Create app.py

app.py

@@ -0,0 +1,63 @@
+import gradio as gr
+import cv2
+import numpy as np
+import json
+
+def detect_aruco_markers(image, dictionary_preset,
+                         cornerRefinementMethod, detectInvertedMarker, adaptiveThreshConstant):
+    # Convert to grayscale
+    gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
+
+    # Load predefined dictionary
+    aruco_dict = cv2.aruco.getPredefinedDictionary(getattr(cv2.aruco, dictionary_preset))
+
+    # Initialize detector parameters
+    parameters = cv2.aruco.DetectorParameters()
+    parameters.cornerRefinementMethod = getattr(cv2.aruco, cornerRefinementMethod)
+    parameters.detectInvertedMarker = detectInvertedMarker
+    parameters.adaptiveThreshConstant = adaptiveThreshConstant
+
+    detector = cv2.aruco.ArucoDetector(aruco_dict, parameters)
+
+    # Detect markers
+    corners, ids, rejected = detector.detectMarkers(gray)
+    print("Detected IDs:", ids)
+
+    detections = []
+    if ids is not None:
+        annotated_image = cv2.aruco.drawDetectedMarkers(image.copy(), corners, ids)
+        for i in range(len(ids)):
+            marker_info = {
+                "id": int(ids[i][0]),
+                "corners": [np.array(corner).tolist() for corner in corners[i]]
+            }
+            detections.append(marker_info)
+    else:
+        annotated_image = image.copy()
+
+    return annotated_image, json.dumps(detections, indent=4)
+
+# Available ArUco dictionary presets
+dictionary_choices = [name for name in dir(cv2.aruco) if name.startswith("DICT_")]
+corner_refinement_choices = ["CORNER_REFINE_NONE", "CORNER_REFINE_SUBPIX", "CORNER_REFINE_CONTOUR", "CORNER_REFINE_APRILTAG"]
+
+# Gradio interface
+iface = gr.Interface(
+    fn=detect_aruco_markers,
+    inputs=[
+        gr.Image(type="numpy"),
+        gr.Dropdown(choices=dictionary_choices, label="Dictionary Preset", value="DICT_6X6_250"),	
+        gr.Dropdown(choices=corner_refinement_choices, label="Corner Refinement Method", value="CORNER_REFINE_NONE"),
+        gr.Checkbox(label="Detect Inverted Marker", value=False),
+        gr.Slider(minimum=3, maximum=201, step=2, label="Adaptive Thresh", value=7),
+    ],
+    outputs=[
+        gr.Image(type="numpy", label="Detected Markers"),
+        gr.Code(label="Detection Data (JSON)", language="json")
+    ],
+    title="ArUco Marker Detector with Parameter Control",
+    description="Upload an image and adjust ArUco detection parameters. The detected markers are drawn on the image, and the detection data (IDs and corner coordinates) is provided in JSON format.",
+)
+
+if __name__ == "__main__":
+    iface.launch()