update
parent
426b0c34a9
commit
d6f7a97fcb
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
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@ -802,8 +802,7 @@ MonoBehaviour:
|
|||
m_OnCullStateChanged:
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||||
m_PersistentCalls:
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||||
m_Calls: []
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||||
m_text: Tinh hinh cac chi em sau khi check so du tai khoan khi thang luong tiep
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||||
theo chua den
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||||
m_text:
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||||
m_isRightToLeft: 0
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m_fontAsset: {fileID: 11400000, guid: 32ca7ffda2664c077bcf6abfc6f32d0d, type: 2}
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m_sharedMaterial: {fileID: -1949374272958031481, guid: 32ca7ffda2664c077bcf6abfc6f32d0d, type: 2}
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||||
|
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@ -1270,10 +1269,10 @@ MonoBehaviour:
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|||
m_OnCullStateChanged:
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||||
m_PersistentCalls:
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||||
m_Calls: []
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||||
m_text: Soi Ca Phe, Ra Tinh Net!
|
||||
m_text: "Soi C\xE0 Ph\xEA, Ra T\xEDnh N\u1EBFt!"
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||||
m_isRightToLeft: 0
|
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m_fontAsset: {fileID: 11400000, guid: 9fdb488a9e528566eb704ef9b2cfba44, type: 2}
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m_sharedMaterial: {fileID: -8730093448591250526, guid: 9fdb488a9e528566eb704ef9b2cfba44, type: 2}
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m_fontAsset: {fileID: 11400000, guid: 67f211977806da85ba63ff22497a20f3, type: 2}
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m_sharedMaterial: {fileID: -4438248766993109609, guid: 67f211977806da85ba63ff22497a20f3, type: 2}
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m_fontSharedMaterials: []
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m_fontMaterial: {fileID: 0}
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m_fontMaterials: []
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|
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@ -1496,7 +1495,7 @@ MonoBehaviour:
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|||
m_OnCullStateChanged:
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||||
m_PersistentCalls:
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||||
m_Calls: []
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||||
m_text: QUET DE TAI ANH VA NHAN QUA
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||||
m_text: "QU\xC9T \u0110\u1EC2 T\u1EA2I \u1EA2NH V\xC0 NH\u1EACN QU\xC0"
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m_isRightToLeft: 0
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||||
m_fontAsset: {fileID: 11400000, guid: 67f211977806da85ba63ff22497a20f3, type: 2}
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||||
m_sharedMaterial: {fileID: -4438248766993109609, guid: 67f211977806da85ba63ff22497a20f3, type: 2}
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||||
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@ -1697,6 +1696,7 @@ MonoBehaviour:
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|||
_rawImage: {fileID: 1199587752}
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||||
_scanSuccess: {fileID: 11400000, guid: 1d51ed465ae60d5499eed7fd18d21194, type: 2}
|
||||
_guestUpdatedSubscription: {fileID: 11400000, guid: f98ac02dda5623c4c82d342ee9602420, type: 2}
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||||
_descText: {fileID: 523800084}
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||||
--- !u!1 &1388120848
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GameObject:
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||||
m_ObjectHideFlags: 0
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||||
|
|
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|||
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@ -161,8 +161,8 @@ RectTransform:
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|||
m_LocalEulerAnglesHint: {x: 0, y: 0, z: 45}
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||||
m_AnchorMin: {x: 0.5, y: 0.5}
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||||
m_AnchorMax: {x: 0.5, y: 0.5}
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||||
m_AnchoredPosition: {x: -90, y: -10}
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||||
m_SizeDelta: {x: 512, y: 512}
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||||
m_AnchoredPosition: {x: -100, y: 0}
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||||
m_SizeDelta: {x: 700, y: 700}
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||||
m_Pivot: {x: 0.5, y: 0.5}
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||||
--- !u!114 &39214310
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MonoBehaviour:
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||||
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@ -354,15 +354,15 @@ RectTransform:
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|||
m_GameObject: {fileID: 226662302}
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||||
m_LocalRotation: {x: 0, y: 0, z: 0, w: 1}
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||||
m_LocalPosition: {x: 0, y: 0, z: 0}
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||||
m_LocalScale: {x: 1, y: 1, z: 1}
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||||
m_LocalScale: {x: 0.85, y: 0.85, z: 0.85}
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||||
m_ConstrainProportionsScale: 1
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||||
m_Children: []
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||||
m_Father: {fileID: 39214309}
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||||
m_LocalEulerAnglesHint: {x: 0, y: 0, z: 0}
|
||||
m_AnchorMin: {x: 0, y: 0}
|
||||
m_AnchorMax: {x: 1, y: 1}
|
||||
m_AnchoredPosition: {x: 0, y: 0}
|
||||
m_SizeDelta: {x: 0, y: 0}
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||||
m_AnchoredPosition: {x: 0, y: -31}
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||||
m_SizeDelta: {x: -100, y: -100}
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||||
m_Pivot: {x: 0.5, y: 0.5}
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||||
--- !u!114 &226662304
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MonoBehaviour:
