making scripts compatible

datasets/mars.py

@@ -33,19 +33,20 @@ def read_train_test_directory_to_str(directory):
     def to_label(x):
         return int(x) if x.isdigit() else -1
 
-    dirnames = os.listdir(directory)
-    image_filenames, ids, camera_indices, tracklet_indices = [], [], [], []
-    for dirname in dirnames:
-        filenames = os.listdir(os.path.join(directory, dirname))
-        filenames = [
-            f for f in filenames if os.path.splitext(f)[1] == ".jpg"]
-        image_filenames += [
-            os.path.join(directory, dirname, f) for f in filenames]
-        ids += [to_label(dirname) for _ in filenames]
-        camera_indices += [int(f[5]) for f in filenames]
-        tracklet_indices += [int(f[7:11]) for f in filenames]
-
-    return image_filenames, ids, camera_indices, tracklet_indices
+    filenames = os.listdir(directory)
+    print("filenames")
+    print(filenames)
+    image_filenames, ids, tracklet_indices = [], [], []
+    # for dirname in dirnames:
+    # filenames = os.listdir(os.path.join(directory, dirname))
+    filenames = [
+        f for f in filenames if os.path.splitext(f)[1] == ".jpg"]
+    image_filenames += [
+        os.path.join(directory, f) for f in filenames]
+    ids += [int(f[0]) for f in filenames]
+    tracklet_indices += [int(f[f.find('_') + 1: f.find('.')]) for f in filenames]
+
+    return image_filenames, ids, tracklet_indices
 
 
 def read_train_test_directory_to_image(directory, image_shape=(128, 64)):
@@ -65,15 +66,14 @@ def read_train_test_directory_to_image(directory, image_shape=(128, 64)):
 
         * Tensor of images in BGR color space.
         * One dimensional array of unique IDs for the individuals in the images.
-        * One dimensional array of camera indices.
         * One dimensional array of tracklet indices.
 
     """
     reshape_fn = (
         (lambda x: x) if image_shape == IMAGE_SHAPE[:2]
         else (lambda x: cv2.resize(x, image_shape[::-1])))
 
-    filenames, ids, camera_indices, tracklet_indices = (
+    filenames, ids, tracklet_indices = (
         read_train_test_directory_to_str(directory))
 
     images = np.zeros((len(filenames), ) + image_shape + (3, ), np.uint8)
@@ -83,9 +83,8 @@ def read_train_test_directory_to_image(directory, image_shape=(128, 64)):
         image = cv2.imread(filename, cv2.IMREAD_COLOR)
         images[i] = reshape_fn(image)
     ids = np.asarray(ids, dtype=np.int64)
-    camera_indices = np.asarray(camera_indices, dtype=np.int64)
     tracklet_indices = np.asarray(tracklet_indices, dtype=np.int64)
-    return images, ids, camera_indices, tracklet_indices
+    return images, ids, tracklet_indices
 
 
 def read_train_split_to_str(dataset_dir):

datasets/util.py

@@ -113,7 +113,7 @@ def limit_num_elements_per_identity(data_y, max_num_images_per_id, seed=None):
     return valid_mask
 
 
-def create_cmc_probe_and_gallery(data_y, camera_indices=None, seed=None):
+def create_cmc_probe_and_gallery(data_y, seed=None):
     """Create probe and gallery images for evaluation of CMC top-k statistics.
 
     For every identity, this function selects one image as probe and one image
@@ -138,8 +138,7 @@ def create_cmc_probe_and_gallery(data_y, camera_indices=None, seed=None):
 
     """
     data_y = np.asarray(data_y)
-    if camera_indices is None:
-        camera_indices = np.zeros_like(data_y, dtype=np.int)
+    camera_indices = np.zeros_like(data_y, dtype=np.int)
     camera_indices = np.asarray(camera_indices)
 
     random_generator = np.random.RandomState(seed=seed)

train_app.py

@@ -292,7 +292,7 @@ def create_trainer(preprocess_fn, network_factory, read_from_file, image_shape,
     return trainer, train_op
 
 
-def eval_loop(preprocess_fn, network_factory, data_x, data_y, camera_indices,
+def eval_loop(preprocess_fn, network_factory, data_x, data_y,
               log_dir, eval_log_dir, image_shape=None, run_id=None,
               loss_mode="cosine-softmax", num_galleries=10, random_seed=4321):
     """Evaluate a running training session using CMC metric averaged over
@@ -358,7 +358,7 @@ def eval_loop(preprocess_fn, network_factory, data_x, data_y, camera_indices,
     probes, galleries = [], []
     for i in range(num_galleries):
         probe_indices, gallery_indices = util.create_cmc_probe_and_gallery(
-            data_y, camera_indices, seed=random_seed + i)
+            data_y, seed=random_seed + i)
         probes.append(probe_indices)
         galleries.append(gallery_indices)
     probes, galleries = np.asarray(probes), np.asarray(galleries)

train_mars.py

@@ -20,15 +20,14 @@ def __init__(self, dataset_dir, num_validation_y=0.1, seed=1234):
         self._seed = seed
 
     def read_train(self):
-        filenames, ids, camera_indices, _ = mars.read_train_split_to_str(
+        filenames, ids, _ = mars.read_train_split_to_str(
             self._dataset_dir)
         train_indices, _ = util.create_validation_split(
             np.asarray(ids, np.int64), self._num_validation_y, self._seed)
 
         filenames = [filenames[i] for i in train_indices]
         ids = [ids[i] for i in train_indices]
-        camera_indices = [camera_indices[i] for i in  train_indices]
-        return filenames, ids, camera_indices
+        return filenames, ids
 
     def read_validation(self):
         filenames, ids, camera_indices, _ = mars.read_train_split_to_str(
@@ -38,8 +37,7 @@ def read_validation(self):
 
         filenames = [filenames[i] for i in valid_indices]
         ids = [ids[i] for i in valid_indices]
-        camera_indices = [camera_indices[i] for i in valid_indices]
-        return filenames, ids, camera_indices
+        return filenames, ids
 
     def read_test_filenames(self):
         filename = os.path.join(self._dataset_dir, "info", "test_name.txt")
@@ -60,7 +58,7 @@ def main():
     dataset = Mars(args.dataset_dir, num_validation_y=0.1, seed=1234)
 
     if args.mode == "train":
-        train_x, train_y, _ = dataset.read_train()
+        train_x, train_y = dataset.read_train()
         print("Train set size: %d images, %d identities" % (
             len(train_x), len(np.unique(train_y))))
 
@@ -72,7 +70,7 @@ def main():
             net.preprocess, network_factory, train_x, train_y,
             num_images_per_id=4, image_shape=IMAGE_SHAPE, **train_kwargs)
     elif args.mode == "eval":
-        valid_x, valid_y, camera_indices = dataset.read_validation()
+        valid_x, valid_y = dataset.read_validation()
         print("Validation set size: %d images, %d identities" % (
             len(valid_x), len(np.unique(valid_y))))
 
@@ -81,7 +79,7 @@ def main():
             add_logits=args.loss_mode == "cosine-softmax")
         eval_kwargs = train_app.to_eval_kwargs(args)
         train_app.eval_loop(
-            net.preprocess, network_factory, valid_x, valid_y, camera_indices,
+            net.preprocess, network_factory, valid_x, valid_y,
             image_shape=IMAGE_SHAPE, num_galleries=20, **eval_kwargs)
     elif args.mode == "export":
         filenames = dataset.read_test_filenames()