From fab127a029f1707ba01c4e72a8a6d5653183d184 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Fran=C3=A7ois=20Laurent?= <francois.laurent@posteo.net>
Date: Fri, 27 May 2022 12:04:06 +0200
Subject: [PATCH] metadata handling moved to TaggingBackends (see commit
https://gitlab.pasteur.fr/nyx/TaggingBackends/-/commit/8e3e2fd5d5f24bf6523a9e969cbfe94f34b5457b)
---
src/maggotuba/models/denselayer.py | 2 +-
src/maggotuba/models/predict_model.py | 27 +++++++++++----------------
2 files changed, 12 insertions(+), 17 deletions(-)
diff --git a/src/maggotuba/models/denselayer.py b/src/maggotuba/models/denselayer.py
index 82a6f7a..7196bcd 100644
--- a/src/maggotuba/models/denselayer.py
+++ b/src/maggotuba/models/denselayer.py
@@ -169,7 +169,7 @@ class DenseLayer:
if train:
return y
else:
- return y.numpy()
+ return y.cpu().numpy()
def train(self, all_spines=None, tags=None):
if all_spines is None or tags is None:
diff --git a/src/maggotuba/models/predict_model.py b/src/maggotuba/models/predict_model.py
index 681cf4b..4299dd2 100644
--- a/src/maggotuba/models/predict_model.py
+++ b/src/maggotuba/models/predict_model.py
@@ -25,13 +25,10 @@ def predict_model(backend):
# we could go and pick files in `data/interim` as well:
input_files += backend.list_interim_files()
assert 0 < len(input_files)
- metadata = None
- metadata_file = [file for file in input_files if file.name == "metadata"]
- if metadata_file:
- metadata_file = metadata_file[0]
- input_files.remove(metadata_file)
- with open(metadata_file, "r") as f:
- metadata = json.load(f)
+ # initialize output labels
+ input_files, labels = backend.prepare_labels(input_files)
+ assert 0 < len(input_files)
+ #
for file in input_files:
# load the input data (or features)
if file.name.endswith(".spine"):
@@ -40,7 +37,7 @@ def predict_model(backend):
larvae = spine["larva_id"].values
t = spine["time"].values
data = spine.iloc[:,3:].values
- elif file.name == "trx.mat":
+ elif file.name.endswith(".mat"):
trx = TrxMat(file)
t = trx["t"]
data = trx["spine"]
@@ -49,11 +46,14 @@ def predict_model(backend):
run, data = next(iter(data.items()))
t = t[run]
elif file.name.endswith(".csv"):
- print("assuming 30 fps")
- t, data = fimtrack.read_spines(file, fps=30)
+ if labels.camera_framerate:
+ print(f"camera frame rate: {labels.camera_framerate}fps")
+ else:
+ print("assuming 30-fps camera frame rate")
+ labels.camera_framerate = 30
+ t, data = fimtrack.read_spines(file, fps=labels.camera_framerate)
run = "NA"
else:
- # TODO: support more file formats
continue
# downsample the skeleton
if isinstance(data, dict):
@@ -68,7 +68,6 @@ def predict_model(backend):
config_file = [file for file in config_file if file.name.endswith("clf_config.json")]
model = Clf(config_file[-1])
# assign labels
- labels = Labels(tracking=input_files)
if isinstance(data, dict):
ref_length = np.mean(np.concatenate([
model.body_length(spines) for spines in data.values()
@@ -93,10 +92,6 @@ def predict_model(backend):
else:
labels[run, larva] = dict(zip(t[mask], predictions))
# save the predicted labels to file
- if metadata:
- labels.metadata = metadata
- else:
- labels.metadata = {'filename': file.name}
labels.labelspec = {
"names": ["run", "bend", "stop", "hunch", "back", "roll"],
"colors": ["#000000", "#ff0000", "#00ff00", "#0000ff",
--
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