HandTracker.py

import numpy as np
from collections import namedtuple
import mediapipe_utils as mpu
import depthai as dai
import cv2
from pathlib import Path
from FPS import FPS, now
import time
import sys
from math import sin, cos


SCRIPT_DIR = Path(__file__).resolve().parent
PALM_DETECTION_MODEL = str(SCRIPT_DIR / "models/palm_detection_sh4.blob")
LANDMARK_MODEL_FULL = str(SCRIPT_DIR / "models/hand_landmark_full_sh4.blob")
LANDMARK_MODEL_LITE = str(SCRIPT_DIR / "models/hand_landmark_lite_sh4.blob")
LANDMARK_MODEL_SPARSE = str(SCRIPT_DIR / "models/hand_landmark_sparse_sh4.blob")

def to_planar(arr: np.ndarray, shape: tuple) -> np.ndarray:
    return cv2.resize(arr, shape).transpose(2,0,1)#.flatten()

class HandTracker:
    """
    Mediapipe Hand Tracker for depthai
    Arguments:
    - input_src: frame source, 
                    - "rgb" or None: OAK* internal color camera,
                    - "rgb_laconic": same as "rgb" but without sending the frames to the host (Edge mode only),
                    - a file path of an image or a video,
                    - an integer (eg 0) for a webcam id,
    - pd_model: palm detection model blob file,
    - pd_nms_thresh: NMS threshold,
    - pd_score: confidence score to determine whether a detection is reliable (a float between 0 and 1).
    - use_lm: boolean. When True, run landmark model. Otherwise, only palm detection model is run
    - lm_model: landmark model. Either:
                    - 'full' for LANDMARK_MODEL_FULL,
                    - 'lite' for LANDMARK_MODEL_LITE,
                    - 'sparse' for LANDMARK_MODEL_SPARSE,
                    - a path of a blob file. 
    - lm_score_thresh : confidence score to determine whether landmarks prediction is reliable (a float between 0 and 1).
    - use_world_landmarks: boolean. The landmarks model yields 2 types of 3D coordinates : 
                    - coordinates expressed in pixels in the image, always stored in hand.landmarks,
                    - coordinates expressed in meters in the world, stored in hand.world_landmarks 
                    only if use_world_landmarks is True.
    - solo: boolean, when True detect one hand max (much faster since we run the pose detection model only if no hand was detected in the previous frame)
    - xyz : boolean, when True get the (x, y, z) coords of the detected hands (if the device supports depth measure).
    - crop : boolean which indicates if square cropping on source images is applied or not
    - internal_fps : when using the internal color camera as input source, set its FPS to this value (calling setFps()).
    - resolution : sensor resolution "full" (1920x1080) or "ultra" (3840x2160),
    - internal_frame_height : when using the internal color camera, set the frame height (calling setIspScale()).
                    The width is calculated accordingly to height and depends on value of 'crop'
    - use_gesture : boolean, when True, recognize hand poses froma predefined set of poses
                    (ONE, TWO, THREE, FOUR, FIVE, OK, PEACE, FIST)
    - use_handedness_average : boolean, when True the handedness is the average of the last collected handednesses.
                    This brings robustness since the inferred robustness is not reliable on ambiguous hand poses.
                    When False, handedness is the last inferred handedness.
    - single_hand_tolerance_thresh (Duo mode only) : In Duo mode, if there is only one hand in a frame, 
                    in order to know when a second hand will appear you need to run the palm detection 
                    in the following frames. Because palm detection is slow, you may want to delay 
                    the next time you will run it. 'single_hand_tolerance_thresh' is the number of 
                    frames during only one hand is detected before palm detection is run again. 
    - lm_nb_threads : 1 or 2 (default=2), number of inference threads for the landmark model
    - stats : boolean, when True, display some statistics when exiting.   
    - trace : int, 0 = no trace, otherwise print some debug messages or show output of ImageManip nodes
            if trace & 1, print application level info like number of palm detections
    
    """
    def __init__(self, input_src=None,
                pd_model=PALM_DETECTION_MODEL, 
                pd_score_thresh=0.5, pd_nms_thresh=0.3,
                use_lm=True,
                lm_model="lite",
                lm_score_thresh=0.5,
                use_world_landmarks=False,
                solo=False,
                xyz=False,
                crop=False,
                internal_fps=23,
                resolution="full",
                internal_frame_height=640,
                use_gesture=False,
                use_handedness_average=True,
                single_hand_tolerance_thresh=10,
                lm_nb_threads=2,
                stats=False,
                trace=0, 
                ):

        self.pd_model = pd_model
        print(f"Palm detection blob : {self.pd_model}")
        if use_lm:
            if lm_model == "full":
                self.lm_model = LANDMARK_MODEL_FULL
            elif lm_model == "lite":
                self.lm_model = LANDMARK_MODEL_LITE
            elif lm_model == "sparse":
                self.lm_model = LANDMARK_MODEL_SPARSE
            else:
                self.lm_model = lm_model
            print(f"Landmark blob       : {self.lm_model}")
        self.pd_score_thresh = pd_score_thresh
        self.pd_nms_thresh = pd_nms_thresh
        self.use_lm = use_lm
        self.lm_score_thresh = lm_score_thresh
        if not use_lm and solo:
            print("Warning: solo mode desactivated when not using landmarks")
            self.solo = False
        else:
            self.solo = solo
        if self.solo:
            print("In Solo mode, # of landmark model threads is forced to 1")
            self.lm_nb_threads = 1
        else:
            assert lm_nb_threads in [1, 2]
            self.lm_nb_threads = lm_nb_threads
        if self.use_lm:
            self.max_hands = 1 if self.solo else 2
        else:
            self.max_hands = 20
        self.xyz = False
        self.crop = crop 
        self.use_world_landmarks = use_world_landmarks
        self.internal_fps = internal_fps     
        self.stats = stats
        self.trace = trace
        self.use_gesture = use_gesture
        self.use_handedness_average = use_handedness_average
        self.single_hand_tolerance_thresh = single_hand_tolerance_thresh

        self.device = dai.Device()

        if input_src == None or input_src == "rgb" or input_src == "rgb_laconic":
            # Note that here (in Host mode), specifying "rgb_laconic" has no effect
            # Color camera frames are systematically transferred to the host
            self.input_type = "rgb" # OAK* internal color camera
            self.internal_fps = internal_fps 
            print(f"Internal camera FPS set to: {self.internal_fps}")
            if resolution == "full":
                self.resolution = (1920, 1080)
            elif resolution == "ultra":
                self.resolution = (3840, 2160)
            else:
                print(f"Error: {resolution} is not a valid resolution !")
                sys.exit()
            print("Sensor resolution:", self.resolution)

            if xyz:
                # Check if the device supports stereo
                cameras = self.device.getConnectedCameras()
                if dai.CameraBoardSocket.LEFT in cameras and dai.CameraBoardSocket.RIGHT in cameras:
                    self.xyz = True
                else:
                    print("Warning: depth unavailable on this device, 'xyz' argument is ignored")

            self.video_fps = self.internal_fps # Used when saving the output in a video file. Should be close to the real fps
            
            if self.crop:
                self.frame_size, self.scale_nd = mpu.find_isp_scale_params(internal_frame_height, self.resolution)
                self.img_h = self.img_w = self.frame_size
                self.pad_w = self.pad_h = 0
                self.crop_w = (int(round(self.resolution[0] * self.scale_nd[0] / self.scale_nd[1])) - self.img_w) // 2
            else:
                width, self.scale_nd = mpu.find_isp_scale_params(internal_frame_height * self.resolution[0] / self.resolution[1], self.resolution, is_height=False)
                self.img_h = int(round(self.resolution[1] * self.scale_nd[0] / self.scale_nd[1]))
                self.img_w = int(round(self.resolution[0] * self.scale_nd[0] / self.scale_nd[1]))
                self.pad_h = (self.img_w - self.img_h) // 2
                self.pad_w = 0
                self.frame_size = self.img_w
                self.crop_w = 0

