diff options
| -rwxr-xr-x | train.py | 4 | ||||
| -rwxr-xr-x | util/image-to-array | 35 |
2 files changed, 37 insertions, 2 deletions
@@ -18,13 +18,13 @@ def normalise_faces(average_face, raw_faces): parser = argparse.ArgumentParser() parser.add_argument("-i", "--data", help="Input CSV file", required=True) parser.add_argument("-o", "--model", help="Output model file", required=True) -parser.add_argument("-m", "--M", help="Number of eigenvalues in model", type=int) +parser.add_argument("-m", "--eigen", help="Number of eigenvalues in model", required=True, type=int) args = parser.parse_args() assert args.data, "No input CSV data (-i, --input-data)" assert args.model, "No model specified (-o, --model)" -M = args.M | -1; +M = args.eigen raw_faces = genfromtxt(args.data, delimiter=',').T diff --git a/util/image-to-array b/util/image-to-array new file mode 100755 index 0000000..72abc5c --- /dev/null +++ b/util/image-to-array @@ -0,0 +1,35 @@ +#!/usr/bin/env python +import numpy as np +import sys, os + +from PIL import Image; + +def jpg_image_to_array(image_path): + """ + Loads JPEG image into 3D Numpy array of shape + (width, height, channels) + """ + with Image.open(image_path) as image: + im_arr = np.frombuffer(image.tobytes(), dtype=np.uint8) + # im_arr = im_arr.reshape((image.size[1], image.size[0], 3)) + return im_arr + +def main(files): + for i in range(0, len(files)): + inputfile = files[i] + print(inputfile); + + if not os.path.exists(inputfile): + sys.exit('ERROR: File %s was not found!' % inputfile) + + print(jpg_image_to_array(inputfile)) + +if __name__ == "__main__": + if len(sys.argv) > 1: + main(sys.argv[1:]) + else: + print("%s: You must specify files to convert to vectors" % sys.argv[0]) + +# def compute_average_vector + +# def compute_covariance_of_normalised_faces |