def test_augment_keypoints__kernel_size_is_two__no_keep_size(self):
from imgaug.augmentables.kps import Keypoint, KeypointsOnImage
kps = [Keypoint(x=1.5, y=5.5), Keypoint(x=5.5, y=1.5)]
kpsoi = KeypointsOnImage(kps, shape=(6, 6, 3))
expected = KeypointsOnImage.from_xy_array(np.float32(
[[1.5 / 2, 5.5 / 2], [5.5 / 2, 1.5 / 2]]),
shape=(3, 3, 3))
self._test_augment_cbaoi__kernel_size_is_two__no_keep_size(
kpsoi, expected, "augment_keypoints")
def _test_augment_keypoints__kernel_size_is_noop(self, kernel_size):
from imgaug.augmentables.kps import Keypoint, KeypointsOnImage
kps = [Keypoint(x=1.5, y=5.5), Keypoint(x=5.5, y=1.5)]
kpsoi = KeypointsOnImage(kps, shape=(6, 6, 3))
aug = self.augmenter(kernel_size)
kpsoi_aug = aug.augment_keypoints(kpsoi)
assert_cbaois_equal(kpsoi_aug, kpsoi)
def test_keypoint_alignment(self):
from imgaug.augmentables.kps import Keypoint, KeypointsOnImage
kps = [Keypoint(x=10, y=10), Keypoint(x=30, y=30)]
kpsoi = KeypointsOnImage(kps, shape=(40, 40, 1))
kpsoi_empty = KeypointsOnImage([], shape=(40, 40, 1))
self._test_cbaoi_alignment(kpsoi, kpsoi_empty, [[(5, 5)], [(15, 15)]],
[[(10, 10)], [(30, 30)]],
"augment_keypoints")
def show_pts_on_img(image, pts,
img_pth = '/home/workspace2/dataset/3dhand/dataset/pts_on_img.png'):
if pts.shape[1] == 3:
kps = [Keypoint(x, y) for x,y,_ in pts]
else :
kps = [Keypoint(x, y) for x,y in pts]
kpsoi = KeypointsOnImage(kps, shape=image.shape)
misc.imsave('/home/workspace2/dataset/3dhand/dataset/img.png', image)
misc.imsave(img_pth, kpsoi.draw_on_image(image, size = 7))
def _test_augment_keypoints__kernel_size_is_noop(self, kernel_size):
from imgaug.augmentables.kps import Keypoint, KeypointsOnImage
kps = [Keypoint(x=1.5, y=5.5), Keypoint(x=5.5, y=1.5)]
kpsoi = KeypointsOnImage(kps, shape=(6, 6, 3))
aug = self.augmenter(kernel_size)
kpsoi_aug = aug.augment_keypoints(kpsoi)
assert kpsoi_aug.shape == (6, 6, 3)
assert np.allclose(kpsoi_aug.to_xy_array(),
[[1.5, 5.5],
[5.5, 1.5]])
def test_augment_keypoints__kernel_size_is_two__no_keep_size(self):
from imgaug.augmentables.kps import Keypoint, KeypointsOnImage
kps = [Keypoint(x=1.5, y=5.5), Keypoint(x=5.5, y=1.5)]
kpsoi = KeypointsOnImage(kps, shape=(6, 6, 3))
aug = self.augmenter(2, keep_size=False)
kpsoi_aug = aug.augment_keypoints(kpsoi)
expected = KeypointsOnImage.from_xy_array(np.float32(
[[1.5 / 2, 5.5 / 2], [5.5 / 2, 1.5 / 2]]),
shape=(3, 3, 3))
assert_cbaois_equal(kpsoi_aug, expected)
def test_augment_keypoints__kernel_size_is_two__no_keep_size(self):
from imgaug.augmentables.kps import Keypoint, KeypointsOnImage
kps = [Keypoint(x=1.5, y=5.5), Keypoint(x=5.5, y=1.5)]
kpsoi = KeypointsOnImage(kps, shape=(6, 6, 3))
aug = self.augmenter(2, keep_size=False)
kpsoi_aug = aug.augment_keypoints(kpsoi)
assert kpsoi_aug.shape == (3, 3, 3)
assert np.allclose(kpsoi_aug.to_xy_array(),
[[1.5/2, 5.5/2],
[5.5/2, 1.5/2]])
def img2xy(self, img_path, gal, gpr, augment=0):
'''
Single img file to xys for all blocks and channels
returns:
xs:
cropped window, grayscale[0~255]=>[0~1]
shape ( #blocks, 1, win_shape[0], win_shape[1] )
ys:
gaussian heatmaps of same w, h as xs, one channel for one kpt
idx:
(img_path, block) for one x sample
'''
idxs, imgs, df = super().img2x(img_path, gal,
gpr) # imgs: (N, 1, h, w)
h, w = imgs.shape[2], imgs.shape[3]
gpr = Gpr(gpr) if type(gpr) == str else gpr
xs, ys, coords = [], [], []
for (img_path, b), img, block_df in zip(idxs, imgs, df):
nrows = gal.header[f'Block{b}'][Gal.N_ROWS]
ncols = gal.header[f'Block{b}'][Gal.N_COLS]
kpts = KeypointsOnImage([
Keypoint(x=block_df.loc[1, 1]['X'], y=block_df.loc[1, 1]['Y']),
Keypoint(x=block_df.loc[1, ncols]['X'],
y=block_df.loc[1, ncols]['Y']),
Keypoint(x=block_df.loc[nrows, ncols]['X'],
y=block_df.loc[nrows, ncols]['Y']),
Keypoint(x=block_df.loc[nrows, 1]['X'],
y=block_df.loc[nrows, 1]['Y'])
],
shape=(h, w))
if augment <= 0:
xs.append(img)
coord = self.to_Lcoord(kpts.to_xy_array())
ys.append(
self.coord2heatmap(coord, (img.shape[-2], img.shape[-1])))
coords.append(coord.flatten())
else:
for i in range(augment):
img_aug, kpts_aug = self.aug_seq(
image=(img[0] * 255).astype('uint8'),
keypoints=kpts) # img: (1, w, h) -> (w, h)
coord = self.to_Lcoord(kpts_aug.to_xy_array()) # (3, 2)
if self.check_out_of_bounds(img_aug.shape, coord):
continue # skip if coord out of bounds
xs.append(np.array([img_aug / 255]))
ys.append(
self.coord2heatmap(
coord, (img_aug.shape[-2], img_aug.shape[-1])))
coords.append(coord.flatten())
return np.stack(xs), np.stack(ys), np.stack(coords)
def test_augment_keypoints__kernel_size_differs__requires_padding(self):
from imgaug.augmentables.kps import Keypoint, KeypointsOnImage
kps = [Keypoint(x=1.5, y=5.5), Keypoint(x=5.5, y=1.5)]
kpsoi = KeypointsOnImage(kps, shape=(5, 6, 3))
aug = self.augmenter(
(iap.Deterministic(3), iap.Deterministic(2)),
keep_size=False)
kpsoi_aug = aug.augment_keypoints(kpsoi)
assert kpsoi_aug.shape == (2, 3, 3)
assert np.allclose(kpsoi_aug.to_xy_array(),
[[(1.5/6)*3, (5.5/5)*2],
[(5.5/6)*3, (1.5/5)*2]])
def _test_augment_keypoints__kernel_size_differs(self, shape, shape_exp):
from imgaug.augmentables.kps import Keypoint, KeypointsOnImage
kps = [Keypoint(x=1.5, y=5.5), Keypoint(x=5.5, y=1.5)]
kpsoi = KeypointsOnImage(kps, shape=shape)
aug = self.augmenter((iap.Deterministic(3), iap.Deterministic(2)),
keep_size=False)
kpsoi_aug = aug.augment_keypoints(kpsoi)
expected = KeypointsOnImage.from_xy_array(np.float32([[
(1.5 / shape[1]) * shape_exp[1], (5.5 / shape[0]) * shape_exp[0]
], [(5.5 / shape[1]) * shape_exp[1],
(1.5 / shape[0]) * shape_exp[0]]]),
shape=shape_exp)
assert_cbaois_equal(kpsoi_aug, expected)
def pts_2_imgaug_keypts(points, img):
kps = []
for point in points:
keypoint = Keypoint(x=point[0], y=point[1])
kps.append(keypoint)
kpsoi = KeypointsOnImage(kps, shape=img.shape)
return kpsoi
def _augment_batch(self, images, corners):
aug = iaa.SomeOf(
(0, None),
[
iaa.KeepSizeByResize(
iaa.Affine(
translate_px={"x": (10, 30)},
rotate=(-5, 5),
mode="edge",
fit_output=True,
)),
iaa.KeepSizeByResize(
iaa.Affine(shear=(-10, 10), mode="edge", fit_output=True)),
iaa.AddToHueAndSaturation((-50, 50)),
iaa.AverageBlur(k=(2, 5)),
],
random_order=True,
)
