diff --git a/__pycache__/config.cpython-39.pyc b/__pycache__/config.cpython-39.pyc
index 0698941d817bfe555d127c72c721f9f90f935909..09be7823ac68d7545b66f9874e821f52c7b5cc06 100644
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diff --git a/__pycache__/faceAnalysis.cpython-39.pyc b/__pycache__/faceAnalysis.cpython-39.pyc
index a72c1c6e674ae8798d8693507826a11ca7213559..9da9aaba520f5915277c7b1f95b1d04246a2ea10 100644
Binary files a/__pycache__/faceAnalysis.cpython-39.pyc and b/__pycache__/faceAnalysis.cpython-39.pyc differ
diff --git a/__pycache__/imageProcess.cpython-39.pyc b/__pycache__/imageProcess.cpython-39.pyc
index 0aefb70d5cbcd6f5e7e3c737a2c723fca207c3cd..e7d8303a2280ab8e835ca610cec3303e6107676d 100644
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diff --git a/__pycache__/loadExpWDS.cpython-39.pyc b/__pycache__/loadExpWDS.cpython-39.pyc
index 8ffc7096932508775a674e6928b0c5b5d5eaf0f9..4963d553a1abb0d28d00a2286da369136ffbe81c 100644
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diff --git a/__pycache__/utils.cpython-39.pyc b/__pycache__/utils.cpython-39.pyc
index a5344a291dabb4584bdf2026e912330768c9dce7..e89e1b3c25003428354d2326b863319855205de8 100644
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diff --git a/buildEmotionModel.ipynb b/buildEmotionModel.ipynb
index b1a19e9c22b00ef69ae0e5289d71efcc72332843..fc0551780c275dd009c6d03baec1e57746cb102f 100644
--- a/buildEmotionModel.ipynb
+++ b/buildEmotionModel.ipynb
@@ -10,7 +10,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.9.4-final"
+ "version": "3.9.4"
},
"orig_nbformat": 2,
"kernelspec": {
@@ -78,451 +78,39 @@
]
},
{
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": [
- "#CHargement des données\n",
- "# Xf, Yf = loadFer2013Data()\n",
- "# Xr, Yr = loadRavdessData()\n",
- "# Xe, Ye = loadExpWData(90000, count=True)\n",
- "# Xa, Ya = loadAffwildData()\n",
+ "#Chargement des données\n",
+ "print(\"Array loading...\")\n",
+ "Xf = np.load(\"data/array/Xf\")\n",
+ "Xe = np.load(\"data/array/Xe\")\n",
+ "Xa = np.load(\"data/array/Xa\")\n",
+ "Xr = np.load(\"data/array/Xr\")\n",
"\n",
- "#X_train, Y_train, X_test, Y_test = mergeToDatabase([Xf, Xr, Xe, Xa], [Yf, Yr, Ye, Ya])"
- ],
- "cell_type": "code",
- "metadata": {
- "tags": [
- "outputPrepend"
- ]
- },
- "execution_count": 2,
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "ideo 62/120\n",
- "Traitement de 02-01-01-01-02-01-23.mp4, video 63/120\n",
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- "Emotion 'Calme', non prise en compte\n",
- "Traitement de 02-01-02-01-01-02-23.mp4, video 66/120\n",
- "Emotion 'Calme', non prise en compte\n",
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- "Emotion 'Calme', non prise en compte\n",
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- "Emotion 'Calme', non prise en compte\n",
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- "Emotion 'Calme', non prise en compte\n",
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- "Emotion 'Calme', non prise en compte\n",
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- "Emotion 'Calme', non prise en compte\n",
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- "Erreur pour la donnée : Aucun ou plusieurs visages détectés\n",
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- "TRAITEMENT ACTEUR N°24\n",
- "Traitement de 01-01-01-01-01-01-24.mp4, video 1/120\n",
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- "Erreur pour la donnée : Aucun ou plusieurs visages détectés\n",
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- "TRAITEMENT RAVDESS: traitement des 10282 visages détectés sur les vidéos de Ravdess...\n",
- "10282 données chargées depuis Ravdess.\n",
- "\n",
- "CHARGEMENT DE 90000 DONNEES DEPUIS EXPW...\n",
- "1000 données chargées depuis expW (sur 1000 données traités).\n",
- "2000 données chargées depuis expW (sur 2000 données traités).\n",
- "3000 données chargées depuis expW (sur 3000 données traités).\n",
- "4000 données chargées depuis expW (sur 4000 données traités).\n",
- "5000 données chargées depuis expW (sur 5000 données traités).\n",
- "6000 données chargées depuis expW (sur 6000 données traités).\n",
- "7000 données chargées depuis expW (sur 7000 données traités).\n",
- "8000 données chargées depuis expW (sur 8000 données traités).\n",
- "9000 données chargées depuis expW (sur 9000 données traités).\n",
- "10000 données chargées depuis expW (sur 10000 données traités).\n",
- "11000 données chargées depuis expW (sur 11000 données traités).\n",
- "12000 données chargées depuis expW (sur 12000 données traités).\n",
- "13000 données chargées depuis expW (sur 13000 données traités).\n",
- "14000 données chargées depuis expW (sur 14000 données traités).\n",
- "15000 données chargées depuis expW (sur 15000 données traités).\n",
- "16000 données chargées depuis expW (sur 16000 données traités).\n",
- "17000 données chargées depuis expW (sur 17000 données traités).\n",
- "18000 données chargées depuis expW (sur 18000 données traités).\n",
- "19000 données chargées depuis expW (sur 19000 données traités).\n",
- "20000 données chargées depuis expW (sur 20000 données traités).\n",
- "21000 données chargées depuis expW (sur 21000 données traités).\n",
- "22000 données chargées depuis expW (sur 22000 données traités).\n",
- "23000 données chargées depuis expW (sur 23000 données traités).\n",
- "24000 données chargées depuis expW (sur 24000 données traités).\n",
- "25000 données chargées depuis expW (sur 25000 données traités).\n",
- "26000 données chargées depuis expW (sur 26000 données traités).\n",
- "27000 données chargées depuis expW (sur 27000 données traités).\n",
- "28000 données chargées depuis expW (sur 28000 données traités).\n",
- "29000 données chargées depuis expW (sur 29000 données traités).\n",
- "30000 données chargées depuis expW (sur 30000 données traités).\n",
- "31000 données chargées depuis expW (sur 31000 données traités).\n",
- "32000 données chargées depuis expW (sur 32000 données traités).\n",
- "33000 données chargées depuis expW (sur 33000 données traités).\n",
- "34000 données chargées depuis expW (sur 34000 données traités).\n",
- "35000 données chargées depuis expW (sur 35000 données traités).\n",
- "36000 données chargées depuis expW (sur 36000 données traités).\n",
- "37000 données chargées depuis expW (sur 37000 données traités).\n",
- "38000 données chargées depuis expW (sur 38000 données traités).\n",
- "39000 données chargées depuis expW (sur 39000 données traités).\n",
- "40000 données chargées depuis expW (sur 40000 données traités).\n",
- "41000 données chargées depuis expW (sur 41000 données traités).\n",
- "42000 données chargées depuis expW (sur 42000 données traités).\n",
- "43000 données chargées depuis expW (sur 43000 données traités).\n",
- "44000 données chargées depuis expW (sur 44000 données traités).\n",
- "45000 données chargées depuis expW (sur 45000 données traités).\n",
- "46000 données chargées depuis expW (sur 46000 données traités).\n",
- "47000 données chargées depuis expW (sur 47000 données traités).\n",
- "48000 données chargées depuis expW (sur 48000 données traités).\n",
- "49000 données chargées depuis expW (sur 49000 données traités).\n",
- "50000 données chargées depuis expW (sur 50000 données traités).\n",
- "51000 données chargées depuis expW (sur 51000 données traités).\n",
- "52000 données chargées depuis expW (sur 52000 données traités).\n",
- "53000 données chargées depuis expW (sur 53000 données traités).\n",
- "54000 données chargées depuis expW (sur 54000 données traités).\n",
