test new model

app.py

@@ -1,31 +1,144 @@
+# import torch
+# import torchvision.transforms as transforms
+# import numpy as np
+# import gradio as gr
+# from PIL import Image, ImageDraw
+# from facenet_pytorch import MTCNN, InceptionResnetV1
+# import time
+
+# # Initialize MTCNN for face detection with smaller face size detection
+# mtcnn = MTCNN(keep_all=True, device='cuda' if torch.cuda.is_available() else 'cpu', min_face_size=20)
+
+# # Load the pre-trained FaceNet model
+# facenet = InceptionResnetV1(pretrained='vggface2').eval().to('cuda' if torch.cuda.is_available() else 'cpu')
+# model_path = r'faceNet_update_transformation.pth'
+# model_state_dict = torch.load(model_path)
+# facenet.load_state_dict(model_state_dict)
+# facenet.eval()  # Set the model to evaluation mode
+
+# # Define the transformation with normalization
+# val_test_transform = transforms.Compose([
+#     transforms.Resize((160, 160)),  # FaceNet expects 160x160 input
+#     transforms.ToTensor(),
+#     transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+# ])
+
+# def compare_faces(embedding1, embedding2, threshold=0.2):  # Adjusted threshold
+#     dist = np.linalg.norm(embedding1 - embedding2)
+#     return dist, dist < threshold
+
+# def align_face(frame):
+#     # Convert the frame to a PIL image if it's a numpy array
+#     if isinstance(frame, np.ndarray):
+#         frame = Image.fromarray(frame)
+#         boxes, _ = mtcnn.detect(frame)
+#     if boxes is not None and len(boxes) > 0:
+#         faces = mtcnn(frame)
+#         if faces is not None and len(faces) > 0:
+#             face = faces[0]
+#             # Convert the face tensor to PIL Image
+#             face = transforms.ToPILImage()(face)
+#             return face, boxes[0]
+#     return None, None
+
+# def draw_bounding_box(image, box):
+#     draw = ImageDraw.Draw(image)
+#     draw.rectangle(box.tolist(), outline="red", width=3)
+#     return image
+
+# def l2_normalize(tensor):
+#     norm = np.linalg.norm(tensor, ord=2, axis=1, keepdims=True)
+#     return tensor / norm
+
+# def process_images(image1, image2):
+#     start_time = time.time()
+    
+#     frame1 = np.array(image1)
+#     frame2 = np.array(image2)
+    
+#     face1, box1 = align_face(frame1)
+#     face2, box2 = align_face(frame2)
+    
+#     if face1 is None or face2 is None:
+#         return None, "Face not detected in one or both images."
+    
+#     face1 = val_test_transform(face1).unsqueeze(0).to('cuda' if torch.cuda.is_available() else 'cpu')
+#     face2 = val_test_transform(face2).unsqueeze(0).to('cuda' if torch.cuda.is_available() else 'cpu')
+    
+#     with torch.no_grad():
+#         embedding1 = facenet(face1).cpu().numpy()
+#         embedding2 = facenet(face2).cpu().numpy()
+    
+#     embedding1 = l2_normalize(embedding1)
+#     embedding2 = l2_normalize(embedding2)
+    
+#     distance, is_match = compare_faces(embedding1, embedding2, threshold=0.2)
+    
+#     # Calculate confidence
+#     confidence = max(0.0, 1.0 - distance / 1.0)  # Ensure confidence is between 0 and 1
+#     print(f'confidence={confidence}')
+#     end_time = time.time()
+#     inference_time = end_time - start_time
+    
+#     # Draw bounding boxes on the original images
+#     image1_with_box = draw_bounding_box(image1, box1)
+#     image2_with_box = draw_bounding_box(image2, box2)
+    
+#     result = f"Distance: {distance:.2f}\nMatch: {is_match}\nInference time: {inference_time:.2f} seconds"
+    
+#     return [image1_with_box, image2_with_box], result
+
+# # Create the Gradio interface
+# iface = gr.Interface(
+#     fn=process_images,
+#     inputs=[gr.Image(type="pil"), gr.Image(type="pil")],
+#     outputs=[gr.Gallery(), gr.Textbox()],
+#     title="Face Verification with FaceNet",
+#     description="Upload two images and the model will verify if the faces in both images are of the same person."
+# )
+
+# # Launch the interface
+# iface.launch(share=True, debug=True)
+
 import torch
-import torchvision.transforms as transforms
+import torch.nn as nn
 import numpy as np
-import gradio as gr
 from PIL import Image, ImageDraw
-from facenet_pytorch import MTCNN, InceptionResnetV1
+from torchvision import transforms
+from transformers import ViTImageProcessor, ViTModel
+from facenet_pytorch import MTCNN
+import gradio as gr
 import time
 
-# Initialize MTCNN for face detection with smaller face size detection
-mtcnn = MTCNN(keep_all=True, device='cuda' if torch.cuda.is_available() else 'cpu', min_face_size=12)
+# Define the Vision Transformer (ViT) architecture
+class ViT(nn.Module):
+    def __init__(self, base_model):
+        super(ViT, self).__init__()
+        self.base_model = base_model
+        self.dropout = nn.Dropout(p=0.2)
+        self.fc = nn.Linear(base_model.config.hidden_size, 512)
+        self.dropout2 = nn.Dropout(p=0.2)
+        self.l2_norm = nn.functional.normalize
 
