refine the gradio ui

app.py

@@ -58,7 +58,7 @@ class FingerCutOverlapError(Exception):
     """There was an overlap with fingercuts... Please try again to generate dxf."""
     pass
 # ---------------------
-# Global Model Initialization with caching and print statements
+# Global Model Initialization with caching and print statements (Original code preserved)
 # ---------------------
 print("Loading YOLOWorld model...")
 start_time = time.time()
@@ -110,7 +110,7 @@ transform_image_global = transforms.Compose([
 ])
 
 # ---------------------
-# Model Reload Function (if needed)
+# Helper Functions (Preserved as is)
 # ---------------------
 def unload_and_reload_models():
     global drawer_detector_global, reference_detector_global, birefnet_global, u2net_global
@@ -708,198 +708,6 @@ def draw_single_polygon(poly, image_rgb, scaling_factor, image_height, color=(0,
     pts_px = np.array(pts_px, dtype=np.int32)
     cv2.polylines(image_rgb, [pts_px], isClosed=True, color=color, thickness=thickness, lineType=cv2.LINE_AA)
 
-# def draw_and_pad(polygons_inch, scaling_factor,boundary_polygon, padding=50, 
-#                  color=(0,0,255), thickness=2):
-#     """
-#     - polygons_inch: list of Shapely Polygons in inch units (already including boundary).
-#     - scaling_factor: inches per pixel.
-#     - padding: padding in pixels.
-#     """
-#     all_x = []
-#     all_y = []
-#     pixel_polys = []
-    
-#     # 1) Convert to pixel coords and collect bounds
-#     for poly in polygons_inch:
-#         coords = list(poly.exterior.coords)
-#         pts = []
-#         for x_in, y_in in coords:
-#             px = int(round(x_in / scaling_factor))
-#             py = int(round(y_in / scaling_factor))
-#             pts.append([px, py])
-#             all_x.append(px)
-#             all_y.append(py)
-#         pixel_polys.append(np.array(pts, dtype=np.int32))
-    
-#     # 2) Compute canvas size
-    
-#     min_x, max_x = min(all_x), max(all_x)
-#     min_y, max_y = min(all_y), max(all_y)
-#     width  = max_x - min_x + 1
-#     height = max_y - min_y + 1
-    
-#     # 3) Create blank white canvas
-#     canvas = 255 * np.ones((height, width, 3), dtype=np.uint8)
-    
-#     # 4) Draw each polygon, flipping y within the local box
-#     for pts in pixel_polys:
-#         # Offset so min corner is (0,0)
-#         pts_off = pts - np.array([[min_x, min_y]])
-#         # Flip y: new_y = height-1 - old_y
-#         pts_off[:,1] = (height - 1) - pts_off[:,1]
-#         cv2.polylines(canvas, [pts_off], isClosed=True, 
-#                       color=color, thickness=thickness, lineType=cv2.LINE_AA)
-    
-#     # 5) Pad the canvas
-
-#         padded = cv2.copyMakeBorder(
-#             canvas,
-#             top=padding, bottom=padding,
-#             left=padding, right=padding,
-#             borderType=cv2.BORDER_CONSTANT,
-#             value=[255,255,255]
-#         )
-#     return padded
-
-# import numpy as np
-# import cv2
-
-# def draw_and_pad(polygons_inch, scaling_factor, boundary_polygon, padding=50,
-#                  color=(0,0,255), thickness=2):
-#     """
-#     - polygons_inch: list of Shapely Polygons in inch units.
-#     - scaling_factor: inches per pixel.
-#     - boundary_polygon: the Shapely boundary polygon, or None.
-#     - padding: base padding in pixels.
-#     """
-#     all_x, all_y = [], []
-#     pixel_polys = []
-    
-#     # 1) Convert to pixel coords and collect bounds
-#     for poly in polygons_inch:
-#         coords = list(poly.exterior.coords)
-#         pts = []
-#         for x_in, y_in in coords:
-#             px = int(round(x_in / scaling_factor))
-#             py = int(round(y_in / scaling_factor))
-#             pts.append([px, py])
-#             all_x.append(px)
-#             all_y.append(py)
-#         pixel_polys.append(np.array(pts, dtype=np.int32))
-    
-#     # 2) Compute canvas size
-#     min_x, max_x = min(all_x), max(all_x)
-#     min_y, max_y = min(all_y), max(all_y)
-#     width  = max_x - min_x + 1
-#     height = max_y - min_y + 1
-    
-#     # 3) Create blank white canvas