- "55000 données chargées depuis expW (sur 55000 données traités).\n",
- "56000 données chargées depuis expW (sur 56000 données traités).\n",
- "57000 données chargées depuis expW (sur 57000 données traités).\n",
- "58000 données chargées depuis expW (sur 58000 données traités).\n",
- "59000 données chargées depuis expW (sur 59000 données traités).\n",
- "60000 données chargées depuis expW (sur 60000 données traités).\n",
- "61000 données chargées depuis expW (sur 61000 données traités).\n",
- "62000 données chargées depuis expW (sur 62000 données traités).\n",
- "63000 données chargées depuis expW (sur 63000 données traités).\n",
- "64000 données chargées depuis expW (sur 64000 données traités).\n",
- "65000 données chargées depuis expW (sur 65000 données traités).\n",
- "66000 données chargées depuis expW (sur 66000 données traités).\n",
- "67000 données chargées depuis expW (sur 67000 données traités).\n",
- "68000 données chargées depuis expW (sur 68000 données traités).\n",
- "69000 données chargées depuis expW (sur 69000 données traités).\n",
- "70000 données chargées depuis expW (sur 70000 données traités).\n",
- "71000 données chargées depuis expW (sur 71000 données traités).\n",
- "72000 données chargées depuis expW (sur 72000 données traités).\n",
- "73000 données chargées depuis expW (sur 73000 données traités).\n",
- "74000 données chargées depuis expW (sur 74000 données traités).\n",
- "75000 données chargées depuis expW (sur 75000 données traités).\n",
- "76000 données chargées depuis expW (sur 76000 données traités).\n",
- "77000 données chargées depuis expW (sur 77000 données traités).\n",
- "78000 données chargées depuis expW (sur 78000 données traités).\n",
- "79000 données chargées depuis expW (sur 79000 données traités).\n",
- "80000 données chargées depuis expW (sur 80000 données traités).\n",
- "81000 données chargées depuis expW (sur 81000 données traités).\n",
- "82000 données chargées depuis expW (sur 82000 données traités).\n",
- "83000 données chargées depuis expW (sur 83000 données traités).\n",
- "84000 données chargées depuis expW (sur 84000 données traités).\n",
- "85000 données chargées depuis expW (sur 85000 données traités).\n",
- "86000 données chargées depuis expW (sur 86000 données traités).\n",
- "87000 données chargées depuis expW (sur 87000 données traités).\n",
- "88000 données chargées depuis expW (sur 88000 données traités).\n",
- "89000 données chargées depuis expW (sur 89000 données traités).\n",
- "90000 données chargées depuis expW (sur 90000 données traités).\n",
- "\n",
- "\n",
- "\n",
- "CHARGEMENT DE 10000000000 DONNEES DEPUIS AFFWILD...\n",
- "Traitement de 1-30-1280x720.mp4, video 1/79\n",
- "Traitement de 108-15-640x480.mp4, video 2/79\n",
- "Traitement de 111-25-1920x1080.mp4, video 3/79\n",
- "Traitement de 117-25-1920x1080.mp4, video 4/79\n",
- "Traitement de 118-30-640x480.mp4, video 5/79\n",
- "Traitement de 121-24-1920x1080.mp4, video 6/79\n",
- "Traitement de 122-60-1920x1080-5.mp4, video 7/79\n",
- "Traitement de 126-30-1080x1920.mp4, video 8/79\n",
- "Traitement de 13-30-1920x1080.mp4, video 9/79\n",
- "Traitement de 130-25-1280x720.mp4, video 10/79\n",
- "Traitement de 132-30-426x240.mp4, video 11/79\n",
- "Traitement de 133-30-1280x720.mp4, video 12/79\n",
- "Traitement de 134-30-1280x720.mp4, video 13/79\n",
- "Traitement de 135-24-1920x1080.mp4, video 14/79\n",
- "Traitement de 136-30-1920x1080.mp4, video 15/79\n",
- "Traitement de 138-30-1280x720.mp4, video 16/79\n",
- "Traitement de 139-14-720x480.mp4, video 17/79\n",
- "Traitement de 14-30-1920x1080.mp4, video 18/79\n",
- "Traitement de 16-30-1920x1080.mp4, video 19/79\n",
- "Traitement de 18-24-1920x1080.mp4, video 20/79\n",
- "Traitement de 198.avi, video 21/79\n",
- "Traitement de 20-24-1920x1080.mp4, video 22/79\n",
- "Traitement de 207.mp4, video 23/79\n",
- "Traitement de 21-24-1920x1080.mp4, video 24/79\n",
- "Traitement de 212.mp4, video 25/79\n",
- "Traitement de 221.mp4, video 26/79\n",
- "Traitement de 225.mp4, video 27/79\n",
- "Traitement de 24-30-1920x1080-2.mp4, video 28/79\n",
- "Traitement de 28-30-1280x720-1.mp4, video 29/79\n",
- "Traitement de 28-30-1280x720-2.mp4, video 30/79\n",
- "Traitement de 28-30-1280x720-3.mp4, video 31/79\n",
- "Traitement de 28-30-1280x720-4.mp4, video 32/79\n",
- "Traitement de 282.mp4, video 33/79\n",
- "Traitement de 38-30-1920x1080.mp4, video 34/79\n",
- "Traitement de 40-30-1280x720.mp4, video 35/79\n",
- "Traitement de 43-30-406x720.mp4, video 36/79\n",
- "Traitement de 44-25-426x240.mp4, video 37/79\n",
- "Traitement de 45-24-1280x720.mp4, video 38/79\n",
- "Traitement de 46-30-484x360.mp4, video 39/79\n",
- "Traitement de 58-30-640x480.mp4, video 40/79\n",
- "Traitement de 6-30-1920x1080.mp4, video 41/79\n",
- "Traitement de 7-60-1920x1080.mp4, video 42/79\n",
- "Traitement de 79-30-960x720.mp4, video 43/79\n",
- "Traitement de 8-30-1280x720.mp4, video 44/79\n",
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- "Traitement de 9-15-1920x1080.mp4, video 48/79\n",
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- "Traitement de 99-30-720x720.mp4, video 50/79\n",
- "Traitement de video24.mp4, video 51/79\n",
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- "Traitement de video4.mp4, video 53/79\n",
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- "Traitement de video45_1.mp4, video 55/79\n",
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- "Traitement de video95.mp4, video 79/79\n",
- "TRAITEMENT AFFWILD: traitement des 6495 visages détectés sur les vidéos de AffWild...\n",
- "6495 données chargées depuis AffWild.\n"
- ]
- },
- {
- "output_type": "error",
- "ename": "MemoryError",
- "evalue": "",
- "traceback": [
- "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[1;31mMemoryError\u001b[0m Traceback (most recent call last)",
- "\u001b[1;32m<ipython-input-2-3417f05429fb>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mXa\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mYa\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mloadAffwildData\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m \u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mY_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mY_test\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmergeToDatabase\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mXf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mXr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mXe\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mXa\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mYf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mYr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mYe\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mYa\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
- "\u001b[1;32mc:\\Users\\timot\\facial-expression-detection\\utils.py\u001b[0m in \u001b[0;36mmergeToDatabase\u001b[1;34m(listOfX, listOfY, validation_repart)\u001b[0m\n\u001b[0;32m 69\u001b[0m \u001b[0mlistOfY_test\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mY_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 70\u001b[0m \u001b[1;31m# Merge\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 71\u001b[1;33m \u001b[0mBigX_train\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mstackImages\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlistOfX_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 72\u001b[0m \u001b[0mBigY_train\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mstackImages\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlistOfY_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 73\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
