def create_image_csv(proto_stimulus_set, target_path):
_logger.debug(f"Writing csv to {target_path}")
specific_columns = extract_specific(proto_stimulus_set)
specific_stimulus_set = proto_stimulus_set[specific_columns]
specific_stimulus_set.to_csv(target_path, index=False)
sha1 = sha1_hash(target_path)
return sha1
def create_image_zip(proto_stimulus_set, target_zip_path):
"""
Create zip file for images in StimulusSet.
Files in the zip will follow a flat directory structure with each row's filename equal to the `image_id` by default,
or `image_path_within_store` if passed.
:param proto_stimulus_set: a `StimulusSet` with a `get_image: image_id -> local path` method, an `image_id` column,
and optionally an `image_path_within_store` column.
:param target_zip_path: path to write the zip file to
:return: SHA1 hash of the zip file
"""
_logger.debug(f"Zipping stimulus set to {target_zip_path}")
os.makedirs(os.path.dirname(target_zip_path), exist_ok=True)
arcnames = []
with zipfile.ZipFile(target_zip_path, 'w') as target_zip:
for _, row in proto_stimulus_set.iterrows(
): # using iterrows instead of itertuples for very large StimulusSets
image_path = proto_stimulus_set.get_image(row['image_id'])
extension = os.path.splitext(image_path)[1]
arcname = row['image_path_within_store'] if hasattr(
row, 'image_path_within_store') else row['image_id']
arcname = arcname + extension
target_zip.write(image_path, arcname=arcname)
arcnames.append(arcname)
sha1 = sha1_hash(target_zip_path)
return sha1, arcnames
def verify_sha1(filepath, sha1):
actual_hash = sha1_hash(filepath)
if sha1 != actual_hash:
raise IOError(
f"File '{filepath}': invalid SHA-1 hash {actual_hash} (expected {sha1})"
)
_logger.debug(f"sha1 OK: {filepath}")
def write_netcdf(assembly, target_netcdf_file):
_logger.debug(f"Writing assembly to {target_netcdf_file}")
assembly = DataArray(assembly) # if we're passed a BrainIO DataAssembly, it will automatically re-index otherwise
for index in assembly.indexes.keys():
assembly.reset_index(index, inplace=True)
assembly.to_netcdf(target_netcdf_file)
sha1 = sha1_hash(target_netcdf_file)
return sha1
def np_to_png(img_array, img_temp_path):
meta = []
for i, img_np in enumerate(img_array):
img_pil = Image.fromarray((img_np).astype('uint8'))
img_path = img_temp_path / f"img{i}.png"
img_pil.save(img_path)
sha1 = sha1_hash(img_path)
img_path_sha1 = img_temp_path / f"{sha1}.png"
img_path.rename(img_path_sha1)
meta.append({
"image_id": sha1,
"image_index": i,
"image_current_local_file_path": img_path_sha1
})
_logger.debug(f"{img_path} -> {img_path_sha1}")
return pd.DataFrame(meta)
def write_netcdf(assembly, target_netcdf_file):
_logger.debug(f"Writing assembly to {target_netcdf_file}")
assembly = assembly.reset_index(list(assembly.indexes))
assembly.to_netcdf(target_netcdf_file)
sha1 = sha1_hash(target_netcdf_file)
return sha1