            print(f"Internal camera image size: {self.img_w} x {self.img_h} - crop_w:{self.crop_w} pad_h: {self.pad_h}")

        elif input_src.endswith('.jpg') or input_src.endswith('.png') :
            self.input_type= "image"
            self.img = cv2.imread(input_src)
            self.video_fps = 25
            self.img_h, self.img_w = self.img.shape[:2]
        else:
            self.input_type = "video"
            if input_src.isdigit():
                input_src = int(input_src)
            self.cap = cv2.VideoCapture(input_src)
            self.video_fps = int(self.cap.get(cv2.CAP_PROP_FPS))
            self.img_w = int(self.cap.get(cv2.CAP_PROP_FRAME_WIDTH))
            self.img_h = int(self.cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
            print("Video FPS:", self.video_fps)
        
        if self.input_type != "rgb":
            print(f"Original frame size: {self.img_w}x{self.img_h}")
            if self.crop:
                self.frame_size = min(self.img_w, self.img_h)
            else:
                self.frame_size = max(self.img_w, self.img_h)
            self.crop_w = max((self.img_w - self.frame_size) // 2, 0)
            if self.crop_w: print("Cropping on width :", self.crop_w)
            self.crop_h = max((self.img_h - self.frame_size) // 2, 0)
            if self.crop_h: print("Cropping on height :", self.crop_h)

            self.pad_w = max((self.frame_size - self.img_w) // 2, 0)
            if self.pad_w: print("Padding on width :", self.pad_w)
            self.pad_h = max((self.frame_size - self.img_h) // 2, 0)
            if self.pad_h: print("Padding on height :", self.pad_h)
                     
            if self.crop: self.img_h = self.img_w = self.frame_size
            print(f"Frame working size: {self.img_w}x{self.img_h}")
        

        # Create SSD anchors 
        self.pd_input_length = 128 # Palm detection
        # self.pd_input_length = 192 # Palm detection
        self.anchors = mpu.generate_handtracker_anchors(self.pd_input_length, self.pd_input_length)
        self.nb_anchors = self.anchors.shape[0]
        print(f"{self.nb_anchors} anchors have been created")

        # Define and start pipeline
        usb_speed = self.device.getUsbSpeed()
        self.device.startPipeline(self.create_pipeline())
        print(f"Pipeline started - USB speed: {str(usb_speed).split('.')[-1]}")

        # Define data queues 
        if self.input_type == "rgb":
            self.q_video = self.device.getOutputQueue(name="cam_out", maxSize=1, blocking=False)
            self.q_pd_out = self.device.getOutputQueue(name="pd_out", maxSize=1, blocking=False)
            self.q_manip_cfg = self.device.getInputQueue(name="manip_cfg")
            if self.use_lm:
                self.q_lm_out = self.device.getOutputQueue(name="lm_out", maxSize=2, blocking=False)
                self.q_lm_in = self.device.getInputQueue(name="lm_in")
            if self.xyz:
                self.q_spatial_data = self.device.getOutputQueue(name="spatial_data_out", maxSize=4, blocking=False)
                self.q_spatial_config = self.device.getInputQueue("spatial_calc_config_in")

        else:
            self.q_pd_in = self.device.getInputQueue(name="pd_in")
            self.q_pd_out = self.device.getOutputQueue(name="pd_out", maxSize=4, blocking=True)
            if self.use_lm:
                self.q_lm_out = self.device.getOutputQueue(name="lm_out", maxSize=4, blocking=True)
                self.q_lm_in = self.device.getInputQueue(name="lm_in")

        self.fps = FPS()

        self.nb_frames_pd_inference = 0
        self.nb_frames_lm_inference = 0
        self.nb_lm_inferences = 0
        self.nb_failed_lm_inferences = 0
        self.nb_frames_lm_inference_after_landmarks_ROI = 0
        self.nb_frames_no_hand = 0
        self.nb_spatial_requests = 0
        self.glob_pd_rtrip_time = 0
        self.glob_lm_rtrip_time = 0
        self.glob_spatial_rtrip_time = 0

