Working with most parameters

app.py

@@ -5,9 +5,35 @@ import cv2
 import gradio as gr
 import os
 
-dict_list = ['DICT_4X4_50', 'DICT_4X4_100', 'DICT_4X4_250', 'DICT_4X4_1000', 'DICT_5X5_50', 'DICT_5X5_100', 'DICT_5X5_250', 'DICT_5X5_1000', 'DICT_6X6_50', 'DICT_6X6_100', 'DICT_6X6_250', 'DICT_6X6_1000', 'DICT_7X7_50', 'DICT_7X7_100', 'DICT_7X7_250', 'DICT_7X7_1000', 'DICT_ARUCO_ORIGINAL', 'DICT_APRILTAG_16h5', 'DICT_APRILTAG_25h9', 'DICT_APRILTAG_36h10', 'DICT_APRILTAG_36h11']
+dict_list = ['DICT_4X4_50', 
+             'DICT_4X4_100', 
+             'DICT_4X4_250', 
+             'DICT_4X4_1000',
+             'DICT_5X5_50',
+             'DICT_5X5_100',
+             'DICT_5X5_250', 
+             'DICT_5X5_1000', 
+             'DICT_6X6_50', 
+             'DICT_6X6_100', 
+                         'DICT_6X6_250',
+                         'DICT_6X6_1000',
+                         'DICT_7X7_50',
+                         'DICT_7X7_100',
+                         'DICT_7X7_250',
+                         'DICT_7X7_1000',
+                         'DICT_ARUCO_ORIGINAL',
+                         'DICT_APRILTAG_16h5',
+                         'DICT_APRILTAG_25h9',
+                         'DICT_APRILTAG_36h10',
+                         'DICT_APRILTAG_36h11',
+                         'DICT_ARUCO_MIP_36h12']
 
-def inference(image_path, dict_name):
+def inference(image_path, dict_name, draw_rejects,
+              adaptiveThreshWinSizeMin, adaptiveThreshWinSizeMax, adaptiveThreshWinSizeStep, adaptiveThreshConstant, 
+              minMarkerPerimeterRate, maxMarkerPerimeterRate,
+                            polygonalApproxAccuracyRate, minCornerDistanceRate, minDistanceToBorder, minMarkerDistanceRate,
+              cornerRefinementMethod, cornerRefinementWinSize, cornerRefinementMaxIterations, cornerRefinementMinAccuracy,
+              markerBoderBits, perspectiveRemovePixelPerCell, perspectiveRemoveIgnoredMarginPerCell, maxErroneousBitsInBorderRate, minOtsuStdDev, errorCorrectionRate):
     if not dict_name:
         raise gr.Error("No model selected. Please select a model.")
     if not image_path:
@@ -17,17 +43,47 @@ def inference(image_path, dict_name):
     aruco_dict = cv2.aruco.getPredefinedDictionary(dict_index)
 
     aruco_params = cv2.aruco.DetectorParameters()
+    
+    aruco_params.adaptiveThreshWinSizeMin       =      int(adaptiveThreshWinSizeMin)
+    aruco_params.adaptiveThreshWinSizeMax       =      int(adaptiveThreshWinSizeMax)
+    aruco_params.adaptiveThreshWinSizeStep      =     int(adaptiveThreshWinSizeStep)
+    aruco_params.adaptiveThreshConstant         =     int(adaptiveThreshConstant)
+    aruco_params.minMarkerPerimeterRate         =     minMarkerPerimeterRate
+    aruco_params.maxMarkerPerimeterRate         =     maxMarkerPerimeterRate
+    aruco_params.polygonalApproxAccuracyRate    =     polygonalApproxAccuracyRate
+    aruco_params.minCornerDistanceRate          =     minCornerDistanceRate
+    aruco_params.minDistanceToBorder            =     minDistanceToBorder
+    aruco_params.minMarkerDistanceRate          =     minMarkerDistanceRate
+    
+
+    aruco_params.cornerRefinementMethod         =     cornerRefinementMethods.index(cornerRefinementMethod)
+    aruco_params.cornerRefinementWinSize        =     int(cornerRefinementWinSize)
+    aruco_params.cornerRefinementMaxIterations  =     int(cornerRefinementMaxIterations)
+    aruco_params.cornerRefinementMinAccuracy    =     cornerRefinementMinAccuracy
+
+    aruco_params.markerBorderBits                      =     int(markerBoderBits)
+    aruco_params.perspectiveRemovePixelPerCell         =     int(perspectiveRemovePixelPerCell)
+    aruco_params.perspectiveRemoveIgnoredMarginPerCell =     perspectiveRemoveIgnoredMarginPerCell
+    aruco_params.maxErroneousBitsInBorderRate          =     maxErroneousBitsInBorderRate
+    aruco_params.minOtsuStdDev                         =     minOtsuStdDev
+    aruco_params.errorCorrectionRate                   =     errorCorrectionRate
+    
+
     detector = cv2.aruco.ArucoDetector(aruco_dict, aruco_params)
     image = cv2.imread(image_path)
     