- "\u001b[1;32mc:\\Users\\timot\\facial-expression-detection\\utils.py\u001b[0m in \u001b[0;36mstackImages\u001b[1;34m(listOfArrayImage)\u001b[0m\n\u001b[0;32m 46\u001b[0m \u001b[0mliste\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 47\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mX\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mlistOfArrayImage\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 48\u001b[1;33m \u001b[0mliste\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 49\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstack\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mliste\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 50\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
- "\u001b[1;31mMemoryError\u001b[0m: "
- ]
- }
+ "Yf = np.load(\"data/array/Yf\")\n",
+ "Ye = np.load(\"data/array/Ye\")\n",
+ "Ya = np.load(\"data/array/Ya\")\n",
+ "Yr = np.load(\"data/array/Yr\")\n",
+ "\n",
+ "print(\"Concatenation...\")\n",
+ "X = np.concatenate([Xf, Xa, Xe, Xr])\n",
+ "Y = np.concatenate([Yf, Ya, Ye, Yr])"
]
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "output_type": "error",
- "ename": "MemoryError",
- "evalue": "",
- "traceback": [
- "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[1;31mMemoryError\u001b[0m Traceback (most recent call last)",
- "\u001b[1;32m<ipython-input-12-1df8f39f5f0e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mX_train\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mstackImages\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mXa\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mXr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mXe\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m5000\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mXf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m5000\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0mX_test\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mstackImages\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mXe\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m25000\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mXf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m35000\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
- "\u001b[1;32mc:\\Users\\timot\\facial-expression-detection\\utils.py\u001b[0m in \u001b[0;36mstackImages\u001b[1;34m(listOfArrayImage)\u001b[0m\n\u001b[0;32m 46\u001b[0m \u001b[0mliste\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 47\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mX\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mlistOfArrayImage\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 48\u001b[1;33m \u001b[0mliste\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 49\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstack\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mliste\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 50\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
- "\u001b[1;31mMemoryError\u001b[0m: "
- ]
- }
- ],
+ "outputs": [],
"source": [
- "\n",
- "X_train = stackImages([Xa, Xr])\n",
- "X_test = stackImages([Xe[25000:], Xf[35000:]])"
+ "#Enregistre X et Y directement, à faire si assez de ram\n",
+ "np.save(\"data/array/X\", X)\n",
+ "np.save(\"data/array/Y\", Y)\n",
+ "def loadData():\n",
+ " return np.load(\"data/array/X\"), np.load(\"data/array/Y\")"
]
},
{
@@ -553,21 +141,9 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "output_type": "error",
- "ename": "NameError",
- "evalue": "name 'X_train' is not defined",
- "traceback": [
- "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
- "\u001b[1;32m<ipython-input-5-6d0d9ec25cf6>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m#Visualisation du dataset global\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"X_train:\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX_train\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Y_train:\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mY_train\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"\\nX_test:\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
- "\u001b[1;31mNameError\u001b[0m: name 'X_train' is not defined"
- ]
- }
- ],
+ "outputs": [],
"source": [
"#Visualisation du dataset global\n",
"print(\"X_train:\", X_train.shape)\n",
@@ -593,44 +169,32 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#@title Hyperparamètres\n",
- "epochs = \n",
+ "epochs = 2\n",
"batch_size = 128\n",
"validation_size = 0.1"
]
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "X_train: (81000, 48, 48, 1)\nY_train: (81000, 7)\n\nX_test_cat: (9001, 48, 48, 1)\nY_test_cat: (9001, 7)\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"#Labels catégoriques\n",
- "Y_train_cat = keras.utils.to_categorical(Y_train)\n",
- "Y_test_cat = keras.utils.to_categorical(Y_test)\n",
- "\n",
- "print(\"X_train:\", X_train.shape)\n",
- "print(\"Y_train:\", Y_train_cat.shape)\n",
+ "Ycat = keras.utils.to_categorical(Y)\n",
"\n",
- "print(\"\\nX_test_cat:\", X_test.shape)\n",
- "print(\"Y_test_cat:\", Y_test_cat.shape)"
+ "print(\"X\", X_train.shape)\n",
+ "print(\"Y\", Y_train_cat.shape)"
]
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -639,24 +203,39 @@
"\n",
" def __init__(self, input_shape):\n",
" super(MyModel, self).__init__()\n",
+ " #Pre processing\n",
+ " self.add(keras.layers.experimental.preprocessing.RandomContrast(factor=(0.5,0.5)))\n",
+ " self.add(keras.layers.experimental.preprocessing.RandomFlip(mode=\"horizontal\"))\n",
+ " \n",
+ " #48*48 *1\n",
" self.add(keras.layers.Conv2D(32, kernel_size = (3, 3), activation = 'relu', input_shape = input_shape)) \n",
" self.add(keras.layers.MaxPooling2D(pool_size = 2))\n",
" self.add(keras.layers.BatchNormalization())\n",
"\n",
+ " #23*23 *32\n",
" self.add(keras.layers.Conv2D(64, kernel_size = (3, 3), activation = 'relu'))\n",
" self.add(keras.layers.MaxPooling2D(pool_size = 2))\n",
" self.add(keras.layers.BatchNormalization())\n",
"\n",
- " self.add(keras.layers.Conv2D(96, kernel_size = (3, 3), activation = 'relu'))\n",
+ " #10*10 *64\n",
+ " self.add(keras.layers.Conv2D(128, kernel_size = (3, 3), activation = 'relu'))\n",
" self.add(keras.layers.MaxPooling2D(pool_size = 2))\n",
" self.add(keras.layers.BatchNormalization())\n",
"\n",
- " self.add(keras.layers.Flatten())\n",
+ " #4*4 *128\n",
+ " self.add(keras.layers.Conv2D(256, kernel_size = (3, 3), activation = 'relu'))\n",
+ " self.add(keras.layers.MaxPooling2D(pool_size = 2))\n",
+ " self.add(keras.layers.BatchNormalization())\n",
"\n",
+ " #1*1 *256\n",
+ " self.add(keras.layers.Flatten())\n",
+ " self.add(keras.layers.Dense(128, activation = 'relu'))\n",
+ " self.add(keras.layers.Dropout(0.2))\n",
" self.add(keras.layers.Dense(64, activation = 'relu'))\n",
- " self.add(keras.layers.BatchNormalization())\n",
- " \n",
+ " self.add(keras.layers.Dropout(0.2))\n",
+ " #self.add(keras.layers.BatchNormalization())\n",
" self.add(keras.layers.Dense(7, activation = 'softmax'))\n",
+ " #7\n",
" \n",
" def predir(self, monImage):\n",
" return self.predict(np.array([monImage]))[0,:]\n",
@@ -670,50 +249,22 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "[0.15433358 0.14506394 0.16671737 0.1411671 0.13488784 0.13087346\n 0.12695667]\n"
- ]
- },
- {
- "output_type": "display_data",
- "data": {
- "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