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@ -442,8 +442,8 @@ RectTransform:
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|||
m_GameObject: {fileID: 263418221}
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||||
m_LocalRotation: {x: 0, y: 0, z: -0.38268343, w: 0.92387956}
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||||
m_LocalPosition: {x: 0, y: 0, z: 0}
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||||
m_LocalScale: {x: 1, y: 1, z: 1}
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||||
m_ConstrainProportionsScale: 0
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||||
m_LocalScale: {x: 1.2, y: 1.2, z: 1.2}
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||||
m_ConstrainProportionsScale: 1
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||||
m_Children:
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||||
- {fileID: 39214309}
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- {fileID: 1701610192}
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||||
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@ -549,7 +549,7 @@ MonoBehaviour:
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|||
m_OnCullStateChanged:
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||||
m_PersistentCalls:
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||||
m_Calls: []
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||||
m_text: <size=80>Soi Ca Phe, Ra Tinh Net!</size>
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||||
m_text: "<size=80>Soi C\xE0 Ph\xEA, Ra T\xEDnh N\u1EBFt!</size>"
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||||
m_isRightToLeft: 0
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||||
m_fontAsset: {fileID: 11400000, guid: 67f211977806da85ba63ff22497a20f3, type: 2}
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||||
m_sharedMaterial: {fileID: -4438248766993109609, guid: 67f211977806da85ba63ff22497a20f3, type: 2}
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||||
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@ -683,10 +683,11 @@ MonoBehaviour:
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|||
m_OnCullStateChanged:
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||||
m_PersistentCalls:
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||||
m_Calls: []
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||||
m_text: Nhin man hinh va khong deo khay trang nhe
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m_text: "Nh\xECn th\u1EB3ng v\xE0o m\xE0n h\xECnh v\xE0 kh\xF4ng \u0111eo kh\u1EA9u
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trang nh\xE9"
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||||
m_isRightToLeft: 0
|
||||
m_fontAsset: {fileID: 11400000, guid: 8f586378b4e144a9851e7b34d9b748ee, type: 2}
|
||||
m_sharedMaterial: {fileID: 2180264, guid: 8f586378b4e144a9851e7b34d9b748ee, type: 2}
|
||||
m_fontAsset: {fileID: 11400000, guid: 67f211977806da85ba63ff22497a20f3, type: 2}
|
||||
m_sharedMaterial: {fileID: -4438248766993109609, guid: 67f211977806da85ba63ff22497a20f3, type: 2}
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||||
m_fontSharedMaterials: []
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||||
m_fontMaterial: {fileID: 0}
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||||
m_fontMaterials: []
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||||
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@ -710,8 +711,8 @@ MonoBehaviour:
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|||
m_faceColor:
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||||
serializedVersion: 2
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||||
rgba: 4294967295
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||||
m_fontSize: 50
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||||
m_fontSizeBase: 50
|
||||
m_fontSize: 45
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||||
m_fontSizeBase: 45
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||||
m_fontWeight: 400
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||||
m_enableAutoSizing: 0
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||||
m_fontSizeMin: 18
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||||
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@ -1196,7 +1197,7 @@ RectTransform:
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|||
m_AnchorMin: {x: 0, y: 0}
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||||
m_AnchorMax: {x: 1, y: 1}
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||||
m_AnchoredPosition: {x: 0, y: 0}
|
||||
m_SizeDelta: {x: 0, y: 0}
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||||
m_SizeDelta: {x: 100, y: 100}
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||||
m_Pivot: {x: 0.5, y: 0.5}
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||||
--- !u!114 &1087577779
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MonoBehaviour:
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||||
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@ -1622,7 +1623,7 @@ RectTransform:
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|||
m_AnchorMin: {x: 0, y: 0}
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||||
m_AnchorMax: {x: 1, y: 1}
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||||
m_AnchoredPosition: {x: 0, y: 0}
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||||
m_SizeDelta: {x: 200, y: 200}
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||||
m_SizeDelta: {x: 100, y: 100}
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||||
m_Pivot: {x: 0.5, y: 0.5}
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||||
--- !u!222 &1391297539
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CanvasRenderer:
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@ -1665,7 +1666,7 @@ MonoBehaviour:
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|||
m_OnCullStateChanged:
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||||
m_PersistentCalls:
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||||
m_Calls: []
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||||
m_Sprite: {fileID: 21300000, guid: be9c33f3a6bc25e4f9ec0dcb6dd26e5e, type: 3}
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||||
m_Sprite: {fileID: 0}
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||||
m_Type: 0
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||||
m_PreserveAspect: 1
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||||
m_FillCenter: 1
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||||
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@ -18,7 +18,6 @@ namespace GadGame.Scripts
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_image = GetComponent<Image>();
