feat: Add image IDs for CSV parser

The CSV parser now stores a list of image IDs for the dataset. An image ID consists of the portion of an image's name before the first dot.

This change will be irrelevant for most use cases, but having a list of image IDs has advantages in some cases.

data_generator/object_detection_2d_data_generator.py

@@ -215,7 +215,7 @@ def parse_csv(self,
                 Defaults to `False`.
 
         Returns:
-            None by default, optionally the image filenames and labels.
+            None by default, optionally the image filenames, labels, and image IDs.
         '''
 
         # Set class members.
@@ -230,6 +230,7 @@ def parse_csv(self,
 
         # Erase data that might have been parsed before
         self.filenames = []
+        self.image_ids = []
         self.labels = []
 
         # First, just read in the CSV file lines and sort them.
@@ -253,6 +254,7 @@ def parse_csv(self,
         # we can compile the actual samples and labels lists
 
         current_file = data[0][0] # The current image for which we're collecting the ground truth boxes
+        current_image_id = data[0][0].split('.')[0] # The image ID will be the portion of the image name before the first dot.
         current_labels = [] # The list where we collect all ground truth boxes for a given image
         add_to_dataset = False
         for i, box in enumerate(data):
@@ -265,33 +267,40 @@ def parse_csv(self,
                         if p >= (1-random_sample):
                             self.labels.append(np.stack(current_labels, axis=0))
                             self.filenames.append(os.path.join(self.images_dir, current_file))
+                            self.image_ids.append(current_image_id)
                     else:
                         self.labels.append(np.stack(current_labels, axis=0))
                         self.filenames.append(os.path.join(self.images_dir, current_file))
+                        self.image_ids.append(current_image_id)
             else: # If this box belongs to a new image file
                 if random_sample: # In case we're not using the full dataset, but a random sample of it.
                     p = np.random.uniform(0,1)
                     if p >= (1-random_sample):
                         self.labels.append(np.stack(current_labels, axis=0))
                         self.filenames.append(os.path.join(self.images_dir, current_file))
+                        self.image_ids.append(current_image_id)
                 else:
                     self.labels.append(np.stack(current_labels, axis=0))
                     self.filenames.append(os.path.join(self.images_dir, current_file))
+                    self.image_ids.append(current_image_id)
                 current_labels = [] # Reset the labels list because this is a new file.
                 current_file = box[0]
+                current_image_id = box[0].split('.')[0]
                 current_labels.append(box[1:])
                 if i == len(data)-1: # If this is the last line of the CSV file
                     if random_sample: # In case we're not using the full dataset, but a random sample of it.
                         p = np.random.uniform(0,1)
                         if p >= (1-random_sample):
                             self.labels.append(np.stack(current_labels, axis=0))
                             self.filenames.append(os.path.join(self.images_dir, current_file))
+                            self.image_ids.append(current_image_id)
                     else:
                         self.labels.append(np.stack(current_labels, axis=0))
                         self.filenames.append(os.path.join(self.images_dir, current_file))
+                        self.image_ids.append(current_image_id)
 
         if ret: # In case we want to return these
-            return self.filenames, self.labels
+            return self.filenames, self.labels, self.image_ids
 
     def parse_xml(self,
                   images_dirs,