adapt compare.py to compare images from 2 folders

contributed/Facenet1vN

@@ -0,0 +1,204 @@
+# author : Maelig Jacquet
+# June 2020
+# adapted from davidsanberg/facenet
+
+"""Performs face alignment and calculates L2 distance between the embeddings of images from 2 folders."""
+
+# MIT License
+#
+# Copyright (c) 2016 David Sandberg
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from scipy import misc
+import tensorflow as tf
+import numpy as np
+import sys
+import os
+import copy
+import argparse
+import facenet
+import align.detect_face
+import xlsxwriter
+import itertools
+import cv2
+from tqdm import tqdm
+#import imageio
+
+parser = argparse.ArgumentParser()
+
+
+parser.add_argument('image_files', type=str, nargs='+', help='Images to compare')
+parser.add_argument('--image_size', type=int,
+    help='Image size (height, width) in pixels.', default=160)
+parser.add_argument('--margin', type=int,
+    help='Margin for the crop around the bounding box (height, width) in pixels.', default=44)
+parser.add_argument('--gpu_memory_fraction', type=float,
+    help='Upper bound on the amount of GPU memory that will be used by the process.', default=0.01)
+parser.add_argument('--model', type=str,
+    help='Could be either a directory containing the meta_file and ckpt_file or a model protobuf (.pb) file', default='../data/model/20180402-114759.pb')
+parser.add_argument('out_file', type=str,
+    help='Output file.', default='../output/results.csv')
+
+args = parser.parse_args()
+
+a_same = args.a_same
+a_different = args.a_different
+b_same = args.b_same
+b_different = args.b_different
+outfile = args.out_file
+
+dirimg1 = args.image_files[0]
+dirimg2 = args.image_files[1]
+
+if os.path.isdir(dirimg1):
+    listimg1 = []
+    for f in os.listdir(dirimg1):
+        listimg1.append(dirimg1 + "/" + f )
+else :
+    listimg1=[dirimg1]
+
+
+if os.path.isdir(dirimg2):
+    listimg2 = []
+    for f in os.listdir(dirimg2):
+        listimg2.append(dirimg2 + "/" + f )
+else :
+    listimg2=[dirimg2]
+
+
+def main(listimg1, listimg2):
+    with tf.Graph().as_default():
+
+        with tf.Session() as sess:
+
+            # Load the model
+            facenet.load_model(args.model)
+
+            # Get input and output tensors
+            images_placeholder = tf.get_default_graph().get_tensor_by_name("input:0")
+            embeddings = tf.get_default_graph().get_tensor_by_name("embeddings:0")
+            phase_train_placeholder = tf.get_default_graph().get_tensor_by_name("phase_train:0")
+
+            # Run forward pass to calculate embeddings
+            ###### original compare.py modif
+            nrof_traces = len(listimg1)
+            nrof_bdd = len(listimg2)
+
+            emb1 = np.array([])
+            emb2 = np.array([])
+
+            feed_dict1 = { images_placeholder: images1, phase_train_placeholder:False }
+            emb1 = sess.run(embeddings, feed_dict=feed_dict1)
+
+            feed_dict2 = { images_placeholder: images2, phase_train_placeholder:False }
+            emb2 = sess.run(embeddings, feed_dict=feed_dict2)
+
+            print (nrof_traces, "images in folder 1")
+            print (nrof_bdd, "images in folder 2")
+            print (nrof_traces * nrof_bdd, "comparisons to run")
+
+            # Create output folder if doesnt exist
+            out_path = os.path.dirname(outfile)
+            if not os.path.exists(out_path):
+                os.makedirs(out_path)
+
+            outf = open(outfile, "w+")
+            outf.write("Image 1;Image 2;Score\n")
+
+            print ('MATCHING...')
+
+            for (i, a) in zip(range(len(images1)), listimg1):
+                nom_img1 = os.path.basename(a)
+                for (j, b) in zip(range(len(images2)), listimg2):
+                    nom_img2 = os.path.basename(b)
+                    dist = np.sqrt(np.sum(np.square(np.subtract(emb1[i,:], emb2[j,:]))))
+
+                    outf.write("%s;%s;%.3f\n" % (nom_img1, nom_img2, dist))
+
+            outf.close()
+
+
+            print ('END')
+
+
+def load_and_align_data(image_paths, image_size, margin, gpu_memory_fraction):
+
+    minsize = 20 # minimum size of face
+    threshold = [ 0.6, 0.7, 0.7 ]  # three steps's threshold
+    factor = 0.709 # scale factor
+
+
+    images1 = []
+    images2 = []
+
+    print('Creating networks and loading parameters for :', (os.path.basename(os.path.dirname(image_paths[0]))))
+    with tf.Graph().as_default():
+        gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=gpu_memory_fraction)
+        sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, log_device_placement=False))
+        with sess.as_default():
+            pnet, rnet, onet = align.detect_face.create_mtcnn(sess, None)
+
+    with tqdm(total=len(image_paths), desc = 'Loading and aligning images from %s' % (os.path.basename(os.path.dirname(image_paths[0])))) as pbar2:
+        img_list = []
+        for image in image_paths:
+            img = misc.imread(os.path.expanduser(image), mode='RGB')
+
+            img_size = np.asarray(img.shape)[0:2]
+            bounding_boxes, _ = align.detect_face.detect_face(img, minsize, pnet, rnet, onet, threshold, factor)
+            if len(bounding_boxes) < 1:
+            # image_paths.remove(image)
+                print("can't detect face, remove ", image)
+                if image in image_paths:
+                    image_paths.remove(image)
+                continue
+            det = np.squeeze(bounding_boxes[0,0:4])
+            bb = np.zeros(4, dtype=np.int32)
+            bb[0] = np.maximum(det[0]-margin/2, 0)
+            bb[1] = np.maximum(det[1]-margin/2, 0)
+            bb[2] = np.minimum(det[2]+margin/2, img_size[1])
+            bb[3] = np.minimum(det[3]+margin/2, img_size[0])
+            cropped = img[bb[1]:bb[3],bb[0]:bb[2],:]
+            aligned = misc.imresize(cropped, (image_size, image_size), interp='bilinear')
+            prewhitened = facenet.prewhiten(aligned)
+
+            img_list.append(prewhitened)
+            pbar2.update(1)
+
+            if image_paths == listimg1:
+                images1 = np.stack(img_list)
+
+            elif image_paths == listimg2:
+                images2 = np.stack(img_list)
+
+        return images1 if image_paths == listimg1 else images2
+
+
+images1= load_and_align_data(listimg1, args.image_size, args.margin, args.gpu_memory_fraction)
+images2= load_and_align_data(listimg2, args.image_size, args.margin, args.gpu_memory_fraction)
+
+main(listimg1, listimg2)
+
+# if __name__ == '__main__':
+#     main(parse_arguments(sys.argv[1:]))
+# print (sys.argv)