package visual search object array

.gitignore

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+# Data files
+mkgu_packaging/klab/search_datasets*
+
+brainio_contrib.egg-info*
+build*
+dist*
+.ipynb_checkpoints*

mkgu_packaging/klab/search_naturaldesign.py

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+import os
+
+import h5py
+import numpy as np
+import pandas as pd
+import scipy.misc
+import xarray as xr
+from pathlib import Path
+from result_caching import store
+from sklearn.model_selection import StratifiedShuffleSplit
+from tqdm import tqdm
+from scipy.io import loadmat
+
+from brainio_collection.knownfile import KnownFile as kf
+from brainio_base.assemblies import BehavioralAssembly
+from brainio_base.stimuli import StimulusSet
+from brainio_contrib.packaging import package_stimulus_set, package_data_assembly
+
+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_data(data_path, sub_id):
+    image_id = ['stimuli_' + str(i) for i in range(1, 241)]
+    subjects = []
+    for i in sub_id:
+        subjects += [i]*len(image_id)
+
+    S_data = np.load(os.path.join(data_path / 'human_data', 'human_all.npy'))
+    I_data = np.load(os.path.join(data_path / 'human_data', 'I_human_all.npy'))
+    data = np.zeros((240*len(sub_id), 66, 2), dtype=int)
+    data[:,:65,:] = S_data
+    data[:,65,:] = I_data
+
+    assembly = BehavioralAssembly(data,
+                               coords={'image_id': ('presentation', image_id*len(sub_id)),
+                                       'subjects': ('presentation', subjects),
+                                       'fixation': [*range(66)],
+                                       'position': ['x', 'y']},
+                               dims=['presentation', 'fixation', 'position'])
+    return assembly
+
+def main():
+    data_dir = Path(__file__).parent / 'search_datasets'
+    data_path = data_dir / 'naturaldesign'
+
+    # create stimuli
+    stimuli = collect_stimuli(data_path)
+    stimuli.name = 'klab.Zhang2018.search_naturaldesign'
+
+    # create assembly for different subjects
+    assembly = collect_data(data_path, [*range(1, 16)])
+    assembly.name = 'klab.Zhang2018search_naturaldesign'
+
+    # package
+    print("\nPackaging Stimuli ----------")
+    package_stimulus_set(stimuli, stimulus_set_name=stimuli.name)
+
+    print("\nPackaging Assembly ----------")
+    package_data_assembly(assembly, data_assembly_name=assembly.name, stimulus_set_name=stimuli.name)
+
+
+if __name__ == '__main__':
+    main()

mkgu_packaging/klab/search_obj_array.py

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+import os
+
+import h5py
+import numpy as np
+import pandas as pd
+import scipy.misc
+import xarray as xr
+from pathlib import Path
+from result_caching import store
+from sklearn.model_selection import StratifiedShuffleSplit
+from tqdm import tqdm
+from scipy.io import loadmat
+
+from brainio_base.assemblies import BehavioralAssembly
+from brainio_base.stimuli import StimulusSet
+from brainio_contrib.packaging import package_stimulus_set, package_data_assembly
+
+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
+
+def collect_data(data_path, sub_id):
+    image_id = ['stimuli_' + str(i) for i in range(1, 301)]
+    subjects = []
+    for i in sub_id:
+        subjects += [i]*len(image_id)
+
+    S_data = np.load(os.path.join(data_path / 'human_data', 'human_all.npy'))
+    I_data = np.load(os.path.join(data_path / 'human_data', 'I_human_all.npy'))
+    data = np.zeros((300*len(sub_id), 8, 2), dtype=int)
+    data[:,:7,:] = S_data
+    data[:,7,:] = I_data
+
+    assembly = BehavioralAssembly(data,
+                               coords={'image_id': ('presentation', image_id*len(sub_id)),
+                                       'subjects': ('presentation', subjects),
+                                       'fixation': [*range(8)],
+                                       'position': ['x', 'y']},
+                               dims=['presentation', 'fixation', 'position'])
+    return assembly
+
+def main():
+    data_dir = Path(__file__).parent / 'search_datasets'
+    data_path = data_dir / 'array'
+
+    # create stimuli
+    stimuli = collect_stimuli(data_path)
+    stimuli.name = 'klab.Zhang2018.search_obj_array'
+
+    # create assembly for different subjects
+    assembly = collect_data(data_path, [*range(1, 16)])
+    assembly.name = 'klab.Zhang2018search_obj_array'
+
+    # package
+    print("\nPackaging Stimuli ----------")
+    package_stimulus_set(stimuli, stimulus_set_name=stimuli.name)
+
+    print("\nPackaging Assembly ----------")
+    package_data_assembly(assembly, data_assembly_name=assembly.name, stimulus_set_name=stimuli.name)
+
+
+if __name__ == '__main__':
+    main()

mkgu_packaging/klab/search_waldo.py

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+import os
+
+import h5py
+import numpy as np
+import pandas as pd
+import scipy.misc
+import xarray as xr
+from pathlib import Path
+from result_caching import store
+from sklearn.model_selection import StratifiedShuffleSplit
+from tqdm import tqdm
+from scipy.io import loadmat
+
+from brainio_base.assemblies import BehavioralAssembly
+from brainio_base.stimuli import StimulusSet
+from brainio_contrib.packaging import package_stimulus_set, package_data_assembly
+
+def collect_stimuli(data_path):
+    stimuli = []
+
+    # search images
+    for i in range(1, 68):
+        target_path = os.path.join(data_path / 'stimuli', 's_' + str(i) + '.jpg')
+        filename = 's_' + str(i) + '.jpg'
+        image_id = 'klab_vs_waldo_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, 68):
+        target_path = os.path.join(data_path / 'target', 't_' + str(i) + '.jpg')
+        filename = 't_' + str(i) + '.jpg'
+        image_id = 'klab_vs_waldo_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, 68):
+        target_path = os.path.join(data_path / 'gt', 'gt_' + str(i) + '.jpg')
+        filename = 'gt_' + str(i) + '.jpg'
+        image_id = 'klab_vs_waldo_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_data(data_path, sub_id):
+    image_id = ['stimuli_' + str(i) for i in range(1, 68)]
+    subjects = []
+    for i in sub_id:
+        subjects += [i]*len(image_id)
+
+    S_data = np.load(os.path.join(data_path / 'human_data', 'human_all.npy'))
+    I_data = np.load(os.path.join(data_path / 'human_data', 'I_human_all.npy'))
+    data = np.zeros((67*len(sub_id), 81, 2), dtype=int)
+    data[:,:80,:] = S_data
+    data[:,80,:] = I_data
+
+    assembly = BehavioralAssembly(data,
+                               coords={'image_id': ('presentation', image_id*len(sub_id)),
+                                       'subjects': ('presentation', subjects),
+                                       'fixation': [*range(81)],
+                                       'position': ['x', 'y']},
+                               dims=['presentation', 'fixation', 'position'])
+    return assembly
+
+def main():
+    data_dir = Path(__file__).parent / 'search_datasets'
+    data_path = data_dir / 'waldo'
+
+    # create stimuli
+    stimuli = collect_stimuli(data_path)
+    stimuli.name = 'klab.Zhang2018.search_waldo'
+
+    # create assembly for different subjects
+    assembly = collect_data(data_path, [*range(1, 16)])
+    assembly.name = 'klab.Zhang2018search_waldo'
+
+    # package
+    print("\nPackaging Stimuli ----------")
+    package_stimulus_set(stimuli, stimulus_set_name=stimuli.name)
+
+    print("\nPackaging Assembly ----------")
+    package_data_assembly(assembly, data_assembly_name=assembly.name, stimulus_set_name=stimuli.name)
+
+
+if __name__ == '__main__':
+    main()