safe path joining with os.path.join

3 files changed, 22 insertions, 20 deletions

diff --git a/Code/constants.py b/Code/constants.py
index 61e9fea..dfd3660 100644
--- a/Code/constants.py
+++ b/Code/constants.py
@@ -43,7 +43,7 @@ def clear_dir(directory):
             print(e)
 
 def get_test_frame_dims():
-    img_path = glob(TEST_DIR + '*/*')[0]
+    img_path = glob(os.path.join(TEST_DIR, '*/*'))[0]
     img = imread(img_path, mode='RGB')
     shape = np.shape(img)
 
@@ -63,12 +63,12 @@ def set_test_dir(directory):
 # root directory for all data
 DATA_DIR = get_dir('../Data/')
 # directory of unprocessed training frames
-TRAIN_DIR = DATA_DIR + 'Ms_Pacman/Train/'
+TRAIN_DIR = os.path.join(DATA_DIR, 'Ms_Pacman/Train/')
 # directory of unprocessed test frames
-TEST_DIR = DATA_DIR + 'Ms_Pacman/Test/'
+TEST_DIR = os.path.join(DATA_DIR, 'Ms_Pacman/Test/')
 # Directory of processed training clips.
 # hidden so finder doesn't freeze w/ so many files. DON'T USE `ls` COMMAND ON THIS DIR!
-TRAIN_DIR_CLIPS = get_dir(DATA_DIR + '.Clips/')
+TRAIN_DIR_CLIPS = get_dir(os.path.join(DATA_DIR, '.Clips/'))
 
 # For processing clips. l2 diff between frames must be greater than this
 MOVEMENT_THRESHOLD = 100
@@ -93,9 +93,9 @@ def set_save_name(name):
     global SAVE_NAME, MODEL_SAVE_DIR, SUMMARY_SAVE_DIR, IMG_SAVE_DIR
 
     SAVE_NAME = name
-    MODEL_SAVE_DIR = get_dir(SAVE_DIR + 'Models/' + SAVE_NAME)
-    SUMMARY_SAVE_DIR = get_dir(SAVE_DIR + 'Summaries/' + SAVE_NAME)
-    IMG_SAVE_DIR = get_dir(SAVE_DIR + 'Images/' + SAVE_NAME)
+    MODEL_SAVE_DIR = get_dir(os.path.join(SAVE_DIR, 'Models/', SAVE_NAME))
+    SUMMARY_SAVE_DIR = get_dir(os.path.join(SAVE_DIR, 'Summaries/', SAVE_NAME))
+    IMG_SAVE_DIR = get_dir(os.path.join(SAVE_DIR, 'Images/', SAVE_NAME))
 
 def clear_save_name():
     """
@@ -112,11 +112,11 @@ SAVE_DIR = get_dir('../Save/')
 # inner directory to differentiate between runs
 SAVE_NAME = 'Default/'
 # directory for saved models
-MODEL_SAVE_DIR = get_dir(SAVE_DIR + 'Models/' + SAVE_NAME)
+MODEL_SAVE_DIR = get_dir(os.path.join(SAVE_DIR, 'Models/', SAVE_NAME))
 # directory for saved TensorBoard summaries
-SUMMARY_SAVE_DIR = get_dir(SAVE_DIR + 'Summaries/' + SAVE_NAME)
+SUMMARY_SAVE_DIR = get_dir(os.path.join(SAVE_DIR, 'Summaries/', SAVE_NAME))
 # directory for saved images
-IMG_SAVE_DIR = get_dir(SAVE_DIR + 'Images/' + SAVE_NAME)
+IMG_SAVE_DIR = get_dir(os.path.join(SAVE_DIR, 'Images/', SAVE_NAME))
 
 
 STATS_FREQ      = 10     # how often to print loss/train error stats, in # steps
diff --git a/Code/g_model.py b/Code/g_model.py
index 9db07da..4e0df31 100644
--- a/Code/g_model.py
+++ b/Code/g_model.py
@@ -3,6 +3,7 @@ import numpy as np
 from scipy.misc import imsave
 from skimage.transform import resize
 from copy import deepcopy
+import os
 
 import constants as c
 from loss_functions import combined_loss
@@ -318,20 +319,20 @@ class GeneratorModel:
 
