update wake up python

Assets/GadGame/SO/Event/GenerateImageSuccess.asset

@@ -9,6 +9,6 @@ MonoBehaviour:
   m_GameObject: {fileID: 0}
   m_Enabled: 1
   m_EditorHideFlags: 0
-  m_Script: {fileID: 11500000, guid: 2c1390943a3e4db69adfdfa5c2bfb8ed, type: 3}
+  m_Script: {fileID: 11500000, guid: 835f0e9a0e0b4ceeac7446d5f7384fb3, type: 3}
   m_Name: GenerateImageSuccess
   m_EditorClassIdentifier: 

Assets/GadGame/Scenes/PassBy_coffee.unity

@@ -38,7 +38,7 @@ RenderSettings:
   m_ReflectionIntensity: 1
   m_CustomReflection: {fileID: 0}
   m_Sun: {fileID: 0}
-  m_IndirectSpecularColor: {r: 0.44657826, g: 0.49641263, b: 0.57481676, a: 1}
+  m_IndirectSpecularColor: {r: 0.44657898, g: 0.4964133, b: 0.5748178, a: 1}
   m_UseRadianceAmbientProbe: 0
 --- !u!157 &3
 LightmapSettings:
@@ -708,7 +708,7 @@ MonoBehaviour:
   m_Name: 
   m_EditorClassIdentifier: 
   m_Material: {fileID: 0}
-  m_Color: {r: 1, g: 1, b: 1, a: 0.39215687}
+  m_Color: {r: 1, g: 1, b: 1, a: 0.078431375}
   m_RaycastTarget: 1
   m_RaycastPadding: {x: 0, y: 0, z: 0, w: 0}
   m_Maskable: 1

Assets/GadGame/Scripts/Coffee/CoffeeController.cs

@@ -12,7 +12,7 @@ namespace GadGame.Scripts.Coffee
     public class CoffeeController : MonoBehaviour
     {
         [SerializeField] private VoidEvent _engageReadyEvent;
-        [SerializeField] private VoidEvent _generateImageSuccessEvent;
+        [SerializeField] private StringEvent _generateImageSuccessEvent;
         [SerializeField] private BoolEvent _playPassByAnimEvent;
         [SerializeField] private BoolEvent _playVideoEvent;
         [SerializeField] private FloatEvent _readyCountDownEvent;
@@ -81,15 +81,17 @@ namespace GadGame.Scripts.Coffee
             _hintText.text = _texts[0];
         }
 
-        private void OnGenerateImageSuccess()
+
+        private void OnGenerateImageSuccess(string desc)
         {
+            _isLoading = false;
             _loading.DOFade(0.255f, 0.5f);
-            _hintText.text = "Mô tả";
+            _hintText.text = desc;
         }
 
-        private void OnGetEncodeImage(string encode)
+        private void OnGetEncodeImage(string filePath)
         {
-            _userImager.LoadImage(encode);
+            _userImager.LoadImage(filePath);
         }
 
         private void OnEngageReady()

Assets/GadGame/Scripts/LoadImageEncoded.cs

@@ -1,10 +1,12 @@
 using System;
 using UnityEngine;
 using UnityEngine.UI;
+using System.IO;
+using GadGame.Network;
 
 namespace GadGame.Scripts
 {
-    [RequireComponent(typeof(Image))]
+    [RequireComponent(typeof(RawImage))]
     public class LoadImageEncoded : MonoBehaviour
     {
         [SerializeField] private bool _preserveAspect;
@@ -19,13 +21,15 @@ namespace GadGame.Scripts
 
         }
 
-
-        public void LoadImage(string encodeString)
+        public void LoadImage(string streamingData)
         {
             //Decode the Base64 string to a byte array
-            byte[] imageBytes = Convert.FromBase64String(encodeString);
+            byte[] imageBytes = UdpSocket.Instance.DataReceived.GenerateImageSuccess ? File.ReadAllBytes(streamingData) : Convert.FromBase64String(streamingData);
+
+            // byte[] imageBytes = Convert.FromBase64String(encodeString);
 
             _texture.LoadImage(imageBytes); // Automatically resizes the texture dimensions
+            // _texture.Apply();
             var sprite = Sprite.Create(_texture, new Rect(0, 0, _texture.width, _texture.height), new Vector2(0.5f, 0.5f), 100);
             _image.sprite = sprite;
         }

Assets/GadGame/Scripts/MainFlow.cs

@@ -17,7 +17,7 @@ namespace GadGame
         public BoolEvent PlayPassByAnim;
         public BoolEvent PlayVideo;
         public FloatEvent ReadyCountDown;
-        public VoidEvent GenerateImageSuccess;
+        public StringEvent GenerateImageSuccess;
         public StringEvent EncodeImage;
 
         protected override async void Awake()

Assets/GadGame/Scripts/Network/ReceiverData.cs

@@ -22,5 +22,6 @@ namespace GadGame.Network
         public Vector2[] PosPoints;
         public string StreamingData;
         [FormerlySerializedAs("Success")] public bool GenerateImageSuccess;
+        public string Description;
     }
 }

Assets/GadGame/Scripts/Network/UdpSocket.cs

@@ -6,6 +6,9 @@ using System.Threading;
 using GadGame.Singleton;
 using Newtonsoft.Json;
 using UnityEngine;
+using System.Diagnostics;
+using Debug = UnityEngine.Debug;
+using Unity.VisualScripting;
 
 namespace GadGame.Network
 {
@@ -23,9 +26,28 @@ namespace GadGame.Network
         private UdpClient _client;
         private IPEndPoint _remoteEndPoint;
 
-
+        private Process _process;
         private void Start()
         {
+            _process = new Process();
+            _process.StartInfo.FileName = "/bin/sh";
+            _process.StartInfo.Arguments = $"{Application.streamingAssetsPath}/MergeFace/run.sh";
+            _process.StartInfo.WorkingDirectory = $"{Application.streamingAssetsPath}/MergeFace";
+
+            _process.StartInfo.RedirectStandardOutput = true;
+            _process.StartInfo.RedirectStandardError = true;
+
+            _process.StartInfo.CreateNoWindow = false;
+            _process.StartInfo.UseShellExecute = false;
+            _process.OutputDataReceived += (sender, a) => {
+                Debug.Log(a.Data);
+            };
+            _process.ErrorDataReceived += (sender, a) => {
+                Debug.LogError(a.Data);
+            };
+            _process.Start();
+
+
             // Create remote endpoint
             _remoteEndPoint = new IPEndPoint(IPAddress.Parse(_ip), _sendPort);
 
@@ -87,6 +109,10 @@ namespace GadGame.Network
                 _receiveThread.Abort();
 
             _client.Close();
+
+            _process.Close();
+            _process.CloseMainWindow();
+            _process.WaitForExit();
         }
     }
 }

Assets/GadGame/Scripts/State/MainFlowState/EngageState.cs

@@ -20,6 +20,7 @@ namespace GadGame.State.MainFlowState
 
         public override void Update(float time)
         {
+            Runner.EncodeImage.Raise(UdpSocket.Instance.DataReceived.StreamingData);
             if(!UdpSocket.Instance.DataReceived.PassBy) {
                 Runner.SetState<IdleState>();
                 return;

Assets/GadGame/Scripts/State/MainFlowState/ShowImageState.cs

@@ -6,7 +6,7 @@ namespace GadGame.State.MainFlowState
     {
         public override void Enter()
         {
-            Runner.GenerateImageSuccess.Raise();
+            Runner.GenerateImageSuccess.Raise(UdpSocket.Instance.DataReceived.Description);
         }
 
         public override void Update(float time)

Assets/GadGame/Texture/merge_face.png.meta

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Assets/Plugins/Febucci/Text Animator/Extra/Typewriter Sound - Package.unitypackage.meta

@@ -0,0 +1,7 @@
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Assets/Plugins/Febucci/Text Animator/Integrations/PlayMaker - Integration.unitypackage.meta

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Assets/StreamingAssets/MergeFace.meta

@@ -0,0 +1,8 @@
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Assets/StreamingAssets/MergeFace/AgeGenderPrediction.py

