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MMathisLab merged 2 commits into from
Mar 7, 2023
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V2.3.1 #2168

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9faf21e
v2.3.1
AlexEMG Mar 7, 2023
731feae
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AlexEMG Mar 7, 2023
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2 changes: 1 addition & 1 deletion deeplabcut/__init__.py
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Expand Up @@ -107,7 +107,7 @@
visualize_paf,
extract_save_all_maps,
export_model,
video_inference_superanimal
video_inference_superanimal,
)


Expand Down
1 change: 0 additions & 1 deletion deeplabcut/benchmark/metrics.py
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Expand Up @@ -110,7 +110,6 @@ def calc_prediction_errors(preds, gt):
return errors



def _map(strings, substrings):
"""
Map image paths from predicted data to GT as the first are typically
Expand Down
2 changes: 1 addition & 1 deletion deeplabcut/create_project/add.py
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Expand Up @@ -25,7 +25,7 @@ def add_new_videos(
A list of strings containing the full paths of the videos to include in the project.

copy_videos : bool, optional
If this is set to True, the videos will be copied to your project/videos directory. If False, the symlink of the
If this is set to True, the videos will be copied to your project/videos directory. If False, the symlink of the
videos will be copied instead. The default is
``False``; if provided it must be either ``True`` or ``False``.

Expand Down
21 changes: 13 additions & 8 deletions deeplabcut/gui/tabs/modelzoo.py
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Expand Up @@ -46,9 +46,11 @@ def _set_page(self):
supermodels = parse_available_supermodels()
self.model_combo.addItems(supermodels.keys())

scales_label = QtWidgets.QLabel('Scale list')
scales_label = QtWidgets.QLabel("Scale list")
self.scales_line = QtWidgets.QLineEdit("", parent=self)
self.scales_line.setPlaceholderText("Optionally input a list of integer sizes separated by commas...")
self.scales_line.setPlaceholderText(
"Optionally input a list of integer sizes separated by commas..."
)
validator = RegExpValidator(self._val_pattern, self)
validator.validationChanged.connect(self._handle_validation_change)
self.scales_line.setValidator(validator)
Expand All @@ -66,13 +68,13 @@ def _set_page(self):

def _handle_validation_change(self, state):
if state == RegExpValidator.Invalid:
color = 'red'
color = "red"
elif state == RegExpValidator.Intermediate:
color = 'gold'
color = "gold"
elif state == RegExpValidator.Acceptable:
color = 'lime'
self.scales_line.setStyleSheet(f'border: 1px solid {color}')
QTimer.singleShot(500, lambda: self.scales_line.setStyleSheet(''))
color = "lime"
self.scales_line.setStyleSheet(f"border: 1px solid {color}")
QTimer.singleShot(500, lambda: self.scales_line.setStyleSheet(""))

def run_video_adaptation(self):
videos = list(self.files)
Expand All @@ -89,7 +91,10 @@ def run_video_adaptation(self):
scales = []
scales_ = self.scales_line.text()
if scales_:
if (self.scales_line.validator().validate(scales_, 0)[0] == RegExpValidator.Acceptable):
if (
self.scales_line.validator().validate(scales_, 0)[0]
== RegExpValidator.Acceptable
):
scales = list(map(int, scales_.split(",")))
supermodel_name = self.model_combo.currentText()
videotype = self.video_selection_widget.videotype_widget.currentText()
Expand Down
18 changes: 9 additions & 9 deletions deeplabcut/modelzoo/api/spatiotemporal_adapt.py
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Expand Up @@ -62,7 +62,9 @@ def __init__(
"""
supermodels = parse_available_supermodels()
if supermodel_name not in supermodels:
raise ValueError(f"`supermodel_name` should be one of: {', '.join(supermodels)}.")
raise ValueError(
f"`supermodel_name` should be one of: {', '.join(supermodels)}."
)

self.video_path = video_path
self.supermodel_name = supermodel_name
Expand All @@ -85,9 +87,7 @@ def __init__(
else:
self.customized_pose_config = customized_pose_config

