Allow Pickle in detect_face

contributed/batch_represent.py

@@ -75,7 +75,8 @@
 import facenet
 import numpy as np
 from sklearn.datasets import load_files
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
+tf.disable_v2_behavior()
 from six.moves import xrange
 
 def main(args):

contributed/cluster.py

@@ -27,7 +27,8 @@
 from __future__ import print_function
 
 from scipy import misc
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
+tf.disable_v2_behavior()
 import numpy as np
 import os
 import sys

contributed/clustering.py

@@ -1,5 +1,6 @@
 """ Face Cluster """
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
+tf.disable_v2_behavior()
 import numpy as np
 import importlib
 import argparse
@@ -190,8 +191,8 @@ def get_onedir(paths):
         for x in image_paths:
             if os.path.getsize(x)>0:
                 dataset.append(x)
-        
-    return dataset 
+
+    return dataset
 
 
 def main(args):
@@ -216,22 +217,22 @@ def main(args):
             #image_list, label_list = facenet.get_image_paths_and_labels(train_set)
 
             meta_file, ckpt_file = facenet.get_model_filenames(os.path.expanduser(args.model_dir))
-            
+
             print('Metagraph file: %s' % meta_file)
             print('Checkpoint file: %s' % ckpt_file)
             load_model(args.model_dir, meta_file, ckpt_file)
-            
+
             # 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")
-            
+
             image_size = images_placeholder.get_shape()[1]
             print("image_size:",image_size)
             embedding_size = embeddings.get_shape()[1]
-        
+
             # Run forward pass to calculate embeddings
-            print('Runnning forward pass on images') 
+            print('Runnning forward pass on images')
 
             nrof_images = len(image_paths)
             nrof_batches = int(math.ceil(1.0*nrof_images / args.batch_size))
@@ -240,7 +241,7 @@ def main(args):
                 embedding_size,nrof_images,nrof_batches,emb_array,args.batch_size,image_paths)
             sorted_clusters = cluster_facial_encodings(facial_encodings)
             num_cluster = len(sorted_clusters)
-                
+
             # Copy image files to cluster folders
             for idx, cluster in enumerate(sorted_clusters):
                 #save all the cluster

contributed/export_embeddings.py

@@ -52,7 +52,8 @@
 
 import time
 from scipy import misc
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
+tf.disable_v2_behavior()
 import numpy as np
 import sys
 import os

contributed/face.py

@@ -34,7 +34,8 @@
 
 import cv2
 import numpy as np
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
+tf.disable_v2_behavior()
 from scipy import misc
 
 import align.detect_face

contributed/predict.py

@@ -29,7 +29,8 @@
 #----------------------------------------------------
 
 
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
+tf.disable_v2_behavior()
 import numpy as np
 import argparse
 import facenet
@@ -43,12 +44,12 @@
 from six.moves import xrange
 
 def main(args):
-  
+
     images, cout_per_image, nrof_samples = load_and_align_data(args.image_files,args.image_size, args.margin, args.gpu_memory_fraction)
     with tf.Graph().as_default():
 
        with tf.Session() as sess:
-      
+
             # Load the model
                 facenet.load_model(args.model)
             # Get input and output tensors
@@ -66,36 +67,36 @@ def main(args):
                 predictions = model.predict_proba(emb)
                 best_class_indices = np.argmax(predictions, axis=1)
                 best_class_probabilities = predictions[np.arange(len(best_class_indices)), best_class_indices]
-                k=0     
-	    #print predictions       
+                k=0
+	    #print predictions
                 for i in range(nrof_samples):
                     print("\npeople in image %s :" %(args.image_files[i]))
                     for j in range(cout_per_image[i]):
                         print('%s: %.3f' % (class_names[best_class_indices[k]], best_class_probabilities[k]))
                         k+=1
-                    
+
 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
-    
+
     print('Creating networks and loading parameters')
     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)
-  
+
     nrof_samples = len(image_paths)
-    img_list = [] 
+    img_list = []
     count_per_image = []
     for i in xrange(nrof_samples):
         img = misc.imread(os.path.expanduser(image_paths[i]))
         img_size = np.asarray(img.shape)[0:2]
         bounding_boxes, _ = align.detect_face.detect_face(img, minsize, pnet, rnet, onet, threshold, factor)
         count_per_image.append(len(bounding_boxes))
-        for j in range(len(bounding_boxes)):	
+        for j in range(len(bounding_boxes)):
                 det = np.squeeze(bounding_boxes[j,0:4])
                 bb = np.zeros(4, dtype=np.int32)
                 bb[0] = np.maximum(det[0]-margin/2, 0)
@@ -105,17 +106,17 @@ def load_and_align_data(image_paths, image_size, margin, gpu_memory_fraction):
                 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)		
+                img_list.append(prewhitened)
     images = np.stack(img_list)
     return images, count_per_image, nrof_samples
 