@@ -0,0 +1,126 @@
+from Facenet.models.mtcnn import MTCNN
+from AgeNet.models import Model
+import torch
+from torchvision import transforms as T
+from PIL import Image
+import numpy as np
+import cv2
+import matplotlib.pyplot as plt
+import os
+import argparse
+
+
+age_range_list = ["1-10", "11-20", "21-30", "31-40", "41-50", "51-60", "61-70", "71-80", "81-90", "91-100"]
+
+
+class AgeEstimator():
+    def __init__(self, face_size=224, weights=None, device='cpu', tpx=500):
+        self.thickness_per_pixels = tpx
+
+        if isinstance(face_size, int):
+            self.face_size = (face_size, face_size)
+        else:
+            self.face_size = face_size
+
+        # Set device
+        self.device = device
+        if isinstance(device, str):
+            if (device == 'cuda' or device == 'gpu') and torch.cuda.is_available():
+                self.device = torch.device(device)
+            else:
+                self.device = torch.device('cpu')
+
+        self.facenet_model = MTCNN(device=self.device)
+
+        self.model = Model().to(self.device)
+        self.model.eval()
+        if weights:
+            self.model.load_state_dict(torch.load(weights, map_location=torch.device('cpu')))
+            # print('Weights loaded successfully from path:', weights)
+            # print('====================================================')
+
+    def transform(self, image):
+        return T.Compose(
+            [
+                T.Resize(self.face_size),
+                T.ToTensor(),
+                T.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
+            ]
+        )(image)
+
+    @staticmethod
+    def preprocess_image(image, face_size=(224, 224)):
+
+        # Resize the image to the target size
+        resized_transform = T.Resize(face_size)
+
+        resized_image = resized_transform(image)
+
+        return resized_image
+
+    @staticmethod
+    def padding_face(box, padding=20):
+        return [
+            box[0] - padding,
+            box[1] - padding,
+            box[2] + padding,
+            box[3] + padding
+        ]
+
+    def predict_from_frame(self, frame, min_prob=0.9):
+        image = Image.fromarray(frame)
+
+        ndarray_image = np.array(image)
+        image_shape = ndarray_image.shape
+
+        try:
+
+            bboxes, prob = self.facenet_model.detect(image)
+            bboxes = bboxes[prob > min_prob]
+
+            face_images = []
+
+            for box in bboxes:
+                box = np.clip(box, 0, np.inf).astype(np.uint32)
+
+                padding = max(image_shape) * 5 / self.thickness_per_pixels
+                padding = int(max(padding, 10))
+                box = self.padding_face(box, padding)
+
+                face = image.crop(box)
+                transformed_face = self.transform(face)
+                face_images.append(transformed_face)
+
+            face_images = torch.stack(face_images, dim=0).to(self.device)
+
+            genders, ages = self.model(face_images)
+            ages = torch.round(ages).long()
+
+            for i, box in enumerate(bboxes):
+                box = np.clip(box, 0, np.inf).astype(np.uint32)
+                thickness = max(image_shape) / 400
+                thickness = int(max(np.ceil(thickness), 1))
+                age_range = age_range_list[int(ages[i].item() / 10)]
+                gender = round(genders[i].item(), 2)
+
+                return [gender, age_range]
+
+        except:
+            return None
+
+        return None
+
+
+def Prediction(frame, weights="weights.pt", face_size=64, device='cuda'):
+    # Initialize the AgeEstimator
+    model = AgeEstimator(weights=weights, face_size=face_size, device=device)
+
+    # Open a connection to the camera (use 0 for default camera)
+
+    # Convert the frame to RGB format (OpenCV uses BGR by default)
+    rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+
+    # Predict age and gender from the frame
+    predicted_image = model.predict_from_frame(rgb_frame)
+
+    return predicted_image

Assets/StreamingAssets/MergeFace/AgeGenderPrediction.py.meta

@@ -0,0 +1,7 @@
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Assets/StreamingAssets/MergeFace/AgeNet.meta

@@ -0,0 +1,8 @@
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Assets/StreamingAssets/MergeFace/AgeNet/__pycache__.meta

@@ -0,0 +1,8 @@
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Assets/StreamingAssets/MergeFace/AgeNet/__pycache__/models.cpython-311.pyc.meta

@@ -0,0 +1,7 @@
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Assets/StreamingAssets/MergeFace/AgeNet/dataset.py

@@ -0,0 +1,65 @@
+from torch.utils.data import Dataset, DataLoader
+import os
+from PIL import Image
+from torchvision import transforms as T
+
+class UTKFaceDataset(Dataset):
+    def __init__(self, root_dir, transform = None):
+        super().__init__()
+        
+        self.root_dir = root_dir
+        self.transform = transform
+        self.filename_list = os.listdir(root_dir)
+        
+    def __len__(self):
+        return len(self.filename_list)
+    
+    def age_to_class(self, age):
+        range_ages = [0, 4, 9, 15, 25, 35, 45, 60, 75]
+        if age > max(range_ages):
+                return len(range_ages) - 1
+        for i in range(len(range_ages) - 1):
+            if range_ages[i] <= age <= range_ages[i + 1]:
+                return i
+    
+    def __getitem__(self, idx):
+        filename = self.filename_list[idx]
+        
+        info = filename.split("_")
+        age = int(info[0])
+        age_label = self.age_to_class(age)
+        gender = int(info[1])
+        
+        filename = os.path.join(self.root_dir, filename)
+        
+        image = Image.open(filename)
+        
+        if self.transform:
+            image = self.transform(image)
+        
+        return image, gender, age_label, age
+
+if __name__ == '__main__':
+    
+    image_size = (64,64)
+    root_dir = '/kaggle/input/utkface-new/UTKFace'
+    batch_size = 512
+    num_workers = os.cpu_count()
+    
+    train_transform = T.Compose(
+        [
+            T.Resize(image_size),
+            T.RandomHorizontalFlip(0.5),
+            T.RandomRotation(10),
+            T.ToTensor(),
+            T.Normalize(mean = [0.5, 0.5, 0.5], std = [0.5, 0.5, 0.5])
+        ]
+    )
+
+    train_dataset = UTKFaceDataset(root_dir, transform = train_transform)
+    trainloader = DataLoader(train_dataset, batch_size = batch_size, shuffle = True, num_workers = num_workers, drop_last = True)
+
+    for images, genders, age_labels, ages in trainloader:
+        print(images.shape, genders.shape, ages.shape)
+        break
+

Assets/StreamingAssets/MergeFace/AgeNet/dataset.py.meta

@@ -0,0 +1,7 @@
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Assets/StreamingAssets/MergeFace/AgeNet/eval.py

@@ -0,0 +1,66 @@
+from torch import nn
+from tqdm import tqdm
+
+import torch
+
+def evaluate(gender_model, age_range_model, age_estimation_model, val_dataloader, device = 'cpu', verbose = 0):
+    # set device
+    if isinstance(device, str):
+        if (device == 'cuda' or device == 'gpu') and torch.cuda.is_available():
+            device = torch.device(device)
+        else:
+            device = torch.device('cpu')
+
+    # Loss function
+    AgeRangeLoss = nn.CrossEntropyLoss()
+    GenderLoss = nn.BCELoss()
+    AgeEstimationLoss = nn.L1Loss()
+    
+    gender_model = gender_model.to(device)
+    age_range_model = age_range_model.to(device)
+    age_estimation_model = age_estimation_model.to(device)
+    
+    with torch.no_grad():
+        age_range_model.eval()
+        gender_model.eval()
+        age_estimation_model.eval()
+        age_accuracy = 0
+        gender_accuracy = 0
+        total_age_loss = 0
+        total_gender_loss = 0
+        total_age_estimation_loss = 0
+        if verbose == 1:
+            val_dataloader = tqdm(val_dataloader, desc = 'Evaluate: ', ncols = 100)
+            
+        for images, genders, age_labels, ages in val_dataloader:
+            batch_size = images.shape[0]
+            
+            images, genders, age_labels, ages = images.to(device), genders.to(device), age_labels.to(device), ages.to(device)
+            
+            pred_genders = gender_model(images).view(-1)
+            pred_age_labels = age_range_model(images)
+                        
+            age_loss = AgeRangeLoss(pred_age_labels, age_labels.long())
+            gender_loss = GenderLoss(pred_genders, genders.float())
+            
+            total_age_loss += age_loss.item()
+            total_gender_loss += gender_loss.item()
+            
+            gender_acc = torch.sum(torch.round(pred_genders) == genders)/batch_size
+            age_acc = torch.sum(torch.argmax(pred_age_labels, dim = 1) == age_labels)/batch_size
+            
+            age_accuracy += age_acc
+            gender_accuracy += gender_acc
+            
+            estimated_ages  = age_estimation_model(images, age_labels).view(-1)
+            age_estimation_loss = AgeEstimationLoss(ages, estimated_ages)
+            
+            total_age_estimation_loss += age_estimation_loss.item()
+            
+        val_age_loss = total_age_loss / len(val_dataloader)
+        val_gender_loss = total_gender_loss / len(val_dataloader)
+        val_age_accuracy = age_accuracy / len(val_dataloader)
+        val_gender_accuracy = gender_accuracy / len(val_dataloader)
+        val_age_estimation_loss = total_age_estimation_loss / len(val_dataloader)
+        
+        return val_age_loss, val_gender_loss, val_age_accuracy, val_gender_accuracy, val_age_estimation_loss

