How to convert horizontal bounding box to oriented bounding box in object detection task
I am trying to detect oriented bounding boxes with faster rcnn for a long time but I couldn't make it to do so. I aim to detect objects in DOTA dataset. I was using built-in faster rcnn model in pytorch but realized that it does not support OBB. Then I found another library named detectron2 that is built on the pytorch framework. Built-in faster rcnn network in detectron2 is actually compatible with OBB but I could not make that model work with DOTA. Because I could not convert DOTA box annotations to (cx, cy, w, h, a). In DOTA, objects are annotated by coordinates of 4 corners which are (x1,y1,x2,y2,x3,y3,x4,y4).
I cant come up with a solution that converts these 4 coordinates to (cx, cy, w, h, a), where cx and cy are the center point of OBB and w, h and a are width, height and angle respectively.
Is there any suggestion?

If you have your boxes in an Nx8 tensor/array, you can conver them to (cx, cy, w, h, a) by doing (assuming first point is top left, second point is bottom left, then bottom right, then top right...):
def DOTA_2_OBB(boxes):
    #calculate the angle of the box using arctan (degrees here)
    angle = (torch.atan((boxes[:,7] - boxes[:,5])/(boxes[:,6] - boxes[:,4]))*180/np.pi).float()
    #centrepoint is the mean of adjacent points
    cx = boxes[:,[4,0]].mean(1)
    cy = boxes[:,[7,3]].mean(1)
    #calculate w and h based on the distance between adjacent points
    w = ((boxes[:,7] - boxes[:,1])**2+(boxes[:,6] - boxes[:,0])**2)**0.5
    h = ((boxes[:,1] - boxes[:,3])**2+(boxes[:,0] - boxes[:,2])**2)**0.5
    return torch.stack([cx,cy,w,h,angle]).T   

Then giving this a test...
In [40]: boxes = torch.tensor([[0,2,1,0,2,2,1,3],[4,12,8,2,12,12,8,22]]).float()    
    
In [43]: DOTA_2_OBB(boxes)                                                                                            
Out[43]: 
tensor([[  1.0000,   1.5000,   1.4142,   2.2361, -45.0000],
        [  8.0000,  12.0000,  10.7703,  10.7703, -68.1986]])
        

