def trans(self, boxes):
"""
# Args
boxes : array, shape of (N, 4)
(x1, y1, x2, y2)-ordered & input image size scale coordinate
# Returns
norm_boxes : array, same shape of boxes
(cx, cy, w, h)-ordered & rescaled to grid-size
"""
# 1. minimax box -> centroid box
centroid_boxes = to_centroid(boxes).astype(np.float32)
# 2. image scale -> grid scale
norm_boxes = centroid_boxes * (self._grid_size / self._input_size)
return norm_boxes
def trans(self, boxes):
"""
# Args
boxes : array, shape of (N, 4)
(x1, y1, x2, y2)-ordered & input image size scale coordinate
# Returns
norm_boxes : array, same shape of boxes
(cx, cy, w, h)-ordered & rescaled to grid-size
"""
# 1. [[100, 120, 140, 200]] minimax box -> centroid box
centroid_boxes = to_centroid(boxes).astype(np.float32)
# 2. [[120. 160. 40. 80.]] image scale -> grid scale [[4. 5. 1.3333334 2.5 ]]
norm_boxes = np.zeros_like(centroid_boxes)
norm_boxes[:,0::2] = centroid_boxes[:,0::2] * (self._grid_size[0] / self._input_size[0])
norm_boxes[:,1::2] = centroid_boxes[:,1::2] * (self._grid_size[1] / self._input_size[1])
#print(norm_boxes)
return norm_boxes