def test_centroid_crop_layer():
xv, yv = make_grid_vectors(image_height=12, image_width=12, output_stride=1)
points = tf.cast([[[1.75, 2.75]], [[3.75, 4.75]], [[5.75, 6.75]]], tf.float32)
cms = tf.expand_dims(make_multi_confmaps(points, xv, yv, sigma=1.5), axis=0)
x_in = tf.keras.layers.Input([12, 12, 1])
x_out = tf.keras.layers.Lambda(lambda x: x, name="CentroidConfmapsHead")(x_in)
model = tf.keras.Model(inputs=x_in, outputs=x_out)
layer = CentroidCrop(
keras_model=model,
input_scale=1.0,
crop_size=3,
pad_to_stride=1,
output_stride=None,
refinement="local",
integral_patch_size=5,
peak_threshold=0.2,
return_confmaps=False,
)
out = layer(cms)
assert tuple(out["centroids"].shape) == (1, None, 2)
assert tuple(out["centroid_vals"].shape) == (1, None)
assert tuple(out["crops"].shape) == (1, None, 3, 3, 1)
assert tuple(out["crop_offsets"].shape) == (1, None, 2)
assert tuple(out["centroids"].bounding_shape()) == (1, 3, 2)
assert tuple(out["centroid_vals"].bounding_shape()) == (1, 3)
assert tuple(out["crops"].bounding_shape()) == (1, 3, 3, 3, 1)
assert tuple(out["crop_offsets"].bounding_shape()) == (1, 3, 2)
assert_allclose(out["centroids"][0].numpy(), points.numpy().squeeze(axis=1))
assert_allclose(out["centroid_vals"][0].numpy(), [1, 1, 1], atol=0.1)
def test_find_local_peaks_rough():
xv, yv = make_grid_vectors(image_height=16,
image_width=16,
output_stride=1)
instances = tf.cast(
[
[[1, 2], [3, 4]],
[[5, 6], [7, 8]],
[[np.nan, np.nan], [11, 12]],
],
tf.float32,
)
cms = make_multi_confmaps(instances, xv=xv, yv=yv, sigma=1.0)
instances2 = tf.cast([[[2, 3], [4, 5]], [[6, 7], [8, 9]]], tf.float32)
cms = tf.stack(
[cms, make_multi_confmaps(instances2, xv=xv, yv=yv, sigma=1.0)],
axis=0)
peak_points, peak_vals, peak_sample_inds, peak_channel_inds = find_local_peaks(
cms, threshold=0.1, refinement=None)
assert peak_points.shape == (9, 2)
assert peak_vals.shape == (9, )
assert peak_sample_inds.shape == (9, )
assert peak_channel_inds.shape == (9, )
assert_array_equal(
peak_points.numpy(),
[
[1, 2],
[3, 4],
[5, 6],
[7, 8],
[11, 12],
[2, 3],
[4, 5],
[6, 7],
[8, 9],
],
)
assert_array_equal(peak_vals, [1, 1, 1, 1, 1, 1, 1, 1, 1])
assert_array_equal(peak_sample_inds, [0, 0, 0, 0, 0, 1, 1, 1, 1])
assert_array_equal(peak_channel_inds, [0, 1, 0, 1, 1, 0, 1, 0, 1])
peak_points, peak_vals, peak_sample_inds, peak_channel_inds = find_local_peaks(
tf.zeros([1, 4, 4, 3], tf.float32), threshold=0.1, refinement=None)
assert peak_points.shape == (0, 2)
assert peak_vals.shape == (0, )
assert peak_sample_inds.shape == (0, )
assert peak_channel_inds.shape == (0, )
def test_single_instance_inference():
xv, yv = make_grid_vectors(image_height=12, image_width=12, output_stride=1)
points = tf.cast([[1.75, 2.75], [3.75, 4.75], [5.75, 6.75]], tf.float32)
points = np.stack([points, points + 1], axis=0)
cms = tf.stack(
[
make_confmaps(points[0], xv, yv, sigma=1.0),
make_confmaps(points[1], xv, yv, sigma=1.0),
],
axis=0,
)
x_in = tf.keras.layers.Input([12, 12, 3])
x = tf.keras.layers.Lambda(lambda x: x, name="SingleInstanceConfmapsHead")(x_in)
keras_model = tf.keras.Model(x_in, x)