-#     canvas = 255 * np.ones((height, width, 3), dtype=np.uint8)
-    
-#     # 4) Draw each polygon, flipping y within the local box
-#     for pts in pixel_polys:
-#         pts_off = pts - np.array([[min_x, min_y]])
-#         pts_off[:,1] = (height - 1) - pts_off[:,1]
-#         cv2.polylines(canvas, [pts_off], isClosed=True, 
-#                       color=color, thickness=thickness, lineType=cv2.LINE_AA)
-    
-#     # 5) Decide padding amounts
-#     if boundary_polygon is not None:
-#         top = bottom = left = right = padding
-#     else:
-#         # Double the padding if there's no boundary, to avoid clipping
-#         top = bottom = left = right = padding * 2
-
-#     # 6) Pad the canvas
-#     padded = cv2.copyMakeBorder(
-#         canvas,
-#         top=top, bottom=bottom,
-#         left=left, right=right,
-#         borderType=cv2.BORDER_CONSTANT,
-#         value=[255,255,255]
-#     )
-#     return padded
-
-import numpy as np
-import cv2
-
-# def draw_and_pad(polygons_inch, scaling_factor, boundary_polygon, padding=50, 
-#                  color=(0, 0, 255), thickness=2):
-#     """
-#     Draws Shapely Polygons (in inch units) on a white canvas.
-    
-#     When boundary_polygon is None, the computed bounds are expanded by the padding value 
-#     so that the drawn contours are not clipped at the edges after adding the final padding.
-    
-#     Arguments:
-#         polygons_inch: list of Shapely Polygons in inch units (already including boundary).
-#         scaling_factor: inches per pixel.
-#         boundary_polygon: the Shapely boundary polygon, or None.
-#         padding: padding in pixels.
-#         color: color of the drawn polylines (in BGR format).
-#         thickness: line thickness.
-
-#     Returns:
-#         padded: an image (numpy array) of the drawn polygons with an external white border.
-#     """
-#     all_x = []
-#     all_y = []
-#     pixel_polys = []
-    
-#     # 1) Convert each polygon to pixel coordinates and compute overall bounds.
-#     for poly in polygons_inch:
-#         coords = list(poly.exterior.coords)
-#         pts = []
-#         for x_in, y_in in coords:
-#             px = int(round(x_in / scaling_factor))
-#             py = int(round(y_in / scaling_factor))
-#             pts.append([px, py])
-#             all_x.append(px)
-#             all_y.append(py)
-#         pixel_polys.append(np.array(pts, dtype=np.int32))
-    
-#     # 2) Compute the basic canvas size from the polygon bounds.
-#     min_x, max_x = min(all_x), max(all_x)
-#     min_y, max_y = min(all_y), max(all_y)
-    
-#     # If no boundary polygon is provided, expand the bounds to add margin
-#     # so that later when we pad externally, the contours do not get clipped.
-#     if boundary_polygon is None:
-#         min_x -= padding
-#         max_x += padding
-#         min_y -= padding
-#         max_y += padding
-
-#     width  = max_x - min_x + 1
-#     height = max_y - min_y + 1
-    
-#     # 3) Create a blank white canvas.
-#     canvas = 255 * np.ones((height, width, 3), dtype=np.uint8)
-    
-#     # 4) Draw each polygon, flipping the y-coordinates to match image coordinates.
-#     for pts in pixel_polys:
-#         # Offset so the minimum corner becomes (0,0) on canvas.
-#         pts_off = pts - np.array([[min_x, min_y]])
-#         # Flip y: image coordinates have (0,0) at the top-left.
-#         pts_off[:, 1] = (height - 1) - pts_off[:, 1]
-#         cv2.polylines(canvas, [pts_off], isClosed=True, 
-#                       color=color, thickness=thickness, lineType=cv2.LINE_AA)
-    
-#     # 5) Finally, add external padding on all sides.
-#     padded = cv2.copyMakeBorder(
-#         canvas,
-#         top=padding, bottom=padding,
-#         left=padding, right=padding,
-#         borderType=cv2.BORDER_CONSTANT,
-#         value=[255, 255, 255]
-#     )
-    
-#     return padded
-
-import numpy as np
-import cv2
-from shapely.geometry import Polygon
-
 import numpy as np
 import cv2
 from shapely.geometry import Polygon
@@ -1274,39 +1082,139 @@ def predict(
         str(scaling_factor)
     )
 