        self.use_previous_landmarks = False
        self.nb_hands_in_previous_frame = 0
        if not self.solo: self.single_hand_count = 0

        if use_handedness_average:
            # handedness_avg: for more robustness, instead of using the last inferred handedness, we prefer to use the average 
            # of the inferred handedness since use_previous_landmarks is True.
            self.handedness_avg = [mpu.HandednessAverage() for i in range(self.max_hands)]
        

    def create_pipeline(self):
        print("Creating pipeline...")
        # Start defining a pipeline
        pipeline = dai.Pipeline()
        pipeline.setOpenVINOVersion(version = dai.OpenVINO.Version.VERSION_2021_4)

        if self.input_type == "rgb":
            # ColorCamera
            print("Creating Color Camera...")
            cam = pipeline.createColorCamera()
            if self.resolution[0] == 1920:
                cam.setResolution(dai.ColorCameraProperties.SensorResolution.THE_1080_P)
            else:
                cam.setResolution(dai.ColorCameraProperties.SensorResolution.THE_4_K)
            cam.setBoardSocket(dai.CameraBoardSocket.RGB)
            cam.setInterleaved(False)
            cam.setIspScale(self.scale_nd[0], self.scale_nd[1])
            cam.setFps(self.internal_fps)

            manip = pipeline.createImageManip()
            manip.setMaxOutputFrameSize(self.pd_input_length*self.pd_input_length*3)
            manip.setWaitForConfigInput(True)
            manip.inputImage.setQueueSize(1)
            manip.inputImage.setBlocking(False)
            cam.preview.link(manip.inputImage)
            if self.crop:
                cam.setVideoSize(self.frame_size, self.frame_size)
                cam.setPreviewSize(self.pd_input_length, self.pd_input_length)
                
            else: 
                cam.setVideoSize(self.img_w, self.img_h)
                cam.setPreviewSize(self.img_w, self.img_h)

            manip_cfg_in = pipeline.createXLinkIn()
            manip_cfg_in.setStreamName("manip_cfg")
            manip_cfg_in.out.link(manip.inputConfig)

            cam_out = pipeline.createXLinkOut()
            cam_out.setStreamName("cam_out")
            cam_out.input.setQueueSize(1)
            cam_out.input.setBlocking(False)
            cam.video.link(cam_out.input)

            if self.xyz:
                print("Creating MonoCameras, Stereo and SpatialLocationCalculator nodes...")
                # For now, RGB needs fixed focus to properly align with depth.
                # The value used during calibration should be used here
                calib_data = self.device.readCalibration()
                calib_lens_pos = calib_data.getLensPosition(dai.CameraBoardSocket.RGB)
                print(f"RGB calibration lens position: {calib_lens_pos}")
                cam.initialControl.setManualFocus(calib_lens_pos)

                mono_resolution = dai.MonoCameraProperties.SensorResolution.THE_400_P
                left = pipeline.createMonoCamera()
                left.setBoardSocket(dai.CameraBoardSocket.LEFT)
                left.setResolution(mono_resolution)
                left.setFps(self.internal_fps)

                right = pipeline.createMonoCamera()
                right.setBoardSocket(dai.CameraBoardSocket.RIGHT)
                right.setResolution(mono_resolution)
                right.setFps(self.internal_fps)

                stereo = pipeline.createStereoDepth()
                stereo.setConfidenceThreshold(230)
                # LR-check is required for depth alignment
                stereo.setLeftRightCheck(True)
                stereo.setDepthAlign(dai.CameraBoardSocket.RGB)
                stereo.setSubpixel(False)  # subpixel True -> latency

                spatial_location_calculator = pipeline.createSpatialLocationCalculator()
                spatial_location_calculator.setWaitForConfigInput(True)
                spatial_location_calculator.inputDepth.setBlocking(False)
                spatial_location_calculator.inputDepth.setQueueSize(1)

                spatial_data_out = pipeline.createXLinkOut()
                spatial_data_out.setStreamName("spatial_data_out")
                spatial_data_out.input.setQueueSize(1)
                spatial_data_out.input.setBlocking(False)

                spatial_calc_config_in = pipeline.createXLinkIn()
                spatial_calc_config_in.setStreamName("spatial_calc_config_in")

                left.out.link(stereo.left)
                right.out.link(stereo.right)    

                stereo.depth.link(spatial_location_calculator.inputDepth)

                spatial_location_calculator.out.link(spatial_data_out.input)
                spatial_calc_config_in.out.link(spatial_location_calculator.inputConfig)

