.video_fps,
(video_width, video_height))