layer = SingleInstanceInferenceLayer(keras_model=keras_model, refinement="local")
assert layer.output_stride == 1
out = layer(cms)
assert tuple(out["instance_peaks"].shape) == (2, 1, 3, 2)
out["instance_peaks"] = tf.squeeze(out["instance_peaks"], axis=1)
assert tuple(out["instance_peak_vals"].shape) == (2, 1, 3)
out["instance_peak_vals"] = tf.squeeze(out["instance_peak_vals"], axis=1)
assert_array_equal(out["instance_peaks"], points)
assert_allclose(out["instance_peak_vals"], 1.0, atol=0.1)
assert "confmaps" not in out
out = layer({"image": cms})
assert tuple(out["instance_peaks"].shape) == (2, 1, 3, 2)
out["instance_peaks"] = tf.squeeze(out["instance_peaks"], axis=1)
assert_array_equal(out["instance_peaks"], points)
layer = SingleInstanceInferenceLayer(
keras_model=keras_model, refinement="local", return_confmaps=True
)
out = layer(cms)
assert "confmaps" in out
assert_array_equal(out["confmaps"], cms)
model = SingleInstanceInferenceModel(layer)
preds = model.predict(cms)
assert preds["instance_peaks"].shape == (2, 1, 3, 2)
preds["instance_peaks"] = preds["instance_peaks"].squeeze(axis=1)
assert_array_equal(preds["instance_peaks"], points)
assert "instance_peak_vals" in preds
assert "confmaps" in preds
def test_find_global_peaks_integral():
xv, yv = make_grid_vectors(image_height=12,
image_width=12,
output_stride=1)
points = tf.cast([[1.5, 2.5], [3.5, 4.5], [5.5, 6.5]], tf.float32)
cm = make_confmaps(points, xv, yv, sigma=1.0)
peaks, peak_vals = find_global_peaks(
tf.expand_dims(cm, axis=0),
threshold=0.1,
refinement="integral",
integral_patch_size=5,
)
assert peaks.shape == (1, 3, 2)
assert peak_vals.shape == (1, 3)
assert_allclose(peaks[0].numpy(), points.numpy(), atol=0.1)
assert_allclose(peak_vals[0].numpy(), [1, 1, 1], atol=0.3)
peaks, peak_vals = find_global_peaks(
tf.zeros((1, 8, 8, 3), dtype=tf.float32),
threshold=0.1,
refinement="integral",
integral_patch_size=5,
)
assert peaks.shape == (1, 3, 2)
assert peak_vals.shape == (1, 3)
assert tf.reduce_all(tf.math.is_nan(peaks))
assert_array_equal(peak_vals, [[0, 0, 0]])
peaks, peak_vals = find_global_peaks(
tf.stack([tf.zeros([12, 12, 3], dtype=tf.float32), cm], axis=0),
threshold=0.1,
refinement="integral",
integral_patch_size=5,
)
assert peaks.shape == (2, 3, 2)
assert tf.reduce_all(tf.math.is_nan(peaks[0]))
assert_allclose(peaks[1].numpy(), points.numpy(), atol=0.1)
peaks, peak_vals = find_global_peaks_integral(
tf.stack([tf.zeros([12, 12, 3], dtype=tf.float32), cm], axis=0),
threshold=0.1,
crop_size=5,
)
assert peaks.shape == (2, 3, 2)
assert tf.reduce_all(tf.math.is_nan(peaks[0]))
assert_allclose(peaks[1].numpy(), points.numpy(), atol=0.1)
def test_find_global_peaks_local():
xv, yv = make_grid_vectors(image_height=12,
image_width=12,
output_stride=1)
points = tf.cast([[1.6, 2.6], [3.6, 4.6], [5.6, 6.6]], tf.float32)
cm = make_confmaps(points, xv, yv, sigma=1.0)
peaks, peak_vals = find_global_peaks(tf.expand_dims(cm, axis=0),
threshold=0.1,
refinement="local")
assert peaks.shape == (1, 3, 2)
assert peak_vals.shape == (1, 3)
assert_allclose(peaks[0].numpy(),
np.array([[1.75, 2.75], [3.75, 4.75], [5.75, 6.75]]))
assert_allclose(peak_vals[0].numpy(), [1, 1, 1], atol=0.3)