def before_adapt_inference(self,
make_video=False,
**kwargs):
def before_adapt_inference(self, make_video=False, **kwargs):
if self.init_weights != "":
_ = superanimal_inference.video_inference(
[self.video_path],
Expand All @@ -114,7 +114,7 @@ def before_adapt_inference(self,
init_weights=self.init_weights,
draw_skeleton=True,
superanimal_name=self.supermodel_name,
**kwargs
**kwargs,
)

def train_without_project(self, pseudo_label_path, **kwargs):
Expand All @@ -131,7 +131,7 @@ def train_without_project(self, pseudo_label_path, **kwargs):
init_weights=self.init_weights,
pseudo_labels=pseudo_label_path,
video_path=self.video_path,
**kwargs
**kwargs,
)

def adaptation_training(self, displayiters=500, saveiters=1000, **kwargs):
Expand Down Expand Up @@ -174,7 +174,7 @@ def after_adapt_inference(self, **kwargs):

# spatial pyramid is not for adapted model

scale_list = kwargs.pop('scale_list', [])
scale_list = kwargs.pop("scale_list", [])
_, datafiles = superanimal_inference.video_inference(
[self.video_path],
self.supermodel_name,
Expand All @@ -184,7 +184,7 @@ def after_adapt_inference(self, **kwargs):
customized_test_config=self.customized_pose_config,
)

if kwargs.pop('plot_trajectories', True):
if kwargs.pop("plot_trajectories", True):
_plot_trajectories(datafiles[0])

deeplabcut.create_labeled_video(
Expand All @@ -195,5 +195,5 @@ def after_adapt_inference(self, **kwargs):
init_weights=adapt_weights,
draw_skeleton=True,
superanimal_name=self.supermodel_name,
**kwargs
**kwargs,
)
29 changes: 12 additions & 17 deletions deeplabcut/modelzoo/api/superanimal_inference.py
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Expand Up @@ -13,10 +13,8 @@

from deeplabcut.modelzoo.utils import parse_available_supermodels
from deeplabcut.pose_estimation_tensorflow.config import load_config
from deeplabcut.pose_estimation_tensorflow.core import \
predict as single_predict
from deeplabcut.pose_estimation_tensorflow.core import \
predict_multianimal as predict
from deeplabcut.pose_estimation_tensorflow.core import predict as single_predict
from deeplabcut.pose_estimation_tensorflow.core import predict_multianimal as predict
from deeplabcut.utils import auxiliaryfunctions
from deeplabcut.utils.auxfun_videos import VideoWriter
from dlclibrary.dlcmodelzoo.modelzoo_download import (
Expand Down Expand Up @@ -91,7 +89,7 @@ def _average_multiple_scale_preds(

# Compute cosine similarity
mean_vec = np.nanmedian(xy, axis=0)
dist_ = np.einsum('ijk,jk->ij', xy, mean_vec)
dist_ = np.einsum("ijk,jk->ij", xy, mean_vec)
n = np.linalg.norm(xy, axis=2) * np.linalg.norm(mean_vec, axis=1)
dist = np.nan_to_num(dist_ / n)

Expand All @@ -103,8 +101,8 @@ def _average_multiple_scale_preds(
coords = np.nanmedian(xyp[..., :2], axis=0)
conf = np.nanmedian(xyp[..., 2], axis=0)
dict_ = {
'coordinates': [list(coords[:, None])],
'confidence': list(conf[:, None].astype(np.float32)),
"coordinates": [list(coords[:, None])],
"confidence": list(conf[:, None].astype(np.float32)),
}
return dict_

Expand Down Expand Up @@ -277,39 +275,36 @@ def video_inference(
else:
test_cfg = load_config(customized_test_config)