-# Load the pre-trained FaceNet model
-facenet = InceptionResnetV1(pretrained='vggface2').eval().to('cuda' if torch.cuda.is_available() else 'cpu')
-model_path = r'faceNet_update_transformation.pth'
-model_state_dict = torch.load(model_path)
-facenet.load_state_dict(model_state_dict)
-facenet.eval()  # Set the model to evaluation mode
+    def forward(self, x):
+        x = self.base_model(x).pooler_output
+        x = self.dropout(x)
+        x = self.fc(x)
+        x = self.dropout2(x)
+        x = self.l2_norm(x, p=2, dim=1)  # Apply L2 normalization
+        return x
 
-# Define the transformation with normalization
-val_test_transform = transforms.Compose([
-    transforms.Resize((160, 160)),  # FaceNet expects 160x160 input
-    transforms.ToTensor(),
-    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
-])
+# Load the pre-trained ViT model and processor
+model_name = "google/vit-base-patch16-224"
+processor = ViTImageProcessor.from_pretrained(model_name)
+base_model = ViTModel.from_pretrained(model_name)
+model = ViT(base_model)
+model_path = r'best_vit11.pth'
+model.load_state_dict(torch.load(model_path))
+model.eval().to('cuda' if torch.cuda.is_available() else 'cpu')
 
-def compare_faces(embedding1, embedding2, threshold=0.2):  # Adjusted threshold
-    dist = np.linalg.norm(embedding1 - embedding2)
-    return dist, dist < threshold
+# Initialize MTCNN for face detection
+mtcnn = MTCNN(keep_all=True, min_face_size=20, device='cuda' if torch.cuda.is_available() else 'cpu')
 
 def align_face(frame):
     # Convert the frame to a PIL image if it's a numpy array
@@ -46,9 +159,11 @@ def draw_bounding_box(image, box):
     draw.rectangle(box.tolist(), outline="red", width=3)
     return image
 
-def l2_normalize(tensor):
-    norm = np.linalg.norm(tensor, ord=2, axis=1, keepdims=True)
-    return tensor / norm
+def euclidean_distance(embedding1, embedding2):
+    return np.linalg.norm(embedding1 - embedding2)
+
+def cosine_similarity(embedding1, embedding2):
+    return np.dot(embedding1, embedding2) / (np.linalg.norm(embedding1) * np.linalg.norm(embedding2))
 
 def process_images(image1, image2):
     start_time = time.time()
@@ -62,20 +177,24 @@ def process_images(image1, image2):
     if face1 is None or face2 is None:
         return None, "Face not detected in one or both images."
     
-    face1 = val_test_transform(face1).unsqueeze(0).to('cuda' if torch.cuda.is_available() else 'cpu')
-    face2 = val_test_transform(face2).unsqueeze(0).to('cuda' if torch.cuda.is_available() else 'cpu')
+    # Use processor to preprocess the images
+    face1 = processor(images=face1, return_tensors="pt").pixel_values.to('cuda' if torch.cuda.is_available() else 'cpu')
+    face2 = processor(images=face2, return_tensors="pt").pixel_values.to('cuda' if torch.cuda.is_available() else 'cpu')
     
     with torch.no_grad():
-        embedding1 = facenet(face1).cpu().numpy()
-        embedding2 = facenet(face2).cpu().numpy()
+        embedding1 = model(face1).cpu().numpy()
+        embedding2 = model(face2).cpu().numpy()
     
-    embedding1 = l2_normalize(embedding1)
-    embedding2 = l2_normalize(embedding2)
+    # Flatten the embeddings if necessary (ensuring they are 1D)
+    embedding1 = embedding1.flatten()
+    embedding2 = embedding2.flatten()
     
-    distance, is_match = compare_faces(embedding1, embedding2, threshold=0.2)
+    euclidean_dist = euclidean_distance(embedding1, embedding2)
+    cosine_sim = cosine_similarity(embedding1, embedding2)
+    is_match = euclidean_dist < 0.2
     
     # Calculate confidence
-    confidence = max(0.0, 1.0 - distance / 1.0)  # Ensure confidence is between 0 and 1
+    confidence = max(0.0, 1.0 - euclidean_dist / 1.0)  # Ensure confidence is between 0 and 1
     print(f'confidence={confidence}')
     end_time = time.time()
     inference_time = end_time - start_time
@@ -84,7 +203,10 @@ def process_images(image1, image2):
     image1_with_box = draw_bounding_box(image1, box1)
     image2_with_box = draw_bounding_box(image2, box2)
     
-    result = f"Distance: {distance:.2f}\nMatch: {is_match}\nInference time: {inference_time:.2f} seconds"
+    result = f"Euclidean Distance: {euclidean_dist:.2f}\n"
+    # result += f"Cosine Similarity: {cosine_sim:.2f}\n"
+    result += f"Match: {is_match}\n"
+    result += f"Inference time: {inference_time:.2f} seconds"
     
     return [image1_with_box, image2_with_box], result
 
@@ -93,7 +215,7 @@ iface = gr.Interface(
     fn=process_images,
     inputs=[gr.Image(type="pil"), gr.Image(type="pil")],
     outputs=[gr.Gallery(), gr.Textbox()],
-    title="Face Verification with FaceNet",
+    title="Face Verification with Vision Transformer",
     description="Upload two images and the model will verify if the faces in both images are of the same person."
 )
 

best_vit11.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c7d41bca403110bf291cf0b40749f486072de2bb701c7749eafa4fac9eb04860
+size 347217224