Assets/StreamingAssets/MergeFace/AgeNet/eval.py.meta

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Assets/StreamingAssets/MergeFace/AgeNet/models.py

@@ -0,0 +1,196 @@
+from torch import nn
+import torch
+from torch.nn import functional as F
+
+class GenderClassificationModel(nn.Module):
+    "VGG-Face"
+    def __init__(self):
+        super().__init__()
+        self.conv_1_1 = nn.Conv2d(3, 64, 3, stride=1, padding=1)
+        self.conv_1_2 = nn.Conv2d(64, 64, 3, stride=1, padding=1)
+        self.conv_2_1 = nn.Conv2d(64, 128, 3, stride=1, padding=1)
+        self.conv_2_2 = nn.Conv2d(128, 128, 3, stride=1, padding=1)
+        self.conv_3_1 = nn.Conv2d(128, 256, 3, stride=1, padding=1)
+        self.conv_3_2 = nn.Conv2d(256, 256, 3, stride=1, padding=1)
+        self.conv_3_3 = nn.Conv2d(256, 256, 3, stride=1, padding=1)
+        self.conv_4_1 = nn.Conv2d(256, 512, 3, stride=1, padding=1)
+        self.conv_4_2 = nn.Conv2d(512, 512, 3, stride=1, padding=1)
+        self.conv_4_3 = nn.Conv2d(512, 512, 3, stride=1, padding=1)
+        self.conv_5_1 = nn.Conv2d(512, 512, 3, stride=1, padding=1)
+        self.conv_5_2 = nn.Conv2d(512, 512, 3, stride=1, padding=1)
+        self.conv_5_3 = nn.Conv2d(512, 512, 3, stride=1, padding=1)
+        self.fc6 = nn.Linear(2048, 4096)
+        self.fc7 = nn.Linear(4096, 4096)
+        self.fc8 = nn.Linear(4096, 1)
+        
+    def forward(self, x):
+        """ Pytorch forward
+
+        Args:
+            x: input image (224x224)
+
+        Returns: class logits
+
+        """
+        x = F.relu(self.conv_1_1(x))
+        x = F.relu(self.conv_1_2(x))
+        x = F.max_pool2d(x, 2, 2)
+        x = F.relu(self.conv_2_1(x))
+        x = F.relu(self.conv_2_2(x))
+        x = F.max_pool2d(x, 2, 2)
+        x = F.relu(self.conv_3_1(x))
+        x = F.relu(self.conv_3_2(x))
+        x = F.relu(self.conv_3_3(x))
+        x = F.max_pool2d(x, 2, 2)
+        x = F.relu(self.conv_4_1(x))
+        x = F.relu(self.conv_4_2(x))
+        x = F.relu(self.conv_4_3(x))
+        x = F.max_pool2d(x, 2, 2)
+        x = F.relu(self.conv_5_1(x))
+        x = F.relu(self.conv_5_2(x))
+        x = F.relu(self.conv_5_3(x))
+        x = F.max_pool2d(x, 2, 2)
+        x = x.view(x.size(0), -1)
+        x = F.relu(self.fc6(x))
+        x = F.dropout(x, 0.5, self.training)
+        x = F.relu(self.fc7(x))
+        x = F.dropout(x, 0.5, self.training)
+        return F.sigmoid(self.fc8(x))
+    
+class AgeRangeModel(nn.Module):
+    def __init__(self, in_channels = 3, backbone = 'resnet50', pretrained = False, num_classes = 9):
+        super().__init__()
+        
+        self.Conv1 = nn.Sequential(
+            nn.Conv2d(in_channels, 64, 3, 1, padding = 1),
+            nn.BatchNorm2d(64),
+            nn.ReLU(),
+            nn.Dropout2d(0.3),
+            nn.MaxPool2d(2),             
+        )
+    
+        self.Conv2 = nn.Sequential(
+            nn.Conv2d(64, 256, 3, 1, padding = 1),
+            nn.BatchNorm2d(256),
+            nn.ReLU(),
+            nn.Dropout2d(0.3),
+            nn.MaxPool2d(2), 
+        )
+        
+        self.Conv3 = nn.Sequential(
+            nn.Conv2d(256, 512, 3, 1, padding = 1),
+            nn.BatchNorm2d(512),
+            nn.ReLU(),
+            nn.Dropout2d(0.3),
+            nn.MaxPool2d(2), 
+        )
+        
+        self.adap = nn.AdaptiveAvgPool2d((2,2))
+        
+        self.out_age = nn.Sequential(
+            nn.Linear(2048, num_classes)
+#             nn.Softmax(dim = 1)
+        )
+        
+    def forward(self, x):
+        batch_size = x.shape[0]
+        x = self.Conv1(x)
+        x = self.Conv2(x)
+        x = self.Conv3(x)
+        
+        x = self.adap(x)
+        
+        x = x.view(batch_size, -1)
+        
+        x = self.out_age(x)
+    
+        return x
+
+class AgeEstimationModel(nn.Module):
+    "VGG-Face"
+    def __init__(self):
+        super().__init__()
+        self.embedding_layer = nn.Embedding(9, 64)
+        
+        self.Conv1 = nn.Sequential(
+            nn.Conv2d(3, 64, 3, 1, padding = 1),
+            nn.BatchNorm2d(64),
+            nn.ReLU(),
+            nn.Dropout2d(0.3),
+            nn.MaxPool2d(2),             
+        )
+    
+        self.Conv2 = nn.Sequential(
+            nn.Conv2d(64, 256, 3, 1, padding = 1),
+            nn.BatchNorm2d(256),
+            nn.ReLU(),
+            nn.Dropout2d(0.3),
+            nn.MaxPool2d(2), 
+        )
+        
+        self.Conv3 = nn.Sequential(
+            nn.Conv2d(256, 512, 3, 1, padding = 1),
+            nn.BatchNorm2d(512),
+            nn.ReLU(),
+            nn.Dropout2d(0.3),
+            nn.MaxPool2d(2), 
+        )
+        
+        self.adap = nn.AdaptiveAvgPool2d((2,2))
+        
+        self.out_age = nn.Sequential(
+            nn.Linear(2048 + 64, 1),
+            nn.ReLU()
+        )
+        
+    def forward(self, x, y):
+        batch_size = x.shape[0]
+        x = self.Conv1(x)
+        x = self.Conv2(x)
+        x = self.Conv3(x)
+        
+        x = self.adap(x)
+        
+        x = x.view(batch_size, -1)
+        
+        y = self.embedding_layer(y)
+        
+        x = torch.cat([x,y], dim = 1)
+        
+        x = self.out_age(x)
+    
+        return x
+    
+class Model(nn.Module):
+    def __init__(self):
+        super(Model, self).__init__()
+        
+        self.gender_model = GenderClassificationModel()
+        
+        self.age_range_model = AgeRangeModel()
+        
+        self.age_estimation_model = AgeEstimationModel()
+
+    def forward(self,x):
+        """x: batch, 3, 64, 64"""
+        if len(x.shape) == 3:
+            x = x[None, ...]
+        
+        predicted_genders = self.gender_model(x)
+        
+        age_ranges = self.age_range_model(x)
+        
+        y = torch.argmax(age_ranges, dim = 1).view(-1,)
+        
+        estimated_ages = self.age_estimation_model(x, y)
+        
+        return predicted_genders, estimated_ages
+    
+if __name__ == '__main__':
+    model = Model()
+    x = torch.rand(2,3,64,64)
+    genders, ages = model(x) #
+    
+    print(genders.shape, ages.shape)
+    
+        