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_image.preserveAspect = _preserveAspect;
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_texture = new Texture2D(1, 1);
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}
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public void LoadImage(string streamingData)
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@ -29,23 +29,23 @@ namespace GadGame.Network
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private Process _process;
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private void Start()
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{
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_process = new Process();
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_process.StartInfo.FileName = "/bin/sh";
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_process.StartInfo.Arguments = $"{Application.streamingAssetsPath}/MergeFace/run.sh";
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_process.StartInfo.WorkingDirectory = $"{Application.streamingAssetsPath}/MergeFace";
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// _process = new Process();
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// _process.StartInfo.FileName = "/bin/sh";
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// _process.StartInfo.Arguments = $"{Application.streamingAssetsPath}/MergeFace/run.sh";
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// _process.StartInfo.WorkingDirectory = $"{Application.streamingAssetsPath}/MergeFace";
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_process.StartInfo.RedirectStandardOutput = true;
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_process.StartInfo.RedirectStandardError = true;
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// _process.StartInfo.RedirectStandardOutput = true;
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// _process.StartInfo.RedirectStandardError = true;
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_process.StartInfo.CreateNoWindow = false;
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_process.StartInfo.UseShellExecute = false;
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_process.OutputDataReceived += (sender, a) => {
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Debug.Log(a.Data);
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};
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_process.ErrorDataReceived += (sender, a) => {
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Debug.LogError(a.Data);
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};
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_process.Start();
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// _process.StartInfo.CreateNoWindow = false;
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// _process.StartInfo.UseShellExecute = false;
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// _process.OutputDataReceived += (sender, a) => {
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// Debug.Log(a.Data);
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// };
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// _process.ErrorDataReceived += (sender, a) => {
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// Debug.LogError(a.Data);
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// };
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// _process.Start();
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// Create remote endpoint
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@ -110,29 +110,29 @@ namespace GadGame.Network
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_client.Close();
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Process killProcess = new Process();
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killProcess.StartInfo.FileName = "/bin/sh";
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killProcess.StartInfo.Arguments = $"{Application.streamingAssetsPath}/MergeFace/kill_process.sh";
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killProcess.StartInfo.WorkingDirectory = $"{Application.streamingAssetsPath}/MergeFace";
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// Process killProcess = new Process();
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// killProcess.StartInfo.FileName = "/bin/sh";
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// killProcess.StartInfo.Arguments = $"{Application.streamingAssetsPath}/MergeFace/kill_process.sh";
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// killProcess.StartInfo.WorkingDirectory = $"{Application.streamingAssetsPath}/MergeFace";
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killProcess.StartInfo.RedirectStandardOutput = true;
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killProcess.StartInfo.RedirectStandardError = true;
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// killProcess.StartInfo.RedirectStandardOutput = true;
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// killProcess.StartInfo.RedirectStandardError = true;
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killProcess.StartInfo.CreateNoWindow = false;
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killProcess.StartInfo.UseShellExecute = false;
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killProcess.OutputDataReceived += (sender, a) => {
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Debug.Log(a.Data);
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};
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killProcess.ErrorDataReceived += (sender, a) => {
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Debug.LogError(a.Data);
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};
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killProcess.Start();
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killProcess.BeginOutputReadLine();
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killProcess.BeginErrorReadLine();
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// killProcess.StartInfo.CreateNoWindow = false;
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// killProcess.StartInfo.UseShellExecute = false;
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// killProcess.OutputDataReceived += (sender, a) => {
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// Debug.Log(a.Data);
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// };
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// killProcess.ErrorDataReceived += (sender, a) => {
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// Debug.LogError(a.Data);
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// };
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// killProcess.Start();