+
+# ---------------------
+# Documentation Strings for Gradio
+# ---------------------
+
+QUICK_START = """
+## 1. Quick Start Guide: From Photo to DXF Cut File
+This application converts a single photograph of tools in a drawer/tray into a ready-to-use DXF file for CNC cutting foam inserts.
+
+1.  **Preparation**: Place your tools in a drawer and include a **reference coin** (assumed to be 0.955 inches / 24.25mm wide, e.g., a US Quarter). Ensure clear, even lighting.
+2.  **Upload**: Upload the image.
+3.  **Configure**: Adjust the `Offset Value` (clearance around the tool) and select options like `Finger Clearance` and `Boundary`.
+4.  **Run**: Click the **"Submit"** button (automatically generated by Gradio).
+5.  **Download**: Review the `Outlines` and `Output Image`, then download the final **DXF file** for your cutting machine.
+"""
+
+INPUT_EXPLANATION = """
+## 2. Expected Inputs
+
+### Image Requirements
+*   **Reference Object is Mandatory:** The system *must* detect a specific reference object (default: coin roughly 0.955 inches wide) to correctly calculate the real-world scale (inches/pixel).
+*   **Placement:** The image should be taken from directly above the drawer (minimal perspective distortion).
+*   **Lighting:** Clear, shadow-free lighting is crucial for object detection and masking.
+
+### Processing Parameters
+
+| Parameter | Purpose | Default Value | Guidance for Non-Tech Users |
+| :--- | :--- | :--- | :--- |
+| **Offset Value** | The amount of space (clearance) added around the tool profile. This is the buffer to ensure the tool fits easily into the foam cutout. | 0.075 | Increase this value for thicker tools or if you need looser cutouts. |
+| **Offset Unit** | Specifies whether the offset value is in millimeters or inches. | inches | Match this to your preferred measurement system. |
+| **Add Finger Clearance?** | Adds a small circular cutout to the side of the tool profile to allow easy removal using a finger. | Yes | Recommended for most tools. |
+| **Add Rectangular Boundary?**| Defines the exterior rectangular shape of the entire foam insert (the boundary of the drawer). | Yes | Required if you need a rectangular outer boundary for cutting. |
+| **Boundary Length/Width** | The actual dimensions of the drawer/tray area for the foam insert. | 50.0 (in the selected unit) | Must be larger than the combined area of all tools plus margins. |
+| **Annotation** | Text to engrave/cut into the foam, typically placed at the top of the boundary box. (Max 20 chars) | (Empty) | Use this to label the tray (e.g., "Wrench Set"). |
+"""
+
+OUTPUT_EXPLANATION = """
+## 3. Expected Outputs
+
+| Output Field | Description | Purpose |
+| :--- | :--- | :--- |
+| **Output Image** | The original image overlaid with the detected tool outlines (including offset and finger clearance). | Visual confirmation that the detection and sizing are correct relative to the original photo. |
+| **Outlines of Objects**| A clean, scaled, white-background image showing only the outlines (blue lines). This visually represents the final geometry exported to the DXF. | Final quality check for shape and spacing before CNC cutting. |
+| **DXF file** | The final vector file containing the tool outlines, boundary box, and text annotation (if included). | Ready to upload to CNC machine software (AutoCAD DXF R2000 format). |
+| **Mask** | The binary image used internally to generate the tool contours after applying the offset value. | Technical output for debugging segmentation issues. |
+| **Scaling Factor**| The calculated real-world scale (inches per pixel) determined by the reference coin. | Confirmation of measurement accuracy. Values close to 0.7 are common for phone photos. |
+"""
+
+
 # ---------------------
 # Gradio Interface
 # ---------------------
 if __name__ == "__main__":
     os.makedirs("./outputs", exist_ok=True)
+    
     def gradio_predict(img, offset, offset_unit, finger_clearance, add_boundary, boundary_length, boundary_width, annotation_text):
         try:
             return predict(img, offset, offset_unit, finger_clearance, add_boundary, boundary_length, boundary_width, annotation_text)
         except Exception as e:
+            # Handle specific known errors for clearer user feedback
+            if isinstance(e, (DrawerNotDetectedError, ReferenceBoxNotDetectedError)):
+                gr.Warning(f"Processing Error: {str(e)} Please check image quality and coin placement.")
+            elif isinstance(e, (BoundaryOverlapError, TextOverlapError)):
+                 gr.Error(f"Boundary/Text Placement Error: {str(e)}")
+            else:
+                 gr.Error(f"An unexpected error occurred: {str(e)}")
+                 
             return None, None, None, None, f"Error: {str(e)}"
-    iface = gr.Interface(
-        fn=gradio_predict,
-        inputs=[
-            gr.Image(label="Input Image"),
