def evaluate(self):
"""Load results, or calculate them if not available."""
results = Vessel("predictions.dat")
if "y_" not in results.keys or results.scientific_name != self.scientific_name:
self.y_ = self.cnn.model.predict(self.X)
results.y = self.y
results.y_ = self.y_
results.scientific_name = self.scientific_name
results.save()
self.y_ = results.y_
def load_targets(self):
"""Load example targets."""
y = []
X = []
print("> Assembling the data.")
for scientific_name in tqdm(self.target_species):
annotations = get_specified_target(scientific_name,
nb_annotations=100)
for annotation in tqdm(annotations):
X_ = extract_tiles_from_annotation(annotation, 10)
X.extend(X_)
y_ = [scientific_name] * len(X_)
y.extend(y_)
self.X = X
self.y = y
print("> Assembly complete.")
c = Vessel("confusion_matrix.dat")
c.X = X
c.y = y
c.save()
examples_per_class)
for idx in batch_idx:
X.append(v.X[target_name][idx])
y.append(v.y[target_name][idx])
X = np.array(X)
y = np.array(y)
return X, y
if __name__ == "__main__":
v = Vessel("targets.dat")
# X — images; y — class identities (one-hot vectors).
v.X = {}
v.y = {}
targets = sorted(glob(f"data/*"))
v.targets = {}
for target in targets:
target_name = target.split("/")[-1]
v.targets[target_name] = glob(f"{target}/*")
target_names = list(v.targets.keys())
v.target_names = target_names
# Now generate training/test data.
for itr, target in enumerate(tqdm(v.target_names)):
v.X[target] = []
v.y[target] = []
paths_to_images = v.targets[target]
for path_to_image in paths_to_images: