Add caffe-compute-image-mean.

Author: bamos

python2.7/caffe-compute-image-mean.py

@@ -0,0 +1,52 @@
+#!/usr/bin/env python2
+
+import sys
+sys.path.append("/home/bamos/repos/caffe-local/python")
+
+import argparse
+import numpy as np
+import os
+import time
+
+from caffe.proto import caffe_pb2
+from caffe.io import array_to_blobproto
+from collections import defaultdict
+from skimage import io
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('meanPrefix', type=str, help="TODO")
+    parser.add_argument('imageDir', type=str, help="TODO")
+    args = parser.parse_args()
+
+    exts = ["jpg", "png"]
+
+    mean = np.zeros((1, 3, 152, 152))
+    N = 0
+    classSizes = defaultdict(int)
+
+    beginTime = time.time()
+    for subdir, dirs, files in os.walk(args.imageDir):
+        for fName in files:
+            (imageClass, imageName) = (os.path.basename(subdir), fName)
+            if any(imageName.lower().endswith("." + ext) for ext in exts):
+                img = io.imread(os.path.join(subdir, fName))
+                if img.shape == (152, 152, 3):
+                    mean[0][0] += img[:,:,0]
+                    mean[0][1] += img[:,:,1]
+                    mean[0][2] += img[:,:,2]
+                    N += 1
+                    if N % 1000 == 0:
+                        elapsed = time.time() - beginTime
+                        print("Processed {} images in {:.2f} seconds. "
+                              "{:.2f} images/second.".format(N, elapsed,
+                                                             N/elapsed))
+    mean[0] /= N
+
+    blob = array_to_blobproto(mean)
+    with open("{}.binaryproto".format(args.meanPrefix), 'wb') as f:
+        f.write(blob.SerializeToString())
+    np.save("{}.npy".format(args.meanPrefix), mean[0])
+
+    meanImg = np.transpose(mean[0].astype(np.uint8), (1, 2, 0))
+    io.imsave("{}.png".format(args.meanPrefix), meanImg)