--- a
+++ b/Segmentation/test.py
@@ -0,0 +1,44 @@
+import os
+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+os.environ["SM_FRAMEWORK"] = "tf.keras"
+import segmentation_models as sm
+sm.framework()
+
+my_model = "efficientnetb1"
+model = sm.Unet(my_model, encoder_weights="imagenet", input_shape=( 256,256, 3), classes=3, activation='sigmoid')
+model.load_weights("Submission_segmentation/weights/unet_model_weights.h5")
+
+#test for single image
+def visualize_single_image_prediction(model, image_path):
+    # Load the image
+    original_img = cv2.imread(image_path)
+    resized_img = cv2.resize(original_img, (256, 256))
+
+    # Predict the mask
+    X = np.expand_dims(resized_img, 0)
+    y_pred = model.predict(X)
+    _, y_pred_thr = cv2.threshold(y_pred[0, :, :, 0] * 255, 127, 255, cv2.THRESH_BINARY)
+    y_pred = (y_pred_thr / 255).astype(int)
+
+    # Resize the predicted mask back to the original size
+    y_pred_original = cv2.resize(y_pred.astype(float), (original_img.shape[1], original_img.shape[0]), interpolation=cv2.INTER_LINEAR)
+
+    # Visualize the original image and predicted mask
+    plt.figure(figsize=(12, 4))
+    plt.subplot(131)
+    plt.title("Original Image")
+    plt.imshow(cv2.cvtColor(original_img, cv2.COLOR_BGR2RGB))
+    plt.axis("off")
+
+    plt.subplot(132)
+    plt.title("Predicted Mask")
+    plt.imshow(y_pred_original, cmap="gray")
+    plt.axis("off")
+
+    plt.show()
+
+#example
+visualize_single_image_prediction(model, "img- (10).png")
+print("done")
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