def test_find_local_peaks_local():
xv, yv = make_grid_vectors(image_height=32,
image_width=32,
output_stride=1)
instances = (tf.cast(
[
[[1, 2], [3, 4]],
[[5, 6], [7, 8]],
[[np.nan, np.nan], [11, 12]],
],
tf.float32,
) * 2 + 0.25)
cms = make_multi_confmaps(instances, xv=xv, yv=yv, sigma=1.0)
instances2 = tf.cast([[[2, 3], [4, 5]], [[6, 7], [8, 9]]],
tf.float32) * 2 + 0.25
cms = tf.stack(
[cms, make_multi_confmaps(instances2, xv=xv, yv=yv, sigma=1.0)],
axis=0)
peak_points, peak_vals, peak_sample_inds, peak_channel_inds = find_local_peaks(
cms, threshold=0.1, refinement="local")
assert peak_points.shape == (9, 2)
assert peak_vals.shape == (9, )
assert peak_sample_inds.shape == (9, )
assert peak_channel_inds.shape == (9, )
assert_allclose(
peak_points.numpy(),
np.array([
[1, 2],
[3, 4],
[5, 6],
[7, 8],
[11, 12],
[2, 3],
[4, 5],
[6, 7],
[8, 9],
]) * 2 + 0.25,
)
assert_allclose(peak_vals, [1, 1, 1, 1, 1, 1, 1, 1, 1], atol=0.1)
assert_array_equal(peak_sample_inds, [0, 0, 0, 0, 0, 1, 1, 1, 1])
assert_array_equal(peak_channel_inds, [0, 1, 0, 1, 1, 0, 1, 0, 1])
def test_find_global_peaks_rough():
xv, yv = make_grid_vectors(image_height=8, image_width=8, output_stride=1)
points = tf.cast([[1, 2], [3, 4], [5, 6]], tf.float32)
cm = make_confmaps(points, xv, yv, sigma=1.0)
points2 = points + 1
cms = tf.stack([cm, make_confmaps(points2, xv, yv, sigma=1.0)])
peaks, peak_vals = find_global_peaks(cms, threshold=0.1, refinement=None)
assert peaks.shape == (2, 3, 2)
assert peak_vals.shape == (2, 3)
assert_array_equal(peaks[0], points)
assert_array_equal(peak_vals[0], [1, 1, 1])
assert_array_equal(peaks[1], points2)
assert_array_equal(peak_vals[1], [1, 1, 1])
peaks, peak_vals = find_global_peaks_rough(tf.zeros((1, 8, 8, 3),
dtype=tf.float32),
threshold=0.1)
assert peaks.shape == (1, 3, 2)
assert peak_vals.shape == (1, 3)
assert tf.reduce_all(tf.math.is_nan(peaks))
assert_array_equal(peak_vals, [[0, 0, 0]])
def test_instance_peaks_layer():
xv, yv = make_grid_vectors(image_height=12, image_width=12, output_stride=1)
points = tf.cast([[1.5, 2.5], [3.5, 4.5], [5.5, 6.5]], tf.float32)
cms = tf.stack(
[
make_confmaps(points, xv, yv, sigma=1.0),
make_confmaps(points + 1, xv, yv, sigma=1.0),
],
axis=0,
)
x_in = tf.keras.layers.Input([12, 12, 3])
x_out = tf.keras.layers.Lambda(lambda x: x, name="CenteredInstanceConfmapsHead")(
x_in
)
model = tf.keras.Model(inputs=x_in, outputs=x_out)
instance_peaks_layer = FindInstancePeaks(
keras_model=model,
input_scale=1.0,
peak_threshold=0.2,
return_confmaps=False,
refinement="integral",
)
# Raw tensor
out = instance_peaks_layer(cms)
assert tuple(out["instance_peaks"].shape) == (2, None, 3, 2)
assert tuple(out["instance_peak_vals"].shape) == (2, None, 3)
assert tuple(out["instance_peaks"].bounding_shape()) == (2, 1, 3, 2)
assert tuple(out["instance_peak_vals"].bounding_shape()) == (2, 1, 3)
assert_allclose(out["instance_peaks"][0][0].numpy(), points.numpy(), atol=0.1)
assert_allclose(out["instance_peak_vals"][0][0].numpy(), [1, 1, 1], atol=0.3)
assert_allclose(out["instance_peaks"][1][0].numpy(), points.numpy() + 1, atol=0.1)