     corners, ids, rejectedImgPoints = detector.detectMarkers(image)
+    thresh_image = cv2.adaptiveThreshold(cv2.cvtColor(image, cv2.COLOR_BGR2GRAY), 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, int(adaptiveThreshConstant), 2)
     image = cv2.aruco.drawDetectedMarkers(image, corners, ids, borderColor=(0, 255, 0))
+    if draw_rejects:
+        image = cv2.aruco.drawDetectedMarkers(image, rejectedImgPoints, borderColor=(0, 0, 255))
+
     for corner in corners:
         cv2.polylines(image, [corner.astype(int)], isClosed=True, color=(0, 255, 0), thickness=3)
     cv2.imwrite("output.jpg", image)
     output_image = cv2.cvtColor(cv2.imread("output.jpg"), cv2.COLOR_BGR2RGB)
-    
-    return output_image
+    # TODO make a gif going through the thresh win size
+    return output_image, thresh_image
 
 def get_aruco_dict():
     #PREDEFINED_DICTIONARY_NAME
@@ -35,22 +91,63 @@ def get_aruco_dict():
 
 aruco_dict = get_aruco_dict()
 
-image_paths= [['examples/cans.png', 'DICT_4X4_50', 0.5],
-              ['examples/image4k.png', 'DICT_4X4_50', 0.5],
+image_paths= [['examples/cans.png', 'DICT_4X4_50', 3, 23, 4, 7],
+              ['examples/image4k.png', 'DICT_4X4_50', 3, 23, 4, 7],
+              ['examples/pose.png', 'DICT_5X5_1000', 3, 23, 4, 7],
+              ['examples/singlemarkerssource.jpg', 'DICT_6X6_250', 3, 23, 4, 7],
               ]
 
