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CK.py

@@ -1,90 +1,90 @@
-from __future__ import print_function
-from PIL import Image
-import numpy as np
-import h5py
-import torch.utils.data as data
-
-
-class CK(data.Dataset):
-    """`CK+ Dataset.
-
-    Args:
-        train (bool, optional): If True, creates dataset from training set, otherwise
-            creates from test set.
-        transform (callable, optional): A function/transform that  takes in an PIL image
-            and returns a transformed version. E.g, ``transforms.RandomCrop``
-
-        there are 135,177,75,207,84,249,54 images in data
-        we choose 123,159,66,186,75,225,48 images for training
-        we choose 12,8,9,21,9,24,6 images for testing
-        the split are in order according to the fold number
-    """
-
-    def __init__(self, split='Training', fold = 1, transform=None):
-        self.transform = transform
-        self.split = split  # training set or test set
-        self.fold = fold # the k-fold cross validation
-        self.data = h5py.File('./data/CK_data.h5', 'r', driver='core')
-
-        number = len(self.data['data_label']) #981
-        sum_number = [0,135,312,387,594,678,927,981] # the sum of class number
-        test_number = [12,18,9,21,9,24,6] # the number of each class
-
-        test_index = []
-        train_index = []
-
-        for j in xrange(len(test_number)):
-            for k in xrange(test_number[j]):
-                if self.fold != 10: #the last fold start from the last element
-                    test_index.append(sum_number[j]+(self.fold-1)*test_number[j]+k)
-                else:
-                    test_index.append(sum_number[j+1]-1-k)
-
-        for i in xrange(number):
-            if i not in test_index:
-                train_index.append(i)
-
-        print(len(train_index),len(test_index))
-
-        # now load the picked numpy arrays
-        if self.split == 'Training':
-            self.train_data = []
-            self.train_labels = []
-            for ind in xrange(len(train_index)):
-                self.train_data.append(self.data['data_pixel'][train_index[ind]])
-                self.train_labels.append(self.data['data_label'][train_index[ind]])
-
-        elif self.split == 'Testing':
-            self.test_data = []
-            self.test_labels = []
-            for ind in xrange(len(test_index)):
-                self.test_data.append(self.data['data_pixel'][test_index[ind]])
-                self.test_labels.append(self.data['data_label'][test_index[ind]])
-
-    def __getitem__(self, index):
-        """
-        Args:
-            index (int): Index
-
-        Returns:
-            tuple: (image, target) where target is index of the target class.
-        """
-        if self.split == 'Training':
-            img, target = self.train_data[index], self.train_labels[index]
-        elif self.split == 'Testing':
-            img, target = self.test_data[index], self.test_labels[index]
-        # doing this so that it is consistent with all other datasets
-        # to return a PIL Image
-        img = img[:, :, np.newaxis]
-        img = np.concatenate((img, img, img), axis=2)
-        img = Image.fromarray(img)
-        if self.transform is not None:
-            img = self.transform(img)
-        return img, target
-
-    def __len__(self):
-        if self.split == 'Training':
-            return len(self.train_data)
-        elif self.split == 'Testing':
-            return len(self.test_data)
-
+from __future__ import print_function
+from PIL import Image
+import numpy as np
+import h5py
+import torch.utils.data as data
+
+
+class CK(data.Dataset):
+    """`CK+ Dataset.
+
+    Args:
+        train (bool, optional): If True, creates dataset from training set, otherwise
+            creates from test set.
+        transform (callable, optional): A function/transform that  takes in an PIL image
+            and returns a transformed version. E.g, ``transforms.RandomCrop``
+
+        there are 135,177,75,207,84,249,54 images in data
+        we choose 123,159,66,186,75,225,48 images for training
+        we choose 12,8,9,21,9,24,6 images for testing
+        the split are in order according to the fold number
+    """
+
+    def __init__(self, split='Training', fold = 1, transform=None):
+        self.transform = transform
+        self.split = split  # training set or test set
+        self.fold = fold # the k-fold cross validation
+        self.data = h5py.File('./data/CK_data.h5', 'r', driver='core')
+
+        number = len(self.data['data_label']) #981
+        sum_number = [0,135,312,387,594,678,927,981] # the sum of class number
+        test_number = [12,18,9,21,9,24,6] # the number of each class
+
+        test_index = []
+        train_index = []
+
+        for j in range(len(test_number)):
+            for k in range(test_number[j]):
+                if self.fold != 10: #the last fold start from the last element
+                    test_index.append(sum_number[j]+(self.fold-1)*test_number[j]+k)
+                else:
+                    test_index.append(sum_number[j+1]-1-k)
+
+        for i in range(number):
+            if i not in test_index:
+                train_index.append(i)
+
+        print(len(train_index),len(test_index))
+
+        # now load the picked numpy arrays
+        if self.split == 'Training':
+            self.train_data = []
+            self.train_labels = []
+            for ind in range(len(train_index)):
+                self.train_data.append(self.data['data_pixel'][train_index[ind]])
+                self.train_labels.append(self.data['data_label'][train_index[ind]])
+
+        elif self.split == 'Testing':
+            self.test_data = []
+            self.test_labels = []
+            for ind in range(len(test_index)):
+                self.test_data.append(self.data['data_pixel'][test_index[ind]])
+                self.test_labels.append(self.data['data_label'][test_index[ind]])
+
+    def __getitem__(self, index):
+        """
+        Args:
+            index (int): Index
+
+        Returns:
+            tuple: (image, target) where target is index of the target class.
+        """
+        if self.split == 'Training':
+            img, target = self.train_data[index], self.train_labels[index]
+        elif self.split == 'Testing':
+            img, target = self.test_data[index], self.test_labels[index]
+        # doing this so that it is consistent with all other datasets
+        # to return a PIL Image
+        img = img[:, :, np.newaxis]
+        img = np.concatenate((img, img, img), axis=2)
+        img = Image.fromarray(img)
+        if self.transform is not None:
+            img = self.transform(img)
+        return img, target
+
+    def __len__(self):
+        if self.split == 'Training':
+            return len(self.train_data)
+        elif self.split == 'Testing':
+            return len(self.test_data)
+

