def __init__(self):
stimulus_set = pd.read_csv(
os.path.join(os.path.dirname(__file__), 'imagenet2012.csv'))
stimulus_set = StimulusSet(stimulus_set)
stimulus_set.image_paths = {
row.image_id: row.filepath
for row in stimulus_set.itertuples()
}
self._stimulus_set = stimulus_set
self._similarity_metric = Accuracy()
ceiling = Score([1, np.nan],
coords={'aggregation': ['center', 'error']},
dims=['aggregation'])
super(Imagenet2012, self).__init__(identifier='fei-fei.Deng2009-top1',
version=1,
ceiling_func=lambda: ceiling,
parent='ImageNet',
bibtex="""@INPROCEEDINGS{5206848,
author={J. {Deng} and W. {Dong} and R. {Socher} and L. {Li} and {Kai Li} and {Li Fei-Fei}},
booktitle={2009 IEEE Conference on Computer Vision and Pattern Recognition},
title={ImageNet: A large-scale hierarchical image database},
year={2009},
volume={},
number={},
pages={248-255},
url = {https://ieeexplore.ieee.org/document/5206848}
}""")
def collect_synth(h5, data_dir):
protos_stimuli = []
responses_synth_d = {}
for monkey in h5.root.images.synthetic:
for setting in monkey:
for session_images in setting:
session_neural = h5.root.neural.synthetic[monkey._v_name][
setting._v_name][session_images._v_name]
session_target_inds = h5.root.target_inds[monkey._v_name][
setting._v_name][session_images._v_name]
identifier = f"{monkey._v_name[-1]}_{setting._v_name}_{session_images._v_name}"
img_temp_path = data_dir / "images_temp" / "synthetic" / identifier
img_temp_path.mkdir(parents=True, exist_ok=True)
proto_stimuli = np_to_png(session_images, img_temp_path)
proto_stimuli["animal"] = monkey._v_name
proto_stimuli["setting"] = setting._v_name
proto_stimuli["session"] = session_images._v_name
protos_stimuli.append(proto_stimuli)
proto_neural = np_to_xr(monkey, setting, session_neural,
proto_stimuli, session_target_inds,
"synth")
proto_neural = NeuronRecordingAssembly(proto_neural)
responses_synth_d[proto_neural.name] = proto_neural
proto_stimuli_all = pd.concat(protos_stimuli, axis=0)
assert len(np.unique(
proto_stimuli_all['image_id'])) == len(proto_stimuli_all)
stimuli = StimulusSet(proto_stimuli_all)
stimuli.image_paths = {
row.image_id: row.image_current_local_file_path
for row in stimuli.itertuples()
}
return stimuli, responses_synth_d
def collect_stimuli(data_dir):
stimulus_set = pd.read_csv(data_dir / 'imagenet2012.csv')
stimulus_set = StimulusSet(stimulus_set)
stimulus_set.image_paths = {row.image_id: row.filepath for row in stimulus_set.itertuples()}
stimulus_set['image_path_within_store'] = stimulus_set['filename'].apply(
lambda filename: os.path.splitext(filename)[0])
stimulus_set = stimulus_set[['image_id', 'label', 'synset', 'image_file_sha1', 'image_path_within_store']]
assert len(np.unique(stimulus_set['image_id'])) == len(stimulus_set), "duplicate entries"
return stimulus_set
def collect_stimuli_nat(h5, data_dir):
img_array = h5.root.images.naturalistic
img_temp_path = data_dir / "images_temp" / "naturalistic"
img_temp_path.mkdir(parents=True, exist_ok=True)
proto = np_to_png(img_array, img_temp_path)
assert len(np.unique(proto['image_id'])) == len(proto)
stimuli = StimulusSet(proto)
stimuli.image_paths = {
row.image_id: row.image_current_local_file_path
for row in stimuli.itertuples()
}
return stimuli
def prep_proto_stim():
image_dir = Path(__file__).parent / "images"
csv_path = image_dir / "test_images.csv"
proto = pd.read_csv(csv_path)
proto["image_id"] = [f"{iid}.{now()}" for iid in proto["image_id"]]
proto[f"test_{now()}"] = [f"{iid}.{now()}" for iid in proto["image_id"]]
proto = StimulusSet(proto)
proto.image_paths = {
row.image_id: image_dir / row.image_current_relative_file_path
for row in proto.itertuples()
}
proto['image_file_name'] = proto['image_path_within_store']
return proto
def __init__(self):
stimulus_set = pd.read_csv(
os.path.join(os.path.dirname(__file__), 'imagenet2012.csv'))
stimulus_set = StimulusSet(stimulus_set)
stimulus_set.image_paths = {
row.image_id: row.filepath
for row in stimulus_set.itertuples()
}
self._stimulus_set = stimulus_set
self._similarity_metric = Accuracy()
ceiling = Score([1, np.nan],
coords={'aggregation': ['center', 'error']},
dims=['aggregation'])
super(Imagenet2012, self).__init__(
identifier='fei-fei.Deng2009-top1',
version=1,
ceiling_func=lambda: ceiling,
parent='ImageNet',
paper_link="https://ieeexplore.ieee.org/abstract/document/5206848")
def collect_stimuli(data_path):
stimuli = []
# search images
for i in range(1, 241):
target_path = os.path.join(data_path / 'stimuli',
's_' + str(i) + '.jpg')
filename = 's_' + str(i) + '.jpg'
image_id = 'klab_vs_naturaldesign_stimuli_' + str(i)
image_label = 'stimuli'
sample_number = i
stimuli.append({
'image_current_local_file_path': target_path,
'image_path_within_store': filename,
'image_label': image_label,
'image_id': image_id,
'sample_number': sample_number,
})
# target images
for i in range(1, 241):
target_path = os.path.join(data_path / 'target',
't_' + str(i) + '.jpg')
filename = 't_' + str(i) + '.jpg'
image_id = 'klab_vs_naturaldesign_target_' + str(i)
image_label = 'target'
sample_number = i
stimuli.append({
'image_current_local_file_path': target_path,
'image_path_within_store': filename,
'image_label': image_label,
'image_id': image_id,
'sample_number': sample_number,
})
# target mask
for i in range(1, 241):
target_path = os.path.join(data_path / 'gt', 'gt_' + str(i) + '.jpg')
filename = 'gt_' + str(i) + '.jpg'
image_id = 'klab_vs_naturaldesign_gt_' + str(i)
image_label = 'gt'
sample_number = i
stimuli.append({
'image_current_local_file_path': target_path,
'image_path_within_store': filename,
'image_label': image_label,
'image_id': image_id,
'sample_number': sample_number,
})
stimuli = StimulusSet(stimuli)
stimuli.image_paths = {
row.image_id: row.image_current_local_file_path
for row in stimuli.itertuples()
}
stimuli['image_file_name'] = stimuli['image_path_within_store']
return stimuli
def collect_stimuli(data_path):
stimuli = []
arr_loc = loadmat(os.path.join(data_path / 'gt', 'array.mat'))
for i in range(1, 301):
j = (np.argmax(arr_loc['MyData'][i - 1]['arraycate'][0] ==
arr_loc['MyData'][i - 1]['targetcate'][0]))
target_path = os.path.join(data_path / 'stimuli',
'array_' + str(i) + '.jpg')
filename = 'array_' + str(i) + '.jpg'
image_id = 'stimuli_' + str(i)
image_label = 'stimuli'
sample_number = i
stimuli.append({
'image_current_local_file_path': target_path,
'image_path_within_store': filename,
'image_label': image_label,
'image_id': image_id,
'tar_obj_pos': j,
'sample_number': sample_number,
})
for i in range(1, 301):
j = (np.argmax(arr_loc['MyData'][i - 1]['arraycate'][0] ==
arr_loc['MyData'][i - 1]['targetcate'][0]))
target_path = os.path.join(data_path / 'target',
'target_' + str(i) + '.jpg')
filename = 'target_' + str(i) + '.jpg'
image_id = 'target_' + str(i)
image_label = 'target'
sample_number = i
stimuli.append({
'image_current_local_file_path': target_path,
'image_path_within_store': filename,
'image_label': image_label,
'image_id': image_id,
'tar_obj_pos': j,
'sample_number': sample_number,
})
for i in range(1, 7):
target_path = os.path.join(data_path / 'gt', 'mask' + str(i) + '.jpg')
filename = 'mask' + str(i) + '.jpg'
image_id = 'mask_' + str(i)
image_label = 'mask'
sample_number = i
stimuli.append({
'image_current_local_file_path': target_path,
'image_path_within_store': filename,
'image_label': image_label,
'image_id': image_id,
'tar_obj_pos': i - 1,
'sample_number': sample_number,
})
stimuli = StimulusSet(stimuli)
stimuli.image_paths = {
row.image_id: row.image_current_local_file_path
for row in stimuli.itertuples()
}
stimuli['image_file_name'] = stimuli['image_path_within_store']
return stimuli