assert_allclose(out["instance_peak_vals"][1][0].numpy(), [1, 1, 1], atol=0.3)
# Batched example
crops = tf.RaggedTensor.from_tensor(tf.expand_dims(cms, axis=1), lengths=[1, 1])
out = instance_peaks_layer({"crops": crops})
assert tuple(out["instance_peaks"].shape) == (2, None, 3, 2)
assert tuple(out["instance_peak_vals"].shape) == (2, None, 3)
assert tuple(out["instance_peaks"].bounding_shape()) == (2, 1, 3, 2)
assert tuple(out["instance_peak_vals"].bounding_shape()) == (2, 1, 3)
assert_allclose(out["instance_peaks"][0][0].numpy(), points.numpy(), atol=0.1)
assert_allclose(out["instance_peak_vals"][0][0].numpy(), [1, 1, 1], atol=0.3)
assert_allclose(out["instance_peaks"][1][0].numpy(), points.numpy() + 1, atol=0.1)
assert_allclose(out["instance_peak_vals"][1][0].numpy(), [1, 1, 1], atol=0.3)
# Batch size = 1, multi-instance example
crops = tf.RaggedTensor.from_tensor(tf.expand_dims(cms, axis=0), lengths=[2])
out = instance_peaks_layer({"crops": crops})
assert tuple(out["instance_peaks"].shape) == (1, None, 3, 2)
assert tuple(out["instance_peak_vals"].shape) == (1, None, 3)
assert tuple(out["instance_peaks"].bounding_shape()) == (1, 2, 3, 2)
assert tuple(out["instance_peak_vals"].bounding_shape()) == (1, 2, 3)
assert_allclose(out["instance_peaks"][0][0].numpy(), points.numpy(), atol=0.1)
assert_allclose(out["instance_peak_vals"][0][0].numpy(), [1, 1, 1], atol=0.3)
assert_allclose(out["instance_peaks"][0][1].numpy(), points.numpy() + 1, atol=0.1)
assert_allclose(out["instance_peak_vals"][0][1].numpy(), [1, 1, 1], atol=0.3)
# Offset adjustment and pass through centroids
instance_peaks_layer = FindInstancePeaks(
keras_model=model,
input_scale=1.0,
peak_threshold=0.2,
return_confmaps=True,
refinement="integral",
)
# (samples, h, w, c) -> (samples, ?, h, w, c)
crops = tf.RaggedTensor.from_tensor(tf.expand_dims(cms, axis=1), lengths=[1, 1])
# (samples, centroids, 2) -> (samples, ?, 2)
crop_offsets = tf.RaggedTensor.from_tensor(
tf.reshape(tf.cast([1, 2, 3, 4], tf.float32), [2, 1, 2]), lengths=[1, 1]
)
out = instance_peaks_layer(
{
"crops": crops,
"centroids": tf.zeros([]),
"centroid_vals": tf.zeros([]),
"crop_offsets": crop_offsets,
}
)
assert "centroids" in out
assert "centroid_vals" in out
assert_allclose(
out["instance_peaks"][0][0].numpy(),
points.numpy() + np.array([[1, 2]]),
atol=0.1,
)
assert_allclose(
out["instance_peaks"][1][0].numpy(),
points.numpy() + 1 + np.array([[3, 4]]),
atol=0.1,
)
# Input scaling
scale = 0.5
instance_peaks_layer = FindInstancePeaks(
keras_model=model,
input_scale=scale,
peak_threshold=0.2,
return_confmaps=False,
refinement="integral",
)
xv, yv = make_grid_vectors(
image_height=12 / scale, image_width=12 / scale, output_stride=1
)
points = tf.cast([[1.5, 2.5], [3.5, 4.5], [5.5, 6.5]], tf.float32)
cms = tf.stack(
[
make_confmaps(points / scale, xv, yv, sigma=1.0 / scale),
make_confmaps((points + 1) / scale, xv, yv, sigma=1.0 / scale),
],
axis=0,
)
out = instance_peaks_layer(cms)
assert_allclose(
out["instance_peaks"][0][0].numpy(), points.numpy() / scale, atol=0.15
)
assert_allclose(
out["instance_peaks"][1][0].numpy(), (points.numpy() + 1) / scale, atol=0.15
)