# add a temp folder for checkpoint

weight_folder = superanimal_name + '_weights'
weight_folder = superanimal_name + "_weights"

if superanimal_name in MODELOPTIONS:
if not os.path.exists(weight_folder):
download_huggingface_model(superanimal_name, weight_folder)
else:
print (f"{weight_folder} exists, using the downloaded weights")
print(f"{weight_folder} exists, using the downloaded weights")
else:
print (f'{superanimal_name} not available. Available ones are: ', MODELOPTIONS)
print(f"{superanimal_name} not available. Available ones are: ", MODELOPTIONS)

snapshots = glob.glob(
os.path.join(weight_folder, 'snapshot-*.index')
)
snapshots = glob.glob(os.path.join(weight_folder, "snapshot-*.index"))

test_cfg["partaffinityfield_graph"] = []
test_cfg["partaffinityfield_predict"] = False

if init_weights != "":
test_cfg["init_weights"] = init_weights
else:
init_weights = os.path.abspath(snapshots[0]).replace('.index', '')
test_cfg['init_weights'] = init_weights
init_weights = os.path.abspath(snapshots[0]).replace(".index", "")
test_cfg["init_weights"] = init_weights

test_cfg["num_outputs"] = 1
test_cfg["batch_size"] = batchsize

sess, inputs, outputs = single_predict.setup_pose_prediction(
test_cfg, allow_growth=allow_growth
)
DLCscorer = "DLC_" + Path(test_cfg['init_weights']).stem
DLCscorer = "DLC_" + Path(test_cfg["init_weights"]).stem
videos = auxiliaryfunctions.get_list_of_videos(videos, videotype)

datafiles = []
Expand Down
5 changes: 2 additions & 3 deletions deeplabcut/modelzoo/utils.py
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Expand Up @@ -4,9 +4,8 @@

def parse_available_supermodels():
import deeplabcut

dlc_path = deeplabcut.utils.auxiliaryfunctions.get_deeplabcut_path()
json_path = os.path.join(
dlc_path, "modelzoo", "models.json"
)
json_path = os.path.join(dlc_path, "modelzoo", "models.json")
with open(json_path) as file:
return json.load(file)
4 changes: 2 additions & 2 deletions deeplabcut/pose_estimation_tensorflow/datasets/pose_deterministic.py
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Expand Up @@ -228,7 +228,7 @@ def make_batch(self, data_item, scale, mirror):

def compute_target_part_scoremap(self, joint_id, coords, data_item, size, scale):
dist_thresh = self.cfg["pos_dist_thresh"] * scale
dist_thresh_sq = dist_thresh ** 2
dist_thresh_sq = dist_thresh**2
num_joints = self.cfg["num_joints"]
scmap = np.zeros(np.concatenate([size, np.array([num_joints])]))
locref_size = np.concatenate([size, np.array([num_joints * 2])])
Expand Down Expand Up @@ -260,7 +260,7 @@ def compute_target_part_scoremap(self, joint_id, coords, data_item, size, scale)
pt_x = i * self.stride + self.half_stride
dx = j_x - pt_x
dy = j_y - pt_y
dist = dx ** 2 + dy ** 2
dist = dx**2 + dy**2
# print(la.norm(diff))
if dist <= dist_thresh_sq:
scmap[j, i, j_id] = 1
Expand Down
8 changes: 4 additions & 4 deletions deeplabcut/pose_estimation_tensorflow/datasets/pose_imgaug.py
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Expand Up @@ -487,7 +487,7 @@ def gaussian_scmap(self, joint_id, coords, data_item, size, scale):
width = size[1]
height = size[0]
dist_thresh = float((width + height) / 6)
dist_thresh_sq = dist_thresh ** 2
dist_thresh_sq = dist_thresh**2

std = dist_thresh / 4
# Grid of coordinates
Expand All @@ -503,7 +503,7 @@ def gaussian_scmap(self, joint_id, coords, data_item, size, scale):
map_j = grid.copy()
# Distance between the joint point and each coordinate
dist = np.linalg.norm(grid - (j_y, j_x), axis=2) ** 2
scmap_j = np.exp(-dist / (2 * (std ** 2)))
scmap_j = np.exp(-dist / (2 * (std**2)))
scmap[..., j_id] = scmap_j
locref_mask[dist <= dist_thresh_sq, j_id * 2 + 0] = 1
locref_mask[dist <= dist_thresh_sq, j_id * 2 + 1] = 1
Expand All @@ -528,7 +528,7 @@ def compute_target_part_scoremap_numpy(
self, joint_id, coords, data_item, size, scale
):
dist_thresh = float(self.cfg["pos_dist_thresh"] * scale)
dist_thresh_sq = dist_thresh ** 2
dist_thresh_sq = dist_thresh**2
num_joints = self.cfg["num_joints"]

