Added dexined model to space

app.py

@@ -0,0 +1,27 @@
+import cv2 as cv
+import gradio as gr
+from dexined import Dexined
+from huggingface_hub import hf_hub_download
+
+# Download ONNX model from Hugging Face
+model_path = hf_hub_download(repo_id="opencv/edge_detection_dexined", filename="edge_detection_dexined_2024sep.onnx")
+
+# Initialize model
+model = Dexined(modelPath=model_path)
+
+def detect_edges(input_image):
+    result = model.infer(input_image)
+    return result
+
+# Gradio UI
+demo = gr.Interface(
+    fn=detect_edges,
+    inputs=gr.Image(type="numpy", label="Upload Image"),
+    outputs=gr.Image(type="numpy", label="Output"),
+    title="Edge Detection DexiNed (OpenCV DNN)",
+    allow_flagging="never",
+    description="Upload an image to detect edges using OpenCV's ONNX-based edge detection using DexiNed model."
+)
+
+if __name__ == "__main__":
+    demo.launch()

dexined.py

@@ -0,0 +1,50 @@
+import cv2 as cv
+import numpy as np
+
+class Dexined:
+    def __init__(self, modelPath='edge_detection_dexined_2024sep.onnx', backendId=0, targetId=0):
+        self._modelPath = modelPath
+        self._backendId = backendId
+        self._targetId = targetId
+        
+        # Load the model
+        self._model = cv.dnn.readNetFromONNX(self._modelPath)
+        self.setBackendAndTarget(self._backendId, self._targetId)
+
+    @property
+    def name(self):
+        return self.__class__.__name__
+
+    def setBackendAndTarget(self, backendId, targetId):
+        self._backendId = backendId
+        self._targetId = targetId
+        self._model.setPreferableBackend(self._backendId)
+        self._model.setPreferableTarget(self._targetId)
+
+    @staticmethod
+    def sigmoid(x):
+        return 1.0 / (1.0 + np.exp(-x))
+
+    def postProcessing(self, output, shape):
+        h, w = shape
+        preds = []
+        for p in output:
+            img = self.sigmoid(p)
+            img = np.squeeze(img)
+            img = cv.normalize(img, None, 0, 255, cv.NORM_MINMAX, cv.CV_8U)
+            img = cv.resize(img, (w, h))
+            preds.append(img)
+        fuse = preds[-1]
+        ave = np.array(preds, dtype=np.float32)
+        ave = np.uint8(np.mean(ave, axis=0))
+        return fuse, ave
+
+    def infer(self, image):
+        inp = cv.dnn.blobFromImage(image, 1.0, (512, 512), (103.5, 116.2, 123.6), swapRB=False, crop=False)
+        self._model.setInput(inp)
+        
+        # Forward pass through the model
+        out = self._model.forward()
+        result, _ = self.postProcessing(out, image.shape[:2])
+
+        return result

requirements.txt

@@ -0,0 +1,4 @@
+opencv-python
+gradio
+numpy
+huggingface_hub