Update README.md

README.md

@@ -22,6 +22,62 @@ from huggingface_hub import from_pretrained_keras
 model = from_pretrained_keras("MIDSCapstoneTeam/ContrailSentinel", custom_objects={'dice_loss_plus_5focal_loss': total_loss, 'jaccard_coef': jaccard_coef, 'IOU score' : sm.metrics.IOUScore(threshold=0.9, name="IOU score"), 'Dice Coeficient' : sm.metrics.FScore(threshold=0.6, name="Dice Coeficient")}, compile=False)
 }}
 
+{{
+#Required imports and Huggingface authentication
+import os
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
+os.environ["SM_FRAMEWORK"] = "tf.keras"
+import segmentation_models as sm
+import tensorflow as tf
+from huggingface_hub import from_pretrained_keras
+from huggingface_hub import notebook_login
+from PIL import Image
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+weights = [0.5,0.5] # hyper parameter
+
+dice_loss = sm.losses.DiceLoss(class_weights = weights)
+focal_loss = sm.losses.CategoricalFocalLoss()
+TOTAL_LOSS_FACTOR = 5
+total_loss = dice_loss + (TOTAL_LOSS_FACTOR * focal_loss)
+
+def jaccard_coef(y_true, y_pred):
+    """
+    Defines custom jaccard coefficient metric
+    """
+    
+    y_true_flatten = K.flatten(y_true)
+    y_pred_flatten = K.flatten(y_pred)
+    intersection = K.sum(y_true_flatten * y_pred_flatten)
+    final_coef_value = (intersection + 1.0) / (K.sum(y_true_flatten) + K.sum(y_pred_flatten) - intersection + 1.0)
+    return final_coef_value
+
+metrics = [tf.keras.metrics.MeanIoU(num_classes=2, sparse_y_true= False, sparse_y_pred=False, name="Mean IOU")]
+notebook_login() 
+
+#Load model from Huggingface Hub
+model = from_pretrained_keras("MIDSCapstoneTeam/ContrailSentinel", custom_objects={'dice_loss_plus_5focal_loss': total_loss, 'jaccard_coef': jaccard_coef, 'IOU score' : sm.metrics.IOUScore(threshold=0.9, name="IOU score"), 'Dice Coeficient' : sm.metrics.FScore(threshold=0.6, name="Dice Coeficient")}, compile=False)
+model.compile(metrics=metrics)
+
+#Inference -- User needs to specify the image path where label and ash images are stored
+label = np.load({Image path} + 'human_pixel_masks.npy')
+ash_image = np.load({Image path} + 'ash_image.npy')[...,4] 
+y_pred = model.predict(ash_image.reshape(1,256, 256, 3))
+prediction = np.argmax(y_pred[0], axis=2).reshape(256,256,1)
+
+fig, ax = plt.subplots(1, 2, figsize=(9, 5))
+fig.tight_layout(pad=5.0)
+ax[1].set_title("Contrail prediction")
+ax[1].imshow(ash_image)
+ax[1].imshow(prediction)
+ax[1].axis('off')
+
+ax[0].set_title("False colored satellite image")
+ax[0].imshow(ash_image)
+ax[0].axis('off')}}
+
 
 ## Training and evaluation data