diff --git a/deepof/utils.py b/deepof/utils.py
index 32c8ff14866de03c813253f8d957ad7b09f3df95..00a6544aa002273e769efa573e5f697ada982537 100644
--- a/deepof/utils.py
+++ b/deepof/utils.py
@@ -8,6 +8,7 @@ Functions and general utilities for the deepof package. See documentation for de
"""
+import argparse
import cv2
import matplotlib.pyplot as plt
import multiprocessing
@@ -72,6 +73,26 @@ def connect_mouse_topview(animal_id=None) -> nx.Graph:
# QUALITY CONTROL AND PREPROCESSING #
+def str2bool(v: str) -> bool:
+ """Returns the passed string as a boolean
+
+ Parameters:
+ v (str): string to transform to boolean value
+
+ Returns:
+ boolean value. If conversion is not possible, it raises an error
+ """
+
+ if isinstance(v, bool):
+ return v
+ if v.lower() in ("yes", "true", "t", "y", "1"):
+ return True
+ elif v.lower() in ("no", "false", "f", "n", "0"):
+ return False
+ else:
+ raise argparse.ArgumentTypeError("Boolean compatible value expected.")
+
+
def likelihood_qc(dframe: pd.DataFrame, threshold: float = 0.9) -> np.array:
"""Returns a DataFrame filtered dataframe, keeping only the rows entirely above the threshold.
diff --git a/examples/model_training.py b/examples/model_training.py
index 9947d32f66069969a6a8e95e7346409d16575b6f..22759e008c8ec41fa7d176d7cec48ff753d1b074 100644
--- a/examples/model_training.py
+++ b/examples/model_training.py
@@ -1,24 +1,13 @@
# @author lucasmiranda42
from datetime import datetime
-from deepof.preprocess import *
+import deepof.preprocess
from deepof.models import *
from tensorflow import keras
import argparse
import os, pickle
-def str2bool(v):
- if isinstance(v, bool):
- return v
- if v.lower() in ("yes", "true", "t", "y", "1"):
- return True
- elif v.lower() in ("no", "false", "f", "n", "0"):
- return False
- else:
- raise argparse.ArgumentTypeError("Boolean value expected.")
-
-
parser = argparse.ArgumentParser(
description="Autoencoder training for DeepOF animal pose recognition"
)
@@ -60,7 +49,7 @@ parser.add_argument(
"-v",
help="Sets the model to train to a variational Bayesian autoencoder. Defaults to True",
default=True,
- type=str2bool,
+ type=deepof.preprocess.str2bool,
)
parser.add_argument(
"--loss",
@@ -102,7 +91,7 @@ parser.add_argument(
"-ol",
help="If True, adds the negative MMD between all components of the latent Gaussian mixture to the loss function",
default=False,
- type=str2bool,
+ type=deepof.preprocess.str2bool,
)
parser.add_argument(
"--batch-size",
@@ -201,7 +190,7 @@ bp_dict = {
"B_Tail_base": ["B_Center", "B_Left_flank", "B_Right_flank"],
}
-DLC_social_1_coords = project(
+DLC_social_1_coords = deepof.preprocess.project(
path=train_path, # Path where to find the required files
smooth_alpha=0.50, # Alpha value for exponentially weighted smoothing
distances=[
@@ -228,10 +217,10 @@ DLC_social_1_coords = project(
coords1 = DLC_social_1_coords.get_coords(center="B_Center", align="B_Nose")
distances1 = DLC_social_1_coords.get_distances()
angles1 = DLC_social_1_coords.get_angles()
-coords_distances1 = merge_tables(coords1, distances1)
-coords_angles1 = merge_tables(coords1, angles1)
-dists_angles1 = merge_tables(distances1, angles1)
-coords_dist_angles1 = merge_tables(coords1, distances1, angles1)
+coords_distances1 = deepof.preprocess.merge_tables(coords1, distances1)