-demo = gr.Interface(
-    fn=inference,
-    inputs=[
-        gr.Image(type="filepath", label="Upload Image"),
-        gr.Dropdown(choices=aruco_dict, label="Select aruco library"),
+cornerRefinementMethods = ["CORNER_REFINE_NONE", "CORNER_REFINE_SUBPIX", "CORNER_REFINE_CONTOUR", "CORNER_REFINE_APRILTAG"]
+
+
+with gr.Blocks() as demo:
+    gr.Markdown("# Aruco tag detection\nSelect the aruco library, upload an image, and detect the aruco tags.")
+    
+    with gr.Row():
+        with gr.Column():
+            image_input = gr.Image(type="filepath", label="Upload Image")
+            dict_dropdown = gr.Dropdown(choices=aruco_dict, label="Select aruco library")
+            advanced_params = gr.Accordion("Advanced Parameters", open=False)
+            with advanced_params:
+                rejects_radio       = gr.Checkbox(label="Show Rejects", value=False)
+                thresh_min_slider   = gr.Slider(minimum=3, maximum=100, step=1, value=3, label="adapatativeThreshWinSizeMin")
+                thresh_max_slider   = gr.Slider(minimum=0, maximum=100, step=1, value=23, label="adapatativeThreshWinSizeMax")
+                thresh_step_slider  = gr.Slider(minimum=1, maximum=100, step=1, value=10, label="adapatativeThreshWinSizeStep")
+                thresh_const_slider = gr.Slider(minimum=0, maximum=50, step=1, value=7, label="adapatativeThreshConstant")
+                        
+                min_marker_p_slider             = gr.Slider(minimum=0, maximum=1, step=0.01, value=0.03, label="minMarkerPerimeterRate")
+                max_marker_p_slider             = gr.Slider(minimum=0, maximum=10, step=0.01, value=4.0, label="maxMarkerPerimeterRate")
+                poly_approx_acc_rate_slider     = gr.Slider(minimum=0, maximum=1, step=0.01, value=0.05, label="polygonalApproxAccuracyRate")
+                min_corner_distance_rate_slider = gr.Slider(minimum=0, maximum=1, step=0.01, value=0.05, label="minCornerDistanceRate")
+                min_distance_to_border_slider   = gr.Slider(minimum=0, maximum=25, step=1, value=3, label="minDistanceToBorder")
+                min_marker_distance_rate_slider = gr.Slider(minimum=0, maximum=1, step=0.01, value=0.05, label="minMarkerDistanceRate")
+                        
+                cornerRefinementMethod_radio = gr.Radio(
+                    choices=cornerRefinementMethods,
+                    label="cornerRefinementMethod", value=cornerRefinementMethods[0]
+                )
+                cornerRefinementWinSize_slider                         = gr.Slider(minimum=0, maximum=20, step=1, value=5, label="cornerRefinementWinSize")
+                cornerRefinementMaxIterations_slider                         = gr.Slider(minimum=1, maximum=50, step=1, value=30, label="cornerRefinementMaxIterations")
+                cornerRefinementMinAccuracy_slider                 = gr.Slider(minimum=0.01, maximum=2, step=0.01, value=0.1, label="cornerRefinementMinAccuracy")
+                markerBoderBits_slider = gr.Slider(minimum=1, maximum=100, step=1, value=1, label="markerBoderBits")
+                perspectiveRemovePixelPerCell_slider = gr.Slider(minimum=0, maximum=100, step=1, value=8, label="perspectiveRemovePixelPerCell")
+                perspectiveRemoveIgnoredMarginPerCell_slider = gr.Slider(minimum=0, maximum=1, step=0.01, value=0.13, label="perspectiveRemoveIgnoredMarginPerCell")
+                maxErroneousBitsInBorderRate_slider = gr.Slider(minimum=0, maximum=1, step=0.01, value=0.04, label="maxErroneousBitsInBorderRate")
+                minOtsuStdDev_slider = gr.Slider(minimum=0, maximum=10, step=0.1, value=5.0, label="minOtsuStdDev")
+                errorCorrectionRate_slider = gr.Slider(minimum=0, maximum=1, step=0.01, value=0.6, label="errorCorrectionRate")
+
+
+            submit_button = gr.Button("Submit")
         
-    ],
-    outputs=gr.Image(type="numpy", label="Output Image"),
-    title="Aruco tag detection",
-    description="Select the aruco library, upload an image, and detect the aruco tags.",
-    examples=image_paths,
-   #     flagging_mode="auto"
-)
+        with gr.Column():
+            output_image = gr.Image(type="numpy", label="Output Image")
+            thresh_image = gr.Image(type="numpy", label="Thresh Image")
+    
+    examples = gr.Examples(examples=image_paths, inputs=[image_input, dict_dropdown, thresh_min_slider, thresh_max_slider, thresh_step_slider, thresh_const_slider], outputs=[output_image, thresh_image])
+    submit_button.click(
+        inference,
+        inputs=[image_input, dict_dropdown, rejects_radio, thresh_min_slider, thresh_max_slider, thresh_step_slider, thresh_const_slider, min_marker_p_slider, max_marker_p_slider, poly_approx_acc_rate_slider, min_corner_distance_rate_slider, min_distance_to_border_slider, min_marker_distance_rate_slider, cornerRefinementMethod_radio, cornerRefinementWinSize_slider, cornerRefinementMaxIterations_slider, cornerRefinementMinAccuracy_slider, markerBoderBits_slider, perspectiveRemovePixelPerCell_slider, perspectiveRemoveIgnoredMarginPerCell_slider, maxErroneousBitsInBorderRate_slider, minOtsuStdDev_slider, errorCorrectionRate_slider],
+        outputs=[output_image, thresh_image]
+    )
 
 demo.launch()