# Convert array of corners to list of KeypointsOnImage instances for use with the augmenter.
keypoints_from_corners = [
KeypointsOnImage(
[Keypoint(x=point[0], y=point[1]) for point in img_corners],
shape=self.dim,
) for img_corners in corners
]
images_augm, keypoints_augm = aug.augment(
images=images, keypoints=keypoints_from_corners)
# Convert augmented keypoints back to array of size (batch_size, 4, 2).
corners_augm = np.array(
[keypoints.to_xy_array() for keypoints in keypoints_augm])
return images_augm, corners_augm
def code_anno(self, anno):
coded_anno = np.zeros((6, 6, 5), dtype='float32')
origin_w = int(anno['size']['width'])
origin_h = int(anno['size']['height'])
xmin = int(anno['object'][0]['bndbox']['xmin']) - 1
ymin = int(anno['object'][0]['bndbox']['ymin']) - 1
xmax = int(anno['object'][0]['bndbox']['xmax']) - 1
ymax = int(anno['object'][0]['bndbox']['ymax']) - 1
c_x = ((xmax + xmin) / 2) / origin_w
c_y = ((ymax + ymin) / 2) / origin_h
w = (xmax - xmin) / origin_w
h = (ymax - ymin) / origin_h
if self.aug:
d = KeypointsOnImage([Keypoint(c_x * 400, c_y * 400)], (400, 400))
f**k = self.seq.augment_keypoints([d])[0]
c_x = f**k.keypoints[0].x / 400
c_y = f**k.keypoints[0].y / 400
grid_x = int(np.floor(c_x * 6))
grid_y = int(np.floor(c_y * 6))
relative_x = c_x * 6 - grid_x
relative_y = c_y * 6 - grid_y
coded_anno[grid_y,
grid_x, :] = np.array([1, relative_x, relative_y, w, h])
return coded_anno
def convert_coco_kp_to_imgaug_kp(self, label):
kps = []
valid = np.ones(NUM_COCO_KEYPOINTS)
invalid_xy = -1
for i in range(NUM_COCO_KEYPOINTS):
label_idx = i * NUM_COCO_KP_ATTRBS # index for label
# generate empty heatmap for unlabelled kp
if label[label_idx + (NUM_COCO_KP_ATTRBS - 1)] == 0:
# invalid keypoint
valid[i] = 0
kps.append(Keypoint(x=invalid_xy, y=invalid_xy))
continue
kpx = int(label[label_idx])
kpy = int(label[label_idx + 1])
kps.append(Keypoint(x=kpx, y=kpy))
return kps, valid
def to_keypoints(self):
"""Convert the BB's corners to keypoints (clockwise, from top left).
Returns
-------
list of imgaug.augmentables.kps.Keypoint
Corners of the bounding box as keypoints.
