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app.py

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+import gradio as gr
+from src.model import ImageEmbedding
+
+image_embedding = ImageEmbedding()
+
+def compare_faces(person_image, id_image):
+    embeddings1, img1 = image_embedding.get_face_embedding(person_image)
+    embeddings2, img2 = image_embedding.get_face_embedding(id_image)
+    similarity = image_embedding.cosine_similarity(embeddings1, embeddings2)
+    return similarity, img1, img2
+
+with gr.Blocks() as demo:
+    gr.Markdown('# Image matcher')
+    with gr.Row():
+        with gr.Column():
+            person_image = gr.Image(label="Person Image", type="numpy", height=256)
+            sim_score = gr.Textbox(label="Similarity Score")
+
+        with gr.Column():
+            id_image = gr.Image(label="ID Image", type="numpy", height=256)
+            submit_button = gr.Button("Submit")
+
+    submit_button.click(fn=compare_faces,
+                        inputs=[person_image, id_image],
+                        outputs=[sim_score, person_image, id_image])
+
+
+if __name__ == "__main__":
+    demo.launch()

models/antelopev2/1k3d68.onnx

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models/antelopev2/2d106det.onnx

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models/antelopev2/genderage.onnx

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models/antelopev2/glintr100.onnx

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models/antelopev2/scrfd_10g_bnkps.onnx

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models/buffalo_l/1k3d68.onnx

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models/buffalo_l/2d106det.onnx

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models/buffalo_l/det_10g.onnx

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models/buffalo_l/genderage.onnx

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models/buffalo_l/w600k_r50.onnx

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models/buffalo_s/1k3d68.onnx

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models/buffalo_s/2d106det.onnx

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models/buffalo_s/det_500m.onnx

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models/buffalo_s/genderage.onnx

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models/buffalo_s/w600k_mbf.onnx

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models/custom/model.onnx

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models/custom/webface_r50.onnx

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requirements.txt

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+gradio
+insightface
+onnxruntime
+ipykernel

src/model.py

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+import cv2
+import insightface
+from insightface.app.common import Face
+from insightface.model_zoo import model_zoo
+
+import numpy as np
+
+class ImageEmbedding:
+    def __init__(self):
+
+        det_model_path = 'models/buffalo_l/det_10g.onnx'
+        rec_model_path = 'models/antelopev2/glintr100.onnx'
+
+        self.det_model = model_zoo.get_model(f'{det_model_path}')
+        self.rec_model = model_zoo.get_model(f'{rec_model_path}')
+
+        self.det_model.prepare(ctx_id=0, input_size=(640, 640), det_thres=0.5)
+
+    def cosine_similarity(self, embeddings1, embeddings2):
+        return np.dot(embeddings1, embeddings2) / (np.linalg.norm(embeddings1) * np.linalg.norm(embeddings2))
+
+    def get_face_embedding(self, img):
+        bboxes, kpss = self.det_model.detect(img, max_num=0, metric='defualt')
+
+        bbox = bboxes[0, :4]
+        det_score = bboxes[0, 4]
+        kps = kpss[0]
+        face = Face(bbox=bbox, kps=kps, det_score=det_score)
+
+        self.rec_model.get(img, face)
+        embeddings = face.normed_embedding
+
+        bbox = face.bbox
+        # Convert float coordinates to integers
+        bbox = [int(coord) for coord in bbox]
+
+        # Draw the rectangle on the image
+        cv2.rectangle(img, (bbox[0], bbox[1]), (bbox[2], bbox[3]), (0, 255, 0), 2)
+
+        return embeddings, img