Python3 changes

geist/backends/fake.py

@@ -1,4 +1,6 @@
 import numpy as np
+from six import string_types
+
 from ..finders import Location, LocationList
 
 
@@ -14,7 +16,7 @@ def __init__(self, **kwargs):
                 [Location(0, 0, w=w, h=h, image=self.image)]
             )
         else:
-            if isinstance(image, basestring):
+            if isinstance(image, string_types):
                 image = np.load(image)
             self.image = image
             h, w, _ = image.shape

geist/backends/replay.py

@@ -3,7 +3,7 @@
 import sys
 import json
 import base64
-import StringIO
+from six import StringIO
 import wrapt
 from PIL import Image
 from . import get_platform_backend
@@ -119,7 +119,7 @@ def capture_locations(self):
         location_list = LocationList()
         for b64_location in b64_locations:
             base64_png = b64_location['base64_png']
-            string_file = StringIO.StringIO(
+            string_file = StringIO(
                 base64.b64decode(base64_png)
             )
             x, y, w, h = (
@@ -191,7 +191,7 @@ def _write_action(self, funcname, *args, **kwargs):
     def _write_capture_locations(self, locations):
         b64_locations = []
         for location in locations:
-            string_file = StringIO.StringIO()
+            string_file = StringIO()
             Image.fromarray(location.image).save(string_file, 'png')
             b64_png = base64.b64encode(string_file.getvalue())
             b64_locations.append({

geist/backends/xvfb.py

@@ -57,7 +57,7 @@ def __init__(self, **kwargs):
 
         display = ":%d" % (self.display_num, )
         self._display_dir = XVFB_PATH % (self.display_num,)
-        os.makedirs(self._display_dir, 0700)
+        os.makedirs(self._display_dir, 0o700)
         dev_null = open('/dev/null', 'w')
         self._xvfb_proc = subprocess.Popen(
             [

geist/pyplot.py

@@ -68,7 +68,6 @@ def _get_colour(self, numpy_array):
         smax = sat.max()
         vmin = val.min()
         vmax = val.max()
-        print hmin, hmax, smin, smax, vmin, vmax
         return hsv(lambda h, s, v: (
             (h >= hmin) & (h <= hmax)) &
             ((s >= smin) & (s <= smax)) &

geist/vision.py

@@ -1,7 +1,8 @@
-from __future__ import division
+from __future__ import print_function, division
 from numpy.fft import irfft2, rfft2
 import numpy
 import itertools
+from six import iteritems
 import operator
 
 
@@ -84,10 +85,10 @@ def best_convolution(bin_template, bin_image,
     max_hor_cells = iw // th
 
     # Try to work out how many times we can stack the image
-    usable_factors = {n: factors for n, factors in overlap_table.iteritems()
+    usable_factors = {n: factors for n, factors in iteritems(overlap_table)
                       if ((template_sum + 1) ** (n)) < ACCURACY_LIMIT}
     overlap_options = [(factor, n // factor)
-                       for n, factors in usable_factors.iteritems()
+                       for n, factors in iteritems(usable_factors)
                        for factor in factors
                        if (factor <= max_vert_cells and
                            n // factor <= max_hor_cells)]
@@ -526,5 +527,5 @@ def correlation_coefficient_normed(template, image):
                 (template_distance ** 2).sum() *
                 (image_distance_of_template_area ** 2).sum()
             )
-        print y
+        print(y)
     return corr_num / corr_denum

geist_tests/test_finders.py

@@ -18,3 +18,11 @@ def test_filter(self):
         result = self.gui.wait_find_one(finder)
         self.assertEqual(parent_x + child_x, result.x)
         self.assertEqual(parent_y + child_y, result.y)
+
+
+location_finder_filter_suite = unittest.TestLoader().loadTestsFromTestCase(
+    TestLocationFinderFilter)
+all_tests = unittest.TestSuite([location_finder_filter_suite])
+if __name__ == "__main__":
+    runner = unittest.TextTestRunner(verbosity=1)
+    runner.run(all_tests)

setup.py

@@ -19,6 +19,7 @@
         'pillow',
         'wrapt',
         'mock',
+        'six',
     ],
     description='Visual Automation Library',
     classifiers=[