scmap = np.zeros(np.concatenate([size, np.array([num_joints])]))
Expand All @@ -555,7 +555,7 @@ def compute_target_part_scoremap_numpy(
y = grid.copy()[:, :, 0]
dx = j_x - x * self.stride - self.half_stride
dy = j_y - y * self.stride - self.half_stride
dist = dx ** 2 + dy ** 2
dist = dx**2 + dy**2
mask1 = dist <= dist_thresh_sq
mask2 = (x >= min_x) & (x <= max_x)
mask3 = (y >= min_y) & (y <= max_y)
Expand Down
8 changes: 4 additions & 4 deletions deeplabcut/pose_estimation_tensorflow/datasets/pose_multianimal_imgaug.py
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Expand Up @@ -595,7 +595,7 @@ def compute_target_part_scoremap_numpy(
locref_size = *size, num_joints * 2
locref_map = np.zeros(locref_size)
locref_scale = 1.0 / self.cfg["locref_stdev"]
dist_thresh_sq = dist_thresh ** 2
dist_thresh_sq = dist_thresh**2

partaffinityfield_shape = *size, self.cfg["num_limbs"] * 2
partaffinityfield_map = np.zeros(partaffinityfield_shape)
Expand All @@ -621,7 +621,7 @@ def compute_target_part_scoremap_numpy(
dx_ = dx * locref_scale
dy = coords[:, 1] - yy * stride - half_stride
dy_ = dy * locref_scale
dist = dx ** 2 + dy ** 2
dist = dx**2 + dy**2
mask1 = dist <= dist_thresh_sq
mask2 = (xx >= mins[:, 0]) & (xx <= maxs[:, 0])
mask3 = (yy >= mins[:, 1]) & (yy <= maxs[:, 1])
Expand Down Expand Up @@ -728,7 +728,7 @@ def gaussian_scmap(self, joint_id, coords, data_item, size, scale):
locref_map = np.zeros(locref_size)

locref_scale = 1.0 / self.cfg["locref_stdev"]
dist_thresh_sq = dist_thresh ** 2
dist_thresh_sq = dist_thresh**2

partaffinityfield_shape = np.concatenate(
[size, np.array([self.cfg["num_limbs"] * 2])]
Expand Down Expand Up @@ -760,7 +760,7 @@ def gaussian_scmap(self, joint_id, coords, data_item, size, scale):
map_j = grid.copy()
# Distance between the joint point and each coordinate
dist = np.linalg.norm(grid - (j_y, j_x), axis=2) ** 2
scmap_j = np.exp(-dist / (2 * (std ** 2)))
scmap_j = np.exp(-dist / (2 * (std**2)))
scmap[..., j_id] = scmap_j
locref_mask[dist <= dist_thresh_sq, j_id * 2 + 0] = 1
locref_mask[dist <= dist_thresh_sq, j_id * 2 + 1] = 1
Expand Down
4 changes: 2 additions & 2 deletions deeplabcut/pose_estimation_tensorflow/datasets/pose_tensorpack.py
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Expand Up @@ -351,7 +351,7 @@ def compute_target_part_scoremap(self, components):
locref_map = np.zeros(locref_size)

locref_scale = 1.0 / self.cfg["locref_stdev"]
dist_thresh_sq = dist_thresh ** 2
dist_thresh_sq = dist_thresh**2

