Initial commit of tests for #13.

test.py

@@ -0,0 +1,89 @@
+# Copyright 2015 Carnegie Mellon University
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+import cv2
+import openface
+import os
+
+import numpy as np
+np.set_printoptions(precision=2)
+from numpy.linalg import norm
+
+import scipy
+import scipy.spatial
+
+import time
+
+import openface
+import openface.helper
+from openface.data import iterImgs
+from openface.alignment import NaiveDlib  # Depends on dlib.
+
+from subprocess import Popen, PIPE
+
+import sys
+fileDir = os.path.dirname(os.path.realpath(__file__))
+
+modelDir = os.path.join(fileDir, 'models')
+dlibModelDir = os.path.join(modelDir, 'dlib')
+openfaceModelDir = os.path.join(modelDir, 'openface')
+
+dlibFacePredictor = os.path.join(dlibModelDir,
+                                 "shape_predictor_68_face_landmarks.dat")
+networkModel = os.path.join(openfaceModelDir, 'nn4.v1.t7')
+imgDim = 96
+
+align = NaiveDlib(dlibFacePredictor)
+net = openface.TorchWrap(networkModel, imgDim=imgDim)
+
+def test_pipeline():
+    imgPath = os.path.join('images', 'examples', 'lennon-1.jpg')
+    bgrImg = cv2.imread(imgPath)
+    if bgrImg is None:
+        raise Exception("Unable to load image: {}".format(imgPath))
+    rgbImg = cv2.cvtColor(bgrImg, cv2.COLOR_BGR2RGB)
+    assert np.isclose(norm(rgbImg), 11.1355)
+
+    bb = align.getLargestFaceBoundingBox(rgbImg)
+    assert bb.left() == 341
+    assert bb.right() == 1006
+    assert bb.top() == 193
+    assert bb.bottom() == 859
+
+    alignedFace = align.alignImg("affine", imgDim, rgbImg, bb)
+    assert np.isclose(norm(alignedFace), 8.30662)
+
+    rep = net.forwardImage(alignedFace)
+    assert np.isclose(scipy.spatial.distance.cosine(rep, np.ones(128)),
+                      1.01860434258)
+
+def test_compare_demo():
+    cmd = ['python2', './demos/compare.py',
+           'images/examples/lennon-1.jpg',
+           'images/examples/lennon-2.jpg']
+    p = Popen(cmd, stdout=PIPE, stderr=PIPE)
+    (out, err) = p.communicate()
+    print(err)
+    assert "0.298" in out
+
+def test_classification_demo():
+    cmd = ['python2', './demos/classifier.py',
+           'infer',
+           './models/openface/celeb-classifier.nn4.v1.pkl',
+           'images/examples/carell.jpg']
+    p = Popen(cmd, stdout=PIPE, stderr=PIPE)
+    (out, err) = p.communicate()
+    print(err)
+    assert "Predict SteveCarell with 0.96 confidence." in out