def test_regress_boxes(self):
print('Regress anchors test')
ds = voc.build_dataset('test/data/VOC2007', im_input_size=(512, 512))
anchors = self.generate_anchors(config.AnchorsConfig(), 512)
im, (l, bbs) = next(iter(ds.take(1)))
gt_reg, gt_labels = utils.anchors.anchor_targets_bbox(
anchors, tf.expand_dims(im, 0), tf.expand_dims(bbs, 0),
tf.expand_dims(l, 0), len(voc.IDX_2_LABEL))
near_mask = gt_reg[0, :, -1] == 1
nearest_regressors = tf.expand_dims(
tf.boolean_mask(gt_reg[0], near_mask)[:, :-1], 0)
nearest_anchors = tf.expand_dims(anchors[near_mask], 0)
# apply regression to boxes
regressed_boxes = utils.bndbox.regress_bndboxes(
nearest_anchors, nearest_regressors)
im_random = unnormalize_image(im)
im_random = visualizer.draw_boxes(im_random, regressed_boxes[0])
plt.imshow(im_random)
plt.show(block=True)
def test_compute_gt(self):
ds = voc.build_dataset('test/data/VOC2007',
im_input_size=(512, 512),
shuffle=False)
ds = ds.skip(1).batch(1)
wrapped_ds = utils.training.wrap_detection_dataset(ds, (512, 512), 20)
anchors = self.generate_anchors(config.AnchorsConfig(), 512)
im, (regressors, l) = next(iter(wrapped_ds.take(1)))
im = unnormalize_image(im[0])
near_mask = regressors[0, :, -1] == 1
nearest_regressors = tf.expand_dims(
tf.boolean_mask(regressors[0], near_mask)[:, :-1], 0)
nearest_anchors = tf.expand_dims(anchors[near_mask], 0)
# apply regression to boxes
regressed_boxes = utils.bndbox.regress_bndboxes(
nearest_anchors, nearest_regressors)
im = utils.visualizer.draw_boxes(im,
nearest_anchors[0],
colors=[(255, 255, 0)])
im = utils.visualizer.draw_boxes(im,
regressed_boxes[0],
colors=[(0, 255, 255)])
plt.imshow(im)
plt.axis('off')
plt.show(block=True)
print('GT shapes:', l.shape, regressors.shape)
print('Found any overlapping anchor?',
tf.reduce_any(tf.equal(l[:, :, -1], 1.)))
def test_compute_gt(self):
level = 3
ds = voc.build_dataset('test/data/VOC2007', im_input_size=(512, 512))
anchors = self.generate_anchors(config.AnchorsConfig(), 512)
im, (l, bbs) = next(iter(ds.take(1)))
gt_reg, gt_labels = utils.anchors.anchor_targets_bbox(
anchors, tf.expand_dims(im, 0), tf.expand_dims(bbs, 0),
tf.expand_dims(l, 0), len(voc.IDX_2_LABEL))
nearest_anchors = anchors[gt_reg[0, :, -1] == 1].numpy()
im_random = unnormalize_image(im)
im_random = visualizer.draw_boxes(im_random, nearest_anchors)
im_random = visualizer.draw_boxes(im_random, bbs, colors=[0, 0, 255])
for label in l:
print(voc.IDX_2_LABEL[int(label)])
plt.imshow(im_random)
plt.show(block=True)
print('GT shapes:', gt_labels.shape, gt_reg.shape)
print('Found any overlapping anchor?', np.any(gt_labels[:, :,
-1] == 1.))
def test_compute_gt(self):
classes = ['treecko', 'psyduck', 'greninja', 'solgaleo', 'mewtwo']
class2idx = {c: i for i, c in enumerate(classes)}
ds = labelme.build_dataset('test/data/pokemon',
'test/data/pokemon',
class2idx=class2idx,
im_input_size=(512, 512))
anchors = self.generate_anchors(config.AnchorsConfig(), 512)
im, (l, bbs) = next(iter(ds.take(1)))
im = unnormalize_image(im)
gt_reg, gt_labels = utils.anchors.anchor_targets_bbox(
anchors, tf.expand_dims(im, 0), tf.expand_dims(bbs, 0),
tf.expand_dims(l, 0), len(classes))
nearest_anchors = anchors[gt_reg[0, :, -1] == 1]
im = utils.visualizer.draw_boxes(im, nearest_anchors)
im = utils.visualizer.draw_boxes(im, bbs, colors=[(255, 0, 0)])
plt.imshow(im)
plt.axis('off')
plt.show(block=True)
print('GT shapes:', gt_labels.shape, gt_reg.shape)
print('Found any overlapping anchor?', np.any(gt_labels[:, :,
-1] == 1.))