I am trying to convert the R3Det Model that outputs rotated bounding boxes to a TensorFlow Lite model for on device inference on mobile devices. The problem that I am facing is that a part of the inference model uses python code wrapped by tf.py_func which is not serializable. I am trying to convert the function to TensorFlow but it contains a for loop and some OpenCV funtion calls, and I have no idea how to convert these into TensorFlow code. I would appreciate it, if anybody can help me out with this. The python function is given below.
def nms_rotate_cpu(boxes, scores, iou_threshold, max_output_size):
"""
:param boxes: format [x_c, y_c, w, h, theta]
:param scores: scores of boxes
:param threshold: iou threshold (0.7 or 0.5)
:param max_output_size: max number of output
:return: the remaining index of boxes
"""
keep = []
order = scores.argsort()[::-1]
num = boxes.shape[0]
suppressed = np.zeros((num), dtype=np.int)
for _i in range(num):
if len(keep) >= max_output_size:
break
i = order[_i]
if suppressed[i] == 1:
continue
keep.append(i)
r1 = ((boxes[i, 0], boxes[i, 1]), (boxes[i, 2], boxes[i, 3]), boxes[i, 4])
area_r1 = boxes[i, 2] * boxes[i, 3]
for _j in range(_i + 1, num):
j = order[_j]
if suppressed[i] == 1:
continue
if np.sqrt((boxes[i, 0] - boxes[j, 0])**2 + (boxes[i, 1] - boxes[j, 1])**2) > (boxes[i, 2] + boxes[j, 2] + boxes[i, 3] + boxes[j, 3]):
inter = 0.0
else:
r2 = ((boxes[j, 0], boxes[j, 1]), (boxes[j, 2], boxes[j, 3]), boxes[j, 4])
area_r2 = boxes[j, 2] * boxes[j, 3]
inter = 0.0
try:
int_pts = cv2.rotatedRectangleIntersection(r1, r2)[1]
if int_pts is not None:
order_pts = cv2.convexHull(int_pts, returnPoints=True)
int_area = cv2.contourArea(order_pts)
inter = int_area * 1.0 / (area_r1 + area_r2 - int_area + cfgs.EPSILON)
except:
"""
cv2.error: /io/opencv/modules/imgproc/src/intersection.cpp:247:
error: (-215) intersection.size() <= 8 in function rotatedRectangleIntersection
"""
# print(r1)
# print(r2)
inter = 0.9999
if inter >= iou_threshold:
suppressed[j] = 1
return np.array(keep, np.int64)