Assets/StreamingAssets/MergeFace/AgeNet/models.py.meta

@@ -0,0 +1,7 @@
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+guid: bfdd42c2ab7b1f10ab08c676ab819c1b
+DefaultImporter:
+  externalObjects: {}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 

Assets/StreamingAssets/MergeFace/AgeNet/train.py

@@ -0,0 +1,188 @@
+from tqdm import tqdm
+from torch import nn
+from torch.optim.lr_scheduler import ReduceLROnPlateau
+
+from .utils import *
+from .eval import *
+from .models import * 
+
+import os 
+import torch
+import numpy as np
+
+
+def train(train_data_dir, weights, device = 'cpu', image_size = 64, batch_size = 128, num_epochs = 100, steps_per_epoch = None,
+              val_data_dir = None, validation_split = None, save_history = True):
+    
+    # đặt val_data_dir and validation_split không đồng thời khác None
+    assert not(val_data_dir is not None and validation_split is not None)
+
+    if isinstance(device, str):
+        if (device == 'cuda' or device == 'gpu') and torch.cuda.is_available():
+            device = torch.device(device)
+        else:
+            device = torch.device('cpu')
+
+    # get train_loader
+    train_data = get_dataloader(train_data_dir, image_size = image_size, batch_size = batch_size)
+
+    # chia dữ liệu thành 2 tập train và val    
+    if val_data_dir is not None:
+        val_data = get_dataloader(val_data_dir, image_size = image_size, batch_size = batch_size)
+    elif validation_split is not None: 
+        train_data, val_data = split_dataloader(train_data, validation_split)
+    else: 
+        val_data = None 
+
+    if steps_per_epoch is None:
+        steps_per_epoch = len(train_data)
+
+    # steps per epoch
+    num_steps = len(train_data)
+    iterator = iter(train_data)
+    count_steps = 1
+
+    # add model to device
+    gender_model = GenderClassificationModel().to(device)
+    age_range_model = AgeRangeModel().to(device)
+    age_estimation_model = AgeEstimationModel().to(device)
+
+    # Loss function
+    AgeRangeLoss = nn.CrossEntropyLoss()
+    GenderLoss = nn.BCELoss()
+    AgeEstimationLoss = nn.L1Loss()
+    
+    # Optimizer
+    gen_optimizer = torch.optim.Adam(gender_model.parameters(), lr = 1e-4)
+    age_range_optimizer = torch.optim.Adam(age_range_model.parameters(), lr = 5e-3)
+    age_estimation_optimizer = torch.optim.Adam(age_estimation_model.parameters(), lr = 1e-3)
+
+    #schedular
+    age_range_scheduler = ReduceLROnPlateau(age_range_optimizer, mode = 'min', factor = 0.1, patience = 3, verbose = 1)
+    gender_scheduler = ReduceLROnPlateau(gen_optimizer, mode = 'min', factor = 0.1, patience = 3, verbose = 1)
+    age_estimation_scheduler = ReduceLROnPlateau(age_estimation_optimizer, mode = 'min', factor = 0.1, patience = 3, verbose = 1)
+
+    # history
+    history = {
+        'train_gender_loss': [],
+        'train_age_loss': [],
+        'train_gender_acc': [],
+        'train_age_acc': [],
+        'val_gender_loss': [],
+        'val_age_loss': [],
+        'val_gender_acc': [],
+        'val_age_acc': [],
+    }
+    
+    for epoch in range(1, num_epochs + 1):
+        total_gender_loss = 0
+        total_age_loss = 0 
+        age_accuracy = 0
+        gender_accuracy = 0
+        
+        total_age_estimation_loss = 0
+        
+        gender_model.train()
+        age_range_model.train()
+        age_estimation_model.train()
+        
+        for step in tqdm(range(steps_per_epoch), desc = f'Epoch {epoch}/{num_epochs}: ', ncols = 100):
+            images, genders, age_labels, ages = next(iterator)
+            batch_size = images.shape[0]
+            
+            images, genders, age_labels, ages = images.to(device), genders.to(device), age_labels.to(device), ages.to(device)
+            
+            pred_genders = gender_model(images).view(-1)
+            pred_age_labels = age_range_model(images)
+            
+            age_loss = AgeRangeLoss(pred_age_labels, age_labels.long())
+            
+            gender_loss = GenderLoss(pred_genders, genders.float())
+                        
+            total_age_loss += age_loss.item()
+            total_gender_loss += gender_loss.item()
+            
+            gender_acc = torch.sum(torch.round(pred_genders) == genders)/batch_size
+            age_acc = torch.sum(torch.argmax(pred_age_labels, dim = 1) == age_labels)/batch_size
+            
+            age_accuracy += age_acc
+            gender_accuracy += gender_acc
+            
+            age_range_optimizer.zero_grad()
+            age_loss.backward()
+            age_range_optimizer.step()
+            
+            gen_optimizer.zero_grad()
+            gender_loss.backward()
+            gen_optimizer.step()
+            
+            # age estimation loss
+            estimated_ages = age_estimation_model(images, age_labels).view(-1)
+            age_estimation_loss = AgeEstimationLoss(ages, estimated_ages)
+            
+            age_estimation_optimizer.zero_grad()
+            age_estimation_loss.backward()
+            age_estimation_optimizer.step()
+            
+            total_age_estimation_loss += age_estimation_loss.item()
+            
+            # nếu nó duyệt hết qua tập dữ liệu thì cho nó lặp lại 1 lần nữa
+            if count_steps == num_steps:
+                iterator = iter(train_data)
+                count_steps = 0
+            count_steps += 1
+            
+        train_age_loss = total_age_loss / steps_per_epoch
+        train_gender_loss = total_gender_loss / steps_per_epoch
+        train_age_accuracy = age_accuracy / steps_per_epoch
+        train_gender_accuracy = gender_accuracy / steps_per_epoch
+
+        train_age_estimation_loss = total_age_estimation_loss/steps_per_epoch
+        
+        history['train_age_loss'].append(float(train_age_loss))
+        history['train_gender_loss'].append(float(train_gender_loss))
+        history['train_age_acc'].append(float(train_age_accuracy))
+        history['train_gender_acc'].append(float(train_gender_accuracy))
+
+        print(f'train_age_loss: {train_age_loss: .2f}, train_gender_loss: {train_gender_loss: .3f}, train_age_accuracy: {train_age_accuracy: .2f}, train_gender_accuracy: {train_gender_accuracy: .2f}, train_age_estimation_loss: {train_age_estimation_loss: .3f}')
+        if val_data:
+            val_age_loss, val_gender_loss, val_age_accuracy, val_gender_accuracy, val_age_estimation_loss = evaluate(gender_model, age_range_model, age_estimation_model, val_data, device = device)
+            history['val_age_loss'].append(float(val_age_loss))
+            history['val_gender_loss'].append(float(val_gender_loss))
+            history['val_age_acc'].append(float(val_age_accuracy))
+            history['val_gender_acc'].append(float(val_gender_accuracy))
+            
+            age_range_scheduler.step(np.round(val_age_loss, 3))
+            gender_scheduler.step(np.round(val_gender_loss, 3))
+            age_estimation_scheduler.step(np.round(val_age_estimation_loss, 3))
+            print(f'val_age_loss: {val_age_loss: .2f}, val_gender_loss: {val_gender_loss: .3f}, val_age_accuracy: {val_age_accuracy: .2f}, val_gender_accuracy: {val_gender_accuracy: .2f}, val_age_estimation_loss: {val_age_estimation_loss : .3f}')
+
+    if weights:  
+        class dummy_model(nn.Module):
+            def __init__(self):
+                super().__init__()
+                
+                self.gender_model = gender_model
+                self.age_range_model = age_range_model
+                self.age_estimation_model = age_estimation_model
+            
+            def forward(self, x):
+                return
+        
+        model = dummy_model()
+        torch.save(model.state_dict(), weights)
+        print(f'Saved successfully last weights to:', weights)
+        
+    if save_history:
+        visualize_history(history, save_history)
+
+if __name__ == '__main__':
+    batch_size = 128
+    image_size = 64
+    train_data_dir = '/kaggle/input/utkface-new/UTKFace'
+    device = 'cuda'
+    weights = 'weights\AgeGenderWeights.pt'
+    epochs = 100
+    train(train_data_dir, weights, device = device, steps_per_epoch = None,
+          validation_split = 0.2, image_size = image_size, batch_size = batch_size, save_history = True)
+    

