rename folders "sample_" -> "data-"

Author: Borda

Diff for: README.md

@@ -85,8 +85,8 @@ def setDataDirectory(self, data_directory):
 
         'setDataDirectory()' is required to set the path to which the data/dataset to be used for
                  training is kept. The directory can have any name, but it must have 'train' and 'validation'
-                 sub-directory. In the 'train' and 'validation' sub-directories, there must be 'sample_images' and 'annotations'
-                 sub-directories respectively. The 'sample_images' folder will contain the pictures for the dataset and the
+                 sub-directory. In the 'train' and 'validation' sub-directories, there must be 'data-images' and 'annotations'
+                 sub-directories respectively. The 'data-images' folder will contain the pictures for the dataset and the
                  'annotations' folder will contain the XML files with details of the annotations for each image in the
                  'images folder'.
 
@@ -115,9 +115,9 @@ def setDataDirectory(self, data_directory):
         :return:
         """
 
-        self.__train_images_folder = os.path.join(data_directory, "train/sample_images/")
+        self.__train_images_folder = os.path.join(data_directory, "train/data-images/")
         self.__train_annotations_folder = os.path.join(data_directory, "train/annotations/")
-        self.__validation_images_folder = os.path.join(data_directory, "validation/sample_images/")
+        self.__validation_images_folder = os.path.join(data_directory, "validation/data-images/")
         self.__validation_annotations_folder = os.path.join(data_directory, "validation/annotations/")
 
         if os.path.exists(os.path.join(data_directory, "cache")) == False:

Diff for: sample_images/1.jpg renamed to data-images/1.jpg

@@ -27,7 +27,7 @@ def __init__(self, generator, iou_threshold=0.5, score_threshold=0.05, max_detec
             iou_threshold   : The threshold used to consider when a detection is positive or negative.
             score_threshold : The score confidence threshold to use for detections.
             max_detections  : The maximum number of detections to use per image.
-            save_path       : The path to save sample_images with visualized detections to.
+            save_path       : The path to save images with visualized detections to.
             tensorboard     : Instance of keras.callbacks.TensorBoard used to log the mAP value.
             verbose         : Set the verbosity level, by default this is set to 1.
         """

Diff for: sample_images/2.jpg renamed to data-images/2.jpg

@@ -79,15 +79,15 @@ def image_aspect_ratio(self, image_index):
 
     def load_image(self, image_index):
         image_info = self.coco.loadImgs(self.image_ids[image_index])[0]
-        path       = os.path.join(self.data_dir, 'sample_images', self.set_name, image_info['file_name'])
+        path = os.path.join(self.data_dir, 'data-images', self.set_name, image_info['file_name'])
         return read_image_bgr(path)
 
     def load_annotations(self, image_index):
         # get ground truth annotations
         annotations_ids = self.coco.getAnnIds(imgIds=self.image_ids[image_index], iscrowd=False)
         annotations     = np.zeros((0, 5))
 
-        # some sample_images appear to miss annotations (like image with id 257034)
+        # some images appear to miss annotations (like image with id 257034)
         if len(annotations_ids) == 0:
             return annotations
 

Diff for: sample_images/3.jpg renamed to data-images/3.jpg

@@ -156,7 +156,7 @@ def preprocess_group(self, image_group, annotations_group):
         return image_group, annotations_group
 
     def group_images(self):
-        # determine the order of the sample_images
+        # determine the order of the images
         order = list(range(self.size()))
         if self.group_method == 'random':
             random.shuffle(order)
@@ -173,7 +173,7 @@ def compute_inputs(self, image_group):
         # construct an image batch object
         image_batch = np.zeros((self.batch_size,) + max_shape, dtype=keras.backend.floatx())
 
-        # copy all sample_images to the upper left part of the image batch object
+        # copy all images to the upper left part of the image batch object
         for image_index, image in enumerate(image_group):
             image_batch[image_index, :image.shape[0], :image.shape[1], :image.shape[2]] = image
 
@@ -218,7 +218,7 @@ def compute_targets(self, image_group, annotations_group):
         return [regression_batch, labels_batch]
 
     def compute_input_output(self, group):
-        # load sample_images and annotations
+        # load images and annotations
         image_group       = self.load_image_group(group)
         annotations_group = self.load_annotations_group(group)
 

Diff for: sample_images/4.jpg renamed to data-images/4.jpg

@@ -46,7 +46,7 @@ def __init__(
         self.base_dir = base_dir
 
         label_dir = os.path.join(self.base_dir, subset, 'labels')
-        image_dir = os.path.join(self.base_dir, subset, 'sample_images')
+        image_dir = os.path.join(self.base_dir, subset, 'images')
 
         """
         1    type         Describes the type of object: 'Car', 'Van', 'Truck',

Diff for: sample_images/5.jpg renamed to data-images/5.jpg

@@ -77,7 +77,7 @@ def generate_images_annotations_json(main_dir, metadata_dir, subset, cls_index):
 
             cls_id = cls_index[class_name]
 
-            img_path = os.path.join(main_dir, 'sample_images', subset, frame + '.jpg')
+            img_path = os.path.join(main_dir, 'images', subset, frame + '.jpg')
             if frame in images_sizes:
                 width, height = images_sizes[frame]
             else:
@@ -130,7 +130,7 @@ def __init__(
             fixed_labels=False,
             **kwargs
     ):
-        self.base_dir = os.path.join(main_dir, 'sample_images', subset)
+        self.base_dir = os.path.join(main_dir, 'images', subset)
         metadata_dir = os.path.join(main_dir, version)
         annotation_cache_json = os.path.join(annotation_cache_dir, subset + '.json')
 

Diff for: sample_images/5fast.jpg renamed to data-images/5fast.jpg

@@ -62,7 +62,7 @@ def evaluate_coco(generator, model, threshold=0.05):
             # append detection to results
             results.append(image_result)
 
-        # append image to list of processed sample_images
+        # append image to list of processed images
         image_ids.append(generator.image_ids[index])
 
         # print progress

Diff for: sample_images/5faster.jpg renamed to data-images/5faster.jpg

@@ -62,11 +62,11 @@ def _get_detections(generator, model, score_threshold=0.05, max_detections=100,
         all_detections[num_images][num_classes] = detections[num_detections, 4 + num_classes]
 
     # Arguments
-        generator       : The generator used to run sample_images through the model.
-        model           : The model to run on the sample_images.
+        generator       : The generator used to run images through the model.
+        model           : The model to run on the images.
         score_threshold : The score confidence threshold to use.
         max_detections  : The maximum number of detections to use per image.
-        save_path       : The path to save the sample_images with visualized detections to.
+        save_path       : The path to save the images with visualized detections to.
     # Returns
         A list of lists containing the detections for each image in the generator.
     """
@@ -164,7 +164,7 @@ def evaluate(
         iou_threshold   : The threshold used to consider when a detection is positive or negative.
         score_threshold : The score confidence threshold to use for detections.
         max_detections  : The maximum number of detections to use per image.
-        save_path       : The path to save sample_images with visualized detections to.
+        save_path       : The path to save images with visualized detections to.
     # Returns
         A dict mapping class names to mAP scores.
     """