def __init__(self, src):
self._src = src
self._kinect = Kinect()
self._body = BodyDetector()
self._hand = HandDetector()
self._contour = HandContourDetector()
self._pose = PoseClassifier(MultiPoseClassifier(src))
def __init__(self, id, nsamples, dst):
self._id = id
self._nsamples = nsamples
self._dst = dst
self._kinect = Kinect()
self._body = BodyDetector()
self._hand = HandDetector()
self._contour = HandContourDetector()
self._feature = GaborDescriptors(4, 4)
def __init__(self, id, nsamples, dst):
self._id = id
self._nsamples = nsamples
self._dst = dst
self._kinect = Kinect()
self._body = BodyDetector()
self._hand = HandDetector()
self._contour = HandContourDetector()
self._fdesc = FourierDescriptors()
self._train = []
import cv2
import argparse
import chainer
import hand
from entity import params
from pose import PoseDetector, draw_person_pose
from hand import HandDetector, draw_hand_keypoints
chainer.using_config('enable_backprop', False)
if __name__ == '__main__':
# load model
pose_detector = PoseDetector("posenet", "models/coco_posenet.npz")
hand_detector = HandDetector("handnet", "models/handnet.npz")
# read image
img = cv2.imread('data/dinner.png')
# 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)
# hands estimation
print("Estimating hands keypoints...")
def __init__(self):
self._kinect = Kinect()
self._body = BodyDetector()
self._hand = HandDetector(HandOtsu())
self._contour = HandContourDetector()
self._palm = PalmDetector()
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import cv2
import numpy as np
import config
from kinect import Kinect
from body import BodyDetector
from hand import HandDetector, HandContourDetector
from pose import PoseClassifier, OpenCloseClassifier, MultiPoseClassifier
kinect = Kinect()
body = BodyDetector()
hand = HandDetector()
contour = HandContourDetector()
#pose = PoseClassifier(OpenCloseClassifier())
pose = PoseClassifier(MultiPoseClassifier())
for (depth, depth8, rgb) in kinect.get_data():
b = body.run(depth8)
(h, _) = hand.run(b)
#cv2.imshow('hand', h)
(ccc, box, hc) = contour.run(h)
if len(ccc) < 100:
