def test_transform(self):
with self.subTest("Shape & Homogeneous"): # Just test identity
# Single entry
self.assertEqual(cvext.AffineTransform().forward([1, 1]).ndim, 1)
self.assertEqual(len(cvext.AffineTransform().forward([1, 1])), 2)
self.assertTrue(
np.array_equal(cvext.AffineTransform().forward([1, 1]),
[1, 1]))
self.assertEqual(cvext.AffineTransform().forward([1, 1, 1]).ndim,
1)
self.assertEqual(len(cvext.AffineTransform().forward([1, 1, 1])),
2)
self.assertTrue(
np.array_equal(cvext.AffineTransform().forward([1, 1, 1]),
[1, 1]))
# Multi points
pts, pts_h = [[1, 1], [0, 5], [1, 3]], [[1, 1, 1], [0, 2.5, 0.5],
[2, 6, 2]]
self.assertEqual(cvext.AffineTransform().forward(pts).ndim, 2)
self.assertEqual(len(cvext.AffineTransform().forward(pts)), 3)
self.assertEqual(cvext.AffineTransform().forward(pts).shape[1], 2)
self.assertTrue(
np.array_equal(cvext.AffineTransform().forward(pts), pts))
self.assertEqual(cvext.AffineTransform().forward(pts_h).ndim, 2)
self.assertEqual(len(cvext.AffineTransform().forward(pts_h)), 3)
self.assertEqual(cvext.AffineTransform().forward(pts_h).shape[1],
2)
self.assertTrue(
np.array_equal(cvext.AffineTransform().forward(pts_h), pts))
with self.subTest("Transform"): # Compare with SKAffine
for i in range(10):
mdl = self.random_transform()
affine = cvext.AffineTransform(scale=mdl["S"],
rotation=mdl["R"],
shear=mdl["M"],
translation=mdl["T"])
skaffine = SKAffine(matrix=np.append(
affine.matrix_f, np.asarray([[0, 0, 1]]), axis=0))
pts = np.random.randint(-10, 20, size=[10, 2])
self.assertTrue(np.allclose(affine.forward(pts),
skaffine(pts)))
self.assertTrue(
np.allclose(affine.inverse(pts), skaffine.inverse(pts)))
def plot_annotations(imgid, clustered_annotations):
try:
img = mpimg.imread(IMAGE_DIR + imgid + ".JPG")
pass
except IOError:
print "WARNING: couldn't find image '%s'" % imgid
return False
# Plot all annotations
fig = plt.figure()
ax = plt.gca()
plt.imshow(img)
for annotation, cluster_id in clustered_annotations:
color = COLOR_MAP[cluster_id]
rect = Rectangle((annotation.left, annotation.top),
annotation.width, annotation.height,
fill=False, color=color)
ax.add_patch(rect)
plt.show()
# Plot median human and computer annotations
by_cluster = annotations_by_cluster(clustered_annotations)
all_medians = { clusterid :
(median_annotation(annotations),
median_annotation([annotation for annotation in annotations
if annotation[0].is_human]),
median_annotation([annotation for annotation in annotations
if not annotation[0].is_human]))
for clusterid, annotations in by_cluster.iteritems() }
plt.figure()
ax = plt.gca()
plt.imshow(img)
for clusterid, medians in all_medians.iteritems():
color = COLOR_MAP[clusterid]
for median in medians:
if median is None: continue
rect = Rectangle((median.left, median.top),
median.width, median.height, fill=False, color=color)
ax.add_patch(rect)
plt.show()
# Affine transform image to consistent shape and plot again.
from skimage.transform import AffineTransform, warp
# calculate scale and coreners
row_scale = float(img.shape[0]) / 400.0
col_scale = float(img.shape[1]) / 400.0
src_corners = np.array([[1, 1], [1, 400.0], [400.0, 400.0]]) - 1
dst_corners = np.zeros(src_corners.shape, dtype=np.double)
# take into account that 0th pixel is at position (0.5, 0.5)
dst_corners[:, 0] = col_scale * (src_corners[:, 0] + 0.5) - 0.5
dst_corners[:, 1] = row_scale * (src_corners[:, 1] + 0.5) - 0.5
# do the transformation
tform = AffineTransform()
tform.estimate(src_corners, dst_corners)
resized = warp(img, tform, output_shape=[400.0, 400.0], order=1,
mode='constant', cval=0)
# plot the transformed image
plt.figure()
ax = plt.gca()
plt.imshow(resized)
for clusterid, medians in all_medians.iteritems():
color = COLOR_MAP[clusterid]
for median in medians:
if median is None: continue
# apply the transformation to each rectangle
corners = np.array([[median.left, median.top],
[median.left + median.width,
median.top + median.height]])
new_corners = tform.inverse(corners)
rect = Rectangle(new_corners[0, :],
new_corners[1,0] - new_corners[0,0],
new_corners[1,1] - new_corners[0,1],
fill=False, color=color)
ax.add_patch(rect)
plt.show()
return True
class DefaultRS(ReferenceSpace):
"""Default reference space.
Attributes
----------
tform : skimage.transform.GeometricTransform
Affine transformation.
keypoints : dict
Defining landmarks used for estimating the parameters of the model.
"""
def __init__(self):
"""Construct."""
self.tform = AffineTransform()
self.keypoints = {
'CHIN': (0, 1),
'UPPER_TEMPLE_L': (-1, -1),
'UPPER_TEMPLE_R': (1, -1),
'UPPERMOST_NOSE': (0, -1),
'MIDDLE_NOSTRIL': (0, 0)
}
def estimate(self, lf):
"""Estimate parameters of the affine transformation.
Parameters
----------
lf : pychubby.detect.LandmarFace
Instance of the ``LandmarkFace``.
"""
src = []
dst = []
for name, ref_coordinate in self.keypoints.items():
dst.append(ref_coordinate)
src.append(lf[name])
src = np.array(src)
dst = np.array(dst)
self.tform.estimate(src, dst)
def ref2inp(self, coords):
"""Transform from reference to input space.
Parameters
----------
coords : np.ndarray
Array of shape `(N, 2)` where the columns represent x and y reference coordinates.
Returns
-------
tformed_coords : np.ndarray
Array of shape `(N, 2)` where the columns represent x and y coordinates in the input image
correspoding row-wise to `coords`.
"""
return self.tform.inverse(coords)
def inp2ref(self, coords):
"""Transform from input to reference space.
Parameters
----------
coords : np.ndarray
Array of shape `(N, 2)` where the columns represent x and y coordinates in the input space.
Returns
-------
tformed_coords : np.ndarray
Array of shape `(N, 2)` where the columns represent x and y coordinates in the reference space
correspoding row-wise to `coords`.
"""
return self.tform(coords)
