img = img[:, :, ::-1]
inp = tf.convert_to_tensor(img[None], tf.float32)
inp = tf.concat([inp, inp], 0)
outs = model(inp, training=False)
num = outs["valid_detections"].numpy()[1]
boxes = outs["nmsed_boxes"].numpy()[1]
scores = outs["nmsed_scores"].numpy()[1]
classes = outs["nmsed_classes"].numpy()[1]
for i in range(num):
box = boxes[i]
# if scores[i] < 0.5:
# continue
# box = boxes[i] * np.array([height, width, height, width])
c = classes[i] + 1
print(box, c)
img = draw(img, box, c, scores[i], coco_id_mapping,
random_color(int(c)))
cv2.imshow("img", img)
cv2.waitKey(0)
cv2.destroyAllWindows()
tf.saved_model.save(
model.detector,
"/home/bail/Data/data2/pretrained_weights/%s" % torch_weight_name)
model.save_weights("/home/bail/Data/data2/pretrained_weights/%s.h5" %
torch_weight_name)
_get_weights_from_pretrained(model.detector, "/home/bail/Data/data2/pretrained_weights/%s.pth" % torch_weight_name)
img = cv2.imread("/home/bail/Workspace/TRTNets/images/bus.jpg")
img, _, _, _ = preprocess(img, 1024)
img = img[:, :, ::-1]
inp = tf.convert_to_tensor(img[None], tf.float32)
inp = tf.concat([inp, inp], 0)
outs = model(inp, training=False)
num = outs["valid_detections"].numpy()[1]
boxes = outs["nmsed_boxes"].numpy()[1]
scores = outs["nmsed_scores"].numpy()[1]
classes = outs["nmsed_classes"].numpy()[1]
for i in range(num):
box = boxes[i]
# if scores[i] < 0.5:
# continue
# box = boxes[i] * np.array([height, width, height, width])
c = classes[i] + 1
print(box, c)
img = draw(img, box, c, scores[i], coco_id_mapping, random_color(int(c)))
cv2.imshow("img", img)
cv2.waitKey(0)
cv2.destroyAllWindows()
tf.saved_model.save(model.detector, "/home/bail/Data/data2/pretrained_weights/%s" % torch_weight_name)
model.save_weights("/home/bail/Data/data2/pretrained_weights/%s.h5" % torch_weight_name)