decode_netout correction

[ChiHangChen]

I modify your decode_netout, which can draw a more accurate bounding box, I think it is about your row and col wrong selection:

compare image

def decode_netout(netout, anchors, obj_thresh,  net_h, net_w):
    grid_h, grid_w = netout.shape[:2]
    nb_box = 3
    netout = netout.reshape((grid_h, grid_w, nb_box, -1))
    nb_class = netout.shape[-1] - 5

    boxes = []

    netout[..., :2]  = _sigmoid(netout[..., :2])
    netout[..., 4:]  = _sigmoid(netout[..., 4:])
    netout[..., 5:]  = netout[..., 4][..., np.newaxis] * netout[..., 5:]
    netout[..., 5:] *= netout[..., 5:] > obj_thresh

    for i in range(grid_h*grid_w):
        row = int(i / grid_h)
        col = int(i % grid_w)-1
        
        for b in range(nb_box):
            # 4th element is objectness score
            objectness = netout[row][col][b][4]
            #objectness = netout[..., :4]
            
            if(objectness <= obj_thresh): continue
            
            # first 4 elements are x, y, w, and h
            x, y, w, h = netout[row][col][b][:4]

            x = (col + x) / grid_w # center position, unit: image width
            y = (row + y) / grid_h # center position, unit: image height
            w = anchors[2 * b + 0] * np.exp(w) / net_w # unit: image width
            h = anchors[2 * b + 1] * np.exp(h) / net_h # unit: image height  
            
            # last elements are class probabilities
            classes = netout[row][col][b][5:]
            
            box = BoundBox(x-w/2, y-h/2, x+w/2, y+h/2, objectness, classes)
            #box = BoundBox(x-w/2, y-h/2, x+w/2, y+h/2, None, classes)

            boxes.append(box)

    return boxes