def predict(model_name, input_file, output_file):
model = load_model(model_name)
X = np.load(input_file)
predicted = np.array(model.predict(X))
print(predicted.shape)
create_csv(output_file, predicted)
def start():
global inputdir, outputdir
helper.initialize_sqli()
image_list = list(helper.list_files(inputdir, "image/jpeg"))
for filename in image_list:
print("Processing %s" % (filename, ))
# Creation of the SQLite row for the file
helper.image_row("evidences", filename)
extractor.basic_info(filename)
if gps.get():
extractor.PIL_exif_data_GPS(filename)
if md5.get():
extractor.md5(filename)
if sha256.get():
extractor.sha256(filename)
if sha512:
extractor.sha512(filename)
if exif.get():
extractor.exif_info(filename)
helper.create_csv(outputdir)
def main(args=None):
print """\
##################################################
# imago.py #
# Digital evidences from images! #
# Made with <3 by Matteo Redaelli #
# Twitter: @solventred #
##################################################
"""
if args is None:
args = sys.argv[1:]
parser = argparse.ArgumentParser()
parser.add_argument('-i','--input', help='Input directory path', type=str, required=True)
parser.add_argument('-x','--exif', help='Extract exif metadata', action='store_true')
parser.add_argument('-g','--gps', help='Extract, parse and convert to coordinates, GPS exif metadata from images (if any)It works only with JPEG.', action='store_true')
parser.add_argument('-e','--ela', help='Extract, Error Level Analysis image,It works only with JPEG. *BETA*', action='store_true')
parser.add_argument('-n','--nude', help='Detect Nudity, It works only with JPEG, *BETA*', action='store_true')
parser.add_argument('-d','--digest', help='Calculate perceptual image hashing', type=str, choices=["md5", "sha256", "sha512", "all"])
parser.add_argument('-p','--percentualhash', help='Calculate hash digest', type=str, choices=["ahash", "phash", "dhash","whash","all"])
parser.add_argument('-o','--output', help='Output directory path', type=str)
parser.add_argument('-s','--sqli', help='Keep SQLite file after the computation', action='store_true')
parser.add_argument('-t','--type', help='Select the image, this flag can be JPEG or TIFF, if this argument it is not provided, imago will process all the image types(i.e. JPEG, TIFF)', type=str, choices=["jpeg","tiff"])
args = parser.parse_args()
if (args.exif or args.gps or args.ela or args.digest or args.nude or args.percentualhash):
filetype = ""
if (args.type == "jpeg"):
filetype = "image/jpeg"
elif (args.type == "tiff"):
filetype = "image/tiff"
else:
filetype = "image"
if args.output:
output_path = args.output
else:
output_path = "."
base_dir = args.input
helper.initialize_sqli()
image_list = list(helper.list_files(base_dir, filetype))
for filename in image_list:
print ("Processing %s" % (filename,))
# Creation of the SQLite row for the file
helper.image_row("evidences", filename)
extractor.basic_info(filename)
if args.nude:
extractor.detect_nudity(filename)
if args.gps:
extractor.PIL_exif_data_GPS(filename)
if args.percentualhash == "ahash":
extractor.ahash(filename)
elif args.percentualhash == "phash":
extractor.phash(filename)
elif args.percentualhash == "dhash":
extractor.dhash(filename)
elif args.percentualhash == "whash":
extractor.whash(filename)
elif args.percentualhash == "all":
extractor.ahash(filename)
extractor.phash(filename)
extractor.whash(filename)
extractor.dhash(filename)
if args.digest == "md5":
extractor.md5(filename)
elif args.digest == "sha256":
extractor.sha256(filename)
elif args.digest == "sha512":
extractor.sha512(filename)
elif args.digest == "all":
extractor.md5(filename)
extractor.sha256(filename)
extractor.sha512(filename)
if args.exif:
extractor.exif_info(filename)
if args.ela:
extractor.ela(filename,output_path)
print ("Processing of %s completed!" % (filename,))
# Creation of the file CSV
helper.create_csv(output_path)
if not args.sqli:
os.remove('metadata.db')
elif args.sqli and args.output:
os.rename("metadata.db", os.path.join(args.output,"metadata.db"))
else:
print("ERROR: Select at least one type of extraction")
def convert(npy_file, csv_file):
npy = np.load(npy_file)
create_csv(csv_file, npy)