Assets/StreamingAssets/MergeFace/AgeNet/train.py.meta

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Assets/StreamingAssets/MergeFace/AgeNet/utils.py

@@ -0,0 +1,93 @@
+from torch.utils.data import Dataset, DataLoader, SubsetRandomSampler
+from .dataset import UTKFaceDataset
+from torchvision import transforms as T
+from matplotlib import pyplot as plt
+import os
+
+def get_dataloader(root_dir, image_size = 64, batch_size = 128, shuffle = True, num_workers = 1):
+    if isinstance(image_size, int):
+        image_size = (image_size, image_size)
+
+    train_transform = T.Compose(
+        [
+            T.Resize(image_size),
+            T.RandomHorizontalFlip(0.2),
+            T.RandomRotation(10),
+            T.ToTensor(),
+            T.Normalize(mean = [0.5, 0.5, 0.5], std = [0.5, 0.5, 0.5])
+        ]
+    )
+
+    train_dataset = UTKFaceDataset(root_dir, transform = train_transform)
+    trainloader = DataLoader(train_dataset, batch_size = batch_size, shuffle = shuffle, num_workers = num_workers, drop_last = True)
+    return trainloader
+
+def split_dataloader(train_data, validation_split = 0.2):
+    # Chia DataLoader thành phần train và test
+    train_ratio = 1 - validation_split  # Tỷ lệ phần train (80%)
+    train_size = int(train_ratio * len(train_data.dataset))  # Số lượng mẫu dùng cho train
+
+    indices = list(range(len(train_data.dataset)))  # Danh sách các chỉ số của dataset
+    train_indices = indices[:train_size]  # Chỉ số của mẫu dùng cho train
+    val_indices = indices[train_size:]  # Chỉ số của mẫu dùng cho test
+
+    # lấy dữ liệu từ dataloader
+    dataset = train_data.dataset
+    batch_size = train_data.batch_size
+    num_workers = train_data.num_workers
+    
+    # Tạo ra các SubsetRandomSampler để chọn một phần dữ liệu cho train và test
+    train_sampler = SubsetRandomSampler(train_indices)
+    val_sampler = SubsetRandomSampler(val_indices)
+
+    # Tạo DataLoader mới từ SubsetRandomSampler
+    train_data = DataLoader(dataset, batch_size = batch_size, sampler = train_sampler, num_workers = num_workers, drop_last = True)
+    val_data = DataLoader(dataset, batch_size = batch_size, sampler = val_sampler, num_workers = num_workers, drop_last = True)
+    
+    return train_data, val_data
+
+def visualize_history(history, save_history=True): 
+    plt.figure(figsize = (20,8))
+    plt.subplot(131)
+    plt.plot(range(1, len(history['train_age_acc']) + 1), history['train_age_acc'], label = 'train_age_acc', c = 'r')
+    plt.plot(range(1, len(history['val_age_acc']) + 1), history['val_age_acc'], label = 'val_age_acc', c = 'g')
+    plt.plot(range(1, len(history['train_gender_acc']) + 1), history['train_gender_acc'], label = 'train_gender_acc', c = 'b')
+    plt.plot(range(1, len(history['val_gender_acc']) + 1), history['val_gender_acc'], label = 'val_gender_acc', c = 'y')
+    plt.title('Accuracy')
+    plt.xlabel('Epoch')
+    plt.ylabel('Gender And Age Prediction Accuracy')
+    plt.legend()
+
+    plt.subplot(132)
+    plt.plot(range(1, len(history['train_age_loss']) + 1), history['train_age_loss'], label = 'train_age_loss', c = 'r')
+    plt.plot(range(1, len(history['val_age_loss']) + 1), history['val_age_loss'], label = 'val_age_loss', c = 'g')
+    plt.title('Age Loss')
+    plt.xlabel('Epoch')
+    plt.ylabel('Age Loss')
+    plt.legend()
+
+    
+    plt.subplot(133)
+    plt.plot(range(1, len(history['train_gender_loss']) + 1), history['train_gender_loss'], label = 'train_gender_loss', c = 'b')
+    plt.plot(range(1, len(history['val_gender_loss']) + 1), history['val_gender_loss'], label = 'val_gender_loss', c = 'y')
+    plt.title('Gender Loss')
+    plt.xlabel('Epoch')
+    plt.ylabel('Gender Loss')
+    plt.legend()
+
+    if save_history:
+        if not os.path.exists("runs"):
+            os.mkdir("runs")
+
+        if not os.path.exists(os.path.join('runs', "train")):
+            os.mkdir(os.path.join('runs', "train"))
+
+        exp = os.listdir(os.path.join("runs", 'train'))
+        if len(exp) == 0:
+            last_exp = os.path.join("runs", 'train', 'exp1')
+            os.mkdir(last_exp)
+        else:
+            exp_list = [int(i[3:]) for i in exp]
+            last_exp = os.path.join("runs", 'train', 'exp' + str(int(exp_list[-1]) + 1))
+            os.mkdir(last_exp)
+        plt.savefig(os.path.join(last_exp, "results.png"))

Assets/StreamingAssets/MergeFace/AgeNet/utils.py.meta

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Assets/StreamingAssets/MergeFace/Face_detection

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+Subproject commit 98e25faaf07e489a624e965684ee8de0756f3ebc

Assets/StreamingAssets/MergeFace/Face_detection.meta

@@ -0,0 +1,8 @@
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Assets/StreamingAssets/MergeFace/Facenet.meta

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Assets/StreamingAssets/MergeFace/Facenet/LICENSE.md

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2019 Timothy Esler
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

Assets/StreamingAssets/MergeFace/Facenet/LICENSE.md.meta

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Assets/StreamingAssets/MergeFace/Facenet/__init__.py

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+from .models.inception_resnet_v1 import InceptionResnetV1
+from .models.mtcnn import MTCNN, PNet, RNet, ONet, prewhiten, fixed_image_standardization
+from .models.utils.detect_face import extract_face
+from .models.utils import training
+
+import warnings
+warnings.filterwarnings(
+    action="ignore", 
+    message="This overload of nonzero is deprecated:\n\tnonzero()", 
+    category=UserWarning
+)