- },
- "metadata": {
- "needs_background": "light"
- }
- }
- ],
+ "outputs": [],
"source": [
- "theImage = X_train[5]\n",
+ "theImage = X_train[0]\n",
"afficher(theImage)\n",
"print(predir(myModel, theImage))"
]
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "Epoch 1/5\n",
- " 880/2532 [=========>....................] - ETA: 1:01 - loss: 0.8081 - accuracy: 0.7104"
- ]
- }
- ],
+ "outputs": [],
"source": [
- "history = myModel.fit(X_train, Y_train_cat, epochs=epochs, validation_data=(X_test, Y_test_cat))\n",
+ "history = myModel.fit(X, Y, epochs=5, validation_rate=0.05)\n",
"\n",
"#Affichage de l'historique de l'apprentissage\n",
"plt.plot(history.history['accuracy'], label='accuracy')\n",
@@ -725,19 +276,11 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "INFO:tensorflow:Assets written to: exp901\\assets\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
- "myModel.save('exp901')"
+ "myModel.save('exp904')"
]
},
{
diff --git a/buildEmotionModel2.ipynb b/buildEmotionModel2.ipynb
index b1a19e9c22b00ef69ae0e5289d71efcc72332843..5bedc2075e5b1c89220184b0133487334f638c6b 100644
--- a/buildEmotionModel2.ipynb
+++ b/buildEmotionModel2.ipynb
@@ -10,7 +10,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.9.4-final"
+ "version": "3.9.4"
},
"orig_nbformat": 2,
"kernelspec": {
@@ -77,452 +77,67 @@
"from config import *"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([[1, 2, 3, 4],\n",
+ " [1, 2, 3, 4],\n",
+ " [1, 2, 3, 4],\n",
+ " [1, 2, 3, 4]])"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 14
+ }
+ ],
+ "source": [
+ "a = np.array([[1,2], [3,4]])\t \n",
+ "np.resize(a, (4,4))"
+ ]
+ },
{
"source": [
"#CHargement des données\n",
- "# Xf, Yf = loadFer2013Data()\n",
- "# Xr, Yr = loadRavdessData()\n",
- "# Xe, Ye = loadExpWData(90000, count=True)\n",
- "# Xa, Ya = loadAffwildData()\n",
+ "Xf, Yf = loadFer2013Data()\n",
+ "Xr, Yr = loadRavdessData()\n",
+ "Xe, Ye = loadExpWData(90000, count=True)\n",
+ "Xa, Ya = loadAffwildData()\n",
"\n",
- "#X_train, Y_train, X_test, Y_test = mergeToDatabase([Xf, Xr, Xe, Xa], [Yf, Yr, Ye, Ya])"
+ "X_train, Y_train, X_test, Y_test = mergeToDatabase([Xf, Xr, Xe, Xa], [Yf, Yr, Ye, Ya])"
],
"cell_type": "code",
"metadata": {
- "tags": [
- "outputPrepend"
- ]
+ "tags": []
},
- "execution_count": 2,
+ "execution_count": 3,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
- "ideo 62/120\n",
- "Traitement de 02-01-01-01-02-01-23.mp4, video 63/120\n",
- "Traitement de 02-01-01-01-02-02-23.mp4, video 64/120\n",
- "Traitement de 02-01-02-01-01-01-23.mp4, video 65/120\n",
- "Emotion 'Calme', non prise en compte\n",
- "Traitement de 02-01-02-01-01-02-23.mp4, video 66/120\n",
- "Emotion 'Calme', non prise en compte\n",
- "Traitement de 02-01-02-01-02-01-23.mp4, video 67/120\n",
- "Emotion 'Calme', non prise en compte\n",
- "Traitement de 02-01-02-01-02-02-23.mp4, video 68/120\n",
- "Emotion 'Calme', non prise en compte\n",
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- "Emotion 'Calme', non prise en compte\n",
- "Traitement de 02-01-02-02-01-02-23.mp4, video 70/120\n",
- "Emotion 'Calme', non prise en compte\n",
- "Traitement de 02-01-02-02-02-01-23.mp4, video 71/120\n",
- "Emotion 'Calme', non prise en compte\n",
- "Traitement de 02-01-02-02-02-02-23.mp4, video 72/120\n",
- "Emotion 'Calme', non prise en compte\n",
- "Traitement de 02-01-03-01-01-01-23.mp4, video 73/120\n",
- "Traitement de 02-01-03-01-01-02-23.mp4, video 74/120\n",
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- "Erreur pour la donnée : Aucun ou plusieurs visages détectés\n",
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- "Traitement de 02-01-08-02-01-01-23.mp4, video 117/120\n",
- "Traitement de 02-01-08-02-01-02-23.mp4, video 118/120\n",
- "Traitement de 02-01-08-02-02-01-23.mp4, video 119/120\n",
- "Traitement de 02-01-08-02-02-02-23.mp4, video 120/120\n",
- "TRAITEMENT ACTEUR N°24\n",
- "Traitement de 01-01-01-01-01-01-24.mp4, video 1/120\n",
- "Traitement de 01-01-01-01-01-02-24.mp4, video 2/120\n",
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- "Traitement de 01-01-02-01-01-01-24.mp4, video 5/120\n",
- "Emotion 'Calme', non prise en compte\n",
- "Traitement de 01-01-02-01-01-02-24.mp4, video 6/120\n",
- "Emotion 'Calme', non prise en compte\n",
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- "Emotion 'Calme', non prise en compte\n",
- "Traitement de 01-01-02-01-02-02-24.mp4, video 8/120\n",
- "Emotion 'Calme', non prise en compte\n",
- "Traitement de 01-01-02-02-01-01-24.mp4, video 9/120\n",
- "Emotion 'Calme', non prise en compte\n",
- "Traitement de 01-01-02-02-01-02-24.mp4, video 10/120\n",
- "Emotion 'Calme', non prise en compte\n",
- "Traitement de 01-01-02-02-02-01-24.mp4, video 11/120\n",
- "Emotion 'Calme', non prise en compte\n",
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- "Emotion 'Calme', non prise en compte\n",
- "Traitement de 01-01-03-01-01-01-24.mp4, video 13/120\n",
- "Traitement de 01-01-03-01-01-02-24.mp4, video 14/120\n",
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- "Traitement de 01-01-06-02-02-01-24.mp4, video 43/120\n",
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- "Traitement de 01-01-07-01-01-01-24.mp4, video 45/120\n",
- "Traitement de 01-01-07-01-01-02-24.mp4, video 46/120\n",
- "Traitement de 01-01-07-01-02-01-24.mp4, video 47/120\n",
- "Traitement de 01-01-07-01-02-02-24.mp4, video 48/120\n",
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- "Traitement de 02-01-01-01-01-01-24.mp4, video 61/120\n",
- "Traitement de 02-01-01-01-01-02-24.mp4, video 62/120\n",
- "Traitement de 02-01-01-01-02-01-24.mp4, video 63/120\n",
- "Traitement de 02-01-01-01-02-02-24.mp4, video 64/120\n",
- "Traitement de 02-01-02-01-01-01-24.mp4, video 65/120\n",
- "Emotion 'Calme', non prise en compte\n",
- "Traitement de 02-01-02-01-01-02-24.mp4, video 66/120\n",
- "Emotion 'Calme', non prise en compte\n",
- "Traitement de 02-01-02-01-02-01-24.mp4, video 67/120\n",
- "Emotion 'Calme', non prise en compte\n",
- "Traitement de 02-01-02-01-02-02-24.mp4, video 68/120\n",
- "Emotion 'Calme', non prise en compte\n",
- "Traitement de 02-01-02-02-01-01-24.mp4, video 69/120\n",
- "Emotion 'Calme', non prise en compte\n",
- "Traitement de 02-01-02-02-01-02-24.mp4, video 70/120\n",
- "Emotion 'Calme', non prise en compte\n",
- "Traitement de 02-01-02-02-02-01-24.mp4, video 71/120\n",
- "Emotion 'Calme', non prise en compte\n",
- "Traitement de 02-01-02-02-02-02-24.mp4, video 72/120\n",
- "Emotion 'Calme', non prise en compte\n",
- "Traitement de 02-01-03-01-01-01-24.mp4, video 73/120\n",
- "Traitement de 02-01-03-01-01-02-24.mp4, video 74/120\n",
- "Traitement de 02-01-03-01-02-01-24.mp4, video 75/120\n",
- "Traitement de 02-01-03-01-02-02-24.mp4, video 76/120\n",
- "Traitement de 02-01-03-02-01-01-24.mp4, video 77/120\n",
- "Traitement de 02-01-03-02-01-02-24.mp4, video 78/120\n",
- "Traitement de 02-01-03-02-02-01-24.mp4, video 79/120\n",
- "Traitement de 02-01-03-02-02-02-24.mp4, video 80/120\n",
- "Traitement de 02-01-04-01-01-01-24.mp4, video 81/120\n",
- "Erreur pour la donnée : Aucun ou plusieurs visages détectés\n",
- "Traitement de 02-01-04-01-01-02-24.mp4, video 82/120\n",
- "Traitement de 02-01-04-01-02-01-24.mp4, video 83/120\n",
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- "Traitement de 02-01-04-02-01-02-24.mp4, video 86/120\n",
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- "Traitement de 02-01-05-01-01-02-24.mp4, video 90/120\n",
- "Traitement de 02-01-05-01-02-01-24.mp4, video 91/120\n",
