From 7e5111bfaa6620343ede98988fe43397dc5cbb2a Mon Sep 17 00:00:00 2001 From: dakshitagrawal97 Date: Fri, 13 Jul 2018 23:55:19 +0530 Subject: [PATCH] add Face Extraction from MTCNN code for test dataset --- ...tractor_BB_Landmarks_Test-checkpoint.ipynb | 166 ++++++++++++++++++ ...actor_BB_Landmarks_Train-checkpoint.ipynb} | 51 ++++++ MTCNN/Face_Extractor_BB_Landmarks_Test.ipynb | 166 ++++++++++++++++++ ...> Face_Extractor_BB_Landmarks_Train.ipynb} | 51 ++++++ MTCNN/src/__pycache__/__init__.cpython-36.pyc | Bin 0 -> 254 bytes .../src/__pycache__/box_utils.cpython-36.pyc | Bin 0 -> 6109 bytes MTCNN/src/__pycache__/detector.cpython-36.pyc | Bin 0 -> 2784 bytes .../__pycache__/first_stage.cpython-36.pyc | Bin 0 -> 2626 bytes MTCNN/src/__pycache__/get_nets.cpython-36.pyc | Bin 0 -> 4933 bytes .../visualization_utils.cpython-36.pyc | Bin 0 -> 942 bytes 10 files changed, 434 insertions(+) create mode 100644 MTCNN/.ipynb_checkpoints/Face_Extractor_BB_Landmarks_Test-checkpoint.ipynb rename MTCNN/.ipynb_checkpoints/{Face_Extractor_BB_Landmarks-checkpoint.ipynb => Face_Extractor_BB_Landmarks_Train-checkpoint.ipynb} (93%) create mode 100644 MTCNN/Face_Extractor_BB_Landmarks_Test.ipynb rename MTCNN/{Face_Extractor_BB_Landmarks.ipynb => Face_Extractor_BB_Landmarks_Train.ipynb} (93%) create mode 100644 MTCNN/src/__pycache__/__init__.cpython-36.pyc create mode 100644 MTCNN/src/__pycache__/box_utils.cpython-36.pyc create mode 100644 MTCNN/src/__pycache__/detector.cpython-36.pyc create mode 100644 MTCNN/src/__pycache__/first_stage.cpython-36.pyc create mode 100644 MTCNN/src/__pycache__/get_nets.cpython-36.pyc create mode 100644 MTCNN/src/__pycache__/visualization_utils.cpython-36.pyc diff --git a/MTCNN/.ipynb_checkpoints/Face_Extractor_BB_Landmarks_Test-checkpoint.ipynb b/MTCNN/.ipynb_checkpoints/Face_Extractor_BB_Landmarks_Test-checkpoint.ipynb new file mode 100644 index 0000000..81ac812 --- /dev/null +++ b/MTCNN/.ipynb_checkpoints/Face_Extractor_BB_Landmarks_Test-checkpoint.ipynb @@ -0,0 +1,166 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Import Modules" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import warnings\n", + "warnings.filterwarnings('ignore')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from src import detect_faces, show_bboxes\n", + "from PIL import Image\n", + "\n", + "import torch\n", + "from torchvision import transforms, datasets\n", + "import numpy as np\n", + "import os" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Path Definitions" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "dataset_path = '../Dataset/emotiw/'\n", + "\n", + "processed_dataset_path = '../Dataset/FaceCoordinates/'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Load Test Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "test = sorted(os.listdir(dataset_path + 'test_shared/test/'))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "test_filelist = [x.split('.')