refactored a little and add a script to generate and store the model

makefile

@@ -10,6 +10,12 @@ data/imm_face_db: data/imm_face_db.tar.gz
 data/imm_face_db.tar.gz:
 	(cd data; wget http://www.imm.dtu.dk/~aam/datasets/imm_face_db.tar.gz)
 
+generate_test_model:
+	python src/main.py \
+		--store_pca \
+		--asf data/imm_face_db/*.asf \
+		--file data/pca_test_model
+
 test:
 	py.test -f src/*_test.py
 

scripts/imm_test_set.sh

+files=`ls data/imm_face_db/ | grep -E "^[0-2][0-9].*.asf"`
+
+for f in $files; do
+    echo "data/imm_face_db/$f"
+done

scripts/imm_train_set.sh

+files=`ls data/imm_face_db/ | grep -E "^[3-4][0-9].*.asf"`
+
+for f in $files; do
+    echo "data/imm_face_db/$f"
+done

src/main.py

 import copy
 import argparse
+import logging
 
 import cv2
 import numpy as np
 
-from pca import pca, reconstruct
+import pca
 from aam import get_mean
 from imm_points import IMMPoints, build_feature_vectors, \
     flatten_feature_vectors
 
+logging.basicConfig(level=logging.INFO,
+        format='%(asctime)s %(levelname)s %(name)s: %(message)s')
+logger = logging.getLogger(__name__)
 
-def nothing(_):
-    pass
 
 
 def add_parser_options():
@@ -21,12 +23,12 @@ def add_parser_options():
 
     pca_group.add_argument(
         '--store_pca', action='store_true',
-        help='number of principle components to keep and are able to manipulate'
+        help='Store the pca model'
     )
 
     pca_group.add_argument(
         '--show_pca', action='store_true',
-        help='number of principle components to keep and are able to manipulate'
+        help='Show and manipulate the stored PCA model'
     )
 
     # asf files
@@ -47,6 +49,10 @@ def add_parser_options():
     return parser
 
 
+def nothing(_):
+    pass
+
+
 def init_eigenvalue_trackbars(n_components, s):
     cv2.namedWindow('eigenvalues')
 
@@ -54,10 +60,24 @@ def init_eigenvalue_trackbars(n_components, s):
         cv2.createTrackbar('{}'.format(i), 'eigenvalues', 500, 1000, nothing)
 
 
-def store_model(args):
+def store_pca_model(args):
     """
     Store the U, s, Vt and mean of all the asf datafiles given by the asf
     files.
+
+    It is stored in the following way:
+
+        np.load(filename, np.assary([U, s, Vt, mean_values])
+
+        And accessed by:
+
+        UsVtm = np.load(args.file)
+
+        U = UsVtm[0]
+        s = UsVtm[1]
+        Vt = UsVtm[2]
+        mean_values = UsVtm[3]
+
     """
     assert args.asf, '--asf files should be given'
     assert args.file, '--file needs to be provided to store the pca model'
@@ -65,12 +85,13 @@ def store_model(args):
     imm_points = build_feature_vectors(args.asf, flattened=True)
     mean_values = get_mean(imm_points)
 
-    U, s, Vt = pca(imm_points, mean_values)
+    U, s, Vt = pca.pca(imm_points, mean_values)
 
     np.save(args.file, np.asarray([U, s, Vt, mean_values]))
+    logger.info('Stored pca model in %s', args.file)
 
 
-def show_pca(args):
+def show_pca_model(args):
     assert args.asf, '--asf files should be given to allow the image to be shown'
     assert args.file, '--file needs to be provided to get the pca model'
 
@@ -80,7 +101,6 @@ def show_pca(args):
     U = UsVtm[0]
     s = UsVtm[1]
     Vt = UsVtm[2]
-
     mean_values = UsVtm[3]
 
     # init trackbars
@@ -95,7 +115,7 @@ def show_pca(args):
     s_copy = copy.copy(s)
 
     while True:
-        projection = reconstruct(U, s_copy, Vt, n_components)
+        projection = pca.reconstruct(U, s_copy, Vt, n_components)
         X_reconstructed = (projection[index] + mean_values).reshape((58, 2))
 
         imm = IMMPoints(points=X_reconstructed)
@@ -122,9 +142,11 @@ def main():
     args = parser.parse_args()
 
     if args.show_pca:
-        show_pca(args)
+        show_pca_model(args)
     elif args.store_pca:
-        store_model(args)
+        store_pca_model(args)
+    elif args.reconstruct:
+        reconstruct_with_model(args)
 
 
 if __name__ == '__main__':