+coords_angles1 = deepof.preprocess.merge_tables(coords1, angles1)
+dists_angles1 = deepof.preprocess.merge_tables(distances1, angles1)
+coords_dist_angles1 = deepof.preprocess.merge_tables(coords1, distances1, angles1)
input_dict_train = {
@@ -302,7 +291,7 @@ input_dict_train = {
# If a validation path is specified, use it to build a validation set
if val_path:
- DLC_social_2_coords = project(
+ DLC_social_2_coords = deepof.preprocess.project(
path=val_path, # Path where to find the required files
smooth_alpha=0.50, # Alpha value for exponentially weighted smoothing
distances=[
@@ -328,10 +317,10 @@ if val_path:
coords2 = DLC_social_2_coords.get_coords(center="B_Center", align="B_Nose")
distances2 = DLC_social_2_coords.get_distances()
angles2 = DLC_social_2_coords.get_angles()
- coords_distances2 = merge_tables(coords2, distances2)
- coords_angles2 = merge_tables(coords2, angles2)
- dists_angles2 = merge_tables(distances2, angles2)
- coords_dist_angles2 = merge_tables(coords2, distances2, angles2)
+ coords_distances2 = deepof.preprocess.merge_tables(coords2, distances2)
+ coords_angles2 = deepof.preprocess.merge_tables(coords2, angles2)
+ dists_angles2 = deepof.preprocess.merge_tables(distances2, angles2)
+ coords_dist_angles2 = deepof.preprocess.merge_tables(coords2, distances2, angles2)
input_dict_val = {
"coords": coords2.preprocess(
diff --git a/examples/predict_video.py b/examples/predict_video.py
index ecbc5e4b3d1dc6398bc4402f61986e08861959ec..1ce1eea7dd1d9d6cefcc5e210b7d29935161c942 100644
--- a/examples/predict_video.py
+++ b/examples/predict_video.py
@@ -5,22 +5,12 @@ import sys
sys.path.insert(1, "../")
-from deepof.preprocess import *
-from deepof.models import *
+import deepof.preprocess
import argparse
import cv2
-import os, pickle
-
-
-def str2bool(v):
- if isinstance(v, bool):
- return v
- if v.lower() in ("yes", "true", "t", "y", "1"):
- return True
- elif v.lower() in ("no", "false", "f", "n", "0"):
- return False
- else:
- raise argparse.ArgumentTypeError("Boolean value expected.")
+import os
+import pickle
+from deepof.models import *
parser = argparse.ArgumentParser(
@@ -49,14 +39,14 @@ parser.add_argument(
"-pred",
help="Activates the prediction branch of the variational Seq 2 Seq model. Defaults to True",
default=True,
- type=str2bool,
+ type=deepof.preprocess.str2bool,
)
parser.add_argument(
"--variational",
"-v",
help="Sets the model to train to a variational Bayesian autoencoder. Defaults to True",
default=True,
- type=str2bool,
+ type=deepof.preprocess.str2bool,
)
parser.add_argument(
"--loss",
@@ -161,7 +151,7 @@ bp_dict = {
"B_Tail_base": ["B_Center", "B_Left_flank", "B_Right_flank"],
}
-DLC_social = project(
+DLC_social = deepof.preprocess.project(
path=os.path.join(data_path), # Path where to find the required files
smooth_alpha=0.50, # Alpha value for exponentially weighted smoothing
distances=[
@@ -183,20 +173,19 @@ DLC_social = project(
table_format=".h5",
)
-
DLC_social_coords = DLC_social.run(verbose=True)
# Coordinates for training data
coords1 = DLC_social_coords.get_coords(center="B_Center", align="B_Nose")
distances1 = DLC_social_coords.get_distances()
angles1 = DLC_social_coords.get_angles()
-coords_distances1 = merge_tables(coords1, distances1)
