Update app.py

app.py

@@ -6,6 +6,9 @@ import gdown
 from PIL import Image
 import numpy as np
 
+means = np.array([0.485, 0.456, 0.406])
+stds = np.array([0.229, 0.224, 0.225])
+
 # Define the model URL and output path
 model_url = "https://drive.google.com/file/d/18HYScsRJuRmfzL0E0BW35uaA542Vd5M5/view?usp=sharing"
 model_path = os.path.join(os.getcwd(),"bone_age_model.onnx")
@@ -26,10 +29,15 @@ def inference(sample_name):
     predicted_age = (outputs[0]*41.172)+127.329
     # Get the image data from the MetaTensor and convert it to a format PIL can handle
     image_data = sample['path'].numpy()
-    image_data = (image_data * 255).astype(np.uint8)  # Assuming the image data is normalized
-    image_data = np.squeeze(image_data)  # Remove any singleton dimensions if necessary
-    image = Image.fromarray(image_data.transpose(1, 2, 0))  # Convert to HWC format
-    
+    # Denormalize the image data
+    for i in range(3):  # Assuming the image has 3 channels
+        image_data[i,:,:] = image_data[i,:,:] * stds[i] + means[i]
+    # Rescale to [0, 1]
+    # Convert to [0, 255] and to uint8
+    image_data = (image_data * 255).astype(np.uint8)
+    # Remove any singleton dimensions if necessary
+    image_data = np.moveaxis(image_data,0,-1)
+    image = Image.fromarray(image_data)
     return age, predicted_age[0][0], image
 
 # List of sample file names