[0] for x in test]" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['test_1', 'test_10', 'test_100', 'test_1000', 'test_1001', 'test_1002', 'test_1003', 'test_1004', 'test_1005', 'test_1006']\n" + ] + } + ], + "source": [ + "print(test_filelist[:10])" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3011\n" + ] + } + ], + "source": [ + "print(len(test_filelist))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Extract Faces from Image using MTCNN" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for i in range(len(test_filelist)):\n", + " print(test_filelist[i])\n", + " img_name = os.path.join(dataset_path, 'test_shared/test/', test_filelist[i]+ '.jpg')\n", + " image = Image.open(img_name)\n", + " try:\n", + " if os.path.isfile(processed_dataset_path + 'test/' + test_filelist[i] + '.npz'):\n", + " print(test_filelist[i] + ' Already present')\n", + " continue\n", + " bounding_boxes, landmarks = detect_faces(image)\n", + " bounding_boxes = np.asarray(bounding_boxes)\n", + " if bounding_boxes.size == 0:\n", + " print('MTCNN model handling empty face condition at ' + test_filelist[i])\n", + " np.savez(processed_dataset_path + 'test/' + test_filelist[i] , a=bounding_boxes, b=landmarks)\n", + " \n", + " except ValueError:\n", + " print('No faces detected for ' + test_filelist[i] + \". Also MTCNN failed.\")\n", + " np.savez(processed_dataset_path + 'test/' + test_filelist[i] , a=np.zeros(1), b=np.zeros(1))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/MTCNN/.ipynb_checkpoints/Face_Extractor_BB_Landmarks-checkpoint.ipynb b/MTCNN/.ipynb_checkpoints/Face_Extractor_BB_Landmarks_Train-checkpoint.ipynb similarity index 93% rename from MTCNN/.ipynb_checkpoints/Face_Extractor_BB_Landmarks-checkpoint.ipynb rename to MTCNN/.ipynb_checkpoints/Face_Extractor_BB_Landmarks_Train-checkpoint.ipynb index 03f7eee..8f73eaf 100644 --- a/MTCNN/.ipynb_checkpoints/Face_Extractor_BB_Landmarks-checkpoint.ipynb +++ b/MTCNN/.ipynb_checkpoints/Face_Extractor_BB_Landmarks_Train-checkpoint.ipynb @@ -211,6 +211,57 @@ "# Extract Faces from Image using MTCNN" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for i in range(len(training_dataset)):\n", + " image, label = training_dataset[i]\n", + " print(train_filelist[i])\n", + " try:\n", + " if label == 0:\n", + " if os.path.isfile(processed_dataset_path + 'train/Negative/' + train_filelist[i] + '.npz'):\n", + " print(train_filelist[i] + ' Already present')\n", + " continue\n", + " bounding_boxes, landmarks = detect_faces(image)\n", + " bounding_boxes = np.asarray(bounding_boxes)\n", + " if bounding_boxes.size == 0:\n", + " print('MTCNN model handling empty face condition at ' + train_filelist[i])\n", + " np.savez(processed_dataset_path + 'train/Negative/' + train_filelist[i] , a=bounding_boxes, b=landmarks)\n", + "\n", + " elif label == 1:\n", + " if os.path.isfile(processed_dataset_path + 'train/Neutral/' + train_filelist[i] + '.npz'):\n", + " print(train_filelist[i] + ' Already present')\n", + " continue\n", + " bounding_boxes, landmarks = detect_faces(image)\n", + " bounding_boxes = np.asarray(bounding_boxes)\n", + " if bounding_boxes.size == 0:\n", + " print('MTCNN model handling empty face condition at ' + train_filelist[i]) \n", + " np.savez(processed_dataset_path + 'train/Neutral/' + train_filelist[i] , a=bounding_boxes, b=landmarks)\n", + "\n", + " else:\n", + " if os.path.isfile(processed_dataset_path + 'train/Positive/' + train_filelist[i] + '.npz'):\n", + " print(train_filelist[i] + ' Already present')\n", + " continue\n", + " bounding_boxes, landmarks = detect_faces(image)\n", + " bounding_boxes = np.asarray(bounding_boxes)\n", + " if bounding_boxes.size == 0:\n", + " print('MTCNN model handling empty face condition at ' + train_filelist[i])\n", + " np.savez(processed_dataset_path + 'train/Positive/' + train_filelist[i] , a=bounding_boxes, b=landmarks)\n", + " \n", + " except ValueError:\n", + " print('No faces detected for ' + train_filelist[i] + \". Also MTCNN failed.\")\n", + " if label == 0:\n", + " np.savez(processed_dataset_path + 'train/Negative/' + train_filelist[i] , a=np.zeros(1), b=np.zeros(1))\n", + " elif label == 1:\n", + " np.savez(processed_dataset_path + 'train/Neutral/' + train_filelist[i] , a=np.zeros(1), b=np.zeros(1))\n", + " else:\n", + " np.savez(processed_dataset_path + 'train/Positive/' + train_filelist[i] , a=np.zeros(1), b=np.zeros(1))\n", + " continue" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/MTCNN/Face_Extractor_BB_Landmarks_Test.ipynb b/MTCNN/Face_Extractor_BB_Landmarks_Test.ipynb new file mode 100644 index 0000000..81ac812 --- /dev/null +++ b/MTCNN/Face_Extractor_BB_Landmarks_Test.ipynb @@ -0,0 +1,166 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Import Modules" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import warnings\n", + "warnings.filterwarnings('ignore')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from src import detect_faces, show_bboxes\n", + "from PIL import Image\n", + "\n", + "import torch\n", + "from torchvision import transforms, datasets\n", + "import numpy as np\n", + "import os" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Path Definitions" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "dataset_path = '../Dataset/emotiw/'\n", + "\n", + "processed_dataset_path = '../Dataset/FaceCoordinates/'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Load Test Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "test = sorted(os.listdir(dataset_path + 'test_shared/test/'))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "test_filelist = [x.split('.')[0] for x in test]" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['test_1', 'test_10', 'test_100', 'test_1000', 'test_1001', 'test_1002', 'test_1003', 'test_1004', 'test_1005', 'test_1006']\n" + ] + } + ], + "source": [ + "print(test_filelist[:10])" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3011\n" + ] + } + ], + "source": [ + "print(len(test_filelist))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Extract Faces from Image using MTCNN" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for i in range(len(test_filelist)):\n", + " print(test_filelist[i])\n", + " img_name = os.path.join(dataset_path, 'test_shared/test/', test_filelist[i]+ '.jpg')\n", + " image = Image.open(img_name)\n", + " try:\n", + " if os.path.isfile(processed_dataset_path + 'test/' + test_filelist[i] + '.npz'):\n", + " print(test_filelist[i] + ' Already present')\n", + " continue\n", + " bounding_boxes, landmarks = detect_faces(image)\n", + " bounding_boxes = np.asarray(bounding_boxes)\n", + " if bounding_boxes.size == 0:\n", + " print('MTCNN model handling empty face condition at ' + test_filelist[i])\n", + " np.savez(processed_dataset_path + 'test/' + test_filelist[i] , a=bounding_boxes, b=landmarks)\n", + " \n", + " except ValueError:\n", + " print('No faces detected for ' + test_filelist[i] + \". Also MTCNN failed.