import unittest
import unittest
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
import efficientdet.utils as utils
import efficientdet.data.voc as voc
import efficientdet.config as config
from efficientdet.data.preprocess import unnormalize_image
class VOCDatasetTest(unittest.TestCase):
def generate_anchors(self,
anchors_config: config.AnchorsConfig,
im_shape: int) -> tf.Tensor:
anchors_gen = [utils.anchors.AnchorGenerator(
size=anchors_config.sizes[i - 3],
aspect_ratios=anchors_config.ratios,
stride=anchors_config.strides[i - 3])
for i in range(3, 8)]
shapes = [im_shape // (2 ** x) for x in range(3, 8)]
anchors = [g((size, size, 3))
for g, size in zip(anchors_gen, shapes)]
return tf.concat(anchors, axis=0)
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.)))
if __name__ == "__main__":
unittest.main()