Readme.md

@@ -2,17 +2,19 @@
 A CNN based pytorch implementation on facial expression recognition (FER2013 and CK+), achieving 73.112% (state-of-the-art) in FER2013 and 94.64% in CK+ dataset
 
 ## Demos ##
-![Image text](https://raw.githubusercontent.com/WuJie1010/Facial-Expression-Recognition.Pytorch/master/demo/1.png)
-![Image text](https://raw.githubusercontent.com/WuJie1010/Facial-Expression-Recognition.Pytorch/master/demo/2.png)
+![Image text](https://github.com/xmtybb/Facial-Expression-Recognition.Pytorch/blob/xmtybb-patch-1/images/results/3.png)
+![Image text](https://github.com/xmtybb/Facial-Expression-Recognition.Pytorch/blob/xmtybb-patch-1/images/results/4.png)
 
 ## Dependencies ##
-- Python 2.7
-- Pytorch >=0.2.0
+- Python 3.7
+- Pytorch 1.8.1
 - h5py (Preprocessing)
 - sklearn (plot confusion matrix)
 
 ## Visualize for a test image by a pre-trained model ##
-- Firstly, download the pre-trained model from https://drive.google.com/open?id=1Oy_9YmpkSKX1Q8jkOhJbz3Mc7qjyISzU (or https://pan.baidu.com/s/1gCL0TlCwKctAy_5yhzHy5Q,  key: g2d3) and then put it in the "FER2013_VGG19" folder; Next, Put the test image (rename as 1.jpg) into the "images" folder, then 
+- Download the "FER2013_VGG19" pre-trained model from https://drive.google.com/open?id=1Oy_9YmpkSKX1Q8jkOhJbz3Mc7qjyISzU (or https://pan.baidu.com/s/1gCL0TlCwKctAy_5yhzHy5Q,  key: g2d3) 
+- 
+- for CK+/VGG19
 - python visualize.py
 
 ## FER2013 Dataset ##