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// killProcess.BeginOutputReadLine();
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// killProcess.BeginErrorReadLine();
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_process.Close();
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_process.CloseMainWindow();
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_process.WaitForExit();
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// _process.Close();
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// _process.CloseMainWindow();
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// _process.WaitForExit();
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}
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}
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}
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@ -11,10 +11,12 @@ public class QRShowNewCTA : MonoBehaviour
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[SerializeField] private RawImage _rawImage;
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[SerializeField] private VoidEvent _scanSuccess;
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[SerializeField] private GuestEvent _guestUpdatedSubscription;
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[SerializeField] private TMP_Text _descText;
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async void Start()
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{
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_descText.text = UdpSocket.Instance.DataReceived.Description;
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_rawImage.texture = await P4PGraphqlManager.Instance.GetQrLink();
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// _timer.SetDuration(60).Begin();
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}
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Binary file not shown.
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@ -49,7 +49,7 @@ class MainProgram:
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self.all_record = wks.get_all_records()
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self.client = NovitaClient("bd00a29d-86a8-4bad-9b8c-e085a5860311")
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self.client = NovitaClient("48cc2b16-286f-49c8-9581-f409b68359c4")
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self.ready_success = False
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@ -61,7 +61,7 @@ class MainProgram:
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def convertFrame(self, frame) -> str:
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encode_param = [int(cv2.IMWRITE_JPEG_QUALITY), 90]
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frame = imutils.resize(frame, width=512)
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frame = imutils.resize(frame, width=320)
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result, encoded_frame = cv2.imencode('.jpg', frame, encode_param)
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jpg_as_text = base64.b64encode(encoded_frame.tobytes())
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@ -252,14 +252,16 @@ class MainProgram:
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self.red_zone_height = (self.center_point[1] - 50, self.frame_height)
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self.face_zone_width = (self.center_point[0] - 150, self.center_point[0] + 150)
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self.face_zone_height = (self.center_point[1] - 200, self.center_point[1])
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self.face_zone_height = (self.center_point[1] - 200, self.center_point[1] + 50)
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self.face_zone_center_point = (
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int((self.face_zone_width[1] - self.face_zone_width[0]) / 2) + self.face_zone_width[0],
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int((self.face_zone_height[1] - self.face_zone_height[0]) / 2) + self.face_zone_height[0])
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int((self.face_zone_width[1] - self.face_zone_width[0]) / 2) + self.face_zone_width[0],
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int((self.face_zone_height[1] - self.face_zone_height[0]) / 2) + self.face_zone_height[0])
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ret, frame = cap.read()
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frame = cv2.flip(frame, 1)
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if not ret:
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continue
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@ -281,16 +283,16 @@ class MainProgram:
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self.sock.SendData(self.send_data_unity)
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# cv2.rectangle(self.frame_to_show, (self.face_zone_width[0], self.face_zone_height[0]),
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# (self.face_zone_width[1], self.face_zone_height[1]),
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# (0, 255, 255), 2)
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cv2.rectangle(self.frame_to_show, (self.face_zone_width[0], self.face_zone_height[0]),
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(self.face_zone_width[1], self.face_zone_height[1]),
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(0, 255, 255), 2)
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# cv2.circle(self.frame_to_show, self.face_zone_center_point, 5, (255, 255, 0), -1)
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cv2.circle(self.frame_to_show, self.face_zone_center_point, 5, (255, 255, 0), -1)
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# cv2.imshow("Output", self.frame_to_show)
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#
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# if cv2.waitKey(1) & 0xFF == ord("q"):
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# break
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cv2.imshow("Output", self.frame_to_show)
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if cv2.waitKey(1) & 0xFF == ord("q"):
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break
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cap.release()
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cv2.destroyAllWindows()
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@ -0,0 +1,323 @@
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import cv2
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import numpy as np
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||||
