Create app.py

compute people similarity

app.py

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+import gradio as gr
+import cv2
+from insightface.app import FaceAnalysis
+import torch
+import torch.nn as nn
+import numpy as np
+
+np.int = np.int32
+np.float = np.float64
+np.bool = np.bool_
+
+app = FaceAnalysis(name="buffalo_l", providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
+app.prepare(ctx_id=0, det_size=(640, 640))
+
+def calculate(photo1, photo2):
+    if photo1 is None or photo2 is None:
+        return 0
+    image1 = cv2.imread(photo1)
+    faces1 = app.get(image1)
+    if len(faces1) < 1:
+        return 0
+    faceid_embeds1 = torch.from_numpy(faces1[0].normed_embedding).unsqueeze(0)
+    
+    image2 = cv2.imread(photo2)
+    faces2 = app.get(image2)
+    if len(faces2) < 1:
+        return 0
+    faceid_embeds2 = torch.from_numpy(faces2[0].normed_embedding).unsqueeze(0)
+    
+    cos = nn.CosineSimilarity(dim=1, eps=1e-10)
+    cos_similarity = cos(faceid_embeds1, faceid_embeds2)
+    return cos_similarity
+    
+with gr.Blocks() as demo:
+    with gr.Row():
+        with gr.Column():
+            face_photo1 = gr.Image(label="Photo of Person 1", type="filepath")            
+            face_photo2 = gr.Image(label="Photo of Person 2", type="filepath")
+            greet_btn = gr.Button("Calculate")
+        with gr.Column():
+            output = gr.JSON()
+    
+    greet_btn.click(fn=calculate, inputs=[face_photo1, face_photo2], outputs=output, api_name="calculate_face_similarity")
+
+if __name__ == "__main__":
+    demo.launch()