UPD: added documentation for Image Segmentation and Auto Segmentation

SegmentAnything2AssistApp.py

@@ -319,7 +319,12 @@ def __generate_auto_mask(
 
 
 with gradio.Blocks() as base_app:
-    gradio.Markdown("# SegmentAnything2Assist")
+    gradio.Markdown(
+        """
+        <h1 style="text-align: center;">Segment Anything 2 Assist 🚀</h1>
+        <p style="text-align: center;">A tool for advanced image segmentation and annotation. 🖼️✏️</p>
+        """
+    )
     with gradio.Row():
         with gradio.Column():
             base_model_choice = gradio.Dropdown(
@@ -350,19 +355,27 @@ with gradio.Blocks() as base_app:
             with gradio.Accordion("Image Annotation Documentation", open=False):
                 gradio.Markdown(
                     """
+                    ### 🖼️ Image Annotation Documentation
+
                     Image annotation allows you to mark specific regions of an image with labels. 
-                    In this app, you can annotate an image by drawing boxes and assigning labels to them. 
+                    In this app, you can annotate an image by drawing bounding boxes and/or making points on the image. 
                     The labels can be either '+' or '-'. 
-                    To annotate an image, simply click and drag to draw a box around the desired region. 
-                    You can add multiple boxes with different labels. 
-                    Once you have annotated the image, click the 'Generate Mask' button to generate a mask based on the annotations. 
-                    The mask can be either a binary mask or a segmented mask, depending on the selected output mode. 
-                    You can switch between the output modes using the radio buttons. 
-                    If you make any changes to the annotations or the output mode, you need to regenerate the mask by clicking the button again. 
-                    Note that the advanced options allow you to adjust the SAM mask threshold, maximum hole area, and maximum sprinkle area. 
-                    These options control the sensitivity and accuracy of the segmentation process. 
-                    Experiment with different settings to achieve the desired results. 
-                """
+
+                    **📝 How to Annotate an Image:**
+                    - Bounding Box: Click and drag to draw a box around the desired region.
+                    - Positive or Negative Points: Draw a small box (note that the center point will be used for the annotation) and add either "+" or "-" as the label respectively.
+
+                    **🎨 Generating Masks:**
+                    - Once you have annotated the image, click the 'Generate Mask' button to generate a mask based on the annotations.
+                    - The mask can be either a binary mask or a segmented mask, depending on the selected output mode.
+                    - You can switch between the output modes using the radio buttons.
+                    - If you make any changes to the annotations or the output mode, you need to regenerate the mask by clicking the button again.
+
+                    **⚙️ Advanced Options:**
+                    - The advanced options allow you to adjust the SAM mask threshold, maximum hole area, and maximum sprinkle area.
+                    - These options control the sensitivity and accuracy of the segmentation process.
+                    - Experiment with different settings to achieve the desired results.
+                    """
                 )
             image_input = gradio_image_annotation.image_annotator(
                 example_image_annotation
@@ -440,7 +453,37 @@ with gradio.Blocks() as base_app:
             with gradio.Accordion("Auto Annotation Documentation", open=False):
                 gradio.Markdown(
                     """
-                """
+                    ### 🖼️ Auto Annotation Documentation
+
+                    Auto annotation allows you to automatically generate masks for an image based on advanced parameters. 
+                    In this app, you can configure various settings to control the mask generation process.
+
+                    **📝 How to Use Auto Annotation:**
+                    - Upload or select an image.
+                    - Adjust the advanced options to fine-tune the mask generation process.
+                    - Click the 'Generate Auto Mask' button to generate masks automatically.
+
+                    **⚙️ Advanced Options:**
+                    - **Points Per Side:** Number of points to sample per side of the image.
+                    - **Points Per Batch:** Number of points to process in each batch.
+                    - **Pred IOU Threshold:** Threshold for the predicted Intersection over Union (IOU) score.
+                    - **Stability Score Threshold:** Threshold for the stability score.
+                    - **Stability Score Offset:** Offset for the stability score.
+                    - **Mask Threshold:** Threshold for the mask generation.
+                    - **Box NMS Threshold:** Non-Maximum Suppression (NMS) threshold for boxes.
+                    - **Crop N Layers:** Number of layers to crop.
+                    - **Crop NMS Threshold:** NMS threshold for crops.
+                    - **Crop Overlay Ratio:** Overlay ratio for crops.
+                    - **Crop N Points Downscale Factor:** Downscale factor for the number of points in crops.
+                    - **Min Mask Region Area:** Minimum area for mask regions.
+                    - **Use M2M:** Whether to use M2M (Mask-to-Mask) refinement.
+                    - **Multi Mask Output:** Whether to generate multiple masks.
+
+                    **🎨 Generating Masks:**
+                    - Once you have configured the advanced options, click the 'Generate Auto Mask' button.
+                    - The masks will be generated automatically based on the selected parameters.
+                    - You can view the generated masks and adjust the settings if needed.
+                    """
                 )
             auto_input = gradio.Image("assets/cars.jpg")
             with gradio.Accordion("Advanced Options", open=False):