- "Traitement de 02-01-05-01-02-02-24.mp4, video 92/120\n",
- "Traitement de 02-01-05-02-01-01-24.mp4, video 93/120\n",
- "Traitement de 02-01-05-02-01-02-24.mp4, video 94/120\n",
- "Traitement de 02-01-05-02-02-01-24.mp4, video 95/120\n",
- "Traitement de 02-01-05-02-02-02-24.mp4, video 96/120\n",
- "Traitement de 02-01-06-01-01-01-24.mp4, video 97/120\n",
- "Traitement de 02-01-06-01-01-02-24.mp4, video 98/120\n",
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- "Traitement de 02-01-06-01-02-02-24.mp4, video 100/120\n",
- "Traitement de 02-01-06-02-01-01-24.mp4, video 101/120\n",
- "Traitement de 02-01-06-02-01-02-24.mp4, video 102/120\n",
- "Traitement de 02-01-06-02-02-01-24.mp4, video 103/120\n",
- "Traitement de 02-01-06-02-02-02-24.mp4, video 104/120\n",
- "Traitement de 02-01-07-01-01-01-24.mp4, video 105/120\n",
- "Traitement de 02-01-07-01-01-02-24.mp4, video 106/120\n",
- "Traitement de 02-01-07-01-02-01-24.mp4, video 107/120\n",
- "Traitement de 02-01-07-01-02-02-24.mp4, video 108/120\n",
- "Traitement de 02-01-07-02-01-01-24.mp4, video 109/120\n",
- "Traitement de 02-01-07-02-01-02-24.mp4, video 110/120\n",
- "Traitement de 02-01-07-02-02-01-24.mp4, video 111/120\n",
- "Traitement de 02-01-07-02-02-02-24.mp4, video 112/120\n",
- "Traitement de 02-01-08-01-01-01-24.mp4, video 113/120\n",
- "Traitement de 02-01-08-01-01-02-24.mp4, video 114/120\n",
- "Traitement de 02-01-08-01-02-01-24.mp4, video 115/120\n",
- "Traitement de 02-01-08-01-02-02-24.mp4, video 116/120\n",
- "Traitement de 02-01-08-02-01-01-24.mp4, video 117/120\n",
- "Traitement de 02-01-08-02-01-02-24.mp4, video 118/120\n",
- "Traitement de 02-01-08-02-02-01-24.mp4, video 119/120\n",
- "Traitement de 02-01-08-02-02-02-24.mp4, video 120/120\n",
- "TRAITEMENT RAVDESS: traitement des 10282 visages détectés sur les vidéos de Ravdess...\n",
- "10282 données chargées depuis Ravdess.\n",
"\n",
- "CHARGEMENT DE 90000 DONNEES DEPUIS EXPW...\n",
- "1000 données chargées depuis expW (sur 1000 données traités).\n",
- "2000 données chargées depuis expW (sur 2000 données traités).\n",
- "3000 données chargées depuis expW (sur 3000 données traités).\n",
- "4000 données chargées depuis expW (sur 4000 données traités).\n",
- "5000 données chargées depuis expW (sur 5000 données traités).\n",
- "6000 données chargées depuis expW (sur 6000 données traités).\n",
- "7000 données chargées depuis expW (sur 7000 données traités).\n",
- "8000 données chargées depuis expW (sur 8000 données traités).\n",
- "9000 données chargées depuis expW (sur 9000 données traités).\n",
- "10000 données chargées depuis expW (sur 10000 données traités).\n",
- "11000 données chargées depuis expW (sur 11000 données traités).\n",
- "12000 données chargées depuis expW (sur 12000 données traités).\n",
- "13000 données chargées depuis expW (sur 13000 données traités).\n",
- "14000 données chargées depuis expW (sur 14000 données traités).\n",
- "15000 données chargées depuis expW (sur 15000 données traités).\n",
- "16000 données chargées depuis expW (sur 16000 données traités).\n",
- "17000 données chargées depuis expW (sur 17000 données traités).\n",
- "18000 données chargées depuis expW (sur 18000 données traités).\n",
- "19000 données chargées depuis expW (sur 19000 données traités).\n",
- "20000 données chargées depuis expW (sur 20000 données traités).\n",
- "21000 données chargées depuis expW (sur 21000 données traités).\n",
- "22000 données chargées depuis expW (sur 22000 données traités).\n",
- "23000 données chargées depuis expW (sur 23000 données traités).\n",
- "24000 données chargées depuis expW (sur 24000 données traités).\n",
- "25000 données chargées depuis expW (sur 25000 données traités).\n",
- "26000 données chargées depuis expW (sur 26000 données traités).\n",
- "27000 données chargées depuis expW (sur 27000 données traités).\n",
- "28000 données chargées depuis expW (sur 28000 données traités).\n",
- "29000 données chargées depuis expW (sur 29000 données traités).\n",
- "30000 données chargées depuis expW (sur 30000 données traités).\n",
- "31000 données chargées depuis expW (sur 31000 données traités).\n",
- "32000 données chargées depuis expW (sur 32000 données traités).\n",
- "33000 données chargées depuis expW (sur 33000 données traités).\n",
- "34000 données chargées depuis expW (sur 34000 données traités).\n",
- "35000 données chargées depuis expW (sur 35000 données traités).\n",
- "36000 données chargées depuis expW (sur 36000 données traités).\n",
- "37000 données chargées depuis expW (sur 37000 données traités).\n",
- "38000 données chargées depuis expW (sur 38000 données traités).\n",
- "39000 données chargées depuis expW (sur 39000 données traités).\n",
- "40000 données chargées depuis expW (sur 40000 données traités).\n",
- "41000 données chargées depuis expW (sur 41000 données traités).\n",
- "42000 données chargées depuis expW (sur 42000 données traités).\n",
- "43000 données chargées depuis expW (sur 43000 données traités).\n",
- "44000 données chargées depuis expW (sur 44000 données traités).\n",
- "45000 données chargées depuis expW (sur 45000 données traités).\n",
- "46000 données chargées depuis expW (sur 46000 données traités).\n",
- "47000 données chargées depuis expW (sur 47000 données traités).\n",
- "48000 données chargées depuis expW (sur 48000 données traités).\n",
- "49000 données chargées depuis expW (sur 49000 données traités).\n",
- "50000 données chargées depuis expW (sur 50000 données traités).\n",
- "51000 données chargées depuis expW (sur 51000 données traités).\n",
- "52000 données chargées depuis expW (sur 52000 données traités).\n",
- "53000 données chargées depuis expW (sur 53000 données traités).\n",
- "54000 données chargées depuis expW (sur 54000 données traités).\n",
- "55000 données chargées depuis expW (sur 55000 données traités).\n",
- "56000 données chargées depuis expW (sur 56000 données traités).\n",
- "57000 données chargées depuis expW (sur 57000 données traités).\n",
- "58000 données chargées depuis expW (sur 58000 données traités).\n",
- "59000 données chargées depuis expW (sur 59000 données traités).\n",
- "60000 données chargées depuis expW (sur 60000 données traités).\n",
- "61000 données chargées depuis expW (sur 61000 données traités).\n",
- "62000 données chargées depuis expW (sur 62000 données traités).\n",
- "63000 données chargées depuis expW (sur 63000 données traités).\n",
- "64000 données chargées depuis expW (sur 64000 données traités).\n",
- "65000 données chargées depuis expW (sur 65000 données traités).\n",
- "66000 données chargées depuis expW (sur 66000 données traités).\n",
- "67000 données chargées depuis expW (sur 67000 données traités).\n",
- "68000 données chargées depuis expW (sur 68000 données traités).\n",
- "69000 données chargées depuis expW (sur 69000 données traités).\n",
- "70000 données chargées depuis expW (sur 70000 données traités).\n",
- "71000 données chargées depuis expW (sur 71000 données traités).\n",
- "72000 données chargées depuis expW (sur 72000 données traités).\n",
- "73000 données chargées depuis expW (sur 73000 données traités).\n",
- "74000 données chargées depuis expW (sur 74000 données traités).\n",
- "75000 données chargées depuis expW (sur 75000 données traités).\n",
- "76000 données chargées depuis expW (sur 76000 données traités).\n",
- "77000 données chargées depuis expW (sur 77000 données traités).\n",
- "78000 données chargées depuis expW (sur 78000 données traités).\n",
- "79000 données chargées depuis expW (sur 79000 données traités).\n",
