new params in visualization

Author: Koldim2001

README.md

@@ -172,7 +172,8 @@ Possible arguments of the ```visualize_results``` function:
 | random_object_colors    | bool            | False         | If true, colors for each object are selected randomly.                                        |
 | show_confidences        | bool            | False         | If true and show_class=True, confidences near class are visualized.                           |
 | axis_off                | bool            | True          | If true, axis is turned off in the final visualization.                                       |
-| show_classes_list       | list            | []            | If empty, visualize all classes. Otherwise, visualize only classes in the list.               |
+| show_classes_list       | list            | []            | If empty, visualize all classes. Otherwise, visualize only classes in the list.              |
+| list_of_class_colors      | list      | None         | A list of tuples representing the colors for each class in BGR format. If provided, these colors will be used for displaying the classes instead of random colors. |
 | return_image_array      | bool            | False         | If True, the function returns the image (BGR np.array) instead of displaying it.              |
 
 

patched_yolo_infer/README.md

@@ -7,6 +7,12 @@ The library also provides a sleek customization of the visualization of the infe
 **Model Support**: The library offers support for multiple ultralytics deep learning [models](https://docs.ultralytics.com/models/), such as YOLOv8, YOLOv8-seg, YOLOv9, YOLOv9-seg, YOLOv10, FastSAM, and RTDETR. Users can select from pre-trained options or utilize custom-trained models to best meet their task requirements.
 
 
+__Explanation of how Patch-Based-Inference works:__
+
+<p align="center">
+  <img width="600" alt="patched_inf_explanation" src="https://github.com/Koldim2001/YOLO-Patch-Based-Inference/blob/main/readme_content/patched_inf_explanation.gif?raw=true">
+</p>
+
 ## Installation
 You can install the library via pip:
 
@@ -141,6 +147,7 @@ Visualizes custom results of object detection or segmentation on an image.
 - **show_confidences** (*bool*): If true and show_class=True, confidences near class are visualized. Default is False.
 - **axis_off** (*bool*): If true, axis is turned off in the final visualization. Default is True.
 - **show_classes_list** (*list*): If empty, visualize all classes. Otherwise, visualize only classes in the list.
+- **list_of_class_colors** (*list*) A list of tuples representing the colors for each class in BGR format. If provided, these colors will be used for displaying the classes instead of random colors.
 - **return_image_array** (*bool*): If True, the function returns the image (BGR np.array) instead of displaying it. Default is False.
 
 

patched_yolo_infer/functions_extra.py

@@ -27,6 +27,7 @@ def visualize_results_usual_yolo_inference(
     show_confidences=False,
     axis_off=True,
     show_classes_list=[],
+    list_of_class_colors=None,
     return_image_array=False,
     inference_extra_args=None,
 ):
@@ -44,8 +45,8 @@ def visualize_results_usual_yolo_inference(
         show_class (bool): Whether to show class labels. Default is True.
         fill_mask (bool): Whether to fill the segmented regions with color. Default is False.
         alpha (float): The transparency of filled masks. Default is 0.3.
-        color_class_background (tuple): The background bgr color for class labels. Default is (0, 0, 255) (red).
-        color_class_text (tuple): The text color for class labels. Default is (255, 255, 255) (white).
+        color_class_background (tuple): The background BGR color for class labels. Default is (0, 0, 255) (red).
+        color_class_text (tuple): The text BGR color for class labels. Default is (255, 255, 255) (white).
         thickness (int): The thickness of bounding box and text. Default is 4.
         font: The font type for class labels. Default is cv2.FONT_HERSHEY_SIMPLEX.
         font_scale (float): The scale factor for font size. Default is 1.5.
@@ -56,6 +57,9 @@ def visualize_results_usual_yolo_inference(
         axis_off (bool): If True, axis is turned off in the final visualization.
         show_classes_list (list): If empty, visualize all classes. Otherwise, visualize only classes in the list.
         inference_extra_args (dict/None): Dictionary with extra ultralytics inference parameters.
+        list_of_class_colors (list/None): A list of tuples representing the colors for each class in BGR format. If provided,  
+                    these colors will be used for displaying the classes instead of random colors. The number of tuples 
+                    in the list must match the number of possible classes in the network.
         return_image_array (bool): If True, the function returns the image bgr array instead of displaying it. 
                                    Default is False.
 
@@ -278,6 +282,7 @@ def visualize_results(
     show_confidences=False,
     axis_off=True,
     show_classes_list=[],
+    list_of_class_colors=None,
     return_image_array=False
 ):
     """
@@ -295,8 +300,8 @@ def visualize_results(
         show_class (bool): Whether to show class labels. Default is True.
         fill_mask (bool): Whether to fill the segmented regions with color. Default is False.
         alpha (float): The transparency of filled masks. Default is 0.3.
-        color_class_background (tuple): The background bgr color for class labels. Default is (0, 0, 255) (red).
-        color_class_text (tuple): The text color for class labels. Default is (255, 255, 255) (white).
+        color_class_background (tuple): The background BGR color for class labels. Default is (0, 0, 255) (red).
+        color_class_text (tuple): The text BGR color for class labels. Default is (255, 255, 255) (white).
         thickness (int): The thickness of bounding box and text. Default is 4.
         font: The font type for class labels. Default is cv2.FONT_HERSHEY_SIMPLEX.
         font_scale (float): The scale factor for font size. Default is 1.5.
@@ -306,8 +311,10 @@ def visualize_results(
         show_confidences (bool): If true and show_class=True, confidences near class are visualized. Default is False.
         axis_off (bool): If true, axis is turned off in the final visualization. Default is True.
         show_classes_list (list): If empty, visualize all classes. Otherwise, visualize only classes in the list.
-        return_image_array (bool): If True, the function returns the image bgr array instead of displaying it. 
-                                   Default is False.
+        list_of_class_colors (list/None): A list of tuples representing the colors for each class in BGR format. If provided,  
+                    these colors will be used for displaying the classes instead of random colors. The number of tuples 
+                    in the list must match the number of possible classes in the network.
+        return_image_array (bool): If True, the function returns the image bgr array instead of displaying it. Default is False.
                                    
     Returns:
         None/np.array