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||||
from lib import *
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||||
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||||
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||||
class MainProgram:
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||||
def __init__(self, face_model_path, model_path, reid_weights, tracker_type="deepocsort"):
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self.face_model_path = face_model_path
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self.model_path = model_path
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self.face_model = YOLO(face_model_path)
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self.person_model = YOLO(model_path)
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self.reid_weights = reid_weights
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self.tracker_conf = get_tracker_config(tracker_type)
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self.sock = U.UdpComms(udpIP="192.168.1.122", portTX=8000, portRX=8001, enableRX=True, suppressWarnings=True)
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self.tracker = create_tracker(
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tracker_type=tracker_type,
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tracker_config=self.tracker_conf,
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reid_weights=reid_weights,
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device='0',
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half=False,
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||||
per_class=False
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)
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||||
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||||
self.send_data_unity: dict = {
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||||
"PassBy": False,
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||||
"Engage": False,
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||||
"Ready": False,
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||||
"Gender": None,
|
||||
"AgeMin": None,
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||||
"AgeMax": None,
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||||
"GenerateImageSuccess": False,
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||||
"Description": ""
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||||
}
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||||
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||||
self.focus_id = None
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||||
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||||
self.frame_count_remove_idx = 0
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||||
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||||
sa = gspread.service_account("key.json")
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||||
sh = sa.open("TestData")
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||||
|
||||
wks = sh.worksheet("Sheet1")
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||||
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||||
self.all_record = wks.get_all_records()
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||||
|
||||
self.client = NovitaClient("48cc2b16-286f-49c8-9581-f409b68359c4")
|
||||
|
||||
self.ready_success = False
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||||
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||||
self.show_success = False
|
||||
|
||||
self.check_save, self.check_generate = False, False
|
||||
|
||||
self.forward_face = Face_detection.FaceDetection()
|
||||
|
||||
def convertFrame(self, frame) -> str:
|
||||
encode_param = [int(cv2.IMWRITE_JPEG_QUALITY), 90]
|
||||
frame = imutils.resize(frame, width=512)
|
||||
result, encoded_frame = cv2.imencode('.jpg', frame, encode_param)
|
||||
jpg_as_text = base64.b64encode(encoded_frame.tobytes())
|
||||
|
||||
return jpg_as_text.decode('utf-8')
|
||||
|
||||
def get_image(self):
|
||||
ran_num = random.randint(0, len(self.all_record) - 1)
|
||||
|
||||
image_url = self.all_record[ran_num]["Image link"]
|
||||
des = self.all_record[ran_num]["Note"]
|
||||
|
||||
return image_url, des
|
||||
|
||||
def generate_image(self):
|
||||
image_url, des = self.get_image()
|
||||
|
||||
res = self.client.merge_face(
|
||||
image=image_url,
|
||||
face_image="./image/output.jpg",
|
||||
)
|
||||
|
||||
base64_to_image(res.image_file).save("./image/merge_face.png")
|
||||
|
||||
self.send_data_unity["Description"] = des
|
||||
self.send_data_unity["GenerateImageSuccess"] = True
|
||||
self.send_data_unity["StreamingData"] = "./Assets/StreamingAssets/MergeFace/image/merge_face.png"
|
||||
|
||||
def predict_age_and_gender(self):
|
||||
image_predict = cv2.imread("./image/output.jpg")
|
||||
if AgeGenderPrediction.Prediction(image_predict):
|
||||
|
||||
self.send_data_unity["Gender"] = AgeGenderPrediction.Prediction(image_predict)[0]
|
||||
self.send_data_unity["AgeMin"] = int(
|
||||
AgeGenderPrediction.Prediction(image_predict)[1].split("-")[0])
|
||||
self.send_data_unity["AgeMax"] = int(
|
||||
AgeGenderPrediction.Prediction(image_predict)[1].split("-")[1])
|
||||
|
||||
else:
|
||||
self.send_data_unity["Gender"] = None
|
||||
self.send_data_unity["AgeMin"] = None
|
||||
self.send_data_unity["AgeMax"] = None
|
||||
|
||||
def get_face_bbox(self, frame):
|
||||
outs = self.face_model(frame)
|
||||
results = sv.Detections.from_ultralytics(outs[0])
|
||||
bbox = results.xyxy.astype(np.int_)
|
||||
conf = results.confidence.astype(np.float32)
|
||||
|
||||
return np.concatenate((bbox, conf[:, np.newaxis]), axis=1)
|
||||
|
||||
def get_person_bbox(self, frame):
|
||||
# Perform object detection with YOLOv8 class = 0 indicate person class
|
||||
outs = self.person_model(frame, classes=[0], conf=0.7)
|
||||
if not outs[0].boxes.xyxy.tolist():
|
||||
detection = np.empty((0, 6))
|
||||
# Extract relevant information from detections for boxmot
|
||||
else:
|
||||
boxes = outs[0].boxes.xyxy.tolist()
|
||||
classes = outs[0].boxes.cls.tolist()
|
||||
confidences = outs[0].boxes.conf.tolist()
|
||||
detection = np.array([box + [conf, cls] for box, conf, cls in zip(boxes, confidences, classes)])
|
||||
|
||||
return detection
|
||||
|
||||
def check_engage(self, x1, x2) -> bool:
|
||||
if not (x1 > self.red_zone_width[1] or x2 < self.red_zone_width[0]):
|
||||
return True
|
||||
|
||||
return False
|
||||
|
||||
def cropped_image(self, frame, x1, y1, x2, y2):
|
||||
return frame[y1: y2, x1: x2]
|
||||
|
||||
def calculate_dis_to_cp(self, cx, cy, face_cx, face_cy) -> float:
|
||||
return math.sqrt((face_cx - cx) ** 2 + (face_cy - cy) ** 2)
