def extract_features(self, key, image_array, meta): # key is a placeholder
start = tic()
output_file = self.config["paths"]["features"] + "/{}_{}_{}.npz"
output_file = output_file.format( meta["Metadata_Plate"], meta["Metadata_Well"], meta["Metadata_Site"])
batch_size = self.config["profile"]["batch_size"]
image_key, image_names, outlines = self.dset.getImagePaths(meta)
total_crops = self.profile_crop_generator.prepare_image(
self.sess,
image_array,
meta,
False
)
if total_crops == 0:
print("No cells to profile:", output_file)
return
num_features = self.config["train"]["model"]["params"]["feature_dim"]
repeats = "channel_repeats" in self.config["dataset"]["images"].keys()
# Extract features
crops = next(self.profile_crop_generator.generate(self.sess))[0] # single image crop generator yields one batch
feats = self.feat_extractor.predict(crops, batch_size=batch_size)
if repeats:
feats = np.reshape(feats, (self.num_channels, total_crops, num_features))
feats = np.concatenate(feats, axis=-1)
# Save features
while len(feats.shape) > 2: # 2D mean spatial pooling
feats = np.mean(feats, axis=1)
np.savez_compressed(output_file, f=feats)
toc(image_key + " (" + str(total_crops) + " cells)", start)
def extract_features(self, key, image_array, meta):
output_file = self.config["profiling"]["output_dir"] + "/{}_{}_{}.npz"
output_file = output_file.format(meta["Metadata_Plate"],
meta["Metadata_Well"],
meta["Metadata_Site"])
batch_size = self.config["validation"]["minibatch"]
image_key, image_names, outlines = self.dset.getImagePaths(meta)
total_crops = self.crop_generator.prepare_image(
self.sess, image_array, meta,
self.config["validation"]["sample_first_crops"])
# Initialize data buffer
data = np.zeros(shape=(self.num_channels, total_crops, num_features))
b = 0
start = tic()
# Extract features in batches
batches = []
for batch in self.crop_generator.generate(self.sess):
crops = batch[0]
if self.config["model"]["type"] == "inception_resnet":
feats = self.sess.run(self.endpoints['PreLogitsFlatten'],
feed_dict={self.raw_crops: crops})
if self.config["model"]["type"] in ["convnet", "mixup"]:
feats = self.feat_extractor((batch[0], 0))
feats = np.reshape(feats,
(self.num_channels, batch_size, num_features))
data[:, b * batch_size:(b + 1) * batch_size, :] = feats
b += 1
batches.append(batch)
# Concatentate features of all channels
data = np.moveaxis(data, 0, 1)
data = np.reshape(data, (data.shape[0], data.shape[1] * data.shape[2]))
# Save features
np.savez_compressed(output_file, f=data)
toc(image_key + " (" + str(data.shape[0]) + " cells)", start)
# Save crops TODO: parameterize saving crops or a sample of them.
if False:
batch_data = {"total_crops": total_crops, "batches": batches}
with open(output_file.replace(".npz", ".pkl"), "wb") as batch_file:
pickle.dump(batch_data, batch_file)
def extract_features(self, key, image_array, meta): # key is a placeholder
start = tic()
output_file = self.config["paths"]["features"] + "/{}/{}_{}.npz"
output_file = output_file.format(meta["Metadata_Plate"],
meta["Metadata_Well"],
meta["Metadata_Site"])
os.makedirs(self.config["paths"]["features"] +
"/{}".format(meta["Metadata_Plate"]),
exist_ok=True)
batch_size = self.config["profile"]["batch_size"]
image_key, image_names, outlines = self.dset.get_image_paths(meta)
crop_locations = self.profile_crop_generator.prepare_image(
K.get_session(), image_array, meta, False)
total_crops = len(crop_locations)
if total_crops == 0:
print("No cells to profile:", output_file)
return
repeats = self.config["train"]["model"][
"crop_generator"] == "repeat_channel_crop_generator"
# Extract features
crops = next(self.profile_crop_generator.generate(K.get_session()))[
0] # single image crop generator yields one batch
feats = self.feat_extractor.predict(crops, batch_size=batch_size)
if repeats:
feats = np.reshape(feats, (self.num_channels, total_crops, -1))
feats = np.concatenate(feats, axis=-1)
# Save features
while len(feats.shape) > 2: # 2D mean spatial pooling
feats = np.mean(feats, axis=1)
key_values = {k: meta[k] for k in meta.keys()}
key_values["Metadata_Model"] = self.config["train"]["model"]["name"]
np.savez_compressed(output_file,
features=feats,
metadata=key_values,
locations=crop_locations)
toc(image_key + " (" + str(total_crops) + " cells)", start)