- "80000 données chargées depuis expW (sur 80000 données traités).\n",
- "81000 données chargées depuis expW (sur 81000 données traités).\n",
- "82000 données chargées depuis expW (sur 82000 données traités).\n",
- "83000 données chargées depuis expW (sur 83000 données traités).\n",
- "84000 données chargées depuis expW (sur 84000 données traités).\n",
- "85000 données chargées depuis expW (sur 85000 données traités).\n",
- "86000 données chargées depuis expW (sur 86000 données traités).\n",
- "87000 données chargées depuis expW (sur 87000 données traités).\n",
- "88000 données chargées depuis expW (sur 88000 données traités).\n",
- "89000 données chargées depuis expW (sur 89000 données traités).\n",
- "90000 données chargées depuis expW (sur 90000 données traités).\n",
- "\n",
- "\n",
- "\n",
- "CHARGEMENT DE 10000000000 DONNEES DEPUIS AFFWILD...\n",
- "Traitement de 1-30-1280x720.mp4, video 1/79\n",
- "Traitement de 108-15-640x480.mp4, video 2/79\n",
- "Traitement de 111-25-1920x1080.mp4, video 3/79\n",
- "Traitement de 117-25-1920x1080.mp4, video 4/79\n",
- "Traitement de 118-30-640x480.mp4, video 5/79\n",
- "Traitement de 121-24-1920x1080.mp4, video 6/79\n",
- "Traitement de 122-60-1920x1080-5.mp4, video 7/79\n",
- "Traitement de 126-30-1080x1920.mp4, video 8/79\n",
- "Traitement de 13-30-1920x1080.mp4, video 9/79\n",
- "Traitement de 130-25-1280x720.mp4, video 10/79\n",
- "Traitement de 132-30-426x240.mp4, video 11/79\n",
- "Traitement de 133-30-1280x720.mp4, video 12/79\n",
- "Traitement de 134-30-1280x720.mp4, video 13/79\n",
- "Traitement de 135-24-1920x1080.mp4, video 14/79\n",
- "Traitement de 136-30-1920x1080.mp4, video 15/79\n",
- "Traitement de 138-30-1280x720.mp4, video 16/79\n",
- "Traitement de 139-14-720x480.mp4, video 17/79\n",
- "Traitement de 14-30-1920x1080.mp4, video 18/79\n",
- "Traitement de 16-30-1920x1080.mp4, video 19/79\n",
- "Traitement de 18-24-1920x1080.mp4, video 20/79\n",
- "Traitement de 198.avi, video 21/79\n",
- "Traitement de 20-24-1920x1080.mp4, video 22/79\n",
- "Traitement de 207.mp4, video 23/79\n",
- "Traitement de 21-24-1920x1080.mp4, video 24/79\n",
- "Traitement de 212.mp4, video 25/79\n",
- "Traitement de 221.mp4, video 26/79\n",
- "Traitement de 225.mp4, video 27/79\n",
- "Traitement de 24-30-1920x1080-2.mp4, video 28/79\n",
- "Traitement de 28-30-1280x720-1.mp4, video 29/79\n",
- "Traitement de 28-30-1280x720-2.mp4, video 30/79\n",
- "Traitement de 28-30-1280x720-3.mp4, video 31/79\n",
- "Traitement de 28-30-1280x720-4.mp4, video 32/79\n",
- "Traitement de 282.mp4, video 33/79\n",
- "Traitement de 38-30-1920x1080.mp4, video 34/79\n",
- "Traitement de 40-30-1280x720.mp4, video 35/79\n",
- "Traitement de 43-30-406x720.mp4, video 36/79\n",
- "Traitement de 44-25-426x240.mp4, video 37/79\n",
- "Traitement de 45-24-1280x720.mp4, video 38/79\n",
- "Traitement de 46-30-484x360.mp4, video 39/79\n",
- "Traitement de 58-30-640x480.mp4, video 40/79\n",
- "Traitement de 6-30-1920x1080.mp4, video 41/79\n",
- "Traitement de 7-60-1920x1080.mp4, video 42/79\n",
- "Traitement de 79-30-960x720.mp4, video 43/79\n",
- "Traitement de 8-30-1280x720.mp4, video 44/79\n",
- "Traitement de 82-25-854x480.mp4, video 45/79\n",
- "Traitement de 85-24-1280x720.mp4, video 46/79\n",
- "Traitement de 87-25-1920x1080.mp4, video 47/79\n",
- "Traitement de 9-15-1920x1080.mp4, video 48/79\n",
- "Traitement de 92-24-1920x1080.mp4, video 49/79\n",
- "Traitement de 99-30-720x720.mp4, video 50/79\n",
- "Traitement de video24.mp4, video 51/79\n",
- "Traitement de video34.mp4, video 52/79\n",
- "Traitement de video4.mp4, video 53/79\n",
- "Traitement de video40.mp4, video 54/79\n",
- "Traitement de video45_1.mp4, video 55/79\n",
- "Traitement de video45_2.mp4, video 56/79\n",
- "Traitement de video45_3.mp4, video 57/79\n",
- "Traitement de video45_4.mp4, video 58/79\n",
- "Traitement de video45_5.mp4, video 59/79\n",
- "Traitement de video45_6.mp4, video 60/79\n",
- "Traitement de video45_7.mp4, video 61/79\n",
- "Traitement de video47.mp4, video 62/79\n",
- "Traitement de video48.mp4, video 63/79\n",
- "Traitement de video49.mp4, video 64/79\n",
- "Traitement de video56.mp4, video 65/79\n",
- "Traitement de video58.mp4, video 66/79\n",
- "Traitement de video6.mp4, video 67/79\n",
- "Traitement de video61.mp4, video 68/79\n",
- "Traitement de video63.mp4, video 69/79\n",
- "Traitement de video65.mp4, video 70/79\n",
- "Traitement de video66.mp4, video 71/79\n",
- "Traitement de video67.mp4, video 72/79\n",
- "Traitement de video72.mp4, video 73/79\n",
- "Traitement de video73.mp4, video 74/79\n",
- "Traitement de video79.mp4, video 75/79\n",
- "Traitement de video87.mp4, video 76/79\n",
- "Traitement de video93.mp4, video 77/79\n",
- "Traitement de video94.mp4, video 78/79\n",
- "Traitement de video95.mp4, video 79/79\n",
- "TRAITEMENT AFFWILD: traitement des 6495 visages détectés sur les vidéos de AffWild...\n",
- "6495 données chargées depuis AffWild.\n"
+ "CHARGEMENT DE 35887 DONNEES DEPUIS FER2013 ...\n"
]
},
{
"output_type": "error",
- "ename": "MemoryError",
- "evalue": "",
- "traceback": [
- "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[1;31mMemoryError\u001b[0m Traceback (most recent call last)",
- "\u001b[1;32m<ipython-input-2-3417f05429fb>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mXa\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mYa\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mloadAffwildData\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m \u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mY_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mY_test\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmergeToDatabase\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mXf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mXr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mXe\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mXa\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mYf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mYr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mYe\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mYa\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
- "\u001b[1;32mc:\\Users\\timot\\facial-expression-detection\\utils.py\u001b[0m in \u001b[0;36mmergeToDatabase\u001b[1;34m(listOfX, listOfY, validation_repart)\u001b[0m\n\u001b[0;32m 69\u001b[0m \u001b[0mlistOfY_test\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mY_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 70\u001b[0m \u001b[1;31m# Merge\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 71\u001b[1;33m \u001b[0mBigX_train\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mstackImages\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlistOfX_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 72\u001b[0m \u001b[0mBigY_train\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mstackImages\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlistOfY_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 73\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
- "\u001b[1;32mc:\\Users\\timot\\facial-expression-detection\\utils.py\u001b[0m in \u001b[0;36mstackImages\u001b[1;34m(listOfArrayImage)\u001b[0m\n\u001b[0;32m 46\u001b[0m \u001b[0mliste\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 47\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mX\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mlistOfArrayImage\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 48\u001b[1;33m \u001b[0mliste\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 49\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstack\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mliste\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 50\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
- "\u001b[1;31mMemoryError\u001b[0m: "
- ]
- }
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {},
- "outputs": [
- {
- "output_type": "error",
- "ename": "MemoryError",
+ "ename": "KeyboardInterrupt",
"evalue": "",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[1;31mMemoryError\u001b[0m Traceback (most recent call last)",
- "\u001b[1;32m<ipython-input-12-1df8f39f5f0e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mX_train\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mstackImages\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mXa\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mXr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mXe\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m5000\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mXf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m5000\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0mX_test\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mstackImages\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mXe\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m25000\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mXf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m35000\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
- "\u001b[1;32mc:\\Users\\timot\\facial-expression-detection\\utils.py\u001b[0m in \u001b[0;36mstackImages\u001b[1;34m(listOfArrayImage)\u001b[0m\n\u001b[0;32m 46\u001b[0m \u001b[0mliste\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 47\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mX\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mlistOfArrayImage\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 48\u001b[1;33m \u001b[0mliste\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 49\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstack\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mliste\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 50\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
- "\u001b[1;31mMemoryError\u001b[0m: "
+ "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
+ "\u001b[1;32m<ipython-input-3-3417f05429fb>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m#CHargement des données\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mXf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mYf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mloadFer2013Data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3\u001b[0m \u001b[0mXr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mYr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mloadRavdessData\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0mXe\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mYe\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mloadExpWData\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m90000\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcount\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mXa\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mYa\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mloadAffwildData\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[1;32mc:\\Users\\timot\\facial-expression-detection\\loadFer2013DS.py\u001b[0m in \u001b[0;36mloadFer2013Data\u001b[1;34m(maxNbrImages)\u001b[0m\n\u001b[0;32m 57\u001b[0m \u001b[0memotionNbr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstringImage\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtypeImage\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrow\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 58\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 59\u001b[1;33m \u001b[0mX\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnormAndResize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstrToArray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstringImage\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput_shape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 60\u001b[0m \u001b[0mY\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0memotionNbr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 61\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[1;32mc:\\Users\\timot\\facial-expression-detection\\utils.py\u001b[0m in \u001b[0;36mnormAndResize\u001b[1;34m(image, input_shape)\u001b[0m\n\u001b[0;32m 28\u001b[0m \u001b[1;31m# resize for h and l\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 29\u001b[0m \u001b[1;31m# print(image.shape) #\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 30\u001b[1;33m \u001b[0mimage\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcv2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimage\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mh\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ml\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minterpolation\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcv2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mINTER_CUBIC\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 31\u001b[0m \u001b[1;31m# if we want (h,l,3) -> (h,l,1) , we first transform it in to (h,l) (grey the image)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 32\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimage\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m3\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mp\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mimage\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
]
}
- ],
- "source": [
- "\n",
- "X_train = stackImages([Xa, Xr])\n",
- "X_test = stackImages([Xe[25000:], Xf[35000:]])"
]
},
{
diff --git a/config.py b/config.py
index 39a9b19015377b70329585670715abf52a932d7a..450da6036807e8f5d92361c5d5c7e0eb4a93441b 100644
--- a/config.py
+++ b/config.py
@@ -1,8 +1,8 @@
# Name of model used
-modelName = 'firstModel'
+modelName = 'exp903'
# Emotions provided by the dataset
-emotions = ["Angry", "Disgust", "Fear", "Happy", "Sad", "Suprise", "Neutral"]
+emotions = ["Angry", "Disgust", "Fear", "Happy", "Sad", "Surprise", "Neutral"]
# Shape of input of the model
input_shape = (48, 48, 1)
diff --git a/game.py b/game.py
new file mode 100644
index 0000000000000000000000000000000000000000..c5482446f4b3a17589e8d55329ea01d7cd72d2f6
--- /dev/null
+++ b/game.py
@@ -0,0 +1,49 @@
+#Use your camera for processing the video. Stop by pressing Q
+import cv2
+import imageProcess as ip
+import faceAnalysis as fa
+import random
+from config import emotions
+
+cap = cv2.VideoCapture(0) #0 means we capture the first camera, your webcam probably
+score = 0
+t = 0
+N = 15
+
+def smileyRandom(emotionToDodge):
+ emotionNbr = random.randrange(0,6)
+ emotion = emotions[emotionNbr]
+ if emotion == emotionToDodge: return smileyRandom(emotion)
+ smileyImagePath = "data/smileys/"+emotion+".png"
+ smiley = cv2.imread(smileyImagePath)
+ return smiley, emotion
+
+smiley, emotion = smileyRandom("")
+
+while cap.isOpened(): #or while 1. cap.isOpened() is false if there is a problem
+ ret, frame = cap.read() #Read next video frame, stop if frame not well read
+ if not ret: break
+
+ emotionsList = ip.imageProcess(frame, returnEmotion=True)
+ if len(emotionsList)==1:
+ if emotionsList[0] == emotion: #If emotion recognized, increase score, reset smiley to mimick and write "GG!"