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+profile.txt

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Assets/StreamingAssets/MergeFace/Facenet/examples/finetune.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Face detection and recognition training pipeline\n",
+    "\n",
+    "The following example illustrates how to fine-tune an InceptionResnetV1 model on your own dataset. This will mostly follow standard pytorch training patterns."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from facenet_pytorch import MTCNN, InceptionResnetV1, fixed_image_standardization, training\n",
+    "import torch\n",
+    "from torch.utils.data import DataLoader, SubsetRandomSampler\n",
+    "from torch import optim\n",
+    "from torch.optim.lr_scheduler import MultiStepLR\n",
+    "from torch.utils.tensorboard import SummaryWriter\n",
+    "from torchvision import datasets, transforms\n",
+    "import numpy as np\n",
+    "import os"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Define run parameters\n",
+    "\n",
+    "The dataset should follow the VGGFace2/ImageNet-style directory layout. Modify `data_dir` to the location of the dataset on wish to finetune on."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_dir = '../data/test_images'\n",
+    "\n",
+    "batch_size = 32\n",
+    "epochs = 8\n",
+    "workers = 0 if os.name == 'nt' else 8"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Determine if an nvidia GPU is available"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n",
+    "print('Running on device: {}'.format(device))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Define MTCNN module\n",
+    "\n",
+    "See `help(MTCNN)` for more details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mtcnn = MTCNN(\n",
+    "    image_size=160, margin=0, min_face_size=20,\n",
+    "    thresholds=[0.6, 0.7, 0.7], factor=0.709, post_process=True,\n",
+    "    device=device\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Perfom MTCNN facial detection\n",
+    "\n",
+    "Iterate through the DataLoader object and obtain cropped faces."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "dataset = datasets.ImageFolder(data_dir, transform=transforms.Resize((512, 512)))\n",
+    "dataset.samples = [\n",
+    "    (p, p.replace(data_dir, data_dir + '_cropped'))\n",
+    "        for p, _ in dataset.samples\n",
+    "]\n",
+    "        \n",
+    "loader = DataLoader(\n",
+    "    dataset,\n",
+    "    num_workers=workers,\n",
+    "    batch_size=batch_size,\n",
+    "    collate_fn=training.collate_pil\n",
+    ")\n",
+    "\n",
+    "for i, (x, y) in enumerate(loader):\n",
+    "    mtcnn(x, save_path=y)\n",
+    "    print('\\rBatch {} of {}'.format(i + 1, len(loader)), end='')\n",
+    "    \n",
+    "# Remove mtcnn to reduce GPU memory usage\n",
+    "del mtcnn"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Define Inception Resnet V1 module\n",
+    "\n",
+    "See `help(InceptionResnetV1)` for more details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "resnet = InceptionResnetV1(\n",
+    "    classify=True,\n",
+    "    pretrained='vggface2',\n",
+    "    num_classes=len(dataset.class_to_idx)\n",
+    ").to(device)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Define optimizer, scheduler, dataset, and dataloader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "optimizer = optim.Adam(resnet.parameters(), lr=0.001)\n",
+    "scheduler = MultiStepLR(optimizer, [5, 10])\n",
+    "\n",
+    "trans = transforms.Compose([\n",
+    "    np.float32,\n",
+    "    transforms.ToTensor(),\n",
+    "    fixed_image_standardization\n",
+    "])\n",
+    "dataset = datasets.ImageFolder(data_dir + '_cropped', transform=trans)\n",
+    "img_inds = np.arange(len(dataset))\n",
+    "np.random.shuffle(img_inds)\n",
+    "train_inds = img_inds[:int(0.8 * len(img_inds))]\n",
+    "val_inds = img_inds[int(0.8 * len(img_inds)):]\n",
+    "\n",
+    "train_loader = DataLoader(\n",
+    "    dataset,\n",
+    "    num_workers=workers,\n",
+    "    batch_size=batch_size,\n",
+    "    sampler=SubsetRandomSampler(train_inds)\n",
+    ")\n",
+    "val_loader = DataLoader(\n",
+    "    dataset,\n",
+    "    num_workers=workers,\n",
+    "    batch_size=batch_size,\n",
+    "    sampler=SubsetRandomSampler(val_inds)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Define loss and evaluation functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loss_fn = torch.nn.CrossEntropyLoss()\n",
+    "metrics = {\n",
+    "    'fps': training.BatchTimer(),\n",
+    "    'acc': training.accuracy\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Train model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "writer = SummaryWriter()\n",
+    "writer.iteration, writer.interval = 0, 10\n",
+    "\n",
+    "print('\\n\\nInitial')\n",
+    "print('-' * 10)\n",
+    "resnet.eval()\n",
+    "training.pass_epoch(\n",
+    "    resnet, loss_fn, val_loader,\n",
+    "    batch_metrics=metrics, show_running=True, device=device,\n",
+    "    writer=writer\n",
+    ")\n",
+    "\n",
+    "for epoch in range(epochs):\n",
+    "    print('\\nEpoch {}/{}'.format(epoch + 1, epochs))\n",
+    "    print('-' * 10)\n",
+    "\n",
+    "    resnet.train()\n",
+    "    training.pass_epoch(\n",
+    "        resnet, loss_fn, train_loader, optimizer, scheduler,\n",
+    "        batch_metrics=metrics, show_running=True, device=device,\n",
+    "        writer=writer\n",
+    "    )\n",
+    "\n",
+    "    resnet.eval()\n",
+    "    training.pass_epoch(\n",
+    "        resnet, loss_fn, val_loader,\n",
+    "        batch_metrics=metrics, show_running=True, device=device,\n",
+    "        writer=writer\n",
+    "    )\n",
+    "\n",
+    "writer.close()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

Assets/StreamingAssets/MergeFace/Facenet/examples/finetune.ipynb.meta

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Assets/StreamingAssets/MergeFace/Facenet/examples/infer.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Face detection and recognition inference pipeline\n",
+    "\n",
+    "The following example illustrates how to use the `facenet_pytorch` python package to perform face detection and recogition on an image dataset using an Inception Resnet V1 pretrained on the VGGFace2 dataset.\n",
+    "\n",
+    "The following Pytorch methods are included:\n",
+    "* Datasets\n",
+    "* Dataloaders\n",
+    "* GPU/CPU processing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from facenet_pytorch import MTCNN, InceptionResnetV1\n",
+    "import torch\n",
+    "from torch.utils.data import DataLoader\n",
+    "from torchvision import datasets\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import os\n",
+    "\n",
+    "workers = 0 if os.name == 'nt' else 4"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Determine if an nvidia GPU is available"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running on device: cuda:0\n"
+     ]
+    }
+   ],
+   "source": [
+    "device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n",
+    "print('Running on device: {}'.format(device))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Define MTCNN module\n",
+    "\n",
+    "Default params shown for illustration, but not needed. Note that, since MTCNN is a collection of neural nets and other code, the device must be passed in the following way to enable copying of objects when needed internally.\n",
+    "\n",
+    "See `help(MTCNN)` for more details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mtcnn = MTCNN(\n",
+    "    image_size=160, margin=0, min_face_size=20,\n",
+    "    thresholds=[0.6, 0.7, 0.7], factor=0.709, post_process=True,\n",
+    "    device=device\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Define Inception Resnet V1 module\n",
+    "\n",
+    "Set classify=True for pretrained classifier. For this example, we will use the model to output embeddings/CNN features. Note that for inference, it is important to set the model to `eval` mode.\n",
+    "\n",
+    "See `help(InceptionResnetV1)` for more details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "resnet = InceptionResnetV1(pretrained='vggface2').eval().to(device)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Define a dataset and data loader\n",
+    "\n",
+    "We add the `idx_to_class` attribute to the dataset to enable easy recoding of label indices to identity names later one."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def collate_fn(x):\n",
+    "    return x[0]\n",
+    "\n",
+    "dataset = datasets.ImageFolder('../data/test_images')\n",
+    "dataset.idx_to_class = {i:c for c, i in dataset.class_to_idx.items()}\n",
+    "loader = DataLoader(dataset, collate_fn=collate_fn, num_workers=workers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Perfom MTCNN facial detection\n",
+    "\n",
+    "Iterate through the DataLoader object and detect faces and associated detection probabilities for each. The `MTCNN` forward method returns images cropped to the detected face, if a face was detected. By default only a single detected face is returned - to have `MTCNN` return all detected faces, set `keep_all=True` when creating the MTCNN object above.\n",
+    "\n",
+    "To obtain bounding boxes rather than cropped face images, you can instead call the lower-level `mtcnn.detect()` function. See `help(mtcnn.detect)` for details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Face detected with probability: 0.999957\n",
+      "Face detected with probability: 0.999927\n",
+      "Face detected with probability: 0.999662\n",
+      "Face detected with probability: 0.999873\n",
+      "Face detected with probability: 0.999991\n"
+     ]
+    }
+   ],
+   "source": [
+    "aligned = []\n",
+    "names = []\n",
+    "for x, y in loader:\n",
+    "    x_aligned, prob = mtcnn(x, return_prob=True)\n",
+    "    if x_aligned is not None:\n",
+    "        print('Face detected with probability: {:8f}'.format(prob))\n",
+    "        aligned.append(x_aligned)\n",
+    "        names.append(dataset.idx_to_class[y])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Calculate image embeddings\n",
+    "\n",
+    "MTCNN will return images of faces all the same size, enabling easy batch processing with the Resnet recognition module. Here, since we only have a few images, we build a single batch and perform inference on it. \n",
+    "\n",
+    "For real datasets, code should be modified to control batch sizes being passed to the Resnet, particularly if being processed on a GPU. For repeated testing, it is best to separate face detection (using MTCNN) from embedding or classification (using InceptionResnetV1), as calculation of cropped faces or bounding boxes can then be performed a single time and detected faces saved for future use."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "aligned = torch.stack(aligned).to(device)\n",
+    "embeddings = resnet(aligned).detach().cpu()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Print distance matrix for classes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                angelina_jolie  bradley_cooper  kate_siegel  paul_rudd  \\\n",
+      "angelina_jolie        0.000000        1.344806     0.781201   1.425579   \n",
+      "bradley_cooper        1.344806        0.000000     1.256238   0.922126   \n",
+      "kate_siegel           0.781201        1.256238     0.000000   1.366423   \n",
+      "paul_rudd             1.425579        0.922126     1.366423   0.000000   \n",
+      "shea_whigham          1.448495        0.891145     1.416447   0.985438   \n",
+      "\n",
+      "                shea_whigham  \n",
+      "angelina_jolie      1.448495  \n",
+      "bradley_cooper      0.891145  \n",
+      "kate_siegel         1.416447  \n",
+      "paul_rudd           0.985438  \n",
+      "shea_whigham        0.000000  \n"
+     ]
+    }
+   ],
+   "source": [
+    "dists = [[(e1 - e2).norm().item() for e2 in embeddings] for e1 in embeddings]\n",
+    "print(pd.DataFrame(dists, columns=names, index=names))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

