From c85ee573ed5a33c7741c61ba9fcce0ad64718d34 Mon Sep 17 00:00:00 2001
From: Matt Cooper <matthew_cooper@brown.edu>
Date: Sun, 21 Aug 2016 15:33:05 -0400
Subject: testing a bug where test inputs and GTs are out of order

---
 Code/avg_runner.py   |  6 +++---
 Code/constants.py    | 16 ++++++++++++----
 Code/process_data.py |  5 +++++
 Code/utils.py        | 12 ++++++------
 4 files changed, 26 insertions(+), 13 deletions(-)

diff --git a/Code/avg_runner.py b/Code/avg_runner.py
index 18bc592..9a27426 100644
--- a/Code/avg_runner.py
+++ b/Code/avg_runner.py
@@ -42,8 +42,8 @@ class AVGRunner:
                                       self.summary_writer,
                                       c.TRAIN_HEIGHT,
                                       c.TRAIN_WIDTH,
-                                      c.TEST_HEIGHT,
-                                      c.TEST_WIDTH,
+                                      c.FULL_HEIGHT,
+                                      c.FULL_WIDTH,
                                       c.SCALE_FMS_G,
                                       c.SCALE_KERNEL_SIZES_G)
 
@@ -161,7 +161,7 @@ def main():
 
     # set test frame dimensions
     assert os.path.exists(c.TEST_DIR)
-    c.TEST_HEIGHT, c.TEST_WIDTH = c.get_test_frame_dims()
+    c.FULL_HEIGHT, c.FULL_WIDTH = c.get_test_frame_dims()
 
     ##
     # Init and run the predictor
diff --git a/Code/constants.py b/Code/constants.py
index dfd3660..761448b 100644
--- a/Code/constants.py
+++ b/Code/constants.py
@@ -49,16 +49,23 @@ def get_test_frame_dims():
 
     return shape[0], shape[1]
 
+def get_train_frame_dims():
+    img_path = glob(os.path.join(TRAIN_DIR, '*/*'))[0]
+    img = imread(img_path, mode='RGB')
+    shape = np.shape(img)
+
+    return shape[0], shape[1]
+
 def set_test_dir(directory):
     """
     Edits all constants dependent on TEST_DIR.
 
     @param directory: The new test directory.
     """
-    global TEST_DIR, TEST_HEIGHT, TEST_WIDTH
+    global TEST_DIR, FULL_HEIGHT, FULL_WIDTH
 
     TEST_DIR = directory
-    TEST_HEIGHT, TEST_WIDTH = get_test_frame_dims()
+    FULL_HEIGHT, FULL_WIDTH = get_test_frame_dims()
 
 # root directory for all data
 DATA_DIR = get_dir('../Data/')
@@ -75,8 +82,9 @@ MOVEMENT_THRESHOLD = 100
 # total number of processed clips in TRAIN_DIR_CLIPS
 NUM_CLIPS = len(glob(TRAIN_DIR_CLIPS + '*'))
 
-# the height and width of the full frames to test on. Set in avg_runner.py main.
-TEST_HEIGHT = TEST_WIDTH = 0
+# the height and width of the full frames to test on. Set in avg_runner.py or process_data.py main.
+FULL_HEIGHT = 210
+FULL_WIDTH = 160
 # the height and width of the patches to train on
 TRAIN_HEIGHT = TRAIN_WIDTH = 32
 
diff --git a/Code/process_data.py b/Code/process_data.py
index 446de91..8375035 100644
--- a/Code/process_data.py
+++ b/Code/process_data.py
@@ -2,6 +2,7 @@ import numpy as np
 import getopt
 import sys
 from glob import glob
+import os
 
 import constants as c
 from utils import process_clip
@@ -64,6 +65,10 @@ def main():
             usage()
             sys.exit(2)
 
+    # set train frame dimensions
+    assert os.path.exists(c.TRAIN_DIR)
+    c.FULL_HEIGHT, c.FULL_WIDTH = c.get_train_frame_dims()
+
     ##
     # Process data for training
     ##
diff --git a/Code/utils.py b/Code/utils.py
index c2bf856..042912e 100644
--- a/Code/utils.py
+++ b/Code/utils.py
@@ -1,6 +1,6 @@
 import tensorflow as tf
 import numpy as np
-from scipy.ndimage import imread
+from scipy.misc import imread
 from glob import glob
 import os
 
@@ -69,14 +69,14 @@ def get_full_clips(data_dir, num_clips, num_rec_out=1):
              A batch of frame sequences with values normalized in range [-1, 1].
     """
     clips = np.empty([num_clips,
-                      c.TEST_HEIGHT,
-                      c.TEST_WIDTH,
+                      c.FULL_HEIGHT,
+                      c.FULL_WIDTH,
                       (3 * (c.HIST_LEN + num_rec_out))])
 
     # get num_clips random episodes
     ep_dirs = np.random.choice(glob(data_dir + '*'), num_clips)
 
-    # get a random clip of length HIST_LEN + 1 from each episode
+    # get a random clip of length HIST_LEN + num_rec_out from each episode
     for clip_num, ep_dir in enumerate(ep_dirs):
         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))
@@ -105,8 +105,8 @@ def process_clip():
     take_first = np.random.choice(2, p=[0.95, 0.05])
     cropped_clip = np.empty([c.TRAIN_HEIGHT, c.TRAIN_WIDTH, 3 * (c.HIST_LEN + 1)])
     for i in xrange(100):  # cap at 100 trials in case the clip has no movement anywhere
-        crop_x = np.random.choice(c.TEST_WIDTH - c.TRAIN_WIDTH + 1)
-        crop_y = np.random.choice(c.TEST_HEIGHT - c.TRAIN_HEIGHT + 1)
+        crop_x = np.random.choice(c.FULL_WIDTH - c.TRAIN_WIDTH + 1)
+        crop_y = np.random.choice(c.FULL_HEIGHT - c.TRAIN_HEIGHT + 1)
         cropped_clip = clip[crop_y:crop_y + c.TRAIN_HEIGHT, crop_x:crop_x + c.TRAIN_WIDTH, :]
 
         if take_first or clip_l2_diff(cropped_clip) > c.MOVEMENT_THRESHOLD:
-- 
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