        # Define palm detection model
        print("Creating Palm Detection Neural Network...")
        pd_nn = pipeline.createNeuralNetwork()
        pd_nn.setBlobPath(self.pd_model)
        # Palm detection input        
        if self.input_type == "rgb":
            # Specify that network takes latest arriving frame in non-blocking manner
            pd_nn.input.setQueueSize(1)
            pd_nn.input.setBlocking(False)
            if self.crop:
                cam.preview.link(pd_nn.input)
            else:
                manip.out.link(pd_nn.input)
        else:
            pd_in = pipeline.createXLinkIn()
            pd_in.setStreamName("pd_in")
            pd_in.out.link(pd_nn.input)

        # Palm detection output
        pd_out = pipeline.createXLinkOut()
        pd_out.setStreamName("pd_out")
        pd_nn.out.link(pd_out.input)
        
         # Define hand landmark model
        if self.use_lm:
            print(f"Creating Hand Landmark Neural Network ({'1 thread' if self.lm_nb_threads == 1 else '2 threads'})...")         
            lm_nn = pipeline.createNeuralNetwork()
            lm_nn.setBlobPath(self.lm_model)
            lm_nn.setNumInferenceThreads(self.lm_nb_threads)
            # Hand landmark input
            self.lm_input_length = 224
            lm_in = pipeline.createXLinkIn()
            lm_in.setStreamName("lm_in")
            lm_in.out.link(lm_nn.input)
            # Hand landmark output
            lm_out = pipeline.createXLinkOut()
            lm_out.setStreamName("lm_out")
            lm_nn.out.link(lm_out.input)
            
        print("Pipeline created.")
        return pipeline        
   

    def pd_postprocess(self, inference):
        # print(inference.getAllLayerNames())
        scores = np.array(inference.getLayerFp16("classificators"), dtype=np.float16) # 896
        # scores = np.array(inference.getLayerFp16("Identity_1"), dtype=np.float16)
        bboxes = np.array(inference.getLayerFp16("regressors"), dtype=np.float16).reshape((self.nb_anchors,18)) # 896x18
        # bboxes = np.array(inference.getLayerFp16("Identity"), dtype=np.float16).reshape((self.nb_anchors,18)) 
        # Decode bboxes
        hands = mpu.decode_bboxes(self.pd_score_thresh, scores, bboxes, self.anchors, scale=self.pd_input_length, best_only=self.solo)
        # Non maximum suppression (not needed if solo)
        if not self.solo:
            hands = mpu.non_max_suppression(hands, self.pd_nms_thresh)[:self.max_hands]
        if self.use_lm:
            mpu.detections_to_rect(hands)
            mpu.rect_transformation(hands, self.frame_size, self.frame_size)
        return hands


    def lm_postprocess(self, hand, inference):
        # print(inference.getAllLayerNames())
        # The output names of the landmarks model are :
        # Identity_1 (1x1) : score 
        # Identity_2 (1x1) : handedness
        # Identity_3 (1x63) : world 3D landmarks (in meters)
        # Identity (1x63) : screen 3D landmarks (in pixels)
        hand.lm_score = inference.getLayerFp16("Identity_1")[0]  
        if hand.lm_score > self.lm_score_thresh:  
            hand.handedness = inference.getLayerFp16("Identity_2")[0]
            lm_raw = np.array(inference.getLayerFp16("Identity_dense/BiasAdd/Add")).reshape(-1,3)
            # hand.norm_landmarks contains the normalized ([0:1]) 3D coordinates of landmarks in the square rotated body bounding box
            hand.norm_landmarks = lm_raw / self.lm_input_length
            # hand.norm_landmarks[:,2] /= 0.4

