python port of features matlab code

Author: Subhasis Das

python_features/README.md

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+This directory contains a Python port of the Matlab code in matlab_features_reference/ directory
+
+- This code uses [Caffe](http://caffe.berkeleyvision.org/) and their Python wrapper.
+- I use VGG Net which can be found in the [Model Zoo ](https://github.com/BVLC/caffe/wiki/Model-Zoo) under the title *Models used by the VGG team in ILSVRC-2014*. I use the [16-layer version](https://gist.github.com/ksimonyan/211839e770f7b538e2d8#file-readme-md).
+- Note that I provide my _features deploy network def as well, which is exactly what you see on that page but I chopped off the softmax to get the 4096-D codes below.

python_features/extract_features.py

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+import sys
+import argparse
+
+import numpy as np
+from scipy.misc import imread, imresize
+
+import cPickle as pickle
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--caffe',
+                    help='path to caffe installation')
+parser.add_argument('--model_def',
+                    help='path to model definition prototxt')
+parser.add_argument('--model',
+                    help='path to model parameters')
+parser.add_argument('--files',
+                    help='path to a file contsining a list of images')
+parser.add_argument('--gpu',
+                    action='store_true',
+                    help='whether to use gpu training')
+parser.add_argument('--out',
+                    help='name of the pickle file where to store the features')
+
+args = parser.parse_args()
+
+caffepath = args.caffe + '/python'
+sys.path.append(caffepath)
+
+import caffe
+
+def predict(in_data, net):
+    """
+    Get the features for a batch of data using network
+
+    Inputs:
+    in_data: data batch
+    """
+
+    out = net.forward(**{net.inputs[0]: in_data})
+    features = out[net.outputs[0]].squeeze(axis=(2,3))
+    return features
+
+
+def batch_predict(filenames, net):
+    """
+    Get the features for all images from filenames using a network
+
+    Inputs:
+    filenames: a list of names of image files
+
+    Returns:
+    an array of feature vectors for the images in that file
+    """
+
+    N, C, H, W = net.blobs[net.inputs[0]].data.shape
+    F = net.blobs[net.outputs[0]].data.shape[1]
+    Nf = len(filenames)
+    Hi, Wi, _ = imread(filenames[0]).shape
+    allftrs = np.zeros((Nf, F))
+    for i in range(0, Nf, N):
+        in_data = np.zeros((N, C, H, W), dtype=np.float32)
+
+        batch_range = range(i, min(i+N, Nf))
+        batch_filenames = [filenames[j] for j in batch_range]
+        Nb = len(batch_range)
+
+        batch_images = np.zeros((Nb, 3, H, W))
+        for j,fname in enumerate(batch_filenames):
+            im = imread(fname)
+            if len(im.shape) == 2:
+                im = np.tile(im[:,:,np.newaxis], (1,1,3))
+            # RGB -> BGR
+            im = im[:,:,(2,1,0)]
+            # mean subtraction
+            im = im - np.array([103.939, 116.779, 123.68])
+            # resize
+            im = imresize(im, (H, W))
+            # get channel in correct dimension
+            im = np.transpose(im, (2, 0, 1))
+            batch_images[j,:,:,:] = im
+
+        # insert into correct place
+        in_data[0:len(batch_range), :, :, :] = batch_images
+
+        # predict features
+        ftrs = predict(in_data, net)
+
+        for j in range(len(batch_range)):
+            allftrs[i+j,:] = ftrs[j,:]
+
+        print 'Done %d/%d files' % (i+len(batch_range), len(filenames))
+
+    return allftrs
+
+
+if args.gpu:
+    caffe.set_mode_gpu()
+else:
+    caffe.set_mode_cpu()
+
+net = caffe.Net(args.model_def, args.model)
+caffe.set_phase_test()
+
+filenames = []
+with open(args.files) as fp:
+    for line in fp:
+        filename = line.strip().split()[0]
+        filenames.append(filename)
+
+allftrs = batch_predict(filenames, net)
+
+# store the features in a pickle file
+with open(args.out, 'w') as fp:
+    pickle.dump(allftrs, fp)