face expressions

.idea/Siamese_network_hackathon.iml

@@ -3,7 +3,7 @@
   <component name="NewModuleRootManager" inherit-compiler-output="true">
     <exclude-output />
     <content url="file://$MODULE_DIR$" />
-    <orderEntry type="inheritedJdk" />
+    <orderEntry type="jdk" jdkName="Python 3.12" jdkType="Python SDK" />
     <orderEntry type="sourceFolder" forTests="false" />
   </component>
 </module>

.idea/misc.xml

@@ -1,5 +1,8 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <project version="4">
+  <component name="Black">
+    <option name="sdkName" value="Python 3.12" />
+  </component>
   <component name="ProjectRootManager" version="2" languageLevel="JDK_22" default="true" project-jdk-name="openjdk-22" project-jdk-type="JavaSDK">
     <output url="file://$PROJECT_DIR$/out" />
   </component>

app/Hackathon_setup/exp_recognition.py

@@ -8,6 +8,12 @@ from PIL import Image
 import base64
 import io
 import os
+import torch
+import torch.nn as nn
+from torchvision import models
+import torchvision.transforms as transforms
+import torch.nn.functional as F
+
 ## Add more imports if required
 
 #############################################################################################################################
@@ -50,6 +56,27 @@ def detected_face(image):
 ##Caution: Don't change the definition or function name; for loading the model use the current_path for path example is given in comments to the function
 def get_expression(img):
     device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+    # Recreate the same model architecture
+    num_classes = 7  # 👈 change this to match your training setup
+
+    model = models.resnet18(weights=None)  # no pretrained weights now
+    model.fc = nn.Linear(model.fc.in_features, num_classes)
+
+    model = model.to(device)
+
+    # Create the optimizer (same as training)
+    optimizer = torch.optim.Adam(model.parameters(), lr=0.0001)
+
+    # Load the checkpoint
+    checkpoint = torch.load('resnet_expression_recognition.pth', map_location=device)
+
+    # Restore weights and optimizer
+    model.load_state_dict(checkpoint['model_state_dict'])
+    optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
+
+    # Put the model in evaluation mode
+    model.eval()
     
     ##########################################################################################
     ##Example for loading a model using weight state dictionary:                            ##
@@ -62,10 +89,22 @@ def get_expression(img):
     ##########################################################################################
     ##########################################################################################
 
+    transform = transforms.Compose([
+        transforms.Grayscale(num_output_channels=1),
+        transforms.Resize(256),
+        transforms.CenterCrop(224),
+        transforms.ToTensor(),
+        transforms.Normalize(mean=[0.5], std=[0.5])
+    ])
+
     face = detected_face(img)
     if face==0:
         face = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY))
-    
-    # YOUR CODE HERE, return expression using your model
 
-    return "YET TO BE CODED"
+    face = transform(face).unsqueeze(0).to(device)
+    # YOUR CODE HERE, return expression using your model
+    with torch.no_grad():
+        outputs = model(face)
+        probs = F.softmax(outputs, dim=1)
+        predicted_class = probs.argmax(dim=1).item()
+    return predicted_class

app/Hackathon_setup/expression_model.t7

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3a7893e2bcdf6825103b73328d0d17933ca8d5c7f0201b973cee63dd97ff6248
+size 135777625