\")\n", + " np.savez(processed_dataset_path + 'test/' + test_filelist[i] , a=np.zeros(1), b=np.zeros(1))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/MTCNN/Face_Extractor_BB_Landmarks.ipynb b/MTCNN/Face_Extractor_BB_Landmarks_Train.ipynb similarity index 93% rename from MTCNN/Face_Extractor_BB_Landmarks.ipynb rename to MTCNN/Face_Extractor_BB_Landmarks_Train.ipynb index 03f7eee..8f73eaf 100644 --- a/MTCNN/Face_Extractor_BB_Landmarks.ipynb +++ b/MTCNN/Face_Extractor_BB_Landmarks_Train.ipynb @@ -211,6 +211,57 @@ "# Extract Faces from Image using MTCNN" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for i in range(len(training_dataset)):\n", + " image, label = training_dataset[i]\n", + " print(train_filelist[i])\n", + " try:\n", + " if label == 0:\n", + " if os.path.isfile(processed_dataset_path + 'train/Negative/' + train_filelist[i] + '.npz'):\n", + " print(train_filelist[i] + ' Already present')\n", + " continue\n", + " bounding_boxes, landmarks = detect_faces(image)\n", + " bounding_boxes = np.asarray(bounding_boxes)\n", + " if bounding_boxes.size == 0:\n", + " print('MTCNN model handling empty face condition at ' + train_filelist[i])\n", + " np.savez(processed_dataset_path + 'train/Negative/' + train_filelist[i] , a=bounding_boxes, b=landmarks)\n", + "\n", + " elif label == 1:\n", + " if os.path.isfile(processed_dataset_path + 'train/Neutral/' + train_filelist[i] + '.npz'):\n", + " print(train_filelist[i] + ' Already present')\n", + " continue\n", + " bounding_boxes, landmarks = detect_faces(image)\n", + " bounding_boxes = np.asarray(bounding_boxes)\n", + " if bounding_boxes.size == 0:\n", + " print('MTCNN model handling empty face condition at ' + train_filelist[i]) \n", + " np.savez(processed_dataset_path + 'train/Neutral/' + train_filelist[i] , a=bounding_boxes, b=landmarks)\n", + "\n", + " else:\n", + " if os.path.isfile(processed_dataset_path + 'train/Positive/' + train_filelist[i] + '.npz'):\n", + " print(train_filelist[i] + ' Already present')\n", + " continue\n", + " bounding_boxes, landmarks = detect_faces(image)\n", + " bounding_boxes = np.asarray(bounding_boxes)\n", + " if bounding_boxes.size == 0:\n", + " print('MTCNN model handling empty face condition at ' + train_filelist[i])\n", + " np.savez(processed_dataset_path + 'train/Positive/' + train_filelist[i] , a=bounding_boxes, b=landmarks)\n", + " \n", + " except ValueError:\n", + " print('No faces detected for ' + train_filelist[i] + \". Also MTCNN failed.