Пример #1
0
class Walk():
    def __init__(self, n=5):
        self.cam = scv.Kinect()
        #initialize meanvect
        self.meanvect = CList(n)
        for i in range(n):
            self.meanvect.push(self.getavg())
        self.oldidx = -1 #old
    
    def getcam(self):
        return self.cam
                    
    def getavg(self):
        dm=self.cam.getDepthMatrix()
        #Get depth and height
        h,w = dm.shape #h=480, w=640
        
        left  = dm[:, 0:w/3] #dm[:, 0:w/2]
        center = dm[:, w/3: 2*w/3]#dm[:, w/4:3*w/4]
        right = dm[:, 2*w/3:]#dm[:, w/2:]
        
        leftMean = np.sum(left)/left.size
        centerMean = np.sum(center)/center.size
        rightMean = np.sum(right)/right.size
        mean = np.sum(dm)/dm.size
        return np.array([mean, leftMean, centerMean, rightMean])
    
    #return index of side to walk on;  idxmap[index] equals direction to walk
    def walk(self,n=5):                
        
        newidx =  -1
        smat = self.meanvect.getn(n=n) #sample matrix
        median = np.median(smat, axis=0)
        #print "median is {}".format(median)
        minidx = median.argmin()
        
        #backup if against a wall
        if median[dirmap["all"]] > 1900:            
            newidx=dirmap["back"]
                
        #walk in center if walkable
        elif median[dirmap["center"]] <= 1100 or minidx == dirmap["center"]: 
            newidx = dirmap["center"]
        
        else: #pick left or right based on whichever is cleaner
            newidx = minidx
    
        #store new sample
        mean=self.getavg()
        self.meanvect.push(mean)
        
        #print "{} {}".format(newidx, self.oldidx)             
        if newidx != self.oldidx:
            self.oldidx=newidx
            return newidx
        else:    
            return -1
Пример #2
0
class Walk():
    def __init__(self, n=5):
        self.cam = scv.Kinect()
        #initialize meanvect
        self.meanvect = CList(n)
        for i in range(n):
            self.meanvect.push(self.getavg())
        self.oldidx = -1  #old

    def getcam(self):
        return self.cam

    def getavg(self):
        dm = self.cam.getDepthMatrix()
        #Get depth and height
        h, w = dm.shape  #h=480, w=640

        left = dm[:, 0:w / 3]  #dm[:, 0:w/2]
        center = dm[:, w / 3:2 * w / 3]  #dm[:, w/4:3*w/4]
        right = dm[:, 2 * w / 3:]  #dm[:, w/2:]

        leftMean = np.sum(left) / left.size
        centerMean = np.sum(center) / center.size
        rightMean = np.sum(right) / right.size
        mean = np.sum(dm) / dm.size
        return np.array([mean, leftMean, centerMean, rightMean])

    #return index of side to walk on;  idxmap[index] equals direction to walk
    def walk(self, n=5):

        newidx = -1
        smat = self.meanvect.getn(n=n)  #sample matrix
        median = np.median(smat, axis=0)
        #print "median is {}".format(median)
        minidx = median.argmin()

        #backup if against a wall
        if median[dirmap["all"]] > 1900:
            newidx = dirmap["back"]

        #walk in center if walkable
        elif median[dirmap["center"]] <= 1100 or minidx == dirmap["center"]:
            newidx = dirmap["center"]

        else:  #pick left or right based on whichever is cleaner
            newidx = minidx

        #store new sample
        mean = self.getavg()
        self.meanvect.push(mean)

        #print "{} {}".format(newidx, self.oldidx)
        if newidx != self.oldidx:
            self.oldidx = newidx
            return newidx
        else:
            return -1
Пример #3
0
class ListTests(unittest.TestCase):
    def setUp(self):
        self._list = CList()

    def test_push(self):
        self.assertEqual(self._list.size, 0)
        self._list.push(1)
        self.assertEqual(self._list.size, 1)

    def test_pop(self):
        self.assertEqual(self._list.size, 0)
        self.assertEqual(self._list.pop(), (0, False))
        self._list.push(10)
        self.assertEqual(self._list.size, 1)
        self.assertEqual(self._list.pop(), (10, True))
        self.assertEqual(self._list.size, 0)