input shape

app.py

@@ -183,16 +183,16 @@ def encrypt(user_id, input_image):
         raise gr.Error("Please choose an image first.")
 
     # Resize the image if it hasn't the shape (224, 224, 3)
-    if input_image.shape != (224, 224, 3):
+    if input_image.shape != (32, 32, 3):
         input_image_pil = Image.fromarray(input_image)
-        input_image_pil = input_image_pil.resize((224, 224))
+        input_image_pil = input_image_pil.resize((32, 32))
         input_image = numpy.array(input_image_pil)
 
     # Convert RGB to grayscale
     input_image_gray = numpy.mean(input_image, axis=2).astype(numpy.uint8)
 
     # Reshape to (1, 1, 224, 224)
-    input_image_reshaped = input_image_gray.reshape(1, 1, 224, 224)
+    input_image_reshaped = input_image_gray.reshape(1, 1, 32, 32)
 
     # Convert to int12 (assuming the range is 0-255, we can simply cast to int16)
     input_image_int12 = input_image_reshaped.astype(numpy.int16)
@@ -364,7 +364,7 @@ with demo:
     gr.Markdown("## Client side")
     gr.Markdown("### Step 1: Upload an EEG image. ")
     gr.Markdown(
-        f"The image will automatically be resized to shape (224x224). "
+        f"The image will automatically be resized to shape (32x32). "
         "The image here, however, is displayed in its original resolution."
     )
     with gr.Row():

common.py

@@ -20,7 +20,7 @@ CLIENT_TMP_PATH.mkdir(exist_ok=True)
 SERVER_TMP_PATH.mkdir(exist_ok=True)
 
 # The input images' shape. Images with different input shapes will be cropped and resized by Gradio
-INPUT_SHAPE = (224, 224)
+INPUT_SHAPE = (1, 1, 32, 32)
 
 # Retrieve the input examples directory
 INPUT_EXAMPLES_DIR = REPO_DIR / "input_examples"