Assets/StreamingAssets/MergeFace/Facenet/examples/infer.ipynb.meta

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Assets/StreamingAssets/MergeFace/Facenet/examples/lfw_evaluate.ipynb

@@ -0,0 +1,522 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### facenet-pytorch LFW evaluation\n",
+    "This notebook demonstrates how to evaluate performance against the LFW dataset."
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "outputs": [],
+   "source": [
+    "from facenet_pytorch import MTCNN, InceptionResnetV1, fixed_image_standardization, training, extract_face\n",
+    "import torch\n",
+    "from torch.utils.data import DataLoader, SubsetRandomSampler, SequentialSampler\n",
+    "from torchvision import datasets, transforms\n",
+    "import numpy as np\n",
+    "import os"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "outputs": [],
+   "source": [
+    "data_dir = 'data/lfw/lfw'\n",
+    "pairs_path = 'data/lfw/pairs.txt'\n",
+    "\n",
+    "batch_size = 16\n",
+    "epochs = 15\n",
+    "workers = 0 if os.name == 'nt' else 8"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running on device: cuda:0\n"
+     ]
+    }
+   ],
+   "source": [
+    "device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n",
+    "print('Running on device: {}'.format(device))"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "outputs": [],
+   "source": [
+    "mtcnn = MTCNN(\n",
+    "    image_size=160,\n",
+    "    margin=14,\n",
+    "    device=device,\n",
+    "    selection_method='center_weighted_size'\n",
+    ")"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "outputs": [],
+   "source": [
+    "# Define the data loader for the input set of images\n",
+    "orig_img_ds = datasets.ImageFolder(data_dir, transform=None)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "outputs": [],
+   "source": [
+    "\n",
+    "# overwrites class labels in dataset with path so path can be used for saving output in mtcnn batches\n",
+    "orig_img_ds.samples = [\n",
+    "    (p, p)\n",
+    "    for p, _ in orig_img_ds.samples\n",
+    "]\n",
+    "\n",
+    "loader = DataLoader(\n",
+    "    orig_img_ds,\n",
+    "    num_workers=workers,\n",
+    "    batch_size=batch_size,\n",
+    "    collate_fn=training.collate_pil\n",
+    ")\n"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "crop_paths = []\n",
+    "box_probs = []\n",
+    "\n",
+    "for i, (x, b_paths) in enumerate(loader):\n",
+    "    crops = [p.replace(data_dir, data_dir + '_cropped') for p in b_paths]\n",
+    "    mtcnn(x, save_path=crops)\n",
+    "    crop_paths.extend(crops)\n",
+    "    print('\\rBatch {} of {}'.format(i + 1, len(loader)), end='')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "outputs": [],
+   "source": [
+    "# Remove mtcnn to reduce GPU memory usage\n",
+    "del mtcnn\n",
+    "torch.cuda.empty_cache()"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "outputs": [],
+   "source": [
+    "# create dataset and data loaders from cropped images output from MTCNN\n",
+    "\n",
+    "trans = transforms.Compose([\n",
+    "    np.float32,\n",
+    "    transforms.ToTensor(),\n",
+    "    fixed_image_standardization\n",
+    "])\n",
+    "\n",
+    "dataset = datasets.ImageFolder(data_dir + '_cropped', transform=trans)\n",
+    "\n",
+    "embed_loader = DataLoader(\n",
+    "    dataset,\n",
+    "    num_workers=workers,\n",
+    "    batch_size=batch_size,\n",
+    "    sampler=SequentialSampler(dataset)\n",
+    ")"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "outputs": [],
+   "source": [
+    "# Load pretrained resnet model\n",
+    "resnet = InceptionResnetV1(\n",
+    "    classify=False,\n",
+    "    pretrained='vggface2'\n",
+    ").to(device)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "outputs": [],
+   "source": [
+    "classes = []\n",
+    "embeddings = []\n",
+    "resnet.eval()\n",
+    "with torch.no_grad():\n",
+    "    for xb, yb in embed_loader:\n",
+    "        xb = xb.to(device)\n",
+    "        b_embeddings = resnet(xb)\n",
+    "        b_embeddings = b_embeddings.to('cpu').numpy()\n",
+    "        classes.extend(yb.numpy())\n",
+    "        embeddings.extend(b_embeddings)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "outputs": [],
+   "source": [
+    "embeddings_dict = dict(zip(crop_paths,embeddings))\n",
+    "\n"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### Evaluate embeddings by using distance metrics to perform verification on the official LFW test set.\n",
+    "\n",
+    "The functions in the next block are copy pasted from `facenet.src.lfw`. Unfortunately that module has an absolute import from `facenet`, so can't be imported from the submodule\n",
+    "\n",
+    "added functionality to return false positive and false negatives"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import KFold\n",
+    "from scipy import interpolate\n",
+    "\n",
+    "# LFW functions taken from David Sandberg's FaceNet implementation\n",
+    "def distance(embeddings1, embeddings2, distance_metric=0):\n",
+    "    if distance_metric==0:\n",
+    "        # Euclidian distance\n",
+    "        diff = np.subtract(embeddings1, embeddings2)\n",
+    "        dist = np.sum(np.square(diff),1)\n",
+    "    elif distance_metric==1:\n",
+    "        # Distance based on cosine similarity\n",
+    "        dot = np.sum(np.multiply(embeddings1, embeddings2), axis=1)\n",
+    "        norm = np.linalg.norm(embeddings1, axis=1) * np.linalg.norm(embeddings2, axis=1)\n",
+    "        similarity = dot / norm\n",
+    "        dist = np.arccos(similarity) / math.pi\n",
+    "    else:\n",
+    "        raise 'Undefined distance metric %d' % distance_metric\n",
+    "\n",
+    "    return dist\n",
+    "\n",
+    "def calculate_roc(thresholds, embeddings1, embeddings2, actual_issame, nrof_folds=10, distance_metric=0, subtract_mean=False):\n",
+    "    assert(embeddings1.shape[0] == embeddings2.shape[0])\n",
+    "    assert(embeddings1.shape[1] == embeddings2.shape[1])\n",
+    "    nrof_pairs = min(len(actual_issame), embeddings1.shape[0])\n",
+    "    nrof_thresholds = len(thresholds)\n",
+    "    k_fold = KFold(n_splits=nrof_folds, shuffle=False)\n",
+    "\n",
+    "    tprs = np.zeros((nrof_folds,nrof_thresholds))\n",
+    "    fprs = np.zeros((nrof_folds,nrof_thresholds))\n",
+    "    accuracy = np.zeros((nrof_folds))\n",
+    "\n",
+    "    is_false_positive = []\n",
+    "    is_false_negative = []\n",
+    "\n",
+    "    indices = np.arange(nrof_pairs)\n",
+    "\n",
+    "    for fold_idx, (train_set, test_set) in enumerate(k_fold.split(indices)):\n",
+    "        if subtract_mean:\n",
+    "            mean = np.mean(np.concatenate([embeddings1[train_set], embeddings2[train_set]]), axis=0)\n",
+    "        else:\n",
+    "          mean = 0.0\n",
+    "        dist = distance(embeddings1-mean, embeddings2-mean, distance_metric)\n",
+    "\n",
+    "        # Find the best threshold for the fold\n",
+    "        acc_train = np.zeros((nrof_thresholds))\n",
+    "        for threshold_idx, threshold in enumerate(thresholds):\n",
+    "            _, _, acc_train[threshold_idx], _ ,_ = calculate_accuracy(threshold, dist[train_set], actual_issame[train_set])\n",
+    "        best_threshold_index = np.argmax(acc_train)\n",
+    "        for threshold_idx, threshold in enumerate(thresholds):\n",
+    "            tprs[fold_idx,threshold_idx], fprs[fold_idx,threshold_idx], _, _, _ = calculate_accuracy(threshold, dist[test_set], actual_issame[test_set])\n",
+    "        _, _, accuracy[fold_idx], is_fp, is_fn = calculate_accuracy(thresholds[best_threshold_index], dist[test_set], actual_issame[test_set])\n",
+    "\n",
+    "        tpr = np.mean(tprs,0)\n",
+    "        fpr = np.mean(fprs,0)\n",
+    "        is_false_positive.extend(is_fp)\n",
+    "        is_false_negative.extend(is_fn)\n",
+    "\n",
+    "    return tpr, fpr, accuracy, is_false_positive, is_false_negative\n",
+    "\n",
+    "def calculate_accuracy(threshold, dist, actual_issame):\n",
+    "    predict_issame = np.less(dist, threshold)\n",
+    "    tp = np.sum(np.logical_and(predict_issame, actual_issame))\n",
+    "    fp = np.sum(np.logical_and(predict_issame, np.logical_not(actual_issame)))\n",
+    "    tn = np.sum(np.logical_and(np.logical_not(predict_issame), np.logical_not(actual_issame)))\n",
+    "    fn = np.sum(np.logical_and(np.logical_not(predict_issame), actual_issame))\n",
+    "\n",
+    "    is_fp = np.logical_and(predict_issame, np.logical_not(actual_issame))\n",
+    "    is_fn = np.logical_and(np.logical_not(predict_issame), actual_issame)\n",
+    "\n",
+    "    tpr = 0 if (tp+fn==0) else float(tp) / float(tp+fn)\n",
+    "    fpr = 0 if (fp+tn==0) else float(fp) / float(fp+tn)\n",
+    "    acc = float(tp+tn)/dist.size\n",
+    "    return tpr, fpr, acc, is_fp, is_fn\n",
+    "\n",
+    "def calculate_val(thresholds, embeddings1, embeddings2, actual_issame, far_target, nrof_folds=10, distance_metric=0, subtract_mean=False):\n",
+    "    assert(embeddings1.shape[0] == embeddings2.shape[0])\n",
+    "    assert(embeddings1.shape[1] == embeddings2.shape[1])\n",
+    "    nrof_pairs = min(len(actual_issame), embeddings1.shape[0])\n",
+    "    nrof_thresholds = len(thresholds)\n",
+    "    k_fold = KFold(n_splits=nrof_folds, shuffle=False)\n",
+    "\n",
+    "    val = np.zeros(nrof_folds)\n",
+    "    far = np.zeros(nrof_folds)\n",
+    "\n",
+    "    indices = np.arange(nrof_pairs)\n",
+    "\n",
+    "    for fold_idx, (train_set, test_set) in enumerate(k_fold.split(indices)):\n",
+    "        if subtract_mean:\n",
+    "            mean = np.mean(np.concatenate([embeddings1[train_set], embeddings2[train_set]]), axis=0)\n",
+    "        else:\n",
+    "          mean = 0.0\n",
+    "        dist = distance(embeddings1-mean, embeddings2-mean, distance_metric)\n",
+    "\n",
+    "        # Find the threshold that gives FAR = far_target\n",
+    "        far_train = np.zeros(nrof_thresholds)\n",
+    "        for threshold_idx, threshold in enumerate(thresholds):\n",
+    "            _, far_train[threshold_idx] = calculate_val_far(threshold, dist[train_set], actual_issame[train_set])\n",
+    "        if np.max(far_train)>=far_target:\n",
+    "            f = interpolate.interp1d(far_train, thresholds, kind='slinear')\n",
+    "            threshold = f(far_target)\n",
+    "        else:\n",
+    "            threshold = 0.0\n",
+    "\n",
+    "        val[fold_idx], far[fold_idx] = calculate_val_far(threshold, dist[test_set], actual_issame[test_set])\n",
+    "\n",
+    "    val_mean = np.mean(val)\n",
+    "    far_mean = np.mean(far)\n",
+    "    val_std = np.std(val)\n",
+    "    return val_mean, val_std, far_mean\n",
+    "\n",
+    "def calculate_val_far(threshold, dist, actual_issame):\n",
+    "    predict_issame = np.less(dist, threshold)\n",
+    "    true_accept = np.sum(np.logical_and(predict_issame, actual_issame))\n",
+    "    false_accept = np.sum(np.logical_and(predict_issame, np.logical_not(actual_issame)))\n",
+    "    n_same = np.sum(actual_issame)\n",
+    "    n_diff = np.sum(np.logical_not(actual_issame))\n",
+    "    val = float(true_accept) / float(n_same)\n",
+    "    far = float(false_accept) / float(n_diff)\n",
+    "    return val, far\n",
+    "\n",
+    "\n",
+    "\n",
+    "def evaluate(embeddings, actual_issame, nrof_folds=10, distance_metric=0, subtract_mean=False):\n",
+    "    # Calculate evaluation metrics\n",
+    "    thresholds = np.arange(0, 4, 0.01)\n",
+    "    embeddings1 = embeddings[0::2]\n",
+    "    embeddings2 = embeddings[1::2]\n",
+    "    tpr, fpr, accuracy, fp, fn  = calculate_roc(thresholds, embeddings1, embeddings2,\n",
+    "        np.asarray(actual_issame), nrof_folds=nrof_folds, distance_metric=distance_metric, subtract_mean=subtract_mean)\n",
+    "    thresholds = np.arange(0, 4, 0.001)\n",
+    "    val, val_std, far = calculate_val(thresholds, embeddings1, embeddings2,\n",
+    "        np.asarray(actual_issame), 1e-3, nrof_folds=nrof_folds, distance_metric=distance_metric, subtract_mean=subtract_mean)\n",
+    "    return tpr, fpr, accuracy, val, val_std, far, fp, fn\n",
+    "\n",
+    "def add_extension(path):\n",
+    "    if os.path.exists(path+'.jpg'):\n",
+    "        return path+'.jpg'\n",
+    "    elif os.path.exists(path+'.png'):\n",
+    "        return path+'.png'\n",
+    "    else:\n",
+    "        raise RuntimeError('No file \"%s\" with extension png or jpg.' % path)\n",
+    "\n",
+    "def get_paths(lfw_dir, pairs):\n",
+    "    nrof_skipped_pairs = 0\n",
+    "    path_list = []\n",
+    "    issame_list = []\n",
+    "    for pair in pairs:\n",
+    "        if len(pair) == 3:\n",
+    "            path0 = add_extension(os.path.join(lfw_dir, pair[0], pair[0] + '_' + '%04d' % int(pair[1])))\n",
+    "            path1 = add_extension(os.path.join(lfw_dir, pair[0], pair[0] + '_' + '%04d' % int(pair[2])))\n",
+    "            issame = True\n",
+    "        elif len(pair) == 4:\n",
+    "            path0 = add_extension(os.path.join(lfw_dir, pair[0], pair[0] + '_' + '%04d' % int(pair[1])))\n",
+    "            path1 = add_extension(os.path.join(lfw_dir, pair[2], pair[2] + '_' + '%04d' % int(pair[3])))\n",
+    "            issame = False\n",
+    "        if os.path.exists(path0) and os.path.exists(path1):    # Only add the pair if both paths exist\n",
+    "            path_list += (path0,path1)\n",
+    "            issame_list.append(issame)\n",
+    "        else:\n",
+    "            nrof_skipped_pairs += 1\n",
+    "    if nrof_skipped_pairs>0:\n",
+    "        print('Skipped %d image pairs' % nrof_skipped_pairs)\n",
+    "\n",
+    "    return path_list, issame_list\n",
+    "\n",
+    "def read_pairs(pairs_filename):\n",
+    "    pairs = []\n",
+    "    with open(pairs_filename, 'r') as f:\n",
+    "        for line in f.readlines()[1:]:\n",
+    "            pair = line.strip().split()\n",
+    "            pairs.append(pair)\n",
+    "    return np.array(pairs, dtype=object)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "outputs": [],
+   "source": [
+    "pairs = read_pairs(pairs_path)\n",
+    "path_list, issame_list = get_paths(data_dir+'_cropped', pairs)\n",
+    "embeddings = np.array([embeddings_dict[path] for path in path_list])\n",
+    "\n",
+    "tpr, fpr, accuracy, val, val_std, far, fp, fn = evaluate(embeddings, issame_list)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.995      0.995      0.99166667 0.99       0.99       0.99666667\n",
+      " 0.99       0.995      0.99666667 0.99666667]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": "0.9936666666666666"
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print(accuracy)\n",
+    "np.mean(accuracy)\n",
+    "\n"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}

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