-coords_angles1 = merge_tables(coords1, angles1)
-dists_angles1 = merge_tables(distances1, angles1)
-coords_dist_angles1 = merge_tables(coords1, distances1, angles1)
+coords_distances1 = deepof.preprocess.merge_tables(coords1, distances1)
+coords_angles1 = deepof.preprocess.merge_tables(coords1, angles1)
+dists_angles1 = deepof.preprocess.merge_tables(distances1, angles1)
+coords_dist_angles1 = deepof.preprocess.merge_tables(coords1, distances1, angles1)
input_dict = {
- "coords": table_dict(
+ "coords": deepof.preprocess.table_dict(
((k, coords1[k]) for k in [video_name]), typ="coords"
).preprocess(
window_size=13,
@@ -207,7 +196,7 @@ input_dict = {
shuffle=False,
align="center",
),
- "dists": table_dict(
+ "dists": deepof.preprocess.table_dict(
((k, coords1[k]) for k in [video_name]), typ="coords"
).preprocess(
window_size=13,
@@ -218,7 +207,7 @@ input_dict = {
shuffle=False,
align="center",
),
- "angles": table_dict(
+ "angles": deepof.preprocess.table_dict(
((k, coords1[k]) for k in [video_name]), typ="coords"
).preprocess(
window_size=13,
@@ -229,7 +218,7 @@ input_dict = {
shuffle=False,
align="center",
),
- "coords+dist": table_dict(
+ "coords+dist": deepof.preprocess.table_dict(
((k, coords1[k]) for k in [video_name]), typ="coords"
).preprocess(
window_size=13,
@@ -240,7 +229,7 @@ input_dict = {
shuffle=False,
align="center",
),
- "coords+angle": table_dict(
+ "coords+angle": deepof.preprocess.table_dict(
((k, coords1[k]) for k in [video_name]), typ="coords"
).preprocess(
window_size=13,
@@ -251,7 +240,7 @@ input_dict = {
shuffle=False,
align="center",
),
- "dists+angle": table_dict(
+ "dists+angle": deepof.preprocess.table_dict(
((k, coords1[k]) for k in [video_name]), typ="coords"
).preprocess(
window_size=13,
@@ -262,7 +251,7 @@ input_dict = {
shuffle=False,
align="center",
),
- "coords+dist+angle": table_dict(
+ "coords+dist+angle": deepof.preprocess.table_dict(
((k, coords1[k]) for k in [video_name]), typ="coords"
).preprocess(
window_size=13,
@@ -290,7 +279,9 @@ ae.build(input_dict[input_type].shape)
ae.load_weights(model_path)
# Predict cluster labels per frame
-frame_labels = np.argmax(grouper.predict(input_dict[input_type]), axis=1)
+frame_labels = deepof.preprocess.np.argmax(
+ grouper.predict(input_dict[input_type]), axis=1
+)
print(frame_labels)
print(frame_labels.shape)
@@ -308,7 +299,7 @@ cap = cv2.VideoCapture(
# Loop over the first frames in the video to get resolution and center of the arena
if frame_limit == -1:
- frame_limit = np.inf
+ frame_limit = deepof.preprocess.np.inf
fnum, h, w = 0, 0, 0
if action == "save":
diff --git a/tests/test_utils.py b/tests/test_utils.py
index fd9418ec2ed3ada5ca45cf7c47116230a88154fa..a90d878691026b80046d269d885618ccfda31794 100644
--- a/tests/test_utils.py
+++ b/tests/test_utils.py
@@ -33,6 +33,25 @@ def autocorr(x, t=1):
# QUALITY CONTROL AND PREPROCESSING #
+@settings(deadline=None)
+@given(
+ v=st.one_of(
+ st.just("yes"),
+ st.just("true"),
+ st.just("t"),
+ st.just("y"),
+ st.just("1"),
+ st.just("no"),
+ st.just("false"),
+ st.just("f"),
+ st.just("n"),
+ st.just("0"),
+ )
+)
+def test_str2bool(v):
+ assert type(str2bool(v)) == bool
+
+
@settings(deadline=None)
@given(
mult=st.integers(min_value=1, max_value=10),