width = size[1]
height = size[0]
Expand All @@ -376,7 +376,7 @@ def compute_target_part_scoremap(self, components):
pt_x = i * stride + half_stride
dx = j_x - pt_x
dy = j_y - pt_y
dist = dx ** 2 + dy ** 2
dist = dx**2 + dy**2
# print(la.norm(diff))
if dist <= dist_thresh_sq:
scmap[j, i, j_id] = 1
Expand Down
2 changes: 1 addition & 1 deletion deeplabcut/pose_estimation_tensorflow/lib/inferenceutils.py
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Expand Up @@ -359,7 +359,7 @@ def calc_link_probability(self, link):
ind = _conv_square_to_condensed_indices(i, j, self.n_multibodyparts)
mu = self._kde.mean[ind]
sigma = self._kde.covariance[ind, ind]
z = (link.length ** 2 - mu) / sigma
z = (link.length**2 - mu) / sigma
return 2 * (1 - 0.5 * (1 + erf(abs(z) / sqrt(2))))

@staticmethod
Expand Down
4 changes: 2 additions & 2 deletions deeplabcut/pose_estimation_tensorflow/nnets/utils.py
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Expand Up @@ -86,8 +86,8 @@ def get_batch_spec(cfg):
def make_2d_gaussian_kernel(sigma, size):
sigma = tf.convert_to_tensor(sigma, dtype=tf.float32)
k = tf.range(-size // 2 + 1, size // 2 + 1)
k = tf.cast(k ** 2, sigma.dtype)
k = tf.nn.softmax(-k / (2 * (sigma ** 2)))
k = tf.cast(k**2, sigma.dtype)
k = tf.nn.softmax(-k / (2 * (sigma**2)))
return tf.einsum("i,j->ij", k, k)


Expand Down
3 changes: 2 additions & 1 deletion deeplabcut/pose_estimation_tensorflow/predict_supermodel.py
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Expand Up @@ -77,5 +77,6 @@ def video_inference_superanimal(
adapter.before_adapt_inference(make_video=False)
adapter.adaptation_training()
adapter.after_adapt_inference(
pcutoff=pcutoff, plot_trajectories=plot_trajectories,
pcutoff=pcutoff,
plot_trajectories=plot_trajectories,
)
2 changes: 1 addition & 1 deletion deeplabcut/pose_estimation_tensorflow/util/visualize.py
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Expand Up @@ -30,7 +30,7 @@ def _npcircle(image, cx, cy, radius, color, transparency=0.0):
cx = int(cx)
cy = int(cy)
y, x = np.ogrid[-radius:radius, -radius:radius]
index = x ** 2 + y ** 2 <= radius ** 2
index = x**2 + y**2 <= radius**2
image[cy - radius : cy + radius, cx - radius : cx + radius][index] = (
image[cy - radius : cy + radius, cx - radius : cx + radius][index].astype(
"float32"
Expand Down
2 changes: 1 addition & 1 deletion deeplabcut/pose_estimation_tensorflow/visualizemaps.py
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Expand Up @@ -339,7 +339,7 @@ def visualize_paf(image, paf, step=5, colors=None):
V = paf[:, :, n, 1]
X, Y = np.meshgrid(np.arange(U.shape[1]), np.arange(U.shape[0]))
M = np.zeros(U.shape, dtype=bool)
M[U ** 2 + V ** 2 < 0.5 * 0.5 ** 2] = True
M[U**2 + V**2 < 0.5 * 0.5**2] = True
U = np.ma.masked_array(U, mask=M)
V = np.ma.masked_array(V, mask=M)
ax.quiver(
Expand Down
4 changes: 2 additions & 2 deletions deeplabcut/pose_tracking_pytorch/tracking_utils/metrics.py
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Expand Up @@ -49,8 +49,8 @@ def eval_func(distmat, q_pids, g_pids, q_camids, g_camids, max_rank=50):
max_rank = num_g
print("Note: number of gallery samples is quite small, got {}".format(num_g))
indices = np.argsort(distmat, axis=1)
# 0 2 1 3
# 1 2 3 0
# 0, 2, 1, 3
# 1, 2, 3, 0
matches = (g_pids[indices] == q_pids[:, np.newaxis]).astype(np.int32)
# compute cmc curve for each query
all_cmc = []
Expand Down
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