--- a
+++ b/eval.py
@@ -0,0 +1,127 @@
+import os
+import cv2
+import numpy as np
+import pandas as pd
+from glob import glob
+from tqdm import tqdm
+
+import tensorflow as tf
+from tensorflow.keras.utils import CustomObjectScope
+from sklearn.metrics import (
+    accuracy_score,
+    f1_score,
+    jaccard_score,
+    precision_score,
+    recall_score,
+)
+from metrics import dice_loss, dice_coef, iou
+from train import load_data
+
+IMG_HEIGHT = 512
+IMG_WIDTH = 512
+
+""" Creating a directory """
+
+
+def create_dir(path):
+    if not os.path.exists(path):
+        os.makedirs(path)
+
+
+def save_results(image, mask, y_pred, save_image_path):
+    ## i - m - y
+    line = np.ones((IMG_HEIGHT, 10, 3)) * 128
+
+    """ Mask """
+    mask = np.expand_dims(mask, axis=-1)  ## (512, 512, 1)
+    mask = np.concatenate([mask, mask, mask], axis=-1)  ## (512, 512, 3)
+
+    """ Predicted Mask """
+    y_pred = np.expand_dims(y_pred, axis=-1)  ## (512, 512, 1)
+    y_pred = np.concatenate([y_pred, y_pred, y_pred], axis=-1)  ## (512, 512, 3)
+    y_pred = y_pred * 255
+
+    cat_images = np.concatenate([image, line, mask, line, y_pred], axis=1)
+    cv2.imwrite(save_image_path, cat_images)
+
+
+if __name__ == "__main__":
+    """Seeding"""
+    SEEDS = 42
+    np.random.seed(SEEDS)
+    tf.random.set_seed(SEEDS)
+
+    """ Directory for storing files """
+    create_dir("results")
+
+    """ Loading model """
+    with CustomObjectScope(
+        {"iou": iou, "dice_coef": dice_coef, "dice_loss": dice_loss}
+    ):
+        model = tf.keras.models.load_model("files/model.h5")
+
+    """ Load the dataset """
+    test_x = sorted(glob(os.path.join("new_data", "valid", "image", "*")))
+    test_y = sorted(glob(os.path.join("new_data", "valid", "mask", "*")))
+    print(f"Test: {len(test_x)} - {len(test_y)}")
+
+    """ Evaluation and Prediction """
+    SCORE = []
+    for x, y in tqdm(zip(test_x, test_y), total=len(test_x)):
+        """Extract the name"""
+        name = x.split("/")[-1].split(".")[0]
+
+        """ Reading the image """
+        image = cv2.imread(x, cv2.IMREAD_COLOR)
+        x = image / 255.0
+        x = np.expand_dims(x, axis=0)
+
+        """ Reading the mask """
+        mask = cv2.imread(y, cv2.IMREAD_GRAYSCALE)
+        y = mask / 255.0
+        y = y > 0.5
+        y = y.astype(np.int32)
+
+        """ Prediction """
+        y_pred = model.predict(x)[0]
+        y_pred = np.squeeze(y_pred, axis=-1)
+        y_pred = y_pred > 0.5
+        y_pred = y_pred.astype(np.int32)
+
+        """ Saving the prediction """
+        save_image_path = f"results/{name}.png"
+        save_results(image, mask, y_pred, save_image_path)
+
+        """ Flatten the array """
+        y = y.flatten()
+        y_pred = y_pred.flatten()
+
+        """ Calculating the metrics values """
+        acc_value = accuracy_score(y, y_pred)
+        f1_value = f1_score(y, y_pred, labels=[0, 1], average="binary", zero_division=1)
+        jac_value = jaccard_score(
+            y, y_pred, labels=[0, 1], average="binary", zero_division=1
+        )
+        recall_value = recall_score(
+            y, y_pred, labels=[0, 1], average="binary", zero_division=1
+        )
+        precision_value = precision_score(
+            y, y_pred, labels=[0, 1], average="binary", zero_division=1
+        )
+        SCORE.append(
+            [name, acc_value, f1_value, jac_value, recall_value, precision_value]
+        )
+
+    """ Metrics values """
+    score = [s[1:] for s in SCORE]
+    score = np.mean(score, axis=0)
+    print(f"Accuracy: {score[0]:0.5f}")
+    print(f"F1: {score[1]:0.5f}")
+    print(f"Jaccard: {score[2]:0.5f}")
+    print(f"Recall: {score[3]:0.5f}")
+    print(f"Precision: {score[4]:0.5f}")
+
+    df = pd.DataFrame(
+        SCORE, columns=["Image", "Accuracy", "F1", "Jaccard", "Recall", "Precision"]
+    )
+    df.to_csv("files/score.csv")