            # Now calculate hand.landmarks = the landmarks in the image coordinate system (in pixel)
            src = np.array([(0, 0), (1, 0), (1, 1)], dtype=np.float32)
            dst = np.array([ (x, y) for x,y in hand.rect_points[1:]], dtype=np.float32) # hand.rect_points[0] is left bottom point and points going clockwise!
            mat = cv2.getAffineTransform(src, dst)
            lm_xy = np.expand_dims(hand.norm_landmarks[:,:2], axis=0)
            # lm_z = hand.norm_landmarks[:,2:3] * hand.rect_w_a  / 0.4
            hand.landmarks = np.squeeze(cv2.transform(lm_xy, mat)).astype(np.int32)

            # World landmarks
            if self.use_world_landmarks:
                hand.world_landmarks = np.array(inference.getLayerFp16("Identity_3_dense/BiasAdd/Add")).reshape(-1,3)
            
            if self.use_gesture: mpu.recognize_gesture(hand)
            
    def spatial_loc_roi_from_palm_center(self, hand):
        half_size = int(hand.pd_box[2] * self.frame_size / 2)
        zone_size = max(half_size//2, 8)
        rect_center = dai.Point2f(int(hand.pd_box[0]*self.frame_size) + half_size - zone_size//2 + self.crop_w, int(hand.pd_box[1]*self.frame_size) + half_size - zone_size//2 - self.pad_h)
        rect_size = dai.Size2f(zone_size, zone_size)
        return dai.Rect(rect_center, rect_size)

    def spatial_loc_roi_from_wrist_landmark(self, hand):
        zone_size = max(int(hand.rect_w_a / 10), 8)
        rect_center = dai.Point2f(*(hand.landmarks[0]-np.array((zone_size//2 - self.crop_w, zone_size//2 + self.pad_h))))
        rect_size = dai.Size2f(zone_size, zone_size)
        return dai.Rect(rect_center, rect_size)

    def query_xyz(self, spatial_loc_roi_func):
        conf_datas = []
        for h in self.hands:
            conf_data = dai.SpatialLocationCalculatorConfigData()
            conf_data.depthThresholds.lowerThreshold = 100
            conf_data.depthThresholds.upperThreshold = 10000
            conf_data.roi = spatial_loc_roi_func(h)
            conf_datas.append(conf_data)
        if len(conf_datas) > 0:
            cfg = dai.SpatialLocationCalculatorConfig()
            cfg.setROIs(conf_datas)
            
            spatial_rtrip_time = now()
            self.q_spatial_config.send(cfg)

            # Receives spatial locations
            spatial_data = self.q_spatial_data.get().getSpatialLocations()
            self.glob_spatial_rtrip_time += now() - spatial_rtrip_time
            self.nb_spatial_requests += 1
            for i,sd in enumerate(spatial_data):
                self.hands[i].xyz_zone =  [
                    int(sd.config.roi.topLeft().x) - self.crop_w,
                    int(sd.config.roi.topLeft().y),
                    int(sd.config.roi.bottomRight().x) - self.crop_w,
                    int(sd.config.roi.bottomRight().y)
                    ]
                self.hands[i].xyz = np.array([
                    sd.spatialCoordinates.x,
                    sd.spatialCoordinates.y,
                    sd.spatialCoordinates.z
                    ])

    def next_frame(self):
        hand_label = None
        bag = {}
        self.fps.update()
        if self.input_type == "rgb":
            if not self.use_previous_landmarks:
                # Send image manip config to the device
                cfg = dai.ImageManipConfig()
                # We prepare the input to the Palm detector
                cfg.setResizeThumbnail(self.pd_input_length, self.pd_input_length)
                self.q_manip_cfg.send(cfg)