-            gr.Number(label="Offset value for Mask", value=0.075),
-            gr.Dropdown(label="Offset Unit", choices=["mm", "inches"], value="inches"),
-            gr.Dropdown(label="Add Finger Clearance?", choices=["Yes", "No"], value="Yes"),
-            gr.Dropdown(label="Add Rectangular Boundary?", choices=["Yes", "No"], value="Yes"),
-            gr.Number(label="Boundary Length", value=50, precision=2),
-            gr.Number(label="Boundary Width", value=50, precision=2),
-            gr.Textbox(label="Annotation (max 20 chars)", max_length=20, placeholder="Type up to 20 characters")
-        ],
-        outputs=[
-            gr.Image(format="png",label="Output Image"),
-            gr.Image(format="png",label="Outlines of Objects"),
-            gr.File(label="DXF file"),
-            gr.Image(label="Mask"),
-            gr.Textbox(label="Scaling Factor (inches/pixel)")
-        ],
-        examples=[
-            ["./Test20.jpg", 0.075, "inches", "Yes", "No", 300.0, 200.0, "MyTool"],
-            ["./Test21.jpg", 0.075, "inches", "Yes", "Yes", 300.0, 200.0, "Tool2"]
-        ]
-    )
-    iface.launch(share=True)
-    
+        
+    with gr.Blocks(title="Tool Cutout DXF Generator") as demo:
+        gr.Markdown("<h1 style='text-align: center;'> AI-Powered Tool Cutout DXF Generator </h1>")
+        gr.Markdown("Convert a photo of your tool drawer into precise CNC-ready vector files.")
+        
+        # 1. Guidelines & Instructions
+        with gr.Accordion(" Tips & User Guide", open=False):
+            gr.Markdown(QUICK_START)
+            gr.Markdown("---")
+            gr.Markdown(INPUT_EXPLANATION)
+            gr.Markdown("---")
+            gr.Markdown(OUTPUT_EXPLANATION)
+
+        # 2. Main Interface Setup
+
+        with gr.Row():
+            with gr.Column():
+                gr.Markdown("## Step 1: Upload Image and Configure Parameters")
+                input_image = gr.Image(label="Input Image", type="numpy")
+
+            with gr.Column():
+                gr.Markdown("## Step 2: Configure Parameters")
+                offset_value = gr.Number(label="Offset value for Mask", value=0.075)
+                offset_unit = gr.Dropdown(label="Offset Unit", choices=["mm", "inches"], value="inches")
+                finger_clearance = gr.Dropdown(label="Add Finger Clearance?", choices=["Yes", "No"], value="Yes")
+                add_boundary = gr.Dropdown(label="Add Rectangular Boundary?", choices=["Yes", "No"], value="Yes")
+                boundary_length = gr.Number(label="Boundary Length", value=50.0, precision=2)
+                boundary_width = gr.Number(label="Boundary Width", value=50.0, precision=2)
+                annotation_text = gr.Textbox(label="Annotation (max 20 chars)", max_length=20, placeholder="Type up to 20 characters")
+                
+        gr.Markdown("## Step 3: Click Generate DXF & Previews")
+        submit_button = gr.Button("Generate DXF & Previews", variant="primary")
+        
+        # 3. Outputs
+        gr.Markdown("## Outputs")
+        
+        with gr.Row():
+            output_img = gr.Image(format="png", label="Output Image (Tools overlaid with outlines)")
+            outlines_color = gr.Image(format="png", label="Outlines of Objects (Final DXF Geometry Preview)")
+
+        with gr.Row():
+            dxf_file = gr.File(label="DXF file")
+            with gr.Column():
+                scaling_factor_display = gr.Textbox(label="Scaling Factor (inches/pixel)")
+                dilated_mask = gr.Image(label="Mask (Technical Preview)")
+
+        # 4. Examples
+        gr.Markdown("---")
+        gr.Markdown("## Example Data (Click a row to load the image and parameters)")
+        gr.Examples(
+            examples=[
+                ["data/Test20.jpg", 0.075, "inches", "Yes", "No", 300.0, 200.0, "MyTool"],
+                ["data/Test21.jpg", 0.075, "inches", "Yes", "Yes", 300.0, 200.0, "Tool2"]
+            ],
+            inputs=[input_image, offset_value, offset_unit, finger_clearance, add_boundary, boundary_length, boundary_width, annotation_text],
+            outputs=[output_img, outlines_color, dxf_file, dilated_mask, scaling_factor_display],
+            fn=gradio_predict,
+            cache_examples=False,
+        )
+
+        # Event Handler
+        submit_button.click(
+            fn=gradio_predict,
+            inputs=[input_image, offset_value, offset_unit, finger_clearance, add_boundary, boundary_length, boundary_width, annotation_text],
+            outputs=[output_img, outlines_color, dxf_file, dilated_mask, scaling_factor_display]
+        )
+        
+    demo.launch(share=True)

requirements.txt

@@ -1,11 +1,13 @@
 transformers
 ultralytics==8.3.9
 ezdxf
-gradio
 pydantic==2.10.6
 kornia
 timm
 einops
 shapely
 gevent==22.10.2
-matplotlib
+matplotlib
+numpy<2
+gradio==3.50.2 
+gradio-client==0.6.1