"""
# TODO get rid of this deferred import
from imgaug.augmentables.kps import Keypoint
return [
Keypoint(x=self.x1, y=self.y1),
Keypoint(x=self.x2, y=self.y1),
Keypoint(x=self.x2, y=self.y2),
Keypoint(x=self.x1, y=self.y2)
]
def aug_data(seq, img, pts=None):
if pts is None:
return seq(image=img)
pts_shape = pts.shape
kps = [Keypoint(x=x[0], y=x[1]) for x in pts.reshape(-1, 2)]
kpsoi = KeypointsOnImage(kps, shape=img.shape)
img_auged, kpsoi_auged = seq(image=img, keypoints=kpsoi)
res_pts = kpsoi_auged.to_xy_array().reshape(pts_shape)
return img_auged, res_pts
def np_to_keypoints(np_kpoints, image_size):
np_kpoints = np_kpoints
kps = [
Keypoint(x=np_kpoints[0], y=image_size[0] - np_kpoints[1]),
Keypoint(x=np_kpoints[2], y=image_size[0] - np_kpoints[3]),
Keypoint(x=np_kpoints[4], y=image_size[0] - np_kpoints[5]),
Keypoint(x=np_kpoints[6], y=image_size[0] - np_kpoints[7]),
Keypoint(x=np_kpoints[8], y=image_size[0] - np_kpoints[9]),
Keypoint(x=np_kpoints[10], y=image_size[0] - np_kpoints[11]),
Keypoint(x=np_kpoints[12], y=image_size[0] - np_kpoints[13]),
Keypoint(x=np_kpoints[14], y=image_size[0] - np_kpoints[15]),
]
return kps
def augment(self, img, corners, row_inc=0, col_inc=0):
'''
args:
corners: [top left, bottom left, bottom right, top right] (4, 2)
row_inc (int): changes n_row
returns:
aug_img
coords: corner coords (4, 2)
'''
img_aug, corners_aug, row_cut_coords = self.augment_row(
img, corners, row_inc)
img_aug, corners_aug, col_cut_coords = self.augment_col(
img_aug, corners_aug, col_inc)
kpts = KeypointsOnImage([
Keypoint(x=corners_aug[0, 0], y=corners_aug[0, 1]),
Keypoint(x=corners_aug[1, 0], y=corners_aug[1, 1]),
Keypoint(x=corners_aug[2, 0], y=corners_aug[2, 1]),
Keypoint(x=corners_aug[3, 0], y=corners_aug[3, 1]),
],
shape=img_aug.shape)
return img_aug, kpts, (row_cut_coords, col_cut_coords)
def test_keypoint_alignment(self):
from imgaug.augmentables.kps import Keypoint, KeypointsOnImage
aug = self.augmenter((1, 2), keep_size=False)
image = np.zeros((40, 40, 1), dtype=np.uint8)
kps = [Keypoint(x=10, y=10), Keypoint(x=30, y=30)]
kpsoi = KeypointsOnImage(kps, shape=image.shape)
kpsoi_empty = KeypointsOnImage([], shape=image.shape)
images_batch = [image, image, image, image]
kpsoi_batch = [kpsoi, kpsoi, kpsoi_empty, kpsoi]
nb_iterations = 10
for _ in sm.xrange(nb_iterations):
images_aug, kpsois_aug = aug(images=images_batch,
keypoints=kpsoi_batch)
for index in [0, 1, 3]:
image_aug = images_aug[index]
kpsoi_aug = kpsois_aug[index]
assert image_aug.shape == kpsoi_aug.shape
if image_aug.shape == (20, 20, 1):
assert np.allclose(
kpsoi_aug.to_xy_array(),
[[5, 5], [15, 15]]
)
else:
assert np.allclose(
kpsoi_aug.to_xy_array(),
[[10, 10], [30, 30]]
)
for index in [2]:
image_aug = images_aug[index]
kpsoi_aug = kpsois_aug[index]
assert kpsoi_aug.shape == image_aug.shape
assert len(kpsoi_aug.keypoints) == 0
def tranformKeypoints(self, keypoints, augmentation, imageshape):
imgaug_keypoints = []
for i in range(len(keypoints)):
imgaug_keypoints.append(
Keypoint(x=keypoints[i, 0], y=keypoints[i, 1]))
kpsoi = KeypointsOnImage(imgaug_keypoints, shape=imageshape)
keypoitns_aug = augmentation(keypoints=kpsoi)
if (isinstance(keypoints, np.ndarray)):
keypoints[:, :2] = keypoitns_aug.to_xy_array()
else:
keypoints[:, :2] = torch.from_numpy(
keypoitns_aug.to_xy_array())
return keypoints
def generate_augment_dataset(dataset_dir, name_list, num_aug=500):
for name in name_list:
image_name = os.path.join(dataset_dir, name + '.jpg')
json_name = os.path.join(dataset_dir, name + '.json')
image = Image.open(image_name).convert('RGB')
image = np.asarray(image)
points, labels = parse_annotations(json_name)
kps = [
Keypoint(x=int(points[i][0]), y=int(points[i][1]))
for i in range(len(points))
]
kpsoi = KeypointsOnImage(kps, shape=image.shape)
for j in range(num_aug):
