Upload app.py

app.py

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+import gradio as gr
+from facenet_pytorch import InceptionResnetV1
+import torch.nn as nn
+import torchvision.transforms as tf
+import numpy as np
+import torch
+import faiss
+import h5py
+import os
+import random
+from PIL import Image
+import matplotlib.cm as cm
+import matplotlib as mpl
+
+
+
+
+img_names = []
+with open('list_eval_partition.txt', 'r') as f:
+    for line in f:
+        img_name, dtype = line.rstrip().split(' ')
+        img_names.append(img_name)
+        
+        # if dtype == '0':
+        #     data['train'].append(img_name)
+        # elif dtype == '1':
+        #     data['val'].append(img_name)
+        # else:
+        #     data['test'].append(img_name)
+
+
+# For a model pretrained on VGGFace2
+print('Loading model weights ........')
+
+class SiameseModel(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.backbone = InceptionResnetV1(pretrained='vggface2')
+    def forward(self, x):
+        x = self.backbone(x)
+        x = torch.nn.functional.normalize(x, dim=1)
+        return x
+    
+model = SiameseModel()
+model.load_state_dict(torch.load('model_best_weights_1000.pt', map_location=torch.device('cpu')))
+model.eval()
+
+
+# Make FAISS index
+print('Make index .............')
+index = faiss.IndexFlatL2(512)
+
+hf = h5py.File('face_vecs_full.h5', 'r')
+for key in hf.keys():
+    vec = np.array(hf.get(key))
+    index.add(vec)
+
+hf.close()
+
+# Function to search image
+def image_search(image, k=5):
+    
+    transform = tf.Compose([
+        tf.Resize((160, 160)),
+        tf.ToTensor()
+    ])
+
+    query_img = transform(image)
+    query_img = torch.unsqueeze(query_img, 0)
+
+    model.eval()
+    query_vec = model(query_img).detach().numpy()
+
+    D, I = index.search(query_vec, k=k)
+
+    retrieval_imgs = []
+    
+    FOLDER = 'img_align_celeba'
+    for idx in I[0]:
+        img_file_name = img_names[idx]
+        path = os.path.join(FOLDER, img_file_name)
+
+        image = Image.open(path)
+        retrieval_imgs.append((image, ''))
+        
+    return retrieval_imgs
+
+with gr.Blocks(theme=gr.themes.Monochrome()) as demo:
+    gr.Markdown('''
+    
+    
+    # Face Image Retrieval with Content-based Retrieval Image (CBIR) & Saliency Map
+    --------
+    
+    
+    ''')
+    
+    with gr.Row():
+        with gr.Column():
+            image = gr.Image(type='pil', scale=1)
+            slider = gr.Slider(1, 10, value=5, step=1, label='Number of retrieval image')
+            with gr.Row():
+                btn = gr.Button('Search')
+                clear_btn = gr.ClearButton()
+        
+        gallery = gr.Gallery(label='Retrieval Images', columns=[5], show_label=True, scale=2)
+        
+    img_dir = './img_align_celeba'    
+    examples = random.choices(img_names, k=6)
+    examples = [os.path.join(img_dir, ex) for ex in examples]
+    examples = [Image.open(img) for img in examples]
+                                  
+    with gr.Row():
+        gr.Examples(
+            examples = examples,
+            inputs = image
+        )
+            
+            
+    btn.click(image_search, 
+              inputs= [image, slider], 
+              outputs= [gallery])
+    
+    def clear_image():
+        return None
+    
+    clear_btn.click(
+        fn = clear_image,
+        inputs = [],
+        outputs = [image]
+    )
+
+    
+if __name__ == "__main__":
+    demo.launch()   
+