 def parse_arguments(argv):
     parser = argparse.ArgumentParser()
     parser.add_argument('image_files', type=str, nargs='+', help='Path(s) of the image(s)')
-    parser.add_argument('model', type=str, 
+    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')
-    parser.add_argument('classifier_filename', 
-        help='Classifier model file name as a pickle (.pkl) file. ' + 
+    parser.add_argument('classifier_filename',
+        help='Classifier model file name as a pickle (.pkl) file. ' +
         'For training this is the output and for classification this is an input.')
     parser.add_argument('--image_size', type=int,
         help='Image size (height, width) in pixels.', default=160)

requirements.txt

@@ -1,7 +1,7 @@
-tensorflow==1.7
-scipy
+tensorflow-gpu == 2.2.3
+scipy == 1.2.0
 scikit-learn
-opencv-python
+opencv-python-headless == 3.4.14.53
 h5py
 matplotlib
 Pillow

src/align/align_dataset_mtcnn.py

@@ -1,18 +1,18 @@
 """Performs face alignment and stores face thumbnails in the output directory."""
 # 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
@@ -29,7 +29,8 @@
 import sys
 import os
 import argparse
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
+tf.disable_v2_behavior()
 import numpy as np
 import facenet
 import align.detect_face
@@ -45,23 +46,23 @@ def main(args):
     src_path,_ = os.path.split(os.path.realpath(__file__))
     facenet.store_revision_info(src_path, output_dir, ' '.join(sys.argv))
     dataset = facenet.get_dataset(args.input_dir)
-    
+
     print('Creating networks and loading parameters')
-    
+
     with tf.Graph().as_default():
         gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=args.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)
-    
+
     minsize = 20 # minimum size of face
     threshold = [ 0.6, 0.7, 0.7 ]  # three steps's threshold
     factor = 0.709 # scale factor
 
     # Add a random key to the filename to allow alignment using multiple processes
     random_key = np.random.randint(0, high=99999)
     bounding_boxes_filename = os.path.join(output_dir, 'bounding_boxes_%05d.txt' % random_key)
-    
+
     with open(bounding_boxes_filename, "w") as text_file:
         nrof_images_total = 0
         nrof_successfully_aligned = 0
@@ -92,7 +93,7 @@ def main(args):
                         if img.ndim == 2:
                             img = facenet.to_rgb(img)
                         img = img[:,:,0:3]
-    
+
                         bounding_boxes, _ = align.detect_face.detect_face(img, minsize, pnet, rnet, onet, threshold, factor)
                         nrof_faces = bounding_boxes.shape[0]
                         if nrof_faces>0:
@@ -133,21 +134,21 @@ def main(args):
                         else:
                             print('Unable to align "%s"' % image_path)
                             text_file.write('%s\n' % (output_filename))
-                            
+
     print('Total number of images: %d' % nrof_images_total)
     print('Number of successfully aligned images: %d' % nrof_successfully_aligned)
-            
+
 
 def parse_arguments(argv):
     parser = argparse.ArgumentParser()
-    
+
     parser.add_argument('input_dir', type=str, help='Directory with unaligned images.')
     parser.add_argument('output_dir', type=str, help='Directory with aligned face thumbnails.')
     parser.add_argument('--image_size', type=int,
         help='Image size (height, width) in pixels.', default=182)
     parser.add_argument('--margin', type=int,
         help='Margin for the crop around the bounding box (height, width) in pixels.', default=44)
-    parser.add_argument('--random_order', 
+    parser.add_argument('--random_order',
         help='Shuffles the order of images to enable alignment using multiple processes.', action='store_true')
     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=1.0)