+ score += 1
+ cv2.putText(smiley, "Emotion reconnue !", (50,50), cv2.FONT_HERSHEY_SIMPLEX, 1, (0,255,0), 2)
+ cv2.imshow("Smiley", smiley)
+ smiley, emotion = smileyRandom(emotion)
+
+
+ cv2.imshow("Caméra", frame) #Show you making emotional faces
+ cv2.putText(smiley, "Score: "+str(score), (0,0), cv2.FONT_HERSHEY_SIMPLEX, 1, (0,0,255), 2)
+ cv2.imshow("Smiley", smiley) #Show the smiley to mimic
+
+ if cv2.waitKey(1) & 0xFF == ord('q'): #If you press Q, stop the while and so the capture
+ break
+
+ if cv2.waitKey(1) & 0xFF == ord('q'): #If you press P, pass the smiley but lower your score
+ score -= 1
+ smiley, emotion = smileyRandom(emotion)
+
+
+cap.release()
+cv2.destroyAllWindows()
\ No newline at end of file
diff --git a/imageProcess.py b/imageProcess.py
index ef4f769b095f7bc01d9f680673e108ab7ac7cdf2..0234f78b612312258d355ad927cb34ef68f293b3 100644
--- a/imageProcess.py
+++ b/imageProcess.py
@@ -4,10 +4,11 @@ import numpy as np
import faceAnalysis as fa
import timeit as ti
-def imageProcess(image, writeEmotion=True, writeRectangle=True):
+def imageProcess(image, writeEmotion=True, writeRectangle=True, returnEmotion=False):
#Objectives : detect faces, identify emotion associated on it, modify the image by framing faces and writing their emotions associated
facesList = []
+ emotionsList = []
#Import faces and eyes detectors from cv2
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades+'haarcascade_frontalface_default.xml')
@@ -34,7 +35,9 @@ def imageProcess(image, writeEmotion=True, writeRectangle=True):
if writeEmotion:
emotion = fa.detectEmotion(face_color)
cv2.putText(image, emotion, (x,y), cv2.FONT_HERSHEY_SIMPLEX, 1, (255,0,0), 2)
+ emotionsList.append(emotion)
+ if returnEmotion: return emotionsList
return facesList
def selectFace(image):
diff --git a/loadExpWDS.py b/loadExpWDS.py
index 1e530fd57b494d59c56ae420df11743cc3852733..415f2ede36f4ba873674022b69dfa20867d00e15 100644
--- a/loadExpWDS.py
+++ b/loadExpWDS.py
@@ -32,28 +32,27 @@ def loadExpWData(nbrMaxImages=float('inf'), onlyDetected=False, detectedFace=Fal
break
#Add extracted data to our dataset
- if len(facesDetected) == 1 or not onlyDetected: #Otherwise no face were detected or a no-face was detected as face
-
- #Select detected face (if there is 1) or face according to the dataset
- if detectedFace:
- facesDetected = ip.imageProcess(faceAccordingToDS, writeEmotion=False, writeRectangle=False)
- if len(facesDetected) ==1:
- face = facesDetected[0]
- else:
- face = faceAccordingToDS
+
+ #Select detected face (if there is 1) or face according to the dataset
+ if detectedFace:
+ facesDetected = ip.imageProcess(faceAccordingToDS, writeEmotion=False, writeRectangle=False)
+ if len(facesDetected) ==1:
+ face = facesDetected[0]
else:
face = faceAccordingToDS
+ else:
+ face = faceAccordingToDS
- #Colored N*M*3 face to gray 48*48*1 image.
- gray = normAndResize(face, input_shape)
+ #Colored N*M*3 face to gray 48*48*1 image.
+ gray = normAndResize(face, input_shape)
- X.append(gray)
- Y.append(label) #Emotion order is the same as fer2013.
-
- nbrImages += 1
+ X.append(gray)
+ Y.append(label) #Emotion order is the same as fer2013.
+
+ nbrImages += 1
- #Print number of datas loaded every 1000 datas
- if count and nbrImages%1000==0: print(f"{nbrImages} données chargées depuis expW (sur {k} données traités).")
+ #Print number of datas loaded every 1000 datas
+ if count and nbrImages%1000==0: print(f"{nbrImages} données chargées depuis expW (sur {k} données traités).")
X = np.array(X)
Y = np.array(Y)
diff --git a/models/firstModel/exp901/keras_metadata.pb b/models/exp901/keras_metadata.pb
similarity index 100%
rename from models/firstModel/exp901/keras_metadata.pb
rename to models/exp901/keras_metadata.pb
diff --git a/models/firstModel/exp901/saved_model.pb b/models/exp901/saved_model.pb
similarity index 100%
rename from models/firstModel/exp901/saved_model.pb
rename to models/exp901/saved_model.pb
diff --git a/models/firstModel/exp901/variables/variables.data-00000-of-00001 b/models/exp901/variables/variables.data-00000-of-00001
similarity index 100%
rename from models/firstModel/exp901/variables/variables.data-00000-of-00001
rename to models/exp901/variables/variables.data-00000-of-00001
diff --git a/models/firstModel/exp901/variables/variables.index b/models/exp901/variables/variables.index
similarity index 100%
rename from models/firstModel/exp901/variables/variables.index
rename to models/exp901/variables/variables.index
diff --git a/exp902/keras_metadata.pb b/models/exp902/keras_metadata.pb
similarity index 100%
rename from exp902/keras_metadata.pb
rename to models/exp902/keras_metadata.pb
diff --git a/exp902/saved_model.pb b/models/exp902/saved_model.pb
similarity index 100%
rename from exp902/saved_model.pb
rename to models/exp902/saved_model.pb
diff --git a/exp902/variables/variables.data-00000-of-00001 b/models/exp902/variables/variables.data-00000-of-00001
similarity index 100%
rename from exp902/variables/variables.data-00000-of-00001
rename to models/exp902/variables/variables.data-00000-of-00001
diff --git a/exp902/variables/variables.index b/models/exp902/variables/variables.index
similarity index 100%
rename from exp902/variables/variables.index
rename to models/exp902/variables/variables.index
diff --git a/models/exp903/keras_metadata.pb b/models/exp903/keras_metadata.pb
new file mode 100644
index 0000000000000000000000000000000000000000..88836e51ceac0420b5ce339c2d93ea266fc403a8
--- /dev/null
+++ b/models/exp903/keras_metadata.pb
@@ -0,0 +1,22 @@
+
+��root"_tf_keras_sequential*݄{"name": "my_model_2", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "must_restore_from_config": false, "class_name": "MyModel", "config": {"name": "my_model_2", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": {"class_name": "__tuple__", "items": [null, 48, 48, 1]}, "dtype": "float32", "sparse": false, "ragged": false, "name": "conv2d_6_input"}}, {"class_name": "Conv2D", "config": {"name": "conv2d_6", "trainable": true, "batch_input_shape": {"class_name": "__tuple__", "items": [null, 48, 48, 1]}, "dtype": "float32", "filters": 32, "kernel_size": {"class_name": "__tuple__", "items": [3, 3]}, "strides": {"class_name": "__tuple__", "items": [1, 1]}, "padding": "valid", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": 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