--- a
+++ b/CaraNet/eval_blood.py
@@ -0,0 +1,105 @@
+import os
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torchvision.transforms as transforms
+from PIL import Image
+import numpy as np
+import pandas
+import cv2
+
+class test_dataset:
+    def __init__(self, image_root, gt_root):
+        self.images = [image_root + f for f in os.listdir(image_root) if f.endswith('.jpg') or f.endswith('.png')]
+        self.gts = [gt_root + f for f in os.listdir(gt_root) if f.endswith('.jpg') or f.endswith('.png')]
+        self.images = sorted(self.images)
+        self.gts = sorted(self.gts)
+        self.transform = transforms.ToTensor()
+        self.gt_transform = transforms.ToTensor()
+        self.size = len(self.images)
+        self.index = 0
+
+    def load_data(self):
+        image = self.rgb_loader(self.images[self.index])
+        image = self.transform(image).unsqueeze(0)
+        gt = self.binary_loader(self.gts[self.index])
+        name = self.images[self.index].split('/')[-1]
+        if name.endswith('.jpg'):
+            name = name.split('.jpg')[0] + '.png'
+        self.index += 1
+        return image, gt, name
+
+    def rgb_loader(self, path):
+        with open(path, 'rb') as f:
+            img = Image.open(f)
+            return img.convert('RGB')
+
+    def binary_loader(self, path):
+        with open(path, 'rb') as f:
+            img = Image.open(f)
+            return img.convert('L')
+
+
+if __name__ == '__main__':
+    for _data_name in ['test']:
+        ###########################################################
+        ##### image_root : your model inference results' path   ###
+        ##### gt_root : gt files' path                          ###
+        ###########################################################
+
+        # image_root = 'D:/HarDNet-MSEG-master/results/HarDMSEG/Kvasir_SEG_Validation_120/'
+        # gt_root = 'D:/HarDNet-MSEG-master/Kvasir_SEG_Validation_120/mask/'
+
+        image_root = '/home/data/spleen_blood/CaraNet/results/CaraNet/test/'
+        gt_root = '/home/data/spleen_blood/CaraNet/TestDataset/test/masks/'
+
+        test_loader = test_dataset(image_root, gt_root)
+        b=0.0
+
+    for i in range(test_loader.size):
+        image, gt, name = test_loader.load_data()
+        gt = np.asarray(gt, np.float32)
+        gt /= (gt.max() + 1e-8)
+        image = image
+        input = image[0,1,:,:]
+        input = np.array(input)
+        
+        target = np.array(gt)
+        N = gt.shape
+        smooth = 1
+        
+        
+        input_flat = np.reshape(input,(-1))
+        target_flat = np.reshape(target,(-1))
+        
+        intersection = (input_flat*target_flat)
+        #intersection = (iflat * tflat).sum()
+        #A_sum = input_flat.sum()
+        #B_sum = target_flat.sum()
+        #intersection = (input_flat * target_flat).sum()
+        #a=  ((2. * intersection + smooth) / (A_sum + B_sum + smooth) )
+        #loss = 2 * (intersection.sum(1) + smooth) / (input_flat.sum(1) + target_flat.sum(1) + smooth)
+        loss =  (2 * intersection.sum() + smooth) / (input_flat.sum() + target_flat.sum() + smooth)
+        #loss =  loss.sum() / N[1]
+        a =  '{:.4f}'.format(loss)
+        a = float(a)
+        
+        b = b + a
+        print( i, a)
+
+    print(b/test_loader.size)
+
+
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