|
||||
|
||||
def check_ready(self, x1, y1, x2, y2, frame):
|
||||
person_frame = self.cropped_image(frame, x1, y1, x2, y2)
|
||||
|
||||
# out = self.face_model(person_frame)
|
||||
# results = sv.Detections.from_ultralytics(out[0])
|
||||
# bbox = results.xyxy.astype(np.int_)
|
||||
#
|
||||
# face_cx, face_cy = (int(bbox[0][0] + x1 + (bbox[0][2] - bbox[0][0]) / 2),
|
||||
# int(bbox[0][1] + y1 + (bbox[0][3] - bbox[0][1]) / 2))
|
||||
#
|
||||
# dis = self.calculate_dis_to_cp()
|
||||
|
||||
return self.forward_face.detect_face(person_frame, self.face_zone_center_point[0],
|
||||
self.face_zone_center_point[1], x1, y1)
|
||||
|
||||
def person_process(self, frame):
|
||||
# Perform person detection
|
||||
person_detections = self.get_person_bbox(frame)
|
||||
|
||||
# Update the tracker with person detections
|
||||
tracked_objects = self.tracker.update(person_detections, frame)
|
||||
|
||||
track_list = []
|
||||
|
||||
frame_to_crop = frame.copy()
|
||||
|
||||
engage = False
|
||||
|
||||
for track in tracked_objects.astype(int):
|
||||
x1, y1, x2, y2, track_id, conf, cls, _ = track
|
||||
track_list.append(track_id)
|
||||
# cv2.rectangle(self.frame_to_show, (x1, y1), (x2, y2), (0, 255, 0), 2)
|
||||
# cv2.putText(self.frame_to_show, f"ID: {track_id} Conf: {conf:.2f}", (x1, y1 - 10),
|
||||
# cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
|
||||
#
|
||||
# cv2.rectangle(self.frame_to_show, (self.red_zone_width[0], self.red_zone_height[0]),
|
||||
# (self.red_zone_width[1], self.red_zone_height[1]), (255, 0, 0), 2)
|
||||
|
||||
if not engage:
|
||||
engage = self.check_engage(x1, x2)
|
||||
|
||||
if not self.focus_id:
|
||||
self.focus_id = track_id if self.check_ready(x1, y1, x2, y2, frame_to_crop) else None
|
||||
|
||||
elif track_id != self.focus_id:
|
||||
continue
|
||||
|
||||
else:
|
||||
received_data = self.sock.ReadReceivedData()
|
||||
|
||||
if received_data == "Begin":
|
||||
self.ready_success = True
|
||||
|
||||
elif received_data == "End":
|
||||
self.ready_success = False
|
||||
self.check_save = False
|
||||
self.check_generate = False
|
||||
os.remove("./image/output.jpg")
|
||||
os.remove("./image/merge_face.png")
|
||||
self.send_data_unity: dict = {
|
||||
"PassBy": False,
|
||||
"Engage": False,
|
||||
"Ready": False,
|
||||
"Gender": None,
|
||||
"AgeMin": None,
|
||||
"AgeMax": None,
|
||||
"GenerateImageSuccess": False,
|
||||
"Description": ""
|
||||
}
|
||||
|
||||
if not self.ready_success:
|
||||
self.send_data_unity["Ready"] = True if self.check_ready(x1, y1, x2, y2, frame_to_crop) else False
|
||||
|
||||
elif not self.check_save:
|
||||
cv2.imwrite("./image/output.jpg", self.cropped_image(frame, x1, y1, x2, y2))
|
||||
self.check_save = True
|
||||
|
||||
elif not self.check_generate:
|
||||
if str(self.send_data_unity["Gender"]) == "None":
|
||||
self.predict_age_and_gender()
|
||||
|
||||
else:
|
||||
self.generate_image()
|
||||
self.check_generate = True
|
||||
|
||||
elif self.show_success:
|
||||
self.check_save = False
|
||||
self.check_generate = False
|
||||
|
||||
if track_list:
|
||||
self.send_data_unity["PassBy"] = True
|
||||
self.send_data_unity["Engage"] = engage
|
||||
|
||||
else:
|
||||
self.send_data_unity["Engage"] = False
|
||||
self.send_data_unity["PassBy"] = False
|
||||
self.send_data_unity["Ready"] = False
|
||||
|
||||
if self.focus_id not in track_list:
|
||||
if self.frame_count_remove_idx == 20:
|
||||
self.frame_count_remove_idx = 0
|
||||
self.focus_id = None
|
||||
|
||||
else:
|
||||
self.frame_count_remove_idx += 1
|
||||
|
||||
else:
|
||||
self.frame_count_remove_idx = 0
|
||||
|
||||
# cv2.putText(self.frame_to_show, f"Focus id: {self.focus_id}", (20, 20), cv2.FONT_HERSHEY_SIMPLEX,
|
||||
# 1.0, (0, 255, 255), 2)
|
||||
|
||||
def __call__(self):
|
||||
cap = cv2.VideoCapture(0)
|
||||
|
||||
while cap.isOpened():
|
||||
|
||||
self.frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
|
||||
self.frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
|
||||
|
||||
self.center_point = (int(int(self.frame_width) / 2), int(int(self.frame_height) / 2))
|
||||
|
||||
self.red_zone_width = (self.center_point[0] - 250, self.center_point[0] + 250)
|
||||
self.red_zone_height = (self.center_point[1] - 50, self.frame_height)
|
||||
|
||||
self.face_zone_width = (self.center_point[0] - 100, self.center_point[0] + 100)
|
||||
self.face_zone_height = (self.center_point[1] - 200, self.center_point[1])
|
||||
|
||||
self.face_zone_center_point = (
|
||||
int((self.face_zone_width[1] - self.face_zone_width[0]) / 2) + self.face_zone_width[0],
|
||||
int((self.face_zone_height[1] - self.face_zone_height[0]) / 2) + self.face_zone_height[0])
|
||||
|
||||
ret, frame = cap.read()
|
||||
|
||||
frame = cv2.flip(frame, 1)
|
||||
|
||||
if not ret:
|
||||
continue
|
||||
|
||||
frame_to_handle = frame.copy()
|
||||
self.frame_to_show = frame.copy()
|
||||
|
||||
try:
|
||||
self.person_process(frame_to_handle)
|
||||
|
||||
except Exception as e:
|
||||
print(e)
|
||||
|
||||
if not self.send_data_unity["GenerateImageSuccess"]:
|
||||
self.send_data_unity["StreamingData"] = self.convertFrame(self.cropped_image(frame,
|
||||
self.face_zone_width[0],
|
||||
self.face_zone_height[0],
|
||||
self.face_zone_width[1],
|
||||
self.face_zone_height[1]))
|
||||
|
||||
self.sock.SendData(self.send_data_unity)
|
||||
|
||||
cv2.rectangle(self.frame_to_show, (self.face_zone_width[0], self.face_zone_height[0]),
|
||||
(self.face_zone_width[1], self.face_zone_height[1]),
|
||||
(0, 255, 255), 2)
|
||||
|
||||
cv2.circle(self.frame_to_show, self.face_zone_center_point, 5, (255, 255, 0), -1)
|
||||
|
||||
cv2.imshow("Output", self.frame_to_show)
|
||||
|
||||
if cv2.waitKey(1) & 0xFF == ord("q"):
|
||||
break
|
||||
|
||||
cap.release()
|
||||
cv2.destroyAllWindows()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
print("Starting python...")
|
||||
face_model_path = "face_detect.pt"
|
||||
model_path = "yolov8n.pt"
|
||||
|
||||
tracker_type = "deepocsort"
|
||||
reid_weights = Path('osnet_x0_25_msmt17.pt')
|
||||
|
||||
run_main_program = MainProgram(face_model_path, model_path, reid_weights, tracker_type)
|
||||
|
||||
run_main_program()
|
||||
|
|
@ -0,0 +1,7 @@
|
|||
fileFormatVersion: 2
|
||||
guid: 0914c71de71c715788bbd30a30040d92
|
||||
DefaultImporter:
|
||||
externalObjects: {}
|
||||
userData:
|
||||
assetBundleName:
|
||||
assetBundleVariant:
|
||||
Binary file not shown.
|
After Width: | Height: | Size: 4.5 MiB |
|
|
@ -0,0 +1,7 @@
|
|||
fileFormatVersion: 2
|
||||
guid: b11869eb1d5f7a9f583dba65483f7043
|
||||
DefaultImporter:
|
||||
externalObjects: {}
|
||||
userData:
|
||||
assetBundleName:
|
||||
assetBundleVariant:
|
||||
Binary file not shown.
