def make_anchors(self, feature_pyramid, use_tf=False):
with tf.variable_scope('make_anchors'):
anchor = GenerateAnchors(self.cfgs, self.method)
if use_tf and self.method == 'H':
anchor_list = anchor.generate_all_anchor_tf(feature_pyramid)
else:
anchor_list = anchor.generate_all_anchor(feature_pyramid)
return anchor_list
def make_anchors(self, feature_pyramid):
with tf.variable_scope('make_anchors'):
anchor = GenerateAnchors(self.cfgs, self.anchor_mode)
anchor_list = anchor.generate_all_anchor(feature_pyramid)
return anchor_list
def test_pb(self, frozen_graph_path, test_dir):
graph = self.load_graph(frozen_graph_path)
print("we are testing ====>>>>", frozen_graph_path)
img = graph.get_tensor_by_name("input_img:0")
dets = graph.get_tensor_by_name("DetResults:0")
with tf.Session(graph=graph) as sess:
for img_path in os.listdir(test_dir):
print(img_path)
a_img = cv2.imread(os.path.join(test_dir,
img_path))[:, :, ::-1]
raw_h, raw_w = a_img.shape[0], a_img.shape[1]
short_size, max_len = self.cfgs.IMG_SHORT_SIDE_LEN, cfgs.IMG_MAX_LENGTH
if raw_h < raw_w:
new_h, new_w = short_size, min(
int(short_size * float(raw_w) / raw_h), max_len)
else:
new_h, new_w = min(int(short_size * float(raw_h) / raw_w),
max_len), short_size
img_resize = cv2.resize(a_img, (new_w, new_h))
dets_val = sess.run(dets,
feed_dict={img: img_resize[:, :, ::-1]})
bbox_pred, cls_prob = dets_val[:, :5], dets_val[:, 5:(
5 + self.cfgs.CLASS_NUM)]
anchor = GenerateAnchors(self.cfgs, 'H')
h1, w1 = math.ceil(new_h / 2), math.ceil(new_w / 2)
h2, w2 = math.ceil(h1 / 2), math.ceil(w1 / 2)
h3, w3 = math.ceil(h2 / 2), math.ceil(w2 / 2)
h4, w4 = math.ceil(h3 / 2), math.ceil(w3 / 2)
h5, w5 = math.ceil(h4 / 2), math.ceil(w4 / 2)
h6, w6 = math.ceil(h5 / 2), math.ceil(w5 / 2)
h7, w7 = math.ceil(h6 / 2), math.ceil(w6 / 2)
h_dict = {'P3': h3, 'P4': h4, 'P5': h5, 'P6': h6, 'P7': h7}
w_dict = {'P3': w3, 'P4': w4, 'P5': w5, 'P6': w6, 'P7': w7}
anchors = anchor.generate_all_anchor_pb(h_dict, w_dict)
anchors = np.concatenate(anchors, axis=0)
x_c = (anchors[:, 2] + anchors[:, 0]) / 2
y_c = (anchors[:, 3] + anchors[:, 1]) / 2
h = anchors[:, 2] - anchors[:, 0] + 1
w = anchors[:, 3] - anchors[:, 1] + 1
theta = -90 * np.ones_like(x_c)
anchors = np.transpose(np.stack([x_c, y_c, w, h, theta]))
detected_boxes, detected_scores, detected_categories = self.postprocess_detctions(
bbox_pred, cls_prob, anchors)
if True:
# detected_indices = det_scores_r_ >= self.cfgs.VIS_SCORE
# detected_scores = det_scores_r_[detected_indices]
# detected_boxes = det_boxes_r_[detected_indices]
# detected_categories = det_category_r_[detected_indices]
drawer = DrawBox(self.cfgs)
det_detections_r = drawer.draw_boxes_with_label_and_scores(
img_resize[:, :, ::-1],
boxes=detected_boxes,
labels=detected_categories,
scores=detected_scores,
method=1,
in_graph=True)
save_dir = os.path.join('test_pb', self.cfgs.VERSION,
'pb_img_vis')
tools.makedirs(save_dir)
cv2.imwrite(save_dir + '/{}'.format(img_path),
det_detections_r[:, :, ::-1])