Add file comparison example.

examples/compare.py

@@ -0,0 +1,104 @@
+#!/usr/bin/env python2
+#
+# Example to compare the faces in two images.
+# Brandon Amos
+# 2015/09/29
+#
+# 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 time
+
+start = time.time()
+import argparse
+import cv2
+import os
+
+import numpy as np
+np.set_printoptions(precision=2)
+
+import sys
+fileDir = os.path.dirname(os.path.realpath(__file__))
+sys.path.append(os.path.join(fileDir, ".."))
+
+import facenet
+import facenet.helper
+from facenet.data import iterImgs
+
+modelDir = os.path.join(fileDir, '..', 'models')
+dlibModelDir = os.path.join(modelDir, 'dlib')
+facenetModelDir = os.path.join(modelDir, 'facenet')
+
+parser = argparse.ArgumentParser()
+
+parser.add_argument('img1', type=str, help="Input image 1.")
+parser.add_argument('img2', type=str, help="Input image 2.")
+parser.add_argument('--dlibFaceMean', type=str, help="Path to dlib's face predictor.",
+                    default=os.path.join(dlibModelDir, "mean.csv"))
+parser.add_argument('--dlibFacePredictor', type=str, help="Path to dlib's face predictor.",
+                    default=os.path.join(dlibModelDir, "shape_predictor_68_face_landmarks.dat"))
+parser.add_argument('--dlibRoot', type=str,
+                    default=os.path.expanduser("~/src/dlib-18.15/python_examples"),
+                    help="dlib directory with the dlib.so Python library.")
+parser.add_argument('--networkModel', type=str, help="Path to Torch network model.",
+                    default=os.path.join(facenetModelDir, 'nn4.v1.t7'))
+parser.add_argument('--imgDim', type=int, help="Default image dimension.", default=96)
+parser.add_argument('--cuda', type=bool, default=False)
+
+args = parser.parse_args()
+
+sys.path.append(args.dlibRoot)
+import dlib
+
+from facenet.alignment import NaiveDlib # Depends on dlib.
+print("Argument parsing and loading libraries took {} seconds.".format(time.time()-start))
+
+start = time.time()
+align = NaiveDlib(args.dlibFaceMean, args.dlibFacePredictor)
+net = facenet.TorchWrap(args.networkModel, imgDim=args.imgDim, cuda=args.cuda)
+print("Loading the dlib and FaceNet models took {} seconds.".format(time.time()-start))
+
+def getRep(imgPath):
+    global i
+    print("Processing {}.".format(imgPath))
+    img = cv2.imread(imgPath)
+    if img is None:
+        raise Exception("Unable to load image: {}".format(imgPath))
+    print("  + Original size: {}".format(img.shape))
+
+    start = time.time()
+    bb = align.getLargestFaceBoundingBox(img)
+    if bb is None:
+        raise Exception("Unable to find a face: {}".format(imgPath))
+    print("  + Face detection took {} seconds.".format(time.time()-start))
+
+    start = time.time()
+    alignedFace = align.alignImg("affine", args.imgDim, img, bb)
+    if alignedFace is None:
+        raise Exception("Unable to align image: {}".format(imgPath))
+    print("  + Face alignment took {} seconds.".format(time.time()-start))
+
+    start = time.time()
+    t = '/tmp/facenet-compare.png'
+    cv2.imwrite(t, alignedFace)
+    rep = np.array(net.forward(t))
+    os.remove(t)
+    print("  + FaceNet forward pass took {} seconds.".format(time.time()-start))
+    print("Representation:")
+    print(rep)
+    print("-----\n")
+    return rep
+
+d = getRep(args.img1) - getRep(args.img2)
+print("Squared l2 distance between representations: {}".format(np.dot(d, d)))