|
After Width: | Height: | Size: 27 KiB |
|
|
@ -0,0 +1,7 @@
|
|||
fileFormatVersion: 2
|
||||
guid: 642339e38314c58fd849560f6d2c5fbf
|
||||
DefaultImporter:
|
||||
externalObjects: {}
|
||||
userData:
|
||||
assetBundleName:
|
||||
assetBundleVariant:
|
||||
|
|
@ -28,3 +28,7 @@ import AgeGenderPrediction
|
|||
import Face_detection
|
||||
|
||||
import os
|
||||
|
||||
from facenet_pytorch import MTCNN
|
||||
import torch
|
||||
import math
|
||||
|
|
|
|||
|
|
@ -5,7 +5,7 @@ from lib import *
|
|||
|
||||
|
||||
class MainProgram:
|
||||
def __init__(self, face_model_path, model_path, reid_weights, tracker_type="deepocsort"):
|
||||
def __init__(self, face_model_path, model_path, reid_weights, tracker_type="ocsort"):
|
||||
self.face_model_path = face_model_path
|
||||
self.model_path = model_path
|
||||
|
||||
|
|
@ -49,7 +49,7 @@ class MainProgram:
|
|||
|
||||
self.all_record = wks.get_all_records()
|
||||
|
||||
self.client = NovitaClient("bd00a29d-86a8-4bad-9b8c-e085a5860311")
|
||||
self.client = NovitaClient("48cc2b16-286f-49c8-9581-f409b68359c4")
|
||||
|
||||
self.ready_success = False
|
||||
|
||||
|
|
@ -59,9 +59,14 @@ class MainProgram:
|
|||
|
||||
self.forward_face = Face_detection.FaceDetection()
|
||||
|
||||
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
|
||||
self.detector = MTCNN(keep_all=True, device=device)
|
||||
|
||||
self.count_frame = 0
|
||||
|
||||
def convertFrame(self, frame) -> str:
|
||||
encode_param = [int(cv2.IMWRITE_JPEG_QUALITY), 90]
|
||||
frame = imutils.resize(frame, width=512)
|
||||
frame = imutils.resize(frame, width=480)
|
||||
result, encoded_frame = cv2.imencode('.jpg', frame, encode_param)
|
||||
jpg_as_text = base64.b64encode(encoded_frame.tobytes())
|
||||
|
||||
|
|
@ -104,28 +109,6 @@ class MainProgram:
|
|||
self.send_data_unity["AgeMin"] = None
|
||||
self.send_data_unity["AgeMax"] = None
|
||||
|
||||
def get_face_bbox(self, frame):
|
||||
outs = self.face_model(frame)
|
||||
results = sv.Detections.from_ultralytics(outs[0])
|
||||
bbox = results.xyxy.astype(np.int_)
|
||||
conf = results.confidence.astype(np.float32)
|
||||
|
||||
return np.concatenate((bbox, conf[:, np.newaxis]), axis=1)
|
||||
|
||||
def get_person_bbox(self, frame):
|
||||
# Perform object detection with YOLOv8 class = 0 indicate person class
|
||||
outs = self.person_model(frame, classes=[0], conf=0.7)
|
||||
if not outs[0].boxes.xyxy.tolist():
|
||||
detection = np.empty((0, 6))
|
||||
# Extract relevant information from detections for boxmot
|
||||
else:
|
||||
boxes = outs[0].boxes.xyxy.tolist()
|
||||
classes = outs[0].boxes.cls.tolist()
|
||||
confidences = outs[0].boxes.conf.tolist()
|
||||
detection = np.array([box + [conf, cls] for box, conf, cls in zip(boxes, confidences, classes)])
|
||||
|
||||
return detection
|
||||
|
||||
def check_engage(self, x1, x2) -> bool:
|
||||
if not (x1 > self.red_zone_width[1] or x2 < self.red_zone_width[0]):
|
||||
return True
|
||||
|
|
@ -135,22 +118,63 @@ class MainProgram:
|
|||
def cropped_image(self, frame, x1, y1, x2, y2):
|
||||
return frame[y1: y2, x1: x2]
|
||||
|
||||
def check_ready(self, x1, y1, x2, y2, frame):
|
||||
person_frame = self.cropped_image(frame, x1, y1, x2, y2)
|
||||
def get_face(self, frame):
|
||||
boxes, probs, landmarks = self.detector.detect(frame, landmarks=True)
|
||||
|
||||
return self.forward_face.detect_face(person_frame, self.face_zone_center_point[0],
|
||||
self.face_zone_center_point[1], x1, y1)
|
||||
lm_list = []
|
||||
|
||||
for landmark in landmarks:
|
||||
x1, y1, x2, y2 = int(landmark[0][0]), int(landmark[0][1]), int(landmark[1][0]), int(landmark[2][1])
|
||||
lm_list.append([x1, y1, x2, y2])
|
||||
|
||||
if boxes is not None:
|
||||
bboxes = boxes.astype(np.int_)
|
||||
confs = probs.astype(np.float32).reshape(-1, 1)
|
||||
|
||||
# Create an array of zeros with the same length as bboxes
|
||||
zeros = np.zeros((bboxes.shape[0], 1), dtype=np.float32)
|
||||
|
||||
# Concatenate bboxes, confs, and zeros
|
||||
combined = np.hstack((lm_list, confs, zeros))
|
||||
return combined, landmarks, bboxes
|
||||
else:
|
||||
return np.array([])
|
||||
|
||||
def check_ready(self, nose, left_eye, right_eye):
|
||||
|
||||
distance_left = self.forward_face.calculate_distance(nose, left_eye)
|
||||
distance_right = self.forward_face.calculate_distance(nose, right_eye)
|
||||
|
||||
distance_to_point = self.forward_face.calculate_dis_to_cp(self.face_zone_center_point[0],
|
||||
self.face_zone_center_point[1],
|
||||
nose[0], nose[1])
|
||||
|
||||
cv2.circle(self.frame_to_show, (int(nose[0]), int(nose[1])), 5, (0, 255, 255), -1)
|
||||
