exp rec code

app/Hackathon_setup/exp_recognition.py

@@ -67,5 +67,13 @@ def get_expression(img):
         face = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY))
     
     # YOUR CODE HERE, return expression using your model
+    # YOUR CODE HERE, return expression using your model
+    face_det_net = ExpressionRecognitionCNN()
+    model = torch.load(current_path + '/expression_model.t7', map_location=device)
+    face_det_net.load_state_dict(model['net_dict'])
+    face = trnscm(face).unsqueeze(0)
+    output = face_det_net(face)
+    _, pred = torch.max(output, dim=1)
+    return classes[pred]
 
-    return "YET TO BE CODED"
+    return "YET TO BE CODED"

app/Hackathon_setup/exp_recognition_model.py

@@ -21,6 +21,23 @@ class facExpRec(torch.nn.Module):
     def forward(self, x):
         pass   # remove 'pass' once you have written your code
         #YOUR CODE HERE
+
+class ExpressionRecognitionCNN(nn.Module):
+    def __init__(self, num_classes = 7):
+        super(ExpressionRecognitionCNN, self).__init__()
+        self.conv1 = nn.Conv2d(1, 32, kernel_size=3, padding=1)
+        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)
+        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
+        self.fc1 = nn.Linear(64 * 25 * 25, 128)
+        self.fc2 = nn.Linear(128, num_classes)
+
+    def forward(self, x):
+        x = self.pool(torch.relu(self.conv1(x)))
+        x = self.pool(torch.relu(self.conv2(x)))
+        x = x.view(-1, 64 * 25 * 25)
+        x = torch.relu(self.fc1(x))
+        x = self.fc2(x)
+        return x
         
 # Sample Helper function
 def rgb2gray(image):
@@ -28,4 +45,7 @@ def rgb2gray(image):
     
 # Sample Transformation function
 #YOUR CODE HERE for changing the Transformation values.
-trnscm = transforms.Compose([rgb2gray, transforms.Resize((48,48)), transforms.ToTensor()])
+trnscm = transforms.Compose([transforms.Grayscale(num_output_channels=1),
+    transforms.Resize((100, 100)),
+    transforms.ToTensor(),
+    transforms.Normalize((0.5,), (0.5,)),])