image_aug, kpsoi_aug = seq(image=image, keypoints=kpsoi)
points_aug = kpsoi_aug.keypoints
height, width, _ = image_aug.shape
image_aug_name = os.path.join(dataset_dir,
name + '_' + str(j) + '.jpg')
json_aug_name = os.path.join(dataset_dir,
name + '_' + str(j) + '.json')
data = {}
data['version'] = '4.2.10'
data['flags'] = {}
data['shapes'] = []
for k in range(len(points_aug)):
point_name = labels[k]
p_x = int(points_aug[k].x)
p_y = int(points_aug[k].y)
data['shapes'].append({
'label': point_name,
'points': [[p_x, p_y]],
'group_id': None,
'shape_type': 'point',
'flags': {}
})
data['imagePath'] = name + str(j) + '.jpg'
data['imageData'] = image_to_base64(image_aug)
data['imageHeight'] = height
data['imageWidth'] = width
with open(json_aug_name, 'w') as outfile:
json.dump(data, outfile, indent=4)
image_aug = Image.fromarray(image_aug)
image_aug.save(image_aug_name)
print('success')
def quokka_keypoints(size=None, extract=None):
"""Return example keypoints on the standard example quokke image.
The keypoints cover the eyes, ears, nose and paws.
Added in 0.5.0. (Moved from ``imgaug.imgaug``.)
Parameters
----------
size : None or float or tuple of int or tuple of float, optional
Size of the output image on which the keypoints are placed. If
``None``, then the keypoints are not projected to any new size
(positions on the original image are used). ``float`` s lead to
relative size changes, ``int`` s to absolute sizes in pixels.
extract : None or 'square' or tuple of number or imgaug.augmentables.bbs.BoundingBox or imgaug.augmentables.bbs.BoundingBoxesOnImage
Subarea to extract from the image. See :func:`~imgaug.imgaug.quokka`.
Returns
-------
imgaug.augmentables.kps.KeypointsOnImage
Example keypoints on the quokka image.
"""
# TODO get rid of this deferred import
from imgaug.augmentables.kps import Keypoint, KeypointsOnImage
left, top = 0, 0
if extract is not None:
bb_extract = _quokka_normalize_extract(extract)
left = bb_extract.x1
top = bb_extract.y1
with open(_QUOKKA_ANNOTATIONS_FP, "r") as f:
json_dict = json.load(f)
keypoints = []
for kp_dict in json_dict["keypoints"]:
keypoints.append(Keypoint(x=kp_dict["x"] - left, y=kp_dict["y"] - top))
if extract is not None:
shape = (bb_extract.height, bb_extract.width, 3)
else:
shape = (643, 960, 3)
kpsoi = KeypointsOnImage(keypoints, shape=shape)
if size is not None:
shape_resized = _compute_resized_shape(shape, size)
kpsoi = kpsoi.on(shape_resized)
return kpsoi
def getimage_FAN(self,
imagefile,
augmentations=None,
keypoints=None,
is_it_test_sample=False):
image = cv2.cvtColor(
cv2.imread(
self.GetFullImagePath(self, imagefile,
is_it_test_sample)),
cv2.COLOR_BGR2RGB)
if (augmentations is not None):
keypoints_originalres = self.keypointsToOriginalResolution(
self, imagefile, keypoints)
imgaug_keypoints = []
for i in range(len(keypoints)):
imgaug_keypoints.append(
Keypoint(x=keypoints_originalres[i, 0],
y=keypoints_originalres[i, 1]))
kpsoi = KeypointsOnImage(imgaug_keypoints,
shape=image.shape)
image, keypoitns_aug = self.augmentations(image=image,
keypoints=kpsoi)
keypoints_originalres = np.column_stack(
(keypoitns_aug.to_xy_array(),
keypoints_originalres[:, 2:]))
minx, miny, maxx, maxy = self.getFANBox(
self, imagefile, image.shape[1], image.shape[0],
is_it_test_sample)
image = image[miny:maxy, minx:maxx, :]
image = cv2.resize(image, dsize=(256, 256))
if (keypoints is not None):
augmentedkeypoints = self.keypointsToFANResolution(
self, imagefile, keypoints_originalres, self.W, self.H)
return image, augmentedkeypoints
return image
def __data_generation(self, image_keys_temp):
batch_images = np.empty((self.batch_size, IMG_SIZE, IMG_SIZE, 3),
dtype="int")
batch_keypoints = np.empty((self.batch_size, 1, 1, NUM_KEYPOINTS),
dtype="float32")
for i, key in enumerate(image_keys_temp):
data = get_dog(key)
current_keypoint = np.array(data["joints"])[:, :2]
kps = []
# To apply our data augmentation pipeline, we first need to
# form Keypoint objects with the original coordinates.
for j in range(0, len(current_keypoint)):
kps.append(
Keypoint(x=current_keypoint[j][0],
y=current_keypoint[j][1]))
# We then project the original image and its keypoint coordinates.
current_image = data["img_data"]
kps_obj = KeypointsOnImage(kps, shape=current_image.shape)
# Apply the augmentation pipeline.
(new_image, new_kps_obj) = self.aug(image=current_image,
keypoints=kps_obj)
batch_images[i, ] = new_image
# Parse the coordinates from the new keypoint object.
kp_temp = []
for keypoint in new_kps_obj:
kp_temp.append(np.nan_to_num(keypoint.x))
kp_temp.append(np.nan_to_num(keypoint.y))
# More on why this reshaping later.
batch_keypoints[i, ] = np.array(kp_temp).reshape(1, 1, 24 * 2)
# Scale the coordinates to [0, 1] range.
batch_keypoints = batch_keypoints / IMG_SIZE
return (batch_images, batch_keypoints)
def getimage_FAN(self,
imagefile,
augmentations=None,
keypoints=None,
is_it_test_sample=False):
image = cv2.cvtColor(
cv2.imread(
self.GetFullImagePath(self, imagefile,
is_it_test_sample)),
cv2.COLOR_BGR2RGB)
if (augmentations is not None):
keypoints_originalres = self.keypointsToOriginalResolution(
self, imagefile, keypoints)
imgaug_keypoints = []
for i in range(len(keypoints)):
imgaug_keypoints.append(
Keypoint(x=keypoints_originalres[i, 0],
y=keypoints_originalres[i, 1]))
kpsoi = KeypointsOnImage(imgaug_keypoints,
shape=image.shape)
image, keypoitns_aug = self.augmentations(image=image,
keypoints=kpsoi)
keypoints_originalres = np.column_stack(
(keypoitns_aug.to_xy_array(),
keypoints_originalres[:, 2:]))
scaledImage = self.scaleToFANRes(image=image)
if (keypoints is not None):
augmentedkeypoints = self.keypointsToFANResolution(
self, imagefile, keypoints_originalres)
return scaledImage, augmentedkeypoints
return scaledImage
def augment_image(self, image, landmarks):
"""
Generates a batch of augmented images with landmarks from image and its landmarks
:param image: image which will be augmented
:param landmarks: numpy coordinates ([x], [x, y] or [x, y, z]) of the landmark point of the image.
:return: numpy array of the landmark image.
"""
keypoints = []
for k in list(range(0, 2 * self.landmarks_num - 1, 2)):
keypoint = Keypoint(x=(float(landmarks[k])),
y=float(landmarks[k + 1]))
keypoints.append(keypoint)
keypoints_on_image = KeypointsOnImage(keypoints, shape=image.shape)
images = [image for _ in range(self.batch_size)]
keypoints_on_images = [
keypoints_on_image for _ in range(self.batch_size)
]
images_aug, kpsois_aug = self.seq(images=images,
keypoints=keypoints_on_images)
return images_aug, kpsois_aug
def _invert_normalize_polygons_and_line_strings(inputs, inputs_old,
estimate_ntype_func,
cls_single_name, cls_oi_name,
get_entities_func,
get_points_func):
# TODO get rid of this deferred import
from imgaug.augmentables.kps import Keypoint
ntype = estimate_ntype_func(inputs_old)
if ntype == "None":
assert inputs is None
return inputs
elif ntype in ["array[float]", "array[int]", "array[uint]"]:
input_dtype = inputs_old.dtype
return restore_dtype_and_merge(
[[get_points_func(entity) for entity in get_entities_func(oi)]
for oi in inputs], input_dtype)
elif ntype == cls_single_name:
assert len(inputs) == 1
assert len(get_entities_func(inputs[0])) == 1
return get_entities_func(inputs[0])[0]
elif ntype == cls_oi_name:
assert len(inputs) == 1
return inputs[0]
elif ntype == "iterable[empty]":
assert inputs is None
return []
elif ntype in [
"iterable-array[float]", "iterable-array[int]",
"iterable-array[uint]"
]:
nonempty, _, _ = find_first_nonempty(inputs_old)
input_dtype = nonempty.dtype
return [
restore_dtype_and_merge(
[get_points_func(entity)
for entity in get_entities_func(oi)], input_dtype)
for oi in inputs
]
elif ntype == "iterable-tuple[number,size=2]":
assert len(inputs) == 1
assert len(get_entities_func(inputs[0])) == 1
return [(point[0], point[1])
for point in get_points_func(get_entities_func(inputs[0])[0])]
elif ntype == "iterable-Keypoint":
assert len(inputs) == 1
assert len(get_entities_func(inputs[0])) == 1
return [
Keypoint(x=point[0], y=point[1])
for point in get_points_func(get_entities_func(inputs[0])[0])
]
elif ntype == ("iterable-%s" % (cls_single_name, )):
assert len(inputs) == 1
assert len(get_entities_func(inputs[0])) == len(inputs_old)
return get_entities_func(inputs[0])
elif ntype == ("iterable-%s" % (cls_oi_name, )):
return inputs
elif ntype == "iterable-iterable[empty]":
assert inputs is None
return inputs_old[:]
elif ntype in [
"iterable-iterable-array[float]", "iterable-iterable-array[int]",
"iterable-iterable-array[uint]"
]:
nonempty, _, _ = find_first_nonempty(inputs_old)
input_dtype = nonempty.dtype
return [[
restore_dtype_and_merge(get_points_func(entity), input_dtype)
for entity in get_entities_func(oi)
] for oi in inputs]
elif ntype == "iterable-iterable-tuple[number,size=2]":
assert len(inputs) == 1
return [[(point[0], point[1]) for point in get_points_func(entity)]
for entity in get_entities_func(inputs[0])]
elif ntype == "iterable-iterable-Keypoint":
assert len(inputs) == 1
return [[
Keypoint(x=point[0], y=point[1])
for point in get_points_func(entity)
] for entity in get_entities_func(inputs[0])]
elif ntype == ("iterable-iterable-%s" % (cls_single_name, )):
return [get_entities_func(oi) for oi in inputs]
elif ntype == "iterable-iterable-iterable[empty]":
return inputs_old[:]
elif ntype == "iterable-iterable-iterable-tuple[number,size=2]":
return [[[(point[0], point[1]) for point in get_points_func(entity)]
for entity in get_entities_func(oi)] for oi in inputs]
else:
assert ntype == "iterable-iterable-iterable-Keypoint", (
"Got unknown normalization type '%s'." % (ntype, ))
return [[[
Keypoint(x=point[0], y=point[1])
for point in get_points_func(entity)
] for entity in get_entities_func(oi)] for oi in inputs]
def normalize_keypoints(inputs, shapes=None):
# TODO get rid of this deferred import
from imgaug.augmentables.kps import Keypoint, KeypointsOnImage
shapes = _preprocess_shapes(shapes)
ntype = estimate_keypoints_norm_type(inputs)
_assert_exactly_n_shapes_partial = functools.partial(
_assert_exactly_n_shapes,
from_ntype=ntype,
to_ntype="List[KeypointsOnImage]",
shapes=shapes)
if ntype == "None":
return inputs
elif ntype in ["array[float]", "array[int]", "array[uint]"]:
_assert_single_array_ndim(inputs, 3, "(N,K,2)", "KeypointsOnImage")
_assert_single_array_last_dim_exactly(inputs, 2, "KeypointsOnImage")
_assert_exactly_n_shapes_partial(n=len(inputs))
return [
KeypointsOnImage.from_xy_array(attr_i, shape=shape)
for attr_i, shape in zip(inputs, shapes)
]
elif ntype == "tuple[number,size=2]":
_assert_exactly_n_shapes_partial(n=1)
return [
KeypointsOnImage([Keypoint(x=inputs[0], y=inputs[1])],
shape=shapes[0])
]
elif ntype == "Keypoint":
_assert_exactly_n_shapes_partial(n=1)
return [KeypointsOnImage([inputs], shape=shapes[0])]
elif ntype == "KeypointsOnImage":
return [inputs]
elif ntype == "iterable[empty]":
return None
elif ntype in [
"iterable-array[float]", "iterable-array[int]",
"iterable-array[uint]"
]:
_assert_many_arrays_ndim(inputs, 2, "(K,2)", "KeypointsOnImage")
_assert_many_arrays_last_dim_exactly(inputs, 2, "KeypointsOnImage")
_assert_exactly_n_shapes_partial(n=len(inputs))
return [
KeypointsOnImage.from_xy_array(attr_i, shape=shape)
for attr_i, shape in zip(inputs, shapes)
]
elif ntype == "iterable-tuple[number,size=2]":
_assert_exactly_n_shapes_partial(n=1)
return [
KeypointsOnImage([Keypoint(x=x, y=y) for x, y in inputs],
shape=shapes[0])
]
elif ntype == "iterable-Keypoint":
_assert_exactly_n_shapes_partial(n=1)
return [KeypointsOnImage(inputs, shape=shapes[0])]
elif ntype == "iterable-KeypointsOnImage":
return inputs
elif ntype == "iterable-iterable[empty]":
return None
elif ntype == "iterable-iterable-tuple[number,size=2]":
_assert_exactly_n_shapes_partial(n=len(inputs))
return [
KeypointsOnImage.from_xy_array(np.array(attr_i, dtype=np.float32),
shape=shape)
for attr_i, shape in zip(inputs, shapes)
]
else:
assert ntype == "iterable-iterable-Keypoint", (
"Got unknown normalization type '%s'." % (ntype, ))
_assert_exactly_n_shapes_partial(n=len(inputs))
return [
KeypointsOnImage(attr_i, shape=shape)
for attr_i, shape in zip(inputs, shapes)
]
def _test_augment_keypoints__kernel_size_is_noop(self, kernel_size):
from imgaug.augmentables.kps import Keypoint, KeypointsOnImage
kps = [Keypoint(x=1.5, y=5.5), Keypoint(x=5.5, y=1.5)]
kpsoi = KeypointsOnImage(kps, shape=(6, 6, 3))
self._test_augment_cbaoi__kernel_size_is_noop(kernel_size, kpsoi,
"augment_keypoints")
(pair[1], pair[0]), zip(flipped_kps[1::2], flipped_kps[2::2]))
# Leave nose unchanged, flip every pair of eye, ear, etc...
return flipped_img, flipped_fixed_kps
# %% Load sample image
if __name__ == '__main__':
image = imageio.imread('./data/Macropus_rufogriseus_rufogriseus_Bruny.jpg')
image = ia.imresize_single_image(image, (389, 259))
ymax = 389
xmax = 259
offset = 4
kps = [
Keypoint(x=99, y=81), # left eye (from camera perspective)
Keypoint(x=125, y=80), # right eye
Keypoint(x=112, y=102), # nose
Keypoint(x=102, y=210), # left paw
Keypoint(x=127, y=207), # right paw
# ***** Extrema to test out of bounds keypoints *****
Keypoint(x=offset, y=offset),
Keypoint(x=xmax - offset, y=offset),
Keypoint(x=offset, y=ymax - offset),
Keypoint(x=xmax - offset, y=ymax - offset),
Keypoint(x=xmax * 0.5, y=ymax - offset),
Keypoint(x=xmax * 0.9, y=ymax - offset),
Keypoint(x=-1, y=-1),
]
kpsoi = KeypointsOnImage(kps, shape=image.shape)