How to add custom colors to Tensorflow object detection python model bound boxes?

I sort of found a way - after much trouble. I found nothing documenting how to do this. Sort of, as some colors don't seem to work.

Open "visualizations_utils.py". Should be in Lib\site-packages\utils. Rows 41 to 63 are your colors.

Directly under row 164,

draw = ImageDraw.Draw(image),

enter a new row

color = 'Pink'

Save it, and you have now changed the color to a pinkish color. Row 175, you can make the label text smaller.

Some colors don't seem to work, like "Red".

1. add “color” attribute in the label map pbtxt file. i.e.

   item { name: "/m/01g317" id: 1 display_name: "person" color: "Pink" }

2. Open file “research/object_detection/protos/string_int_label_map.proto”. Add the following line.

   optional string color = 4;
   

   Be aware about the semicolon, curly braces.

3. then it is required to serialize the data, so run the following command from research folder

   protoc object_detection/protos/*.proto --python_out=.
   

   Before that you must install protobuf based on your OS.

4. change the code of function “convert_label_map_to_categories” of the file object_detection/utils/lable_map_util.py

   categories = [] list_of_ids_already_added = [] if not label_map: label_id_offset = 1 for class_id in range(max_num_classes): categories.append({ 'id': class_id + label_id_offset, 'name': 'category_{}'.format(class_id + label_id_offset) }) return categories for item in label_map.item:

   if not 0 < item.id <= max_num_classes:
     logging.info(
         'Ignore item %d since it falls outside of requested '
         'label range.', item.id)
     continue
   if use_display_name and item.HasField('display_name'):
     name = item.display_name
   else:
     name = item.name
   
   if use_display_name and item.HasField('color'):
     color = item.color
   else:
     color = ''
   
   if item.id not in list_of_ids_already_added:
     list_of_ids_already_added.append(item.id)
     categories.append({'id': item.id, 'name': name, 'color': color})          
   return categories
   

5.Open the file “object_detection/utils/visualization_utils.py”. Go to the function named “visualize_boxes_and_labels_on_image_array”. Add the following code

else:
         if classes[i] in category_index.keys():
            class_color = category_index[classes[i]]['color']
            box_to_color_map[box] = class_color

after the code

if agnostic_mode:
          box_to_color_map[box] = 'DarkOrange'