            in_video = self.q_video.get()
            video_frame = in_video.getCvFrame()
            if self.pad_h:
                square_frame = cv2.copyMakeBorder(video_frame, self.pad_h, self.pad_h, self.pad_w, self.pad_w, cv2.BORDER_CONSTANT)
            else:
                square_frame = video_frame
        else:
            if self.input_type == "image":
                frame = self.img.copy()
            else:
                ok, frame = self.cap.read()
                if not ok:
                    return None, None, None
            # Cropping and/or padding of the video frame
            video_frame = frame[self.crop_h:self.crop_h+self.frame_size, self.crop_w:self.crop_w+self.frame_size]
            if self.pad_h or self.pad_w:
                square_frame = cv2.copyMakeBorder(video_frame, self.pad_h, self.pad_h, self.pad_w, self.pad_w, cv2.BORDER_CONSTANT)
            else:
                square_frame = video_frame
           
            if not self.use_previous_landmarks:
                frame_nn = dai.ImgFrame()
                frame_nn.setTimestamp(time.monotonic())
                frame_nn.setWidth(self.pd_input_length)
                frame_nn.setHeight(self.pd_input_length)
                frame_nn.setData(to_planar(square_frame, (self.pd_input_length, self.pd_input_length)))
                self.q_pd_in.send(frame_nn)
                pd_rtrip_time = now()

        # Get palm detection
        if self.use_previous_landmarks:
            self.hands = self.hands_from_landmarks
        else:
            inference = self.q_pd_out.get()
            if self.input_type != "rgb": 
                self.glob_pd_rtrip_time += now() - pd_rtrip_time
            hands = self.pd_postprocess(inference)
            if self.trace & 1:
                print(f"Palm detection - nb hands detected: {len(hands)}")
            self.nb_frames_pd_inference += 1  
            bag["pd_inference"] = 1 
            if not self.solo and self.nb_hands_in_previous_frame == 1 and len(hands) <= 1:
                self.hands = self.hands_from_landmarks
            else:
                self.hands = hands

        if len(self.hands) == 0: self.nb_frames_no_hand += 1
        
        if self.use_lm:
            nb_lm_inferences = len(self.hands)
            # Hand landmarks, send requests
            for i,h in enumerate(self.hands):
                img_hand = mpu.warp_rect_img(h.rect_points, square_frame, self.lm_input_length, self.lm_input_length)
                nn_data = dai.NNData()   
                nn_data.setLayer("input_1", to_planar(img_hand, (self.lm_input_length, self.lm_input_length)))
                self.q_lm_in.send(nn_data)
                if i == 0: lm_rtrip_time = now() # We measure only for the first hand
            # Get inference results
            for i,h in enumerate(self.hands):
                inference = self.q_lm_out.get()
                if i == 0: self.glob_lm_rtrip_time += now() - lm_rtrip_time
                self.lm_postprocess(h, inference)
            bag["lm_inference"] = len(self.hands)
            self.hands = [ h for h in self.hands if h.lm_score > self.lm_score_thresh]

            if self.trace & 1:
                print(f"Landmarks - nb hands detected : {len(self.hands)}")

            # Check that 2 detected hands do not correspond to the same hand in the image
            # That may happen when one hand in the image cross another one
            # A simple method is to assure that the center of the rotated rectangles are not too close
            if len(self.hands) == 2: 
                dist_rect_centers = mpu.distance(np.array((self.hands[0].rect_x_center_a, self.hands[0].rect_y_center_a)), np.array((self.hands[1].rect_x_center_a, self.hands[1].rect_y_center_a)))
                if dist_rect_centers < 5:
                    # Keep the hand with higher landmark score
                    if self.hands[0].lm_score > self.hands[1].lm_score:
                        self.hands = [self.hands[0]]
                    else:
                        self.hands = [self.hands[1]]
                    if self.trace & 1: print("!!! Removing one hand because too close to the other one")

            if self.xyz:
                self.query_xyz(self.spatial_loc_roi_from_wrist_landmark)

            self.hands_from_landmarks = [mpu.hand_landmarks_to_rect(h) for h in self.hands]
            
            nb_hands = len(self.hands)

            if self.use_handedness_average:
                if not self.use_previous_landmarks or self.nb_hands_in_previous_frame != nb_hands:
                    for i in range(self.max_hands):
                        self.handedness_avg[i].reset()
                for i in range(nb_hands):
                    self.hands[i].handedness = self.handedness_avg[i].update(self.hands[i].handedness)