             # for every clip in the batch, save the inputs, scale preds and scale gts
             for pred_num in xrange(len(input_frames)):
-                pred_dir = c.get_dir(c.IMG_SAVE_DIR + 'Step_' + str(global_step) + '/' + str(
-                    pred_num) + '/')
+                pred_dir = c.get_dir(os.path.join(c.IMG_SAVE_DIR, 'Step_' + str(global_step),
+                                                  str(pred_num)))
 
                 # save input images
                 for frame_num in xrange(c.HIST_LEN):
                     img = input_frames[pred_num, :, :, (frame_num * 3):((frame_num + 1) * 3)]
-                    imsave(pred_dir + 'input_' + str(frame_num) + '.png', img)
+                    imsave(os.path.join(pred_dir, 'input_' + str(frame_num) + '.png'), img)
 
                 # save preds and gts at each scale
                 # noinspection PyUnboundLocalVariable
                 for scale_num, scale_pred in enumerate(scale_preds):
                     gen_img = scale_pred[pred_num]
 
-                    path = pred_dir + 'scale' + str(scale_num)
+                    path = os.path.join(pred_dir, 'scale' + str(scale_num))
                     gt_img = scale_gts[scale_num][pred_num]
 
                     imsave(path + '_gen.png', gen_img)
@@ -410,19 +411,19 @@ class GeneratorModel:
 
         if save_imgs:
             for pred_num in xrange(len(input_frames)):
-                pred_dir = c.get_dir(
-                    c.IMG_SAVE_DIR + 'Tests/Step_' + str(global_step) + '/' + str(pred_num) + '/')
+                pred_dir = c.get_dir(os.path.join(
+                    c.IMG_SAVE_DIR, 'Tests/Step_' + str(global_step), str(pred_num)))
 
                 # save input images
                 for frame_num in xrange(c.HIST_LEN):
                     img = input_frames[pred_num, :, :, (frame_num * 3):((frame_num + 1) * 3)]
-                    imsave(pred_dir + 'input_' + str(frame_num) + '.png', img)
+                    imsave(os.path.join(pred_dir, 'input_' + str(frame_num) + '.png'), img)
 
                 # save recursive outputs
                 for rec_num in xrange(num_rec_out):
                     gen_img = rec_preds[rec_num][pred_num]
                     gt_img = gt_frames[pred_num, :, :, 3 * rec_num:3 * (rec_num + 1)]
-                    imsave(pred_dir + 'gen_' + str(rec_num) + '.png', gen_img)
-                    imsave(pred_dir + 'gt_' + str(rec_num) + '.png', gt_img)
+                    imsave(os.path.join(pred_dir, 'gen_' + str(rec_num) + '.png'), gen_img)
+                    imsave(os.path.join(pred_dir, 'gt_' + str(rec_num) + '.png'), gt_img)
 
         print '-' * 30
diff --git a/Code/utils.py b/Code/utils.py
index 2b97bdb..c2bf856 100644
--- a/Code/utils.py
+++ b/Code/utils.py
@@ -2,6 +2,7 @@ import tensorflow as tf
 import numpy as np
 from scipy.ndimage import imread
 from glob import glob
+import os
 
 import constants as c
 from tfutils import log10
@@ -77,7 +78,7 @@ def get_full_clips(data_dir, num_clips, num_rec_out=1):
 
     # get a random clip of length HIST_LEN + 1 from each episode
     for clip_num, ep_dir in enumerate(ep_dirs):
-        ep_frame_paths = glob(ep_dir + '/*')
+        ep_frame_paths = glob(os.path.join(ep_dir, '*'))
         start_index = np.random.choice(len(ep_frame_paths) - (c.HIST_LEN + num_rec_out - 1))
         clip_frame_paths = ep_frame_paths[start_index:start_index + (c.HIST_LEN + num_rec_out)]