diff --git a/__pycache__/config.cpython-39.pyc b/__pycache__/config.cpython-39.pyc
index 3937957761156f90d78407216958f1cc67ab52f8..0698941d817bfe555d127c72c721f9f90f935909 100644
Binary files a/__pycache__/config.cpython-39.pyc and b/__pycache__/config.cpython-39.pyc differ
diff --git a/__pycache__/loadExpWDS.cpython-39.pyc b/__pycache__/loadExpWDS.cpython-39.pyc
index 0da1f517ab834ecb77957834e61eb27c949af828..8ffc7096932508775a674e6928b0c5b5d5eaf0f9 100644
Binary files a