cv2.circle(self.frame_to_show, (int(self.face_zone_center_point[0]), int(self.face_zone_center_point[1])), 5, (0, 255, 255), -1)
|
||||
|
||||
# Check if distances exceed threshold
|
||||
if (distance_left > K_MULTIPLIER * distance_right or distance_right > K_MULTIPLIER * distance_left or
|
||||
distance_to_point > 30):
|
||||
if self.count_frame > 200:
|
||||
self.count_frame = 0
|
||||
return False
|
||||
else:
|
||||
self.count_frame += 1
|
||||
|
||||
return True
|
||||
|
||||
def person_process(self, frame):
|
||||
# Perform person detection
|
||||
person_detections = self.get_person_bbox(frame)
|
||||
face_detections, landmarks, bboxes = self.get_face(frame)
|
||||
|
||||
# Update the tracker with person detections
|
||||
tracked_objects = self.tracker.update(person_detections, frame)
|
||||
tracked_objects = self.tracker.update(face_detections, frame)
|
||||
|
||||
track_list = []
|
||||
|
||||
frame_to_crop = frame.copy()
|
||||
face_info = []
|
||||
|
||||
nose_pose, left_eye_pose, right_eye_pos = (0, 0), (0, 0), (0, 0)
|
||||
|
||||
engage = False
|
||||
|
||||
|
|
@ -158,11 +182,23 @@ class MainProgram:
|
|||
x1, y1, x2, y2, track_id, conf, cls, _ = track
|
||||
track_list.append(track_id)
|
||||
|
||||
for idx in range(len(landmarks) - 1):
|
||||
x1_lm, y1_lm = int(landmarks[idx][0][0]), int(landmarks[idx][0][1])
|
||||
if x1_lm == x1 and y1_lm == y1:
|
||||
nose_pose, left_eye_pose, right_eye_pos = ((landmarks[idx][2][0], landmarks[idx][2][1]),
|
||||
(landmarks[idx][0][0], landmarks[idx][0][1]),
|
||||
(landmarks[idx][1][0], landmarks[idx][1][1]))
|
||||
|
||||
face_info.append((nose_pose, left_eye_pose, right_eye_pos, track_id, bboxes[idx]))
|
||||
|
||||
if not engage:
|
||||
engage = self.check_engage(x1, x2)
|
||||
|
||||
print(self.focus_id)
|
||||
|
||||
if not self.focus_id:
|
||||
self.focus_id = track_id if self.check_ready(x1, y1, x2, y2, frame_to_crop) else None
|
||||
self.focus_id = track_id \
|
||||
if self.check_ready(nose_pose, left_eye_pose, right_eye_pos) else None
|
||||
|
||||
elif track_id != self.focus_id:
|
||||
continue
|
||||
|
|
@ -191,11 +227,17 @@ class MainProgram:
|
|||
}
|
||||
|
||||
if not self.ready_success:
|
||||
self.send_data_unity["Ready"] = True if self.check_ready(x1, y1, x2, y2, frame_to_crop) else False
|
||||
self.send_data_unity["Ready"] = True if self.check_ready(nose_pose, left_eye_pose,
|
||||
right_eye_pos) else False
|
||||
|
||||
elif not self.check_save:
|
||||
cv2.imwrite("./image/output.jpg", self.cropped_image(frame, x1, y1, x2, y2))
|
||||
self.check_save = True
|
||||
for idx in range(len(face_info) - 1):
|
||||
if face_info[idx][3] == self.focus_id:
|
||||
x1_face, y1_face, x2_face, y2_face = (int(face_info[idx][4][0]), int(face_info[idx][4][1]),
|
||||
int(face_info[idx][4][2]), int(face_info[idx][4][3]))
|
||||
cv2.imwrite("./image/output.jpg",
|
||||
self.cropped_image(frame, x1_face, y1_face, x2_face, y2_face))
|
||||
self.check_save = True
|
||||
|
||||
elif not self.check_generate:
|
||||
if str(self.send_data_unity["Gender"]) == "None":
|
||||
|
|
@ -242,8 +284,8 @@ class MainProgram:
|
|||
self.red_zone_width = (self.center_point[0] - 250, self.center_point[0] + 250)
|
||||
self.red_zone_height = (self.center_point[1] - 50, self.frame_height)
|
||||
|
||||
self.face_zone_width = (self.center_point[0] - 150, self.center_point[0] + 150)
|
||||
self.face_zone_height = (self.center_point[1] - 200, self.center_point[1])
|
||||
self.face_zone_width = (self.center_point[0] - 100, self.center_point[0] + 100)
|
||||
self.face_zone_height = (self.center_point[1] - 150, self.center_point[1] + 50)
|
||||
|
||||
self.face_zone_center_point = (int((self.face_zone_width[1] - self.face_zone_width[0]) / 2) + self.face_zone_width[0],
|
||||
int((self.face_zone_height[1] - self.face_zone_height[0]) / 2) + self.face_zone_height[0])
|
||||
|
|
@ -256,6 +298,8 @@ class MainProgram:
|
|||
frame_to_handle = frame.copy()
|
||||
self.frame_to_show = frame.copy()
|
||||
|
||||
# self.person_process(frame_to_handle)
|
||||
|
||||
try:
|
||||
self.person_process(frame_to_handle)
|
||||
|
||||
|
|
@ -272,9 +316,17 @@ class MainProgram:
|
|||
|
||||
self.sock.SendData(self.send_data_unity)
|
||||
|
||||
cv2.imshow("Output", self.frame_to_show)
|
||||
|
||||
if cv2.waitKey(1) & 0xFF == ord("q"):
|
||||
break
|
||||
|
||||
cap.release()
|
||||
cv2.destroyAllWindows()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
print("Starting python...")
|
||||
K_MULTIPLIER = 1.2
|
||||
face_model_path = "face_detect.pt"
|
||||
model_path = "yolov8n.pt"
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue