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 plot_single(self, aug_fn):
image, (labels, boxes) = next(iter(self.ds.take(1)))
aug_image, (aug_labels, aug_boxes) = aug_fn(image, (labels, boxes))
image = data.preprocess.unnormalize_image(image)
aug_image = data.preprocess.unnormalize_image(aug_image)
labels = [self.classes[l] for l in labels.numpy().tolist()]
aug_labels = [self.classes[l] for l in aug_labels.numpy().tolist()]
colors = visualizer.colors_per_labels(labels)
aug_colors = visualizer.colors_per_labels(aug_labels)
image = visualizer.draw_boxes(image, boxes, labels, colors=colors)
aug_image = visualizer.draw_boxes(aug_image,
aug_boxes,
aug_labels,
colors=aug_colors)
plt.subplot(121)
plt.title('No Image augmentation')
plt.imshow(image)
plt.axis('off')
plt.subplot(122)
plt.title('Image augmentation')
plt.imshow(aug_image)
plt.axis('off')
plt.savefig('aug.png')
plt.show(block=True)
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_keras_pretrained(self):
# Load efficientdet pretrained on VOC2007
model = EfficientDet(D=0,
num_classes=20,
weights='D0-VOC',
score_threshold=.4)
print('Done loading...')
image = io.load_image('imgs/cat-dog.jpg', model.config.input_size)
n_image = normalize_image(image)
n_image = tf.expand_dims(n_image, axis=0)
classes = voc.IDX_2_LABEL
boxes, labels, scores = model(n_image, training=False)
labels = [classes[l] for l in labels[0]]
colors = visualizer.colors_per_labels(labels)
im = visualizer.draw_boxes(image,
boxes[0],
labels,
scores[0],
colors=colors)
plt.imshow(im)
plt.axis('off')
plt.savefig('test.png')
plt.show(block=True)
def test_tile_anchors(self):
level = 3
feature_size = 512
im_random = np.zeros((512, 512, 3))
boxes = self.generate_anchors(config.AnchorsConfig(),
im_random.shape[0])
im_random = visualizer.draw_boxes(im_random, boxes)
plt.imshow(im_random)
plt.show(block=True)
image = load_image('sample.jpg', image_size)
image.shape
# In[ ]:
n_image = preprocess.normalize_image(image)
n_image = tf.expand_dims(n_image, 0)
# In[ ]:
predictions = model(n_image, training=False)
boxes, labels, scores = predictions
labels = [IDX_2_LABEL[o] for o in labels[0]]
# In[7]:
colors = visualizer.colors_per_labels(labels)
visualizer.draw_boxes(image,
boxes=boxes[0],
labels=labels,
scores=scores[0],
colors=colors)