python: better Python 3 support

modules/dnn/misc/quantize_face_detector.py

@@ -1,10 +1,14 @@
 from __future__ import print_function
+import sys
 import argparse
 import cv2 as cv
 import tensorflow as tf
 import numpy as np
 import struct
 
+if sys.version_info > (3,):
+    long = int
+
 from tensorflow.python.tools import optimize_for_inference_lib
 from tensorflow.tools.graph_transforms import TransformGraph
 from tensorflow.core.framework.node_def_pb2 import NodeDef

modules/dnn/test/imagenet_cls_test_alexnet.py

@@ -1,3 +1,4 @@
+from __future__ import print_function
 from abc import ABCMeta, abstractmethod
 import numpy as np
 import sys
@@ -156,7 +157,7 @@ class DnnCaffeModel(Framework):
 
 
 class ClsAccEvaluation:
-    log = file
+    log = sys.stdout
     img_classes = {}
     batch_size = 0
 
@@ -198,26 +199,26 @@ class ClsAccEvaluation:
                 fw_accuracy.append(100 * correct_answers[i] / float(samples_handled))
                 frameworks_out.append(out)
                 inference_time[i] += end - start
-                print >> self.log, samples_handled, 'Accuracy for', frameworks[i].get_name() + ':', fw_accuracy[i]
-                print >> self.log, "Inference time, ms ", \
-                    frameworks[i].get_name(), inference_time[i] / samples_handled * 1000
+                print(samples_handled, 'Accuracy for', frameworks[i].get_name() + ':', fw_accuracy[i], file=self.log)
+                print("Inference time, ms ", \
+                    frameworks[i].get_name(), inference_time[i] / samples_handled * 1000, file=self.log)
 
             for i in range(1, len(frameworks)):
                 log_str = frameworks[0].get_name() + " vs " + frameworks[i].get_name() + ':'
                 diff = np.abs(frameworks_out[0] - frameworks_out[i])
                 l1_diff = np.sum(diff) / diff.size
-                print >> self.log, samples_handled, "L1 difference", log_str, l1_diff
+                print(samples_handled, "L1 difference", log_str, l1_diff, file=self.log)
                 blobs_l1_diff[i] += l1_diff
                 blobs_l1_diff_count[i] += 1
                 if np.max(diff) > blobs_l_inf_diff[i]:
                     blobs_l_inf_diff[i] = np.max(diff)
-                print >> self.log, samples_handled, "L_INF difference", log_str, blobs_l_inf_diff[i]
+                print(samples_handled, "L_INF difference", log_str, blobs_l_inf_diff[i], file=self.log)
 
             self.log.flush()
 
         for i in range(1, len(blobs_l1_diff)):
             log_str = frameworks[0].get_name() + " vs " + frameworks[i].get_name() + ':'
-            print >> self.log, 'Final l1 diff', log_str, blobs_l1_diff[i] / blobs_l1_diff_count[i]
+            print('Final l1 diff', log_str, blobs_l1_diff[i] / blobs_l1_diff_count[i], file=self.log)
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()

modules/dnn/test/pascal_semsegm_test_fcn.py

@@ -1,3 +1,4 @@
+from __future__ import print_function
 from abc import ABCMeta, abstractmethod
 import numpy as np
 import sys
@@ -145,7 +146,7 @@ class PASCALDataFetch(DatasetImageFetch):
 
 
 class SemSegmEvaluation:
-    log = file
+    log = sys.stdout
 
     def __init__(self, log_path,):
         self.log = open(log_path, 'w')
@@ -174,28 +175,28 @@ class SemSegmEvaluation:
                 pix_acc, mean_acc, miou = get_metrics(conf_mats[i])
 
                 name = frameworks[i].get_name()
-                print >> self.log, samples_handled, 'Pixel accuracy, %s:' % name, 100 * pix_acc
-                print >> self.log, samples_handled, 'Mean accuracy, %s:' % name, 100 * mean_acc
-                print >> self.log, samples_handled, 'Mean IOU, %s:' % name, 100 * miou
-                print >> self.log, "Inference time, ms ", \
-                    frameworks[i].get_name(), inference_time[i] / samples_handled * 1000
+                print(samples_handled, 'Pixel accuracy, %s:' % name, 100 * pix_acc, file=self.log)
+                print(samples_handled, 'Mean accuracy, %s:' % name, 100 * mean_acc, file=self.log)
+                print(samples_handled, 'Mean IOU, %s:' % name, 100 * miou, file=self.log)
+                print("Inference time, ms ", \
+                    frameworks[i].get_name(), inference_time[i] / samples_handled * 1000, file=self.log)
 
             for i in range(1, len(frameworks)):
                 log_str = frameworks[0].get_name() + " vs " + frameworks[i].get_name() + ':'
                 diff = np.abs(frameworks_out[0] - frameworks_out[i])
                 l1_diff = np.sum(diff) / diff.size
-                print >> self.log, samples_handled, "L1 difference", log_str, l1_diff
+                print(samples_handled, "L1 difference", log_str, l1_diff, file=self.log)
                 blobs_l1_diff[i] += l1_diff
                 blobs_l1_diff_count[i] += 1
                 if np.max(diff) > blobs_l_inf_diff[i]:
                     blobs_l_inf_diff[i] = np.max(diff)
-                print >> self.log, samples_handled, "L_INF difference", log_str, blobs_l_inf_diff[i]
+                print(samples_handled, "L_INF difference", log_str, blobs_l_inf_diff[i], file=self.log)
 
             self.log.flush()
 
         for i in range(1, len(blobs_l1_diff)):
             log_str = frameworks[0].get_name() + " vs " + frameworks[i].get_name() + ':'
-            print >> self.log, 'Final l1 diff', log_str, blobs_l1_diff[i] / blobs_l1_diff_count[i]
+            print('Final l1 diff', log_str, blobs_l1_diff[i] / blobs_l1_diff_count[i], file=self.log)
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()

modules/ts/misc/table_formatter.py

@@ -206,6 +206,8 @@ class table(object):
         cell.width = len(max(cell.text, key = lambda line: len(line)))
 
     def reformatTextValue(self, value):
+        if sys.version_info > (3,):  # PY3 fix
+            unicode = str
         if isinstance(value, str):
             vstr = value
         elif isinstance(value, unicode):

modules/ts/misc/testlog_parser.py

@@ -7,6 +7,10 @@ import os.path
 import sys
 from xml.dom.minidom import parse
 
+if sys.version_info > (3,):
+    long = int
+    def cmp(a, b): return (a>b)-(a<b)
+
 class TestInfo(object):
 
     def __init__(self, xmlnode):

platforms/android/build_sdk.py

@@ -335,6 +335,7 @@ if __name__ == "__main__":
     print(cfg.strip())
     print('=' * 80)
 
+    ABIs = None  # make flake8 happy
     exec(compile(cfg, cpath, 'exec'))
 
     log.info("Android NDK path: %s", os.environ["ANDROID_NDK"])

samples/python/deconvolution.py

@@ -79,7 +79,7 @@ if __name__ == '__main__':
 
     img = cv.imread(fn, 0)
     if img is None:
-        print('Failed to load fn1:', fn1)
+        print('Failed to load file:', fn)
         sys.exit(1)
 
     img = np.float32(img)/255.0