def _draw_bboxes():
img = tf.squeeze(images[i])
bboxes = tf.gather(bbox_regs[i], indices)
class_probs = tf.gather(bbox_probs[i], indices)
# bboxes = tf.zeros_like(bboxes)
anchors = tf.gather(all_anchors, indices)
bboxes = bbox_decode(
bboxes, anchors, self.model_cfg.scale_factors)
# bboxes = tf.expand_dims(bboxes, axis=0)
scores = tf.gather(obj_prob, indices)
selected_indices = tf.image.non_max_suppression(
bboxes, scores,
max_output_size=10,
iou_threshold=0.5)
bboxes = tf.gather(bboxes, selected_indices)
class_probs = tf.gather(class_probs, selected_indices)
top_probs, top_classes = tf.nn.top_k(class_probs, 3)
vis_fn = functools.partial(
vis.visualize_bboxes_on_image,
class_labels=self.labels
)
out_img = tf.py_func(
vis_fn,
[img, bboxes, top_classes, top_probs], tf.uint8)
return tf.expand_dims(out_img, axis=0)
def _get_bboxes():
boxes = tf.gather(bbox_regs[i], indices)
anchors = tf.gather(self.anchors, indices)
boxes = bbox_decode(boxes, anchors,
self.model_cfg.scale_factors)
scores_ = tf.gather(bbox_probs[i], indices)
scores_ = tf.expand_dims(scores_, axis=1)
boxes *= tf.constant(
[self.patch_h, self.patch_w, self.patch_h, self.patch_w],
dtype=tf.float32)
delta = tf.constant([
patches_top[i], patches_left[i], patches_top[i],
patches_left[i]
],
dtype=tf.float32)
boxes += delta
return tf.concat([boxes, scores_], axis=1)
def _get_bboxes():
bboxes = tf.gather(bbox_regs[i], indices)
class_probs = tf.gather(bbox_probs[i], indices)
# bboxes = tf.zeros_like(bboxes)
anchors = tf.gather(self.anchors, indices)
bboxes = bbox_decode(bboxes, anchors,
self.model_cfg.scale_factors)
# bboxes = tf.expand_dims(bboxes, axis=0)
scores = tf.gather(obj_prob, indices)
selected_indices = tf.image.non_max_suppression(
bboxes, scores, max_output_size=30, iou_threshold=0.4)
bboxes = tf.gather(bboxes, selected_indices)
scaling = tf.constant(
[self.img_h, self.img_w, self.img_h, self.img_w],
dtype=tf.float32)
scaling = tf.expand_dims(scaling, axis=0)
bboxes = bboxes * scaling
class_probs = tf.gather(class_probs, selected_indices)
top_probs, top_classes = tf.nn.top_k(class_probs,
self.infer_cfg.top_k)
return bboxes, top_probs, top_classes