            # In duo mode , make sure only one left hand and one right hand max is returned everytime
            if not self.solo and nb_hands == 2 and (self.hands[0].handedness - 0.5) * (self.hands[1].handedness - 0.5) > 0:
                self.hands = [self.hands[0]] # We keep the hand with best score
                nb_hands = 1
                if self.trace & 1: print("!!! Removing one hand because same handedness")

            if not self.solo:
                if nb_hands == 1:
                    self.single_hand_count += 1
                else:
                    self.single_hand_count = 0

            # Stats
            if nb_lm_inferences: self.nb_frames_lm_inference += 1
            self.nb_lm_inferences += nb_lm_inferences
            self.nb_failed_lm_inferences += nb_lm_inferences - nb_hands 
            if self.use_previous_landmarks: self.nb_frames_lm_inference_after_landmarks_ROI += 1

            self.use_previous_landmarks = True
            if nb_hands == 0:
                self.use_previous_landmarks = False
            elif not self.solo and nb_hands == 1:
                    if self.single_hand_count >= self.single_hand_tolerance_thresh:
                        self.use_previous_landmarks = False
                        self.single_hand_count = 0
            
            self.nb_hands_in_previous_frame = nb_hands           
            
            for hand in self.hands:
                # If we added padding to make the image square, we need to remove this padding from landmark coordinates and from rect_points
                if self.pad_h > 0:
                    hand.landmarks[:,1] -= self.pad_h
                    for i in range(len(hand.rect_points)):
                        hand.rect_points[i][1] -= self.pad_h
                if self.pad_w > 0:
                    hand.landmarks[:,0] -= self.pad_w
                    for i in range(len(hand.rect_points)):
                        hand.rect_points[i][0] -= self.pad_w

                # Set the hand label
                hand.label = "right" if hand.handedness > 0.5 else "left"       

        else: # not use_lm
            if self.xyz:
                self.query_xyz(self.spatial_loc_roi_from_palm_center)

        return video_frame, self.hands, bag


    def exit(self):
        self.device.close()
        # Print some stats
        if self.stats:
            nb_frames = self.fps.nb_frames()
            print(f"FPS : {self.fps.get_global():.1f} f/s (# frames = {nb_frames})")
            print(f"# frames w/ no hand           : {self.nb_frames_no_hand} ({100*self.nb_frames_no_hand/nb_frames:.1f}%)")
            print(f"# frames w/ palm detection    : {self.nb_frames_pd_inference} ({100*self.nb_frames_pd_inference/nb_frames:.1f}%)")
            if self.use_lm:
                print(f"# frames w/ landmark inference : {self.nb_frames_lm_inference} ({100*self.nb_frames_lm_inference/nb_frames:.1f}%)- # after palm detection: {self.nb_frames_lm_inference - self.nb_frames_lm_inference_after_landmarks_ROI} - # after landmarks ROI prediction: {self.nb_frames_lm_inference_after_landmarks_ROI}")
                if not self.solo:
                    print(f"On frames with at least one landmark inference, average number of landmarks inferences/frame: {self.nb_lm_inferences/self.nb_frames_lm_inference:.2f}")
                print(f"# lm inferences: {self.nb_lm_inferences} - # failed lm inferences: {self.nb_failed_lm_inferences} ({100*self.nb_failed_lm_inferences/self.nb_lm_inferences:.1f}%)")
            if self.input_type != "rgb":
                print(f"Palm detection round trip            : {self.glob_pd_rtrip_time/self.nb_frames_pd_inference*1000:.1f} ms")
                if self.use_lm and self.nb_lm_inferences:
                    print(f"Hand landmark round trip             : {self.glob_lm_rtrip_time/self.nb_lm_inferences*1000:.1f} ms")
            if self.xyz:
                print(f"Spatial location requests round trip : {self.glob_spatial_rtrip_time/self.nb_spatial_requests*1000:.1f} ms")