update

Assets/GadGame/SO/Api Reference.asset

@@ -18,7 +18,7 @@ MonoBehaviour:
   - name: LoginAsGameMachine
     type: 1
     query: "mutation LoginAsGameMachine{\n    loginAsGameMachine(  input :{ macAddress
-      :\"D85ED3741515\", password :\"Sangta@123\"} ){\n        accessToken\n       
+      :\"D8BBC1004EF5\", password :\"Sangta@123\"} ){\n        accessToken\n       
       refreshToken\n    }\n}"
     queryString: loginAsGameMachine
     returnType: Game
@@ -95,7 +95,7 @@ MonoBehaviour:
     isComplete: 1
   - name: CreateGuest
     type: 1
-    query: "mutation CreateGuest{\n    createGuest(  input :{ password :\"Abc@123\"}
+    query: "mutation CreateGuest{\n    createGuest(  input :{ password :\"Sangta@123\"}
       ){\n        accessToken\n        refreshToken\n        user{\n            id\n       
       }\n    }\n}"
     queryString: createGuest
@@ -195,7 +195,7 @@ MonoBehaviour:
     isComplete: 1
   - name: JoinPromotion
     type: 1
-    query: "mutation JoinPromotion{\n    joinPromotion(  input :{ promotionId :\"63c67c5434403000\"}
+    query: "mutation JoinPromotion{\n    joinPromotion(  input :{ promotionId :\"63d878a42dc03000\"}
       ){\n        id\n        totalScore\n    }\n}"
     queryString: joinPromotion
     returnType: Participant
@@ -281,8 +281,8 @@ MonoBehaviour:
   - name: SubmitGameSession
     type: 1
     query: "mutation SubmitGameSession{\n    submitGameSession(  input :{ playerId
-      :\"63e929e9b3c01000\", promotionId :\"63c67c5434403000\", startAt :\"2024-05-23T15:53:22.407196+07:00\",
+      :\"6415300313c01000\", promotionId :\"63d878a42dc03000\", startAt :\"2024-06-26T20:34:49.291114+07:00\",
-      endAt :\"2024-05-23T15:53:29.062723+07:00\", score :1000} ){\n        startAt\n       
+      endAt :\"2024-06-26T20:38:45.135242+07:00\", score :0} ){\n        startAt\n       
       endAt\n        score\n    }\n}"
     queryString: submitGameSession
     returnType: GameSession
@@ -392,7 +392,7 @@ MonoBehaviour:
   - name: GuestUpdatedSubscription
     type: 2
     query: "subscription GuestUpdatedSubscription{\n    guestUpdatedSubscription( 
-      guestId :\"63e929e9b3c01000\" ){\n        firstName\n        lastName\n       
+      guestId :\"6415300313c01000\" ){\n        firstName\n        lastName\n       
       phone\n        email\n    }\n}"
     queryString: guestUpdatedSubscription
     returnType: Guest

Assets/GadGame/Scenes/CTA.unity

@@ -235,7 +235,7 @@ RectTransform:
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   m_AnchorMax: {x: 1, y: 1}
   m_AnchoredPosition: {x: 0, y: 0}
-  m_SizeDelta: {x: 100, y: 100}
+  m_SizeDelta: {x: 0, y: 0}
   m_Pivot: {x: 0.5, y: 0.5}
 --- !u!114 &153040809
 MonoBehaviour:
@@ -832,8 +832,8 @@ MonoBehaviour:
       m_Calls: []
   m_text: 
   m_isRightToLeft: 0
-  m_fontAsset: {fileID: 11400000, guid: 32ca7ffda2664c077bcf6abfc6f32d0d, type: 2}
+  m_fontAsset: {fileID: 11400000, guid: 9cf4fd6f40135976a80659e0dafb626a, type: 2}
-  m_sharedMaterial: {fileID: -1949374272958031481, guid: 32ca7ffda2664c077bcf6abfc6f32d0d, type: 2}
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   m_fontMaterial: {fileID: 0}
   m_fontMaterials: []
@@ -863,7 +863,7 @@ MonoBehaviour:
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   m_fontSizeMin: 18
   m_fontSizeMax: 72
-  m_fontStyle: 0
+  m_fontStyle: 1
   m_HorizontalAlignment: 2
   m_VerticalAlignment: 512
   m_textAlignment: 65535
@@ -1442,7 +1442,7 @@ RectTransform:
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   m_AnchorMin: {x: 0, y: 1}
   m_AnchorMax: {x: 1, y: 1}
-  m_AnchoredPosition: {x: 0, y: -160}
+  m_AnchoredPosition: {x: 0, y: -110}
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@@ -1465,7 +1465,7 @@ MonoBehaviour:
   m_OnCullStateChanged:
     m_PersistentCalls:
       m_Calls: []
-  m_text: "Soi C\xE0 Ph\xEA, Ra T\xEDnh N\u1EBFt!"
+  m_text: "Soi C\xE0 Ph\xEA, Ra Ti\u1EC1n Ki\u1EBFp"
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   m_sharedMaterial: {fileID: 8449302638115568603, guid: 10a7b9e40a1040b5396b3833a6aa8338, type: 2}
@@ -1492,8 +1492,8 @@ MonoBehaviour:
   m_faceColor:
     serializedVersion: 2
     rgba: 4294967295
-  m_fontSize: 75
+  m_fontSize: 68
-  m_fontSizeBase: 75
+  m_fontSizeBase: 68
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   m_fontSizeMin: 18
@@ -1602,7 +1602,7 @@ Camera:
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   far clip plane: 1000
   field of view: 60
-  orthographic: 0
+  orthographic: 1
   orthographic size: 5
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   m_CullingMask:
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   m_PrefabAsset: {fileID: 0}
   m_GameObject: {fileID: 1559499562}
-  m_LocalRotation: {x: 0, y: 0, z: -0.38268343, w: 0.92387956}
+  m_LocalRotation: {x: 0, y: 0, z: -0.39313957, w: 0.91947883}
   m_LocalPosition: {x: 0, y: 0, z: 0}
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@@ -2020,11 +2020,11 @@ RectTransform:
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   m_AnchorMax: {x: 0.5, y: 0.5}
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+  m_SizeDelta: {x: 960, y: 960}
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 CanvasRenderer:

Assets/GadGame/Scenes/Main.unity

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-  _promotionId: 63fb40435c803000
+  _promotionId: 63d878a42dc03000
   _guestUpdatedSubscription: {fileID: 11400000, guid: f98ac02dda5623c4c82d342ee9602420, type: 2}
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Assets/GadGame/Scenes/PassBy_coffee.unity

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@@ -441,7 +366,7 @@ RectTransform:
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-  m_LocalRotation: {x: 0, y: 0, z: -0.38268343, w: 0.92387956}
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@@ -450,11 +375,11 @@ RectTransform:
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-  m_LocalEulerAnglesHint: {x: 0, y: 0, z: -45}
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   m_AnchorMax: {x: 0.5, y: 0.5}
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+  m_SizeDelta: {x: 960, y: 960}
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 MonoBehaviour:
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   m_AnchorMin: {x: 0, y: 1}
   m_AnchorMax: {x: 1, y: 1}
-  m_AnchoredPosition: {x: 0, y: -160}
+  m_AnchoredPosition: {x: 0, y: -110}
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@@ -551,7 +476,7 @@ MonoBehaviour:
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     m_PersistentCalls:
       m_Calls: []
-  m_text: "Soi C\xE0 Ph\xEA, Ra T\xEDnh N\u1EBFt!"
+  m_text: "Soi C\xE0 Ph\xEA, Ra Ti\u1EC1n Ki\u1EBFp"
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@@ -578,8 +503,8 @@ MonoBehaviour:
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     rgba: 4294967295
-  m_fontSize: 75
+  m_fontSize: 68
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+  m_fontSizeBase: 68
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@@ -653,7 +578,7 @@ RectTransform:
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   m_PrefabAsset: {fileID: 0}
   m_GameObject: {fileID: 334911199}
-  m_LocalRotation: {x: -0, y: -0, z: -0, w: 1}
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@@ -662,8 +587,8 @@ RectTransform:
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   m_AnchorMax: {x: 0.5, y: 0.5}
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 MonoBehaviour:
@@ -688,8 +613,8 @@ MonoBehaviour:
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@@ -713,13 +638,13 @@ MonoBehaviour:
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     rgba: 4294967295
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@@ -852,7 +777,7 @@ VideoPlayer:
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@@ -1021,7 +946,7 @@ CanvasRenderer:
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@@ -1029,45 +954,45 @@ GameObject:
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+  - component: {fileID: 1023304017}
-  - component: {fileID: 879095299}
+  - component: {fileID: 1023304016}
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@@ -1078,82 +1003,23 @@ MonoBehaviour:
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@@ -1290,7 +1365,7 @@ Camera:
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   far clip plane: 1000
   field of view: 60
-  orthographic: 0
+  orthographic: 1
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   m_AnchorMax: {x: 1, y: 1}
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+  m_SizeDelta: {x: 0, y: 0}
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@@ -1892,6 +2042,44 @@ CanvasRenderer:
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+  m_Pivot: {x: 0.5, y: 0.5}
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@@ -1930,7 +2118,9 @@ MonoBehaviour:
   _idleVideo: {fileID: 378183297}
   _userImager: {fileID: 1391297540}
   _process: {fileID: 1701610193}
+  _faceGuide: {fileID: 226662305}
   _hintText: {fileID: 1386430229}
+  _notifyText: {fileID: 334911201}
   _notify: {fileID: 338289613}
   _hint: {fileID: 80211465}
   _loading: {fileID: 1087577779}
@@ -2006,7 +2196,7 @@ MonoBehaviour:
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   m_EditorClassIdentifier: 
   m_Material: {fileID: 0}
-  m_Color: {r: 0.4339623, g: 0.2504109, b: 0.13305448, a: 0.7294118}
+  m_Color: {r: 0, g: 0.6862745, b: 0, a: 0.7294118}
   m_RaycastTarget: 1
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@@ -2208,9 +2398,9 @@ RectTransform:
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   - {fileID: 282395848}
   - {fileID: 338289614}
-  - {fileID: 162662061}
-  - {fileID: 879095298}
   - {fileID: 80211466}
+  - {fileID: 1329164564}
+  - {fileID: 1563200507}
   m_Father: {fileID: 0}
   m_LocalEulerAnglesHint: {x: 0, y: 0, z: 0}
   m_AnchorMin: {x: 0, y: 0}

Assets/GadGame/Scripts/Coffee/CoffeeController.cs

@@ -7,6 +7,7 @@ using UnityEngine;
 using UnityEngine.Serialization;
 using UnityEngine.UI;
 using UnityEngine.Video;
+using GadGame.Network;
 
 namespace GadGame.Scripts.Coffee
 {
@@ -21,7 +22,9 @@ namespace GadGame.Scripts.Coffee
         [SerializeField] private VideoPlayer _idleVideo;
         [SerializeField] private LoadImageEncoded _userImager;
         [SerializeField] private Image _process;
+        [SerializeField] private Image _faceGuide;
         [SerializeField] private TextMeshProUGUI _hintText;
+        [SerializeField] private TextMeshProUGUI _notifyText;
         [SerializeField] private GameObject _notify;
         [SerializeField] private GameObject _hint;
         [SerializeField] private Image _loading;
@@ -35,8 +38,6 @@ namespace GadGame.Scripts.Coffee
 
         private async void Awake()
         {
-            await P4PGraphqlManager.Instance.JoinPromotion();
-            await P4PGraphqlManager.Instance.SubmitGameSession(0);
             _idleVideo.targetCameraAlpha = 1;
             _loading.transform.DOLocalRotate(new Vector3(0, 0, 360), 10 / _loadingSpeed, RotateMode.FastBeyond360)
                 .SetLoops(-1)
@@ -45,6 +46,7 @@ namespace GadGame.Scripts.Coffee
 
             _notify.SetActive(false);
             _hint.SetActive(false);
+            _faceGuide.enabled = true;
         }
 
         private void OnEnable()
@@ -81,6 +83,13 @@ namespace GadGame.Scripts.Coffee
                 }
                 // _hintText.text = _loadingTexts[_indexText];
             }
+
+            if (UdpSocket.Instance.DataReceived.AgeMax != 0)
+            {
+                _notifyText.text = UdpSocket.Instance.DataReceived.Gender < 0.5 ? "Hmmm, để  xem kiếp trước bạn là ai nào, chàng trai" : "Hmmm, để  xem kiếp trước bạn là ai nào, cô gái";
+            } else {
+                _notifyText.text = "Khuấy đều tay, kiếp trước của bạn sẽ hiện ra, chờ một chút nhé";
+            }
         }
 
         private void Play(bool engage) {
@@ -94,8 +103,9 @@ namespace GadGame.Scripts.Coffee
             _isLoading = false;
             _loading.DOFade(0, 0.5f);
             // _hintText.text = desc;
-            _notify.SetActive(false);
+            // _notify.SetActive(false);
             _hint.SetActive(false);
+            _notifyText.text = UdpSocket.Instance.DataReceived.Description;
         }
 
         private void OnGetEncodeImage(string filePath)
@@ -111,6 +121,10 @@ namespace GadGame.Scripts.Coffee
             // _hintText.text = _loadingTexts[_indexText];
             _loading.DOFade(1, 1f);
             _hint.SetActive(true);
+            // _notifyText.text = UdpSocket.Instance.DataReceived.Gender < 0.5 ? "Hmmm, có vẻ thú vị đấy chàng trai" : "Hmmm, có vẻ thú vị đấy cô gái";
+            // _notifyText.text = "Hmmm, tiền kiếp có vẻ thú vị đấy nhỉ!!!";
+            _faceGuide.enabled = false;
+            UdpSocket.Instance.SendDataToPython("Begin");
         }
 
         private async void SetPlayVideo(bool value){
@@ -119,13 +133,22 @@ namespace GadGame.Scripts.Coffee
                 {
                     _idleVideo.targetCameraAlpha += Time.deltaTime * 3;
                     await UniTask.Yield();
+                    if(_idleVideo == null){
+                        return;
+                    }
                 }
                 _idleVideo.targetCameraAlpha = 1;
             } else {
                 while (_idleVideo.targetCameraAlpha > 0) 
                 {
+                    if(_idleVideo == null){
+                        return;
+                    }
                     _idleVideo.targetCameraAlpha -= Time.deltaTime * 3;
                     await UniTask.Yield();
+                    if(_idleVideo == null){
+                        return;
+                    }
                 }
                 _idleVideo.targetCameraAlpha = 0;
             }
@@ -135,6 +158,5 @@ namespace GadGame.Scripts.Coffee
             // _hintText.text = _texts[1];
             _process.fillAmount = 1 - progress ;
         }
-        
     }
 }

Assets/GadGame/Scripts/MainFlow.cs

@@ -24,14 +24,13 @@ namespace GadGame
         {
             base.Awake();
             DontDestroyOnLoad(gameObject);
-            string _macAddress = GetMacAddressString();
-            Debug.Log(_macAddress);
-            await P4PGraphqlManager.Instance.LoginMachine(_macAddress);
-            await P4PGraphqlManager.Instance.CreateGuest();
         }
 
         private  async void Start()
         {
+            string _macAddress = GetMacAddressString();
+            Debug.Log(_macAddress);
+            await P4PGraphqlManager.Instance.LoginMachine(_macAddress);
             await LoadSceneManager.Instance.LoadSceneWithTransitionAsync(SceneFlowConfig.PassByScene.ScenePath);
             SetState<IdleState>();
         }

Assets/GadGame/Scripts/MiniGame/MiniGameController.cs

@@ -104,33 +104,33 @@ namespace GadGame.MiniGame
             // var inputNormalize = new Vector2((inputData.x - 213.33f)/ 213.33f, inputData.y / 480);
             // var inputNormalize = new Vector2(inputData.x/ 200, inputData.y / 480);
 
-            receivedData = UdpSocket.Instance.DataReceived.PosPoints;
+            // receivedData = UdpSocket.Instance.DataReceived.PosPoints;
 
-            for (int i = 0; i < Objects.Length; i++)
+            // for (int i = 0; i < Objects.Length; i++)
-            {
+            // {
-                var inputNormalize = new Vector2((receivedData[i].x - 213.33f)/ 213.33f, receivedData[i].y / 480);
+            //     var inputNormalize = new Vector2((receivedData[i].x - 213.33f)/ 213.33f, receivedData[i].y / 480);
-                if (i == 0)
+            //     if (i == 0)
-                {
+            //     {
-                    var input = new Vector2
+            //         var input = new Vector2
-                    {
+            //         {
-                        x = Mathf.Lerp(0, _canvas.pixelRect.width, inputNormalize.x),
+            //             x = Mathf.Lerp(0, _canvas.pixelRect.width, inputNormalize.x),
-                        y = -Mathf.Lerp(0, _canvas.pixelRect.height, inputNormalize.y)
+            //             y = -Mathf.Lerp(0, _canvas.pixelRect.height, inputNormalize.y)
-                    };
+            //         };
-                    if (input != Vector2.zero)
+            //         if (input != Vector2.zero)
-                    {
+            //         {
-                        var mousePos = input;
+            //             var mousePos = input;
-                        var pos = _camera.ScreenToWorldPoint(mousePos);
+            //             var pos = _camera.ScreenToWorldPoint(mousePos);
-                        var currentPosition = _basket.Position;
+            //             var currentPosition = _basket.Position;
-                        pos.x *= -1;
+            //             pos.x *= -1;
-                        pos.y = currentPosition.y;
+            //             pos.y = currentPosition.y;
-                        pos.z = 0;
+            //             pos.z = 0;
-                        currentPosition= Vector3.Lerp(currentPosition, pos, _lerp * Time.deltaTime);
+            //             currentPosition= Vector3.Lerp(currentPosition, pos, _lerp * Time.deltaTime);
-                        currentPosition.x = Mathf.Clamp(currentPosition.x, -2.25f, 2.25f);
+            //             currentPosition.x = Mathf.Clamp(currentPosition.x, -2.25f, 2.25f);
-                        var dirMove = (_preFramePosition - currentPosition).normalized;
+            //             var dirMove = (_preFramePosition - currentPosition).normalized;
-                        _basket.transform.DORotate(new Vector3(0, 0, 10 * dirMove.x), 0.2f);
+            //             _basket.transform.DORotate(new Vector3(0, 0, 10 * dirMove.x), 0.2f);
-                        _basket.Position = currentPosition;
+            //             _basket.Position = currentPosition;
-                    }
+            //         }
-                }
+            //     }
 
                 // var pos_pose = new Vector2();
                 // var x = Mathf.Clamp01(receivedData[i].x / 640);
@@ -140,7 +140,7 @@ namespace GadGame.MiniGame
                 // pos_pose.y = y;
 
                 // Objects[i].localPosition = pos_pose * -1;
-            }
+            // }
             
         }
 

Assets/GadGame/Scripts/Network/P4PGraphqlManager.cs

@@ -121,7 +121,7 @@ namespace GadGame.Network
                     promotionId = _promotionId
                 }
             });
-            
+            Debug.Log(_userAccessToken);
             _graphApi.SetAuthToken(_userAccessToken);
             var request = await _graphApi.Post(query);
             if (request.result == UnityWebRequest.Result.Success)
@@ -139,7 +139,7 @@ namespace GadGame.Network
 
         public async Task<bool> SubmitGameSession(int gameScore)
         {
-            var endTime = DateTime.Now.AddSeconds(-1);
+            var endTime = DateTime.Now;
             var query = _graphApi.GetQueryByName("SubmitGameSession", GraphApi.Query.Type.Mutation);
             query.SetArgs(new
             {
@@ -183,7 +183,8 @@ namespace GadGame.Network
             var socket = await _graphApi.Subscribe(query);
             if (socket.State == WebSocketState.Open)
             {
-                var link = $"https://play4promo.online/brands/{_promotionId}/scan-qr?token={_userAccessToken}&img=";
+                var imageNameFile = UdpSocket.Instance.DataReceived.FileName;
+                var link = $"https://play4promo.online/brands/{_promotionId}/scan-qr?token={_userAccessToken}&img={imageNameFile}";
                 Debug.Log(link);
                 return EncodeTextToQrCode(link);
             }

Assets/GadGame/Scripts/Network/ReceiverData.cs

@@ -19,9 +19,10 @@ namespace GadGame.Network
         public float Gender;
         public int AgeMin;
         public int AgeMax;
-        public Vector2[] PosPoints;
+        // public Vector2[] PosPoints;
         public string StreamingData;
         [FormerlySerializedAs("Success")] public bool GenerateImageSuccess;
         public string Description;
+        public string FileName;
     }
 }

Assets/GadGame/Scripts/QRShowNewCTA.cs

@@ -1,4 +1,5 @@
 using System;
+using Cysharp.Threading.Tasks;
 using GadGame.Event.Customs;
 using GadGame.Event.Type;
 using GadGame.Network;
@@ -18,6 +19,7 @@ public class QRShowNewCTA : MonoBehaviour
     {
         _descText.text = UdpSocket.Instance.DataReceived.Description;
         _rawImage.texture = await P4PGraphqlManager.Instance.GetQrLink();
+
         // _timer.SetDuration(60).Begin();
     }
 

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

@@ -14,19 +14,19 @@ namespace GadGame.State.MainFlowState
 
         public override async void Enter()
         {
-            await LoadSceneManager.Instance.LoadSceneAsync(Runner.SceneFlowConfig.CTASceneMale.ScenePath);
-            Runner.ScanSuccess.Register(OnScanSuccess);
-
             _leaveTimer = 0;
+            _scanSuccess = false;
+            Runner.ScanSuccess.Register(OnScanSuccess);
+            await LoadSceneManager.Instance.LoadSceneAsync(Runner.SceneFlowConfig.CTASceneMale.ScenePath);
         }
 
         public override void Update(float time)
         {
             Runner.EncodeImage.Raise(UdpSocket.Instance.DataReceived.StreamingData);
-            if(_scanSuccess) 
+            if(_scanSuccess || time >= 60) 
             {
-                UdpSocket.Instance.SendDataToPython("End");
                 Runner.SetState<IdleState>();
+                UdpSocket.Instance.SendDataToPython("End");
                 return;
             }
 
@@ -36,15 +36,16 @@ namespace GadGame.State.MainFlowState
                 if ( _leaveTimer >= 10)
                 {
                     Runner.SetState<IdleState>();
+                    UdpSocket.Instance.SendDataToPython("End");
                 }
             } else {
                 _leaveTimer = 0;
             }
-            if (time >= 60)
+            // if (time >= 30)
-            {
+            // {
-                Runner.SetState<IdleState>();
+            //     Runner.SetState<IdleState>();
-                UdpSocket.Instance.SendDataToPython("End");
+            //     UdpSocket.Instance.SendDataToPython("End");
-            }
+            // }
         }
 
         public override void Exit()
@@ -56,7 +57,6 @@ namespace GadGame.State.MainFlowState
         private async void OnScanSuccess() {
             _scanSuccess = true;
             await UniTask.Delay(TimeSpan.FromSeconds(10));
-            Runner.SetState<IdleState>();
         }
 
         private void LeaveComplete()

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

@@ -31,14 +31,14 @@ namespace GadGame.State.MainFlowState
                 return;
             }
 
-            if (!UdpSocket.Instance.DataReceived.Ready) _readyTimer = 3;
-            Runner.ReadyCountDown.Raise(_readyTimer / 3);
             _readyTimer -= Time.deltaTime;
             if (_readyTimer <= 0)
             {
                 _readyTimer = 0;
                 Runner.SetState<WaitForImageState>();
             }
+            if (!UdpSocket.Instance.DataReceived.Ready) _readyTimer = 3;
+            Runner.ReadyCountDown.Raise(_readyTimer / 3);
             if (time >= 2)
             {
                 // Runner.ReadyCountDown(_readyTimer);

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

@@ -7,8 +7,8 @@ namespace GadGame.State.MainFlowState
     {
         public override async void Enter()
         {
-            await UniTask.Delay(1000);
+            // await UniTask.Delay(1000);
-            Runner.PlayPassByAnim.Raise(false);
+            // Runner.PlayPassByAnim.Raise(false);
             Runner.PlayVideo.Raise(true);
         }
         

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

@@ -10,7 +10,7 @@ namespace GadGame.State.MainFlowState
         public override void Enter()
         {
             // await LoadSceneManager.Instance.LoadSceneWithTransitionAsync(Runner.SceneFlowConfig.PassByScene.ScenePath);
-            Runner.PlayPassByAnim.Raise(false);
+            // Runner.PlayPassByAnim.Raise(false);
         }
         
         public override void Update(float time)

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

@@ -12,7 +12,7 @@ namespace GadGame.State.MainFlowState
         public override void Update(float time)
         {
             Runner.EncodeImage.Raise(UdpSocket.Instance.DataReceived.StreamingData);
-            if (time > 3.0f)
+            if(time >= 2.0f)
             {
                 Runner.SetState<CTAState>();
             }

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

@@ -1,19 +1,23 @@
 using GadGame.Network;
-
 namespace GadGame.State.MainFlowState
 {
     public class WaitForImageState : State<MainFlow>
-
     {
-        public override void Enter()
+        private bool _isLogin;
+
+        public async override void Enter()
         {
+            _isLogin = false;
+            await P4PGraphqlManager.Instance.CreateGuest();
+            await P4PGraphqlManager.Instance.JoinPromotion();
+            await P4PGraphqlManager.Instance.SubmitGameSession(0);
+            _isLogin = true;
             Runner.EngageReady.Raise();
-            UdpSocket.Instance.SendDataToPython("Begin");
         }
 
         public override void Update(float time)
         {
-            if (UdpSocket.Instance.DataReceived.GenerateImageSuccess)
+            if (UdpSocket.Instance.DataReceived.GenerateImageSuccess && _isLogin)
             {
                 Runner.SetState<ShowImageState>();
             }

Assets/GadGame/Sprites/Coffee/Brown Coffee Shop Video Promo Instagram Reels.mp4.meta

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Assets/StreamingAssets/MergeFace/Face_Swap.meta

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Assets/StreamingAssets/MergeFace/Face_Swap/FaceSwapModule.py

@@ -0,0 +1,75 @@
+import requests
+
+
+class FaceSwap:
+    def __init__(self):
+        self.swap_url = "https://faceswap3.p.rapidapi.com/faceswap/v1/image"
+        self.result_url = "https://faceswap3.p.rapidapi.com/result/"
+        self.image_save_path = "/image/result.jpg"
+
+    def download_image_from_url(self, url, output_file_path):
+        """
+        Downloads an image from a URL and saves it to a local file.
+
+        :param url: The URL of the image to download.
+        :param output_file_path: The path where the downloaded image will be saved.
+        """
+        try:
+            # Send a GET request to the URL
+            image_response = requests.get(url)
+
+            # Check if the request was successful
+            if image_response.status_code == 200:
+                # Save the image data to a file
+                with open(output_file_path, "wb") as image_file:
+                    image_file.write(image_response.content)
+                print(f"Image saved as {output_file_path}")
+            else:
+                print(f"Failed to download image. HTTP status code: {image_response.status_code}")
+
+        except Exception as e:
+            print(f"An error occurred: {e}")
+
+    def swap_face(self, target_image):
+        payload = ("-----011000010111000001101001\r\nContent-Disposition: form-data; "
+                   f"name=\"target_url\"\r\n\r\n{target_image}\r\n"
+                   "-----011000010111000001101001\r\nContent-Disposition: form-data; "
+                   "name=\"swap_url\"\r\n\r\nhttps://storage.gadgame.com/play4promo/sid/source_file.jpg\r\n"
+                   "-----011000010111000001101001--\r\n\r\n")
+
+        headers = {
+            "x-rapidapi-key": "34c3c2de43msh9d754fc788c3d36p15c896jsn8796ea559bce",
+            "x-rapidapi-host": "faceswap3.p.rapidapi.com",
+            "Content-Type": "multipart/form-data; boundary=---011000010111000001101001"
+        }
+
+        response = requests.post(self.swap_url, data=payload, headers=headers)
+
+        result = response.json()
+
+        request_id = result["image_process_response"]["request_id"]
+
+        return request_id
+
+    def save_image_result(self, target_image):
+        image_path = "./image/merge_face.jpg"
+        request_id = self.swap_face(target_image)
+
+        payload = ("-----011000010111000001101001\r\nContent-Disposition: form-data; "
+                   f"name=\"request_id\"\r\n\r\n{request_id}\r\n-----011000010111000001101001--\r\n\r\n")
+
+        headers = {
+            "x-rapidapi-key": "34c3c2de43msh9d754fc788c3d36p15c896jsn8796ea559bce",
+            "x-rapidapi-host": "faceswap3.p.rapidapi.com",
+            "Content-Type": "multipart/form-data; boundary=---011000010111000001101001"
+        }
+
+        response = requests.post(self.result_url, data=payload, headers=headers)
+
+        print(response.json())
+
+        url_image = response.json()["image_process_response"]["result_url"]
+
+        self.download_image_from_url(url_image, image_path)
+
+        return image_path

Assets/StreamingAssets/MergeFace/Face_Swap/FaceSwapModule.py.meta

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Assets/StreamingAssets/MergeFace/Face_Swap/__init__.py

@@ -0,0 +1 @@
+from .FaceSwapModule import FaceSwap

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Assets/StreamingAssets/MergeFace/Face_facial/face_detector.py

