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import os
import hashlib
import pandas as pd
from PIL import Image
import PIL.ExifTags as ExifTags
def process_base_dirs(base_dirs):
"""Extract exif data from all jpg files under the provided directory(ies)
:param base_dirs: path or paths to directories to scan all files under
:return: A list of dictionaries containing exif data for each file
files_scanned = set()
exif_data = []
if isinstance(base_dirs, str):
base_dirs = [base_dirs]
for base_dir in base_dirs:
for root, dirs, files in os.walk(base_dir):
jpgs = [os.path.join(root, file_name) for file_name in files
if file_name.lower().endswith('.jpg')]
process_files(jpgs, files_scanned, exif_data)
return exif_data
def process_files(file_paths, files_scanned, exif_data):
"""Extract exif data from each of the provided files, avoiding duplicates
:param file_paths: Files to extract data from
:param files_scanned: Set of hashes of file data
:param exif_data: List of dictionaries of exif data
for file_path in file_paths:
md5 = get_md5(file_path)
if md5 not in files_scanned:
def get_md5(file_path, block_size=2**20):
"""Return The md5 hash of the file's data."""
m = hashlib.md5()
with open(file_path, 'rb') as f:
while True:
buf =
if not buf:
return m.digest()
def get_exif_data(file_path):
"""Extract a dict of exif data from the provided file"""
img =
raw_exif = img._getexif()
if raw_exif is None:
return {}
return {ExifTags.TAGS[k]: v for k, v in raw_exif.items()
if k in ExifTags.TAGS}
def make_df(base_dirs,
columns=('DateTimeOriginal', 'FocalLength', 'Make', 'Model')):
"""Return a dataframe of exif data from all the files under the directories
provided in base_dirs.
:param base_dirs: path or paths to scan all files under
:param columns: fields of exif data to extract
:return: DataFrame
exif_data = process_base_dirs(base_dirs)
result_dict = {column: [] for column in columns}
for exif in exif_data:
for column in columns:
return pd.DataFrame(result_dict)
if __name__ == '__main__':
base = os.path.join('/Users/frank/Pictures',
'Photos Library.photoslibrary/Masters/2015/05/31')
result = make_df(base)
print result.head()