\")\n", + " if label == 0:\n", + " np.savez(processed_dataset_path + 'train/Negative/' + train_filelist[i] , a=np.zeros(1), b=np.zeros(1))\n", + " elif label == 1:\n", + " np.savez(processed_dataset_path + 'train/Neutral/' + train_filelist[i] , a=np.zeros(1), b=np.zeros(1))\n", + " else:\n", + " np.savez(processed_dataset_path + 'train/Positive/' + train_filelist[i] , a=np.zeros(1), b=np.zeros(1))\n", + " continue" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/MTCNN/src/__pycache__/__init__.cpython-36.pyc b/MTCNN/src/__pycache__/__init__.cpython-36.pyc new file mode 100644 index 0000000000000000000000000000000000000000..1fb85c18099e09a307c6dcb57186a47654ac902c GIT binary patch literal 254 zcmXr!<>iXmp%ER#z`*brh~a<<$Z`PUVi_Qj!jQt4!;s4u#mER^GvzSlGDk53+04NV znk+9Ffl4$PZ*doAzbPnGE6rpH90>$FB8O$@eOhI^NT4iN{)$-&jgCa$LkeT-r}&y%}*)K ONwovHwiskB4Cb4N3IZ*H_Mz}wANrK1erHHs_F~s=ErG+~%$fW7 z&iT$)tERk-vc_yp2RK!F#O1@iux!-)xwiiK@tn{A1p*L_ye( zStBQk!ofQ)Rzz7Wpr#-yVi6^_SQ5*47X`n|>b0lnk!{p@lDpM!cH}?lld*5EC%K{T z1%8W$rr&FL3V7%{{5@wZVqda-F8Ge2mOAV%>#(u;E7s=15awe$;=H@3#P zaeiDF+i^a&R+-41WN{(RrMVg#7b9cajO~3scD~}H+YyT$-Lv%-SHI1^@~doIik-N0 zijB*$xoySeea;zL=3?YeF{F+t#HA<3DRzqO8{-9Cm(z8*Lvy8`u1| zA?s+_I9`d5k;AU7tTK{CPna1}J1%uN-o>~U^BwHC;HOx}*yos+^RV$#7MnQaQnQW& z+X(!N{pQH)5BqL791N5UL!9s>$8qudKy`+F=||yJ{Yga+f{_fbx=pv;3!0JZ5Bq~X zx2aTf&kfpc*liA^d(Xe%zJLG1v<trsNAoOhZeS)Z~Ttcjl{V0PQpDxmEJ@}9en%an^!k~5=s?r`cn3qo1fm^{6q!A z!NuG13)#E)aX*M~9vAP(R?zW1%5Q%9`OS@u%}}*A(a+W}@_ON=!QQvdI<2P_^as~Y z)3j)gdG_d}-w!XX_kvcl7hXeVBJ-4n@8H4jU)P_~$~Ll3#u5|zrYw}zoy6=(Kgp@4 z-;s&o50XMtb;3YJNxnH4NM9s{OoNc|d{U((FR+7=OiC?Si)JK!JV{RLK>amkqu<8z zkz-9PI=+TA@LD^GF?u&K_VA1@<9#{FX{~5jO(mO&>4`A0dd)CWWPTc^H+mPz9wnp8 zl)Iep-Gq0OoaaYUC53D-z2|kVXP}B`nwZ!g+KKQo5>_d5%d||_SmlmUHOt)QPT>qc zW71ROH51?bE!4TD#SMPeRA>#WlY++^vt+6pS z$r8r7*o2+1PO>qqVQggi0@|CfMFk+&j*Yk=ay16T+w@*#F&BA}e`1J2+gJyx*Skm)hLEV@&zGE^QXt`!m(&p()fTx*(gPr-AYnGIQ`?$G~F-iToT?3K8EnNa+EOyv_q4$Wyt7 z4MC>^2N-j>T^qJJgNrbq7!w6QT)GYl0|mFWd;A<$FC;BKq%frA52V|NqjG7R-AE1P zA(>RE)`E6Blu@`wE-&!q#Ki=|XfTYX9><5sD?$o+Tl!@KA8?k(`|9`Z{~!2z_1MvR zsugYq91l7&0nVFnW!?h?m$C(n9S0iqU5HOXqfwFq1_;rB&@d^<(V*#zE#dXUB;O6A zW@{%y2^>axZ0b0CxaeJjBq>FtAiXB}<)aW;I55$l(GqB2R+@mLyl{e_=j!_?d%B8b zN~{S*WG`;w3Ezfq2H_<)V#a)%ZyMw>A|obxo7*=xos>wAi(_Y88kfflnEnxr`OQXL+IoYAht_DZoX+_!)^{E7Z}T&Qdj-R3N-4A-m8TMws%%4jiGY|JB)D zNxMo@V4AFeIu8{}3Yx870gW1h5d9!UM7^vkRB6%pNd>H1f%Q%tgN8^>dlHQq(7sUf zd__h)Dbkke?<5aePCeKk-nxBj0Qit_gQki($bXyY>JEgAZUJyTI zg}Gf%L|x!edYx{R7-BCmB@#o7C^Jep@^UZXBfLi1J*5~dqPMhJ#Gj*PanDx?j6f$ET)AS;9587{9G6@J<%8$UN4Ugc%utf6k9_6R<;|9T8{dxrng zO7DOT3U+}agHiDNB?zL(A$%=@z5&f~zHMz<6C5LmGvi{L?^8IJ<i9ilD|*ZIKIqkDj)Ruq`P!efgI+J#rRWC= zCDrVngksoA4=i zBO_LGaWC~tamrmh;R!8^>yXMA#CsdpA^UuC5u)SqE0-xgLh!yfkoCE!DK*gAR8xGQ z1E#-HkLk4Q`P2{Pv`XqoN4<~g#A*eDJ#`YMCl`nDI)G00+Du$wVQw5;Crp?sckBDr{u!(tCIi_PA-0*@Mp_FVi1^kqU z^(#F3<7ZQ^OBxwQ5{6JBd_g2GrH;`Fj)*mO1GV#P7L*d1=icxUxGuP@|1ofQ7fqr1 z=wrfC*09c5NSkWdH(L```{nhzsSb+N&7`Qw_aMOS8IcwBJtR1eb;@fk(uqEpt0?|KuB1!Dd`7+WE+w+fr*2ewdC=5=fjcY z&2G^^VH@Zr&>q`M&%O81Q?KpcFxNo9x1M`xpJd1TK}rtQFmLqndrx}r{Upt$YSr!h z_R-@%8VJ2aM@j+eH{j(01VIEx$iw<;cm{lp$c!z|!ibo}iVCsq*>TY;#*XLczCu)r zUC)inUOBFKmAL9vb-x|e;<{IddXYG!MBKmx2~S}5O;RS6Z?V@P_&#b^ConVGYUA8_ z=(Et@j_3qKPut9`yIWM`);%r11lhK7Gl_X#?fFr-&3r+-+v#3j52)ydu^;Fr<@rJ{ zO?D|0U6FSA)3MJen6I;O((Q+gi!K*{4b;2{UA_@q(&go5(AVYFX7JOGs!TR}))a(J z!?y_UO?bHjy#gJe%n}%6K^P!yX%EchvLvfl5saBxZEqb~?vBNztPE{gm1S95Ld)m?gT0b0E1awm>k7(hPwzfLV5h!! 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