@@ -0,0 +1,189 @@
+import joblib
+import os
+import sys
+import torch
+import torch.nn as nn
+import numpy as np
+import cv2
+import copy
+import scipy
+import pathlib
+import warnings
+
+from math import sqrt
+# sys.path.append(os.path.abspath(os.path.join(os.path.dirname("__file__"), '..')))
+from Face_facial.models.common import Conv
+from Face_facial.models.yolo import Model
+from Face_facial.utils.datasets import letterbox
+from Face_facial.utils.preprocess_utils import align_faces
+from Face_facial.utils.general import check_img_size, non_max_suppression_face, \
+    scale_coords,scale_coords_landmarks,filter_boxes
+
+class YoloDetector:
+    def __init__(self, weights_name='yolov5n_state_dict.pt', config_name='yolov5n.yaml', device='cuda:0', min_face=100, target_size=None, frontal=False):
+            """
+            weights_name: name of file with network weights in weights/ folder.
+            config_name: name of .yaml cornfig with network configuration from models/ folder.
+            device : pytorch device. Use 'cuda:0', 'cuda:1', e.t.c to use gpu or 'cpu' to use cpu.
+            min_face : minimal face size in pixels.
+            target_size : target size of smaller image axis (choose lower for faster work). e.g. 480, 720, 1080. Choose None for original resolution.
+            frontal : if True tries to filter nonfontal faces by keypoints location. CURRENTRLY UNSUPPORTED.
+            """
+            self._class_path = pathlib.Path(__file__).parent.absolute()#os.path.dirname(inspect.getfile(self.__class__))
+            self.device = device
+            self.target_size = target_size
+            self.min_face = min_face
+            self.frontal = frontal
+            if self.frontal:
+                print('Currently unavailable')
+                # self.anti_profile = joblib.load(os.path.join(self._class_path, 'models/anti_profile/anti_profile_xgb_new.pkl'))
+            self.detector = self.init_detector(weights_name,config_name)
+
+    def init_detector(self,weights_name,config_name):
+        print(self.device)
+        model_path = os.path.join(self._class_path,'weights/',weights_name)
+        print(model_path)
+        config_path = os.path.join(self._class_path,'models/',config_name)
+        state_dict = torch.load(model_path)
+        detector = Model(cfg=config_path)
+        detector.load_state_dict(state_dict)
+        detector = detector.to(self.device).float().eval()
+        for m in detector.modules():
+            if type(m) in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU]:
+                m.inplace = True  # pytorch 1.7.0 compatibility
+            elif type(m) is Conv:
+                m._non_persistent_buffers_set = set()  # pytorch 1.6.0 compatibility
+        return detector
+    
+    def _preprocess(self,imgs):
+        """
+            Preprocessing image before passing through the network. Resize and conversion to torch tensor.
+        """
+        pp_imgs = []
+        for img in imgs:
+            h0, w0 = img.shape[:2]  # orig hw
+            if self.target_size:
+                r = self.target_size / min(h0, w0)  # resize image to img_size
+                if r < 1:  
+                    img = cv2.resize(img, (int(w0 * r), int(h0 * r)), interpolation=cv2.INTER_LINEAR)
+
+            imgsz = check_img_size(max(img.shape[:2]), s=self.detector.stride.max())  # check img_size
+            img = letterbox(img, new_shape=imgsz)[0]
+            pp_imgs.append(img)
+        pp_imgs = np.array(pp_imgs)
+        pp_imgs = pp_imgs.transpose(0, 3, 1, 2)
+        pp_imgs = torch.from_numpy(pp_imgs).to(self.device)
+        pp_imgs = pp_imgs.float()  # uint8 to fp16/32
+        pp_imgs /= 255.0  # 0 - 255 to 0.0 - 1.0
+        return pp_imgs
+        
+    def _postprocess(self, imgs, origimgs, pred, conf_thres, iou_thres):
+        """
+            Postprocessing of raw pytorch model output.
+            Returns:
+                bboxes: list of arrays with 4 coordinates of bounding boxes with format x1,y1,x2,y2.
+                points: list of arrays with coordinates of 5 facial keypoints (eyes, nose, lips corners).
+        """
+        bboxes = []
+        landmarks = []
+        
+        pred = non_max_suppression_face(pred, conf_thres, iou_thres)
+        
+        for i in range(len(origimgs)):
+            img_shape = origimgs[i].shape
+            h,w = img_shape[:2]
+            gn = torch.tensor(img_shape)[[1, 0, 1, 0]]  # normalization gain whwh
+            gn_lks = torch.tensor(img_shape)[[1, 0, 1, 0, 1, 0, 1, 0, 1, 0]]  # normalization gain landmarks
+            det = pred[i].cpu()
+            scaled_bboxes = scale_coords(imgs[i].shape[1:], det[:, :4], img_shape).round()
+            scaled_cords = scale_coords_landmarks(imgs[i].shape[1:], det[:, 5:15], img_shape).round()
+
+            for j in range(det.size()[0]):
+                box = (det[j, :4].view(1, 4) / gn).view(-1).tolist()
+                box = list(map(int,[box[0]*w,box[1]*h,box[2]*w,box[3]*h]))
+                if box[3] - box[1] < self.min_face:
+                    continue
+                lm = (det[j, 5:15].view(1, 10) / gn_lks).view(-1).tolist()
+                lm = list(map(int,[i*w if j%2==0 else i*h for j,i in enumerate(lm)]))
+                lm = [lm[i:i+2] for i in range(0,len(lm),2)]
+                bboxes.append(box)
+                landmarks.append(lm)
+        return bboxes, landmarks
+
+    def get_frontal_predict(self, box, points):
+        '''
+            Make a decision whether face is frontal by keypoints.
+            Returns:
+                True if face is frontal, False otherwise.
+        '''
+        cur_points = points.astype('int')
+        x1, y1, x2, y2 = box[0:4]
+        w = x2-x1
+        h = y2-y1
+        diag = sqrt(w**2+h**2)
+        dist = scipy.spatial.distance.pdist(cur_points)/diag
+        predict = self.anti_profile.predict(dist.reshape(1, -1))[0]
+        if predict == 0:
+            return True
+        else:
+            return False
+    def align(self, img, points):
+        '''
+            Align faces, found on images.
+            Params:
+                img: Single image, used in predict method.
+                points: list of keypoints, produced in predict method.
+            Returns:
+                crops: list of croped and aligned faces of shape (112,112,3).
+        '''
+        crops = [align_faces(img,landmark=np.array(i)) for i in points]
+        return crops
+
+    def predict(self, imgs, conf_thres = 0.3, iou_thres = 0.5):
+        '''
+            Get bbox coordinates and keypoints of faces on original image.
+            Params:
+                imgs: image or list of images to detect faces on
+                conf_thres: confidence threshold for each prediction
+                iou_thres: threshold for NMS (filtering of intersecting bboxes)
+            Returns:
+                bboxes: list of arrays with 4 coordinates of bounding boxes with format x1,y1,x2,y2.
+                points: list of arrays with coordinates of 5 facial keypoints (eyes, nose, lips corners).
+        '''
+        one_by_one = False
+        # Pass input images through face detector
+        if type(imgs) != list:
+            images = [imgs]
+        else:
+            images = imgs
+            one_by_one = False
+            shapes = {arr.shape for arr in images}
+            if len(shapes) != 1:
+                one_by_one = True
+                warnings.warn(f"Can't use batch predict due to different shapes of input images. Using one by one strategy.")
+        origimgs = copy.deepcopy(images)
+        
+        
+        if one_by_one:
+            images = [self._preprocess([img]) for img in images]
+            bboxes = []
+            points = []
+            for num, img in enumerate(images):
+                with torch.inference_mode():
+                    single_pred = self.detector(img)[0]
+                bb, pt = self._postprocess(img, [origimgs[num]], single_pred, conf_thres, iou_thres)
+                bboxes.extend(bb)
+                points.extend(pt)
+        else:
+            images = self._preprocess(images)
+            with torch.inference_mode(): # change this with torch.no_grad() for pytorch <1.8 compatibility
+                pred = self.detector(images)[0]
+            bboxes, points = self._postprocess(images, origimgs, pred, conf_thres, iou_thres)
+
+        return bboxes, points
+
+    def __call__(self,*args):
+        return self.predict(*args)
+
+if __name__=='__main__':
+    a = YoloDetector()

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Assets/StreamingAssets/MergeFace/Face_facial/models/common.py

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+# This file contains modules common to various models
+
+import math
+
+import numpy as np
+import requests
+import torch
+import torch.nn as nn
+from PIL import Image, ImageDraw
+
+from Face_facial.utils.datasets import letterbox
+from Face_facial.utils.general import non_max_suppression, make_divisible, scale_coords, xyxy2xywh
+from Face_facial.utils.plots import color_list
+
+def autopad(k, p=None):  # kernel, padding
+    # Pad to 'same'
+    if p is None:
+        p = k // 2 if isinstance(k, int) else [x // 2 for x in k]  # auto-pad
+    return p
+
+def channel_shuffle(x, groups):
+    batchsize, num_channels, height, width = x.data.size()
+    channels_per_group = num_channels // groups
+
+    # reshape
+    x = x.view(batchsize, groups, channels_per_group, height, width)
+    x = torch.transpose(x, 1, 2).contiguous()
+
+    # flatten
+    x = x.view(batchsize, -1, height, width)
+    return x
+
+def DWConv(c1, c2, k=1, s=1, act=True):
+    # Depthwise convolution
+    return Conv(c1, c2, k, s, g=math.gcd(c1, c2), act=act)
+
+class Conv(nn.Module):
+    # Standard convolution
+    def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True):  # ch_in, ch_out, kernel, stride, padding, groups
+        super(Conv, self).__init__()
+        self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False)
+        self.bn = nn.BatchNorm2d(c2)
+        self.act = nn.SiLU() if act is True else (act if isinstance(act, nn.Module) else nn.Identity())
+        #self.act = self.act = nn.LeakyReLU(0.1, inplace=True) if act is True else (act if isinstance(act, nn.Module) else nn.Identity())
+
+    def forward(self, x):
+        return self.act(self.bn(self.conv(x)))
+
+    def fuseforward(self, x):
+        return self.act(self.conv(x))
+
+class StemBlock(nn.Module):
+    def __init__(self, c1, c2, k=3, s=2, p=None, g=1, act=True):
+        super(StemBlock, self).__init__()
+        self.stem_1 = Conv(c1, c2, k, s, p, g, act)
+        self.stem_2a = Conv(c2, c2 // 2, 1, 1, 0)
+        self.stem_2b = Conv(c2 // 2, c2, 3, 2, 1)
+        self.stem_2p = nn.MaxPool2d(kernel_size=2,stride=2,ceil_mode=True)
+        self.stem_3 = Conv(c2 * 2, c2, 1, 1, 0)
+
+    def forward(self, x):
+        stem_1_out  = self.stem_1(x)
+        stem_2a_out = self.stem_2a(stem_1_out)
+        stem_2b_out = self.stem_2b(stem_2a_out)
+        stem_2p_out = self.stem_2p(stem_1_out)
+        out = self.stem_3(torch.cat((stem_2b_out,stem_2p_out),1))
+        return out
+
+class Bottleneck(nn.Module):
+    # Standard bottleneck
+    def __init__(self, c1, c2, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, shortcut, groups, expansion
+        super(Bottleneck, self).__init__()
+        c_ = int(c2 * e)  # hidden channels
+        self.cv1 = Conv(c1, c_, 1, 1)
+        self.cv2 = Conv(c_, c2, 3, 1, g=g)
+        self.add = shortcut and c1 == c2
+
+    def forward(self, x):
+        return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
+
+class BottleneckCSP(nn.Module):
+    # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
+    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansion
+        super(BottleneckCSP, self).__init__()
+        c_ = int(c2 * e)  # hidden channels
+        self.cv1 = Conv(c1, c_, 1, 1)
+        self.cv2 = nn.Conv2d(c1, c_, 1, 1, bias=False)
+        self.cv3 = nn.Conv2d(c_, c_, 1, 1, bias=False)
+        self.cv4 = Conv(2 * c_, c2, 1, 1)
+        self.bn = nn.BatchNorm2d(2 * c_)  # applied to cat(cv2, cv3)
+        self.act = nn.LeakyReLU(0.1, inplace=True)
+        self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
+
+    def forward(self, x):
+        y1 = self.cv3(self.m(self.cv1(x)))
+        y2 = self.cv2(x)
+        return self.cv4(self.act(self.bn(torch.cat((y1, y2), dim=1))))
+
+
+class C3(nn.Module):
+    # CSP Bottleneck with 3 convolutions
+    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansion
+        super(C3, self).__init__()
+        c_ = int(c2 * e)  # hidden channels
+        self.cv1 = Conv(c1, c_, 1, 1)
+        self.cv2 = Conv(c1, c_, 1, 1)
+        self.cv3 = Conv(2 * c_, c2, 1)  # act=FReLU(c2)
+        self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
+
+    def forward(self, x):
+        return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), dim=1))
+
+class ShuffleV2Block(nn.Module):
+    def __init__(self, inp, oup, stride):
+        super(ShuffleV2Block, self).__init__()
+
+        if not (1 <= stride <= 3):
+            raise ValueError('illegal stride value')
+        self.stride = stride
+
+        branch_features = oup // 2
+        assert (self.stride != 1) or (inp == branch_features << 1)
+
+        if self.stride > 1:
+            self.branch1 = nn.Sequential(
+                self.depthwise_conv(inp, inp, kernel_size=3, stride=self.stride, padding=1),
+                nn.BatchNorm2d(inp),
+                nn.Conv2d(inp, branch_features, kernel_size=1, stride=1, padding=0, bias=False),
+                nn.BatchNorm2d(branch_features),
+                nn.SiLU(),
+            )
+        else:
+            self.branch1 = nn.Sequential()
+
+        self.branch2 = nn.Sequential(
+            nn.Conv2d(inp if (self.stride > 1) else branch_features, branch_features, kernel_size=1, stride=1, padding=0, bias=False),
+            nn.BatchNorm2d(branch_features),
+            nn.SiLU(),
+            self.depthwise_conv(branch_features, branch_features, kernel_size=3, stride=self.stride, padding=1),
+            nn.BatchNorm2d(branch_features),
+            nn.Conv2d(branch_features, branch_features, kernel_size=1, stride=1, padding=0, bias=False),
+            nn.BatchNorm2d(branch_features),
+            nn.SiLU(),
+        )
+
+    @staticmethod
+    def depthwise_conv(i, o, kernel_size, stride=1, padding=0, bias=False):
+        return nn.Conv2d(i, o, kernel_size, stride, padding, bias=bias, groups=i)
+
+    def forward(self, x):
+        if self.stride == 1:
+            x1, x2 = x.chunk(2, dim=1)
+            out = torch.cat((x1, self.branch2(x2)), dim=1)
+        else:
+            out = torch.cat((self.branch1(x), self.branch2(x)), dim=1)
+        out = channel_shuffle(out, 2)
+        return out
+
+class SPP(nn.Module):
+    # Spatial pyramid pooling layer used in YOLOv3-SPP
+    def __init__(self, c1, c2, k=(5, 9, 13)):
+        super(SPP, self).__init__()
+        c_ = c1 // 2  # hidden channels
+        self.cv1 = Conv(c1, c_, 1, 1)
+        self.cv2 = Conv(c_ * (len(k) + 1), c2, 1, 1)
+        self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k])
+
+    def forward(self, x):
+        x = self.cv1(x)
+        return self.cv2(torch.cat([x] + [m(x) for m in self.m], 1))
+
+
+class Focus(nn.Module):
+    # Focus wh information into c-space
+    def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True):  # ch_in, ch_out, kernel, stride, padding, groups
+        super(Focus, self).__init__()
+        self.conv = Conv(c1 * 4, c2, k, s, p, g, act)
+        # self.contract = Contract(gain=2)
+
+    def forward(self, x):  # x(b,c,w,h) -> y(b,4c,w/2,h/2)
+        return self.conv(torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1))
+        # return self.conv(self.contract(x))
+
+
+class Contract(nn.Module):
+    # Contract width-height into channels, i.e. x(1,64,80,80) to x(1,256,40,40)
+    def __init__(self, gain=2):
+        super().__init__()
+        self.gain = gain
+
+    def forward(self, x):
+        N, C, H, W = x.size()  # assert (H / s == 0) and (W / s == 0), 'Indivisible gain'
+        s = self.gain
+        x = x.view(N, C, H // s, s, W // s, s)  # x(1,64,40,2,40,2)
+        x = x.permute(0, 3, 5, 1, 2, 4).contiguous()  # x(1,2,2,64,40,40)
+        return x.view(N, C * s * s, H // s, W // s)  # x(1,256,40,40)
+
+
+class Expand(nn.Module):
+    # Expand channels into width-height, i.e. x(1,64,80,80) to x(1,16,160,160)
+    def __init__(self, gain=2):
+        super().__init__()
+        self.gain = gain
+
+    def forward(self, x):
+        N, C, H, W = x.size()  # assert C / s ** 2 == 0, 'Indivisible gain'
+        s = self.gain
+        x = x.view(N, s, s, C // s ** 2, H, W)  # x(1,2,2,16,80,80)
+        x = x.permute(0, 3, 4, 1, 5, 2).contiguous()  # x(1,16,80,2,80,2)
+        return x.view(N, C // s ** 2, H * s, W * s)  # x(1,16,160,160)
+
+
+class Concat(nn.Module):
+    # Concatenate a list of tensors along dimension
+    def __init__(self, dimension=1):
+        super(Concat, self).__init__()
+        self.d = dimension
+
+    def forward(self, x):
+        return torch.cat(x, self.d)
+
+
+class NMS(nn.Module):
+    # Non-Maximum Suppression (NMS) module
+    conf = 0.25  # confidence threshold
+    iou = 0.45  # IoU threshold
+    classes = None  # (optional list) filter by class
+
+    def __init__(self):
+        super(NMS, self).__init__()
+
+    def forward(self, x):
+        return non_max_suppression(x[0], conf_thres=self.conf, iou_thres=self.iou, classes=self.classes)
+
+class autoShape(nn.Module):
+    # input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS
+    img_size = 640  # inference size (pixels)
+    conf = 0.25  # NMS confidence threshold
+    iou = 0.45  # NMS IoU threshold
+    classes = None  # (optional list) filter by class
+
+    def __init__(self, model):
+        super(autoShape, self).__init__()
+        self.model = model.eval()
+
+    def autoshape(self):
+        print('autoShape already enabled, skipping... ')  # model already converted to model.autoshape()
+        return self
+
+    def forward(self, imgs, size=640, augment=False, profile=False):
+        # Inference from various sources. For height=720, width=1280, RGB images example inputs are:
+        #   filename:   imgs = 'data/samples/zidane.jpg'
+        #   URI:             = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/zidane.jpg'
+        #   OpenCV:          = cv2.imread('image.jpg')[:,:,::-1]  # HWC BGR to RGB x(720,1280,3)
+        #   PIL:             = Image.open('image.jpg')  # HWC x(720,1280,3)
+        #   numpy:           = np.zeros((720,1280,3))  # HWC
+        #   torch:           = torch.zeros(16,3,720,1280)  # BCHW
+        #   multiple:        = [Image.open('image1.jpg'), Image.open('image2.jpg'), ...]  # list of images
+
+        p = next(self.model.parameters())  # for device and type
+        if isinstance(imgs, torch.Tensor):  # torch
+            return self.model(imgs.to(p.device).type_as(p), augment, profile)  # inference
+
+        # Pre-process
+        n, imgs = (len(imgs), imgs) if isinstance(imgs, list) else (1, [imgs])  # number of images, list of images
+        shape0, shape1 = [], []  # image and inference shapes
+        for i, im in enumerate(imgs):
+            if isinstance(im, str):  # filename or uri
+                im = Image.open(requests.get(im, stream=True).raw if im.startswith('http') else im)  # open
+            im = np.array(im)  # to numpy
+            if im.shape[0] < 5:  # image in CHW
+                im = im.transpose((1, 2, 0))  # reverse dataloader .transpose(2, 0, 1)
+            im = im[:, :, :3] if im.ndim == 3 else np.tile(im[:, :, None], 3)  # enforce 3ch input
+            s = im.shape[:2]  # HWC
+            shape0.append(s)  # image shape
+            g = (size / max(s))  # gain
+            shape1.append([y * g for y in s])
+            imgs[i] = im  # update
+        shape1 = [make_divisible(x, int(self.stride.max())) for x in np.stack(shape1, 0).max(0)]  # inference shape
+        x = [letterbox(im, new_shape=shape1, auto=False)[0] for im in imgs]  # pad
+        x = np.stack(x, 0) if n > 1 else x[0][None]  # stack
+        x = np.ascontiguousarray(x.transpose((0, 3, 1, 2)))  # BHWC to BCHW
+        x = torch.from_numpy(x).to(p.device).type_as(p) / 255.  # uint8 to fp16/32
+
+        # Inference
+        with torch.no_grad():
+            y = self.model(x, augment, profile)[0]  # forward
+        y = non_max_suppression(y, conf_thres=self.conf, iou_thres=self.iou, classes=self.classes)  # NMS
+
+        # Post-process
+        for i in range(n):
+            scale_coords(shape1, y[i][:, :4], shape0[i])
+
+        return Detections(imgs, y, self.names)
+
+
+class Detections:
+    # detections class for YOLOv5 inference results
+    def __init__(self, imgs, pred, names=None):
+        super(Detections, self).__init__()
+        d = pred[0].device  # device
+        gn = [torch.tensor([*[im.shape[i] for i in [1, 0, 1, 0]], 1., 1.], device=d) for im in imgs]  # normalizations
+        self.imgs = imgs  # list of images as numpy arrays
+        self.pred = pred  # list of tensors pred[0] = (xyxy, conf, cls)
+        self.names = names  # class names
+        self.xyxy = pred  # xyxy pixels
+        self.xywh = [xyxy2xywh(x) for x in pred]  # xywh pixels
+        self.xyxyn = [x / g for x, g in zip(self.xyxy, gn)]  # xyxy normalized
+        self.xywhn = [x / g for x, g in zip(self.xywh, gn)]  # xywh normalized
+        self.n = len(self.pred)
+
+    def display(self, pprint=False, show=False, save=False, render=False):
+        colors = color_list()
+        for i, (img, pred) in enumerate(zip(self.imgs, self.pred)):
+            str = f'Image {i + 1}/{len(self.pred)}: {img.shape[0]}x{img.shape[1]} '
+            if pred is not None:
+                for c in pred[:, -1].unique():
+                    n = (pred[:, -1] == c).sum()  # detections per class
+                    str += f'{n} {self.names[int(c)]}s, '  # add to string
+                if show or save or render:
+                    img = Image.fromarray(img.astype(np.uint8)) if isinstance(img, np.ndarray) else img  # from np
+                    for *box, conf, cls in pred:  # xyxy, confidence, class
+                        # str += '%s %.2f, ' % (names[int(cls)], conf)  # label
+                        ImageDraw.Draw(img).rectangle(box, width=4, outline=colors[int(cls) % 10])  # plot
+            if pprint:
+                print(str)
+            if show:
+                img.show(f'Image {i}')  # show
+            if save:
+                f = f'results{i}.jpg'
+                str += f"saved to '{f}'"
+                img.save(f)  # save
+            if render:
+                self.imgs[i] = np.asarray(img)
+
+    def print(self):
+        self.display(pprint=True)  # print results
+
+    def show(self):
+        self.display(show=True)  # show results
+
+    def save(self):
+        self.display(save=True)  # save results
+
+    def render(self):
+        self.display(render=True)  # render results
+        return self.imgs
+
+    def __len__(self):
+        return self.n
+
+    def tolist(self):
+        # return a list of Detections objects, i.e. 'for result in results.tolist():'
+        x = [Detections([self.imgs[i]], [self.pred[i]], self.names) for i in range(self.n)]
+        for d in x:
+            for k in ['imgs', 'pred', 'xyxy', 'xyxyn', 'xywh', 'xywhn']:
+                setattr(d, k, getattr(d, k)[0])  # pop out of list
+        return x
+
+
+class Classify(nn.Module):
+    # Classification head, i.e. x(b,c1,20,20) to x(b,c2)
+    def __init__(self, c1, c2, k=1, s=1, p=None, g=1):  # ch_in, ch_out, kernel, stride, padding, groups
+        super(Classify, self).__init__()
+        self.aap = nn.AdaptiveAvgPool2d(1)  # to x(b,c1,1,1)
+        self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g)  # to x(b,c2,1,1)
+        self.flat = nn.Flatten()
+
+    def forward(self, x):
+        z = torch.cat([self.aap(y) for y in (x if isinstance(x, list) else [x])], 1)  # cat if list
+        return self.flat(self.conv(z))  # flatten to x(b,c2)

Assets/StreamingAssets/MergeFace/Face_facial/models/common.py.meta

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

Assets/StreamingAssets/MergeFace/Face_facial/models/experimental.py

@@ -0,0 +1,133 @@
+# This file contains experimental modules
+
+import numpy as np
+import torch
+import torch.nn as nn
+
+from Face_facial.models.common import Conv, DWConv
+from Face_facial.utils.google_utils import attempt_download
+
+
+class CrossConv(nn.Module):
+    # Cross Convolution Downsample
+    def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False):
+        # ch_in, ch_out, kernel, stride, groups, expansion, shortcut
+        super(CrossConv, self).__init__()
+        c_ = int(c2 * e)  # hidden channels
+        self.cv1 = Conv(c1, c_, (1, k), (1, s))
+        self.cv2 = Conv(c_, c2, (k, 1), (s, 1), g=g)
+        self.add = shortcut and c1 == c2
+
+    def forward(self, x):
+        return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
+
+
+class Sum(nn.Module):
+    # Weighted sum of 2 or more layers https://arxiv.org/abs/1911.09070
+    def __init__(self, n, weight=False):  # n: number of inputs
+        super(Sum, self).__init__()
+        self.weight = weight  # apply weights boolean
+        self.iter = range(n - 1)  # iter object
+        if weight:
+            self.w = nn.Parameter(-torch.arange(1., n) / 2, requires_grad=True)  # layer weights
+
+    def forward(self, x):
+        y = x[0]  # no weight
+        if self.weight:
+            w = torch.sigmoid(self.w) * 2
+            for i in self.iter:
+                y = y + x[i + 1] * w[i]
+        else:
+            for i in self.iter:
+                y = y + x[i + 1]
+        return y
+
+
+class GhostConv(nn.Module):
+    # Ghost Convolution https://github.com/huawei-noah/ghostnet
+    def __init__(self, c1, c2, k=1, s=1, g=1, act=True):  # ch_in, ch_out, kernel, stride, groups
+        super(GhostConv, self).__init__()
+        c_ = c2 // 2  # hidden channels
+        self.cv1 = Conv(c1, c_, k, s, None, g, act)
+        self.cv2 = Conv(c_, c_, 5, 1, None, c_, act)
+
+    def forward(self, x):
+        y = self.cv1(x)
+        return torch.cat([y, self.cv2(y)], 1)
+
+
+class GhostBottleneck(nn.Module):
+    # Ghost Bottleneck https://github.com/huawei-noah/ghostnet
+    def __init__(self, c1, c2, k, s):
+        super(GhostBottleneck, self).__init__()
+        c_ = c2 // 2
+        self.conv = nn.Sequential(GhostConv(c1, c_, 1, 1),  # pw
+                                  DWConv(c_, c_, k, s, act=False) if s == 2 else nn.Identity(),  # dw
+                                  GhostConv(c_, c2, 1, 1, act=False))  # pw-linear
+        self.shortcut = nn.Sequential(DWConv(c1, c1, k, s, act=False),
+                                      Conv(c1, c2, 1, 1, act=False)) if s == 2 else nn.Identity()
+
+    def forward(self, x):
+        return self.conv(x) + self.shortcut(x)
+
+
+class MixConv2d(nn.Module):
+    # Mixed Depthwise Conv https://arxiv.org/abs/1907.09595
+    def __init__(self, c1, c2, k=(1, 3), s=1, equal_ch=True):
+        super(MixConv2d, self).__init__()
+        groups = len(k)
+        if equal_ch:  # equal c_ per group
+            i = torch.linspace(0, groups - 1E-6, c2).floor()  # c2 indices
+            c_ = [(i == g).sum() for g in range(groups)]  # intermediate channels
+        else:  # equal weight.numel() per group
+            b = [c2] + [0] * groups
+            a = np.eye(groups + 1, groups, k=-1)
+            a -= np.roll(a, 1, axis=1)
+            a *= np.array(k) ** 2
+            a[0] = 1
+            c_ = np.linalg.lstsq(a, b, rcond=None)[0].round()  # solve for equal weight indices, ax = b
+
+        self.m = nn.ModuleList([nn.Conv2d(c1, int(c_[g]), k[g], s, k[g] // 2, bias=False) for g in range(groups)])
+        self.bn = nn.BatchNorm2d(c2)
+        self.act = nn.LeakyReLU(0.1, inplace=True)
+
+    def forward(self, x):
+        return x + self.act(self.bn(torch.cat([m(x) for m in self.m], 1)))
+
+
+class Ensemble(nn.ModuleList):
+    # Ensemble of models
+    def __init__(self):
+        super(Ensemble, self).__init__()
+
+    def forward(self, x, augment=False):
+        y = []
+        for module in self:
+            y.append(module(x, augment)[0])
+        # y = torch.stack(y).max(0)[0]  # max ensemble
+        # y = torch.stack(y).mean(0)  # mean ensemble
+        y = torch.cat(y, 1)  # nms ensemble
+        return y, None  # inference, train output
+
+
+def attempt_load(weights, map_location=None):
+    # Loads an ensemble of models weights=[a,b,c] or a single model weights=[a] or weights=a
+    model = Ensemble()
+    for w in weights if isinstance(weights, list) else [weights]:
+        attempt_download(w)
+        model.append(torch.load(w, map_location=map_location)['model'].float().fuse().eval())  # load FP32 model
+
+    # Compatibility updates
+    for m in model.modules():
+        if type(m) in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU]:
+            m.inplace = True  # pytorch 1.7.0 compatibility
+        elif type(m) is Conv:
+            m._non_persistent_buffers_set = set()  # pytorch 1.6.0 compatibility
+
+    if len(model) == 1:
+        return model[-1]  # return model
+    else:
+        print('Ensemble created with %s\n' % weights)
+        for k in ['names', 'stride']:
+            setattr(model, k, getattr(model[-1], k))
+        return model  # return ensemble

Assets/StreamingAssets/MergeFace/Face_facial/models/experimental.py.meta

@@ -0,0 +1,7 @@
+fileFormatVersion: 2
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+  userData: 
+  assetBundleName: 
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Assets/StreamingAssets/MergeFace/Face_facial/models/export.py

@@ -0,0 +1,71 @@
+"""Exports a YOLOv5 *.pt model to ONNX and TorchScript formats
+
+Usage:
+    $ export PYTHONPATH="$PWD" && python models/export.py --weights ./weights/yolov5s.pt --img 640 --batch 1
+"""
+
+import argparse
+import sys
+import time
+
+sys.path.append('./')  # to run '$ python *.py' files in subdirectories
+
+import torch
+import torch.nn as nn
+
+from yoloface.models.experimental import attempt_load
+from yoloface.models.common import Conv
+from yoloface.utils.activations import Hardswish, SiLU
+from yoloface.utils.general import set_logging, check_img_size
+import onnx
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--weights', type=str, default='./yolov5s.pt', help='weights path')  # from yolov5/models/
+    parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='image size')  # height, width
+    parser.add_argument('--batch-size', type=int, default=1, help='batch size')
+    opt = parser.parse_args()
+    opt.img_size *= 2 if len(opt.img_size) == 1 else 1  # expand
+    print(opt)
+    set_logging()
+    t = time.time()
+
+    # Load PyTorch model
+    model = attempt_load(opt.weights, map_location=torch.device('cpu'))  # load FP32 model
+    model.eval()
+    labels = model.names
+
+    # Checks
+    gs = int(max(model.stride))  # grid size (max stride)
+    opt.img_size = [check_img_size(x, gs) for x in opt.img_size]  # verify img_size are gs-multiples
+
+    # Input
+    img = torch.zeros(opt.batch_size, 3, *opt.img_size)  # image size(1,3,320,192) iDetection
+
+    # Update model
+    for k, m in model.named_modules():
+        m._non_persistent_buffers_set = set()  # pytorch 1.6.0 compatibility
+        if isinstance(m, Conv):  # assign export-friendly activations
+            if isinstance(m.act, nn.Hardswish):
+                m.act = Hardswish()
+            elif isinstance(m.act, nn.SiLU):
+                m.act = SiLU()
+        # elif isinstance(m, models.yolo.Detect):
+        #     m.forward = m.forward_export  # assign forward (optional)
+    model.model[-1].export = True  # set Detect() layer export=True
+    y = model(img)  # dry run
+
+    # ONNX export
+    print('\nStarting ONNX export with onnx %s...' % onnx.__version__)
+    f = opt.weights.replace('.pt', '.onnx')  # filename
+    model.fuse()  # only for ONNX
+    torch.onnx.export(model, img, f, verbose=False, opset_version=12, input_names=['data'],
+                      output_names=['stride_' + str(int(x)) for x in model.stride])
+
+    # Checks
+    onnx_model = onnx.load(f)  # load onnx model
+    onnx.checker.check_model(onnx_model)  # check onnx model
+    # print(onnx.helper.printable_graph(onnx_model.graph))  # print a human readable model
+    print('ONNX export success, saved as %s' % f)
+    # Finish
+    print('\nExport complete (%.2fs). Visualize with https://github.com/lutzroeder/netron.' % (time.time() - t))

Assets/StreamingAssets/MergeFace/Face_facial/models/export.py.meta

@@ -0,0 +1,7 @@
+fileFormatVersion: 2
+guid: 1511d937c41421e678812f05811fbee4
+DefaultImporter:
+  externalObjects: {}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 

Assets/StreamingAssets/MergeFace/Face_facial/models/yolo.py

@@ -0,0 +1,307 @@
+import argparse
+import logging
+import math
+import sys
+from copy import deepcopy
+from pathlib import Path
+
+import torch
+import torch.nn as nn
+
+from Face_facial.models.common import Conv, Bottleneck, SPP, DWConv, Focus, BottleneckCSP, C3, ShuffleV2Block, Concat, NMS, autoShape, StemBlock
+from Face_facial.models.experimental import MixConv2d, CrossConv
+from Face_facial.utils.autoanchor import check_anchor_order
+from Face_facial.utils.general import make_divisible, check_file, set_logging
+from Face_facial.utils.torch_utils import time_synchronized, fuse_conv_and_bn, model_info, scale_img, initialize_weights, \
+    select_device, copy_attr
+
+try:
+    import thop  # for FLOPS computation
+except ImportError:
+    thop = None
+
+
+class Detect(nn.Module):
+    stride = None  # strides computed during build
+    export = False  # onnx export
+
+    def __init__(self, nc=80, anchors=(), ch=()):  # detection layer
+        super(Detect, self).__init__()
+        self.nc = nc  # number of classes
+        #self.no = nc + 5  # number of outputs per anchor
+        self.no = nc + 5 + 10  # number of outputs per anchor
+
+        self.nl = len(anchors)  # number of detection layers
+        self.na = len(anchors[0]) // 2  # number of anchors
+        self.grid = [torch.zeros(1)] * self.nl  # init grid
+        a = torch.tensor(anchors).float().view(self.nl, -1, 2)
+        self.register_buffer('anchors', a)  # shape(nl,na,2)
+        self.register_buffer('anchor_grid', a.clone().view(self.nl, 1, -1, 1, 1, 2))  # shape(nl,1,na,1,1,2)
+        self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch)  # output conv
+
+    def forward(self, x):
+        # x = x.copy()  # for profiling
+        z = []  # inference output
+       # self.training |= self.export
+        if self.export:
+            for i in range(self.nl):
+                x[i] = self.m[i](x[i])
+            return x
+        for i in range(self.nl):
+            x[i] = self.m[i](x[i])  # conv
+            bs, _, ny, nx = x[i].shape  # x(bs,255,20,20) to x(bs,3,20,20,85)
+            x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()
+
+            if not self.training:  # inference
+                if self.grid[i].shape[2:4] != x[i].shape[2:4]:
+                    self.grid[i] = self._make_grid(nx, ny).to(x[i].device)
+
+                y = torch.full_like(x[i], 0)
+                y[..., [0,1,2,3,4,15]] = x[i][..., [0,1,2,3,4,15]].sigmoid()
+                y[..., 5:15] = x[i][..., 5:15]
+                #y = x[i].sigmoid()
+
+                y[..., 0:2] = (y[..., 0:2] * 2. - 0.5 + self.grid[i].to(x[i].device)) * self.stride[i]  # xy
+                y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i]  # wh
+
+                #y[..., 5:15] = y[..., 5:15] * 8 - 4
+                y[..., 5:7]   = y[..., 5:7] *   self.anchor_grid[i] + self.grid[i].to(x[i].device) * self.stride[i] # landmark x1 y1
+                y[..., 7:9]   = y[..., 7:9] *   self.anchor_grid[i] + self.grid[i].to(x[i].device) * self.stride[i]# landmark x2 y2
+                y[..., 9:11]  = y[..., 9:11] *  self.anchor_grid[i] + self.grid[i].to(x[i].device) * self.stride[i]# landmark x3 y3
+                y[..., 11:13] = y[..., 11:13] * self.anchor_grid[i] + self.grid[i].to(x[i].device) * self.stride[i]# landmark x4 y4
+                y[..., 13:15] = y[..., 13:15] * self.anchor_grid[i] + self.grid[i].to(x[i].device) * self.stride[i]# landmark x5 y5
+
+                #y[..., 5:7] = (y[..., 5:7] * 2 -1) * self.anchor_grid[i]  # landmark x1 y1
+                #y[..., 7:9] = (y[..., 7:9] * 2 -1) * self.anchor_grid[i]  # landmark x2 y2
+                #y[..., 9:11] = (y[..., 9:11] * 2 -1) * self.anchor_grid[i]  # landmark x3 y3
+                #y[..., 11:13] = (y[..., 11:13] * 2 -1) * self.anchor_grid[i]  # landmark x4 y4
+                #y[..., 13:15] = (y[..., 13:15] * 2 -1) * self.anchor_grid[i]  # landmark x5 y5
+
+                z.append(y.view(bs, -1, self.no))
+
+        return x if self.training else (torch.cat(z, 1), x)
+
+    @staticmethod
+    def _make_grid(nx=20, ny=20):
+        yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)], indexing='ij')
+        return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float()
+
+
+class Model(nn.Module):
+    def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None):  # model, input channels, number of classes
+        super(Model, self).__init__()
+        if isinstance(cfg, dict):
+            self.yaml = cfg  # model dict
+        else:  # is *.yaml
+            import yaml  # for torch hub
+            self.yaml_file = Path(cfg).name
+            with open(cfg) as f:
+                self.yaml = yaml.load(f, Loader=yaml.FullLoader)  # model dict
+
+        # Define model
+        ch = self.yaml['ch'] = self.yaml.get('ch', ch)  # input channels
+        if nc and nc != self.yaml['nc']:
+            # logger.info('Overriding model.yaml nc=%g with nc=%g' % (self.yaml['nc'], nc))
+            self.yaml['nc'] = nc  # override yaml value
+        self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch])  # model, savelist
+        self.names = [str(i) for i in range(self.yaml['nc'])]  # default names
+        # print([x.shape for x in self.forward(torch.zeros(1, ch, 64, 64))])
+
+        # Build strides, anchors
+        m = self.model[-1]  # Detect()
+        if isinstance(m, Detect):
+            s = 128  # 2x min stride
+            m.stride = torch.tensor([s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))])  # forward
+            m.anchors /= m.stride.view(-1, 1, 1)
+            check_anchor_order(m)
+            self.stride = m.stride
+            self._initialize_biases()  # only run once
+            # print('Strides: %s' % m.stride.tolist())
+
+        # Init weights, biases
+        initialize_weights(self)
+        self.info()
+        # logger.info('')
+
+    def forward(self, x, augment=False, profile=False):
+        if augment:
+            img_size = x.shape[-2:]  # height, width
+            s = [1, 0.83, 0.67]  # scales
+            f = [None, 3, None]  # flips (2-ud, 3-lr)
+            y = []  # outputs
+            for si, fi in zip(s, f):
+                xi = scale_img(x.flip(fi) if fi else x, si)
+                yi = self.forward_once(xi)[0]  # forward
+                # cv2.imwrite('img%g.jpg' % s, 255 * xi[0].numpy().transpose((1, 2, 0))[:, :, ::-1])  # save
+                yi[..., :4] /= si  # de-scale
+                if fi == 2:
+                    yi[..., 1] = img_size[0] - yi[..., 1]  # de-flip ud
+                elif fi == 3:
+                    yi[..., 0] = img_size[1] - yi[..., 0]  # de-flip lr
+                y.append(yi)
+            return torch.cat(y, 1), None  # augmented inference, train
+        else:
+            return self.forward_once(x, profile)  # single-scale inference, train
+
+    def forward_once(self, x, profile=False):
+        y, dt = [], []  # outputs
+        for m in self.model:
+            if m.f != -1:  # if not from previous layer
+                x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f]  # from earlier layers
+
+            if profile:
+                o = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2 if thop else 0  # FLOPS
+                t = time_synchronized()
+                for _ in range(10):
+                    _ = m(x)
+                dt.append((time_synchronized() - t) * 100)
+                print('%10.1f%10.0f%10.1fms %-40s' % (o, m.np, dt[-1], m.type))
+
+            x = m(x)  # run
+            y.append(x if m.i in self.save else None)  # save output
+
+        if profile:
+            print('%.1fms total' % sum(dt))
+        return x
+
+    def _initialize_biases(self, cf=None):  # initialize biases into Detect(), cf is class frequency
+        # https://arxiv.org/abs/1708.02002 section 3.3
+        # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1.
+        m = self.model[-1]  # Detect() module
+        for mi, s in zip(m.m, m.stride):  # from
+            b = mi.bias.view(m.na, -1)  # conv.bias(255) to (3,85)
+            b.data[:, 4] += math.log(8 / (640 / s) ** 2)  # obj (8 objects per 640 image)
+            b.data[:, 5:] += math.log(0.6 / (m.nc - 0.99)) if cf is None else torch.log(cf / cf.sum())  # cls
+            mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)
+
+    def _print_biases(self):
+        m = self.model[-1]  # Detect() module
+        for mi in m.m:  # from
+            b = mi.bias.detach().view(m.na, -1).T  # conv.bias(255) to (3,85)
+            print(('%6g Conv2d.bias:' + '%10.3g' * 6) % (mi.weight.shape[1], *b[:5].mean(1).tolist(), b[5:].mean()))
+
+    # def _print_weights(self):
+    #     for m in self.model.modules():
+    #         if type(m) is Bottleneck:
+    #             print('%10.3g' % (m.w.detach().sigmoid() * 2))  # shortcut weights
+
+    def fuse(self):  # fuse model Conv2d() + BatchNorm2d() layers
+        print('Fusing layers... ')
+        for m in self.model.modules():
+            if type(m) is Conv and hasattr(m, 'bn'):
+                m.conv = fuse_conv_and_bn(m.conv, m.bn)  # update conv
+                delattr(m, 'bn')  # remove batchnorm
+                m.forward = m.fuseforward  # update forward
+        self.info()
+        return self
+
+    def nms(self, mode=True):  # add or remove NMS module
+        present = type(self.model[-1]) is NMS  # last layer is NMS
+        if mode and not present:
+            print('Adding NMS... ')
+            m = NMS()  # module
+            m.f = -1  # from
+            m.i = self.model[-1].i + 1  # index
+            self.model.add_module(name='%s' % m.i, module=m)  # add
+            self.eval()
+        elif not mode and present:
+            print('Removing NMS... ')
+            self.model = self.model[:-1]  # remove
+        return self
+
+    def autoshape(self):  # add autoShape module
+        print('Adding autoShape... ')
+        m = autoShape(self)  # wrap model
+        copy_attr(m, self, include=('yaml', 'nc', 'hyp', 'names', 'stride'), exclude=())  # copy attributes
+        return m
+
+    def info(self, verbose=False, img_size=640):  # print model information
+        model_info(self, verbose, img_size)
+
+
+def parse_model(d, ch):  # model_dict, input_channels(3)
+    # logger.info('\n%3s%18s%3s%10s  %-40s%-30s' % ('', 'from', 'n', 'params', 'module', 'arguments'))
+    anchors, nc, gd, gw = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple']
+    na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors  # number of anchors
+    no = na * (nc + 5)  # number of outputs = anchors * (classes + 5)
+
+    layers, save, c2 = [], [], ch[-1]  # layers, savelist, ch out
+    for i, (f, n, m, args) in enumerate(d['backbone'] + d['head']):  # from, number, module, args
+        m = eval(m) if isinstance(m, str) else m  # eval strings
+        for j, a in enumerate(args):
+            try:
+                args[j] = eval(a) if isinstance(a, str) else a  # eval strings
+            except:
+                pass
+
+        n = max(round(n * gd), 1) if n > 1 else n  # depth gain
+        if m in [Conv, Bottleneck, SPP, DWConv, MixConv2d, Focus, CrossConv, BottleneckCSP, C3, ShuffleV2Block, StemBlock]:
+            c1, c2 = ch[f], args[0]
+
+            # Normal
+            # if i > 0 and args[0] != no:  # channel expansion factor
+            #     ex = 1.75  # exponential (default 2.0)
+            #     e = math.log(c2 / ch[1]) / math.log(2)
+            #     c2 = int(ch[1] * ex ** e)
+            # if m != Focus:
+
+            c2 = make_divisible(c2 * gw, 8) if c2 != no else c2
+
+            # Experimental
+            # if i > 0 and args[0] != no:  # channel expansion factor
+            #     ex = 1 + gw  # exponential (default 2.0)
+            #     ch1 = 32  # ch[1]
+            #     e = math.log(c2 / ch1) / math.log(2)  # level 1-n
+            #     c2 = int(ch1 * ex ** e)
+            # if m != Focus:
+            #     c2 = make_divisible(c2, 8) if c2 != no else c2
+
+            args = [c1, c2, *args[1:]]
+            if m in [BottleneckCSP, C3]:
+                args.insert(2, n)
+                n = 1
+        elif m is nn.BatchNorm2d:
+            args = [ch[f]]
+        elif m is Concat:
+            c2 = sum([ch[-1 if x == -1 else x + 1] for x in f])
+        elif m is Detect:
+            args.append([ch[x + 1] for x in f])
+            if isinstance(args[1], int):  # number of anchors
+                args[1] = [list(range(args[1] * 2))] * len(f)
+        else:
+            c2 = ch[f]
+
+        m_ = nn.Sequential(*[m(*args) for _ in range(n)]) if n > 1 else m(*args)  # module
+        t = str(m)[8:-2].replace('__main__.', '')  # module type
+        np = sum([x.numel() for x in m_.parameters()])  # number params
+        m_.i, m_.f, m_.type, m_.np = i, f, t, np  # attach index, 'from' index, type, number params
+        # logger.info('%3s%18s%3s%10.0f  %-40s%-30s' % (i, f, n, np, t, args))  # print
+        save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1)  # append to savelist
+        layers.append(m_)
+        ch.append(c2)
+    return nn.Sequential(*layers), sorted(save)
+
+
+from thop import profile
+from thop import clever_format
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--cfg', type=str, default='yolov5s.yaml', help='model.yaml')
+    parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
+    opt = parser.parse_args()
+    opt.cfg = check_file(opt.cfg)  # check file
+    set_logging()
+    device = select_device(opt.device)
+    
+    # Create model
+    model = Model(opt.cfg).to(device)
+    stride = model.stride.max()
+    if stride == 32:
+        input = torch.Tensor(1, 3, 480, 640).to(device)
+    else:
+        input = torch.Tensor(1, 3, 512, 640).to(device)
+    model.train()
+    # print(model)
+    flops, params = profile(model, inputs=(input, ))
+    flops, params = clever_format([flops, params], "%.3f")
+    print('Flops:', flops, ',Params:' ,params)

Assets/StreamingAssets/MergeFace/Face_facial/models/yolo.py.meta

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Assets/StreamingAssets/MergeFace/Face_facial/models/yolov5-0.5.yaml

@@ -0,0 +1,46 @@
+# parameters
+nc: 1  # number of classes
+depth_multiple: 1.0  # model depth multiple
+width_multiple: 0.5  # layer channel multiple
+
+# anchors
+anchors:
+  - [4,5,  8,10,  13,16]  # P3/8
+  - [23,29,  43,55,  73,105]  # P4/16
+  - [146,217,  231,300,  335,433]  # P5/32
+
+# YOLOv5 backbone
+backbone:
+  # [from, number, module, args]
+  [[-1, 1, StemBlock, [32, 3, 2]],    # 0-P2/4
+   [-1, 1, ShuffleV2Block, [128, 2]], # 1-P3/8
+   [-1, 3, ShuffleV2Block, [128, 1]], # 2
+   [-1, 1, ShuffleV2Block, [256, 2]], # 3-P4/16
+   [-1, 7, ShuffleV2Block, [256, 1]], # 4
+   [-1, 1, ShuffleV2Block, [512, 2]], # 5-P5/32
+   [-1, 3, ShuffleV2Block, [512, 1]], # 6
+  ]
+
+# YOLOv5 head
+head:
+  [[-1, 1, Conv, [128, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 4], 1, Concat, [1]],  # cat backbone P4
+   [-1, 1, C3, [128, False]],  # 10
+
+   [-1, 1, Conv, [128, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 2], 1, Concat, [1]],  # cat backbone P3
+   [-1, 1, C3, [128, False]],  # 14 (P3/8-small)
+
+   [-1, 1, Conv, [128, 3, 2]],
+   [[-1, 11], 1, Concat, [1]],  # cat head P4
+   [-1, 1, C3, [128, False]],  # 17 (P4/16-medium)
+
+   [-1, 1, Conv, [128, 3, 2]],
+   [[-1, 7], 1, Concat, [1]],  # cat head P5
+   [-1, 1, C3, [128, False]],  # 20 (P5/32-large)
+
+   [[14, 17, 20], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5)
+  ]
+          

Assets/StreamingAssets/MergeFace/Face_facial/models/yolov5-0.5.yaml.meta

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Assets/StreamingAssets/MergeFace/Face_facial/models/yolov5l.yaml

@@ -0,0 +1,47 @@
+# parameters
+nc: 1  # number of classes
+depth_multiple: 1.0  # model depth multiple
+width_multiple: 1.0  # layer channel multiple
+
+# anchors
+anchors:
+  - [4,5,  8,10,  13,16]  # P3/8
+  - [23,29,  43,55,  73,105]  # P4/16
+  - [146,217,  231,300,  335,433]  # P5/32
+
+# YOLOv5 backbone
+backbone:
+  # [from, number, module, args]
+  [[-1, 1, StemBlock, [64, 3, 2]],  # 0-P1/2
+   [-1, 3, C3, [128]],
+   [-1, 1, Conv, [256, 3, 2]],      # 2-P3/8
+   [-1, 9, C3, [256]],
+   [-1, 1, Conv, [512, 3, 2]],      # 4-P4/16
+   [-1, 9, C3, [512]],
+   [-1, 1, Conv, [1024, 3, 2]],     # 6-P5/32
+   [-1, 1, SPP, [1024, [3,5,7]]],
+   [-1, 3, C3, [1024, False]],      # 8
+  ]
+
+# YOLOv5 head
+head:
+  [[-1, 1, Conv, [512, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 5], 1, Concat, [1]],  # cat backbone P4
+   [-1, 3, C3, [512, False]],  # 12
+
+   [-1, 1, Conv, [256, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 3], 1, Concat, [1]],  # cat backbone P3
+   [-1, 3, C3, [256, False]],  # 16 (P3/8-small)
+
+   [-1, 1, Conv, [256, 3, 2]],
+   [[-1, 13], 1, Concat, [1]],  # cat head P4
+   [-1, 3, C3, [512, False]],  # 19 (P4/16-medium)
+
+   [-1, 1, Conv, [512, 3, 2]],
+   [[-1, 9], 1, Concat, [1]],  # cat head P5
+   [-1, 3, C3, [1024, False]],  # 22 (P5/32-large)
+
+   [[16, 19, 22], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5)
+  ]

Assets/StreamingAssets/MergeFace/Face_facial/models/yolov5l.yaml.meta

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Assets/StreamingAssets/MergeFace/Face_facial/models/yolov5l6.yaml

@@ -0,0 +1,60 @@
+# parameters
+nc: 1  # number of classes
+depth_multiple: 1.0  # model depth multiple
+width_multiple: 1.0  # layer channel multiple
+
+# anchors
+anchors:
+  - [6,7,  9,11,  13,16]  # P3/8
+  - [18,23,  26,33,  37,47]  # P4/16
+  - [54,67,  77,104,  112,154]  # P5/32
+  - [174,238,  258,355,  445,568]  # P6/64
+
+# YOLOv5 backbone
+backbone:
+  # [from, number, module, args]
+  [ [ -1, 1, StemBlock, [ 64, 3, 2 ] ],  # 0-P1/2
+    [ -1, 3, C3, [ 128 ] ],
+    [ -1, 1, Conv, [ 256, 3, 2 ] ],  # 2-P3/8
+    [ -1, 9, C3, [ 256 ] ],
+    [ -1, 1, Conv, [ 512, 3, 2 ] ],  # 4-P4/16
+    [ -1, 9, C3, [ 512 ] ],
+    [ -1, 1, Conv, [ 768, 3, 2 ] ],  # 6-P5/32
+    [ -1, 3, C3, [ 768 ] ],
+    [ -1, 1, Conv, [ 1024, 3, 2 ] ],  # 8-P6/64
+    [ -1, 1, SPP, [ 1024, [ 3, 5, 7 ] ] ],
+    [ -1, 3, C3, [ 1024, False ] ],  # 10
+  ]
+
+# YOLOv5 head
+head:
+  [ [ -1, 1, Conv, [ 768, 1, 1 ] ],
+    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
+    [ [ -1, 7 ], 1, Concat, [ 1 ] ],  # cat backbone P5
+    [ -1, 3, C3, [ 768, False ] ],  # 14
+
+    [ -1, 1, Conv, [ 512, 1, 1 ] ],
+    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
+    [ [ -1, 5 ], 1, Concat, [ 1 ] ],  # cat backbone P4
+    [ -1, 3, C3, [ 512, False ] ],  # 18
+
+    [ -1, 1, Conv, [ 256, 1, 1 ] ],
+    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
+    [ [ -1, 3 ], 1, Concat, [ 1 ] ],  # cat backbone P3
+    [ -1, 3, C3, [ 256, False ] ],  # 22 (P3/8-small)
+
+    [ -1, 1, Conv, [ 256, 3, 2 ] ],
+    [ [ -1, 19 ], 1, Concat, [ 1 ] ],  # cat head P4
+    [ -1, 3, C3, [ 512, False ] ],  # 25 (P4/16-medium)
+
+    [ -1, 1, Conv, [ 512, 3, 2 ] ],
+    [ [ -1, 15 ], 1, Concat, [ 1 ] ],  # cat head P5
+    [ -1, 3, C3, [ 768, False ] ],  # 28 (P5/32-large)
+
+    [ -1, 1, Conv, [ 768, 3, 2 ] ],
+    [ [ -1, 11 ], 1, Concat, [ 1 ] ],  # cat head P6
+    [ -1, 3, C3, [ 1024, False ] ],  # 31 (P6/64-xlarge)
+
+    [ [ 22, 25, 28, 31 ], 1, Detect, [ nc, anchors ] ],  # Detect(P3, P4, P5, P6)
+  ]
+

Assets/StreamingAssets/MergeFace/Face_facial/models/yolov5l6.yaml.meta

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Assets/StreamingAssets/MergeFace/Face_facial/models/yolov5m.yaml

@@ -0,0 +1,47 @@
+# parameters
+nc: 1  # number of classes
+depth_multiple: 0.67  # model depth multiple
+width_multiple: 0.75  # layer channel multiple
+
+# anchors
+anchors:
+  - [4,5,  8,10,  13,16]  # P3/8
+  - [23,29,  43,55,  73,105]  # P4/16
+  - [146,217,  231,300,  335,433]  # P5/32
+
+# YOLOv5 backbone
+backbone:
+  # [from, number, module, args]
+  [[-1, 1, StemBlock, [64, 3, 2]],  # 0-P1/2
+   [-1, 3, C3, [128]],
+   [-1, 1, Conv, [256, 3, 2]],      # 2-P3/8
+   [-1, 9, C3, [256]],
+   [-1, 1, Conv, [512, 3, 2]],      # 4-P4/16
+   [-1, 9, C3, [512]],
+   [-1, 1, Conv, [1024, 3, 2]],     # 6-P5/32
+   [-1, 1, SPP, [1024, [3,5,7]]],
+   [-1, 3, C3, [1024, False]],      # 8
+  ]
+
+# YOLOv5 head
+head:
+  [[-1, 1, Conv, [512, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 5], 1, Concat, [1]],  # cat backbone P4
+   [-1, 3, C3, [512, False]],  # 12
+
+   [-1, 1, Conv, [256, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 3], 1, Concat, [1]],  # cat backbone P3
+   [-1, 3, C3, [256, False]],  # 16 (P3/8-small)
+
+   [-1, 1, Conv, [256, 3, 2]],
+   [[-1, 13], 1, Concat, [1]],  # cat head P4
+   [-1, 3, C3, [512, False]],  # 19 (P4/16-medium)
+
+   [-1, 1, Conv, [512, 3, 2]],
+   [[-1, 9], 1, Concat, [1]],  # cat head P5
+   [-1, 3, C3, [1024, False]],  # 22 (P5/32-large)
+
+   [[16, 19, 22], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5)
+  ]

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