Added support for Python 3.6

.gitignore

@@ -9,3 +9,4 @@
 /resources/pascalVocData/
 /results/
 /selectivesearch/
+.idea

fastRCNN/utils36_win64/.gitignore

@@ -0,0 +1,2 @@
+*.c
+*.so

fastRCNN/utils36_win64/__init__.py

@@ -0,0 +1,6 @@
+# --------------------------------------------------------
+# Fast R-CNN
+# Copyright (c) 2015 Microsoft
+# Licensed under The MIT License [see LICENSE for details]
+# Written by Ross Girshick
+# --------------------------------------------------------

fastRCNN/utils36_win64/blob.py

@@ -0,0 +1,45 @@
+# --------------------------------------------------------
+# Fast R-CNN
+# Copyright (c) 2015 Microsoft
+# Licensed under The MIT License [see LICENSE for details]
+# Written by Ross Girshick
+# --------------------------------------------------------
+
+"""Blob helper functions."""
+
+import numpy as np
+import cv2
+
+def im_list_to_blob(ims):
+    """Convert a list of images into a network input.
+
+    Assumes images are already prepared (means subtracted, BGR order, ...).
+    """
+    max_shape = np.array([im.shape for im in ims]).max(axis=0)
+    num_images = len(ims)
+    blob = np.zeros((num_images, max_shape[0], max_shape[1], 3),
+                    dtype=np.float32)
+    for i in xrange(num_images):
+        im = ims[i]
+        blob[i, 0:im.shape[0], 0:im.shape[1], :] = im
+    # Move channels (axis 3) to axis 1
+    # Axis order will become: (batch elem, channel, height, width)
+    channel_swap = (0, 3, 1, 2)
+    blob = blob.transpose(channel_swap)
+    return blob
+
+def prep_im_for_blob(im, pixel_means, target_size, max_size):
+    """Mean subtract and scale an image for use in a blob."""
+    im = im.astype(np.float32, copy=False)
+    im -= pixel_means
+    im_shape = im.shape
+    im_size_min = np.min(im_shape[0:2])
+    im_size_max = np.max(im_shape[0:2])
+    im_scale = float(target_size) / float(im_size_min)
+    # Prevent the biggest axis from being more than MAX_SIZE
+    if np.round(im_scale * im_size_max) > max_size:
+        im_scale = float(max_size) / float(im_size_max)
+    im = cv2.resize(im, None, None, fx=im_scale, fy=im_scale,
+                    interpolation=cv2.INTER_LINEAR)
+
+    return im, im_scale

fastRCNN/utils36_win64/timer.py

@@ -0,0 +1,32 @@
+# --------------------------------------------------------
+# Fast R-CNN
+# Copyright (c) 2015 Microsoft
+# Licensed under The MIT License [see LICENSE for details]
+# Written by Ross Girshick
+# --------------------------------------------------------
+
+import time
+
+class Timer(object):
+    """A simple timer."""
+    def __init__(self):
+        self.total_time = 0.
+        self.calls = 0
+        self.start_time = 0.
+        self.diff = 0.
+        self.average_time = 0.
+
+    def tic(self):
+        # using time.time instead of time.clock because time time.clock
+        # does not normalize for multithreading
+        self.start_time = time.time()
+
+    def toc(self, average=True):
+        self.diff = time.time() - self.start_time
+        self.total_time += self.diff
+        self.calls += 1
+        self.average_time = self.total_time / self.calls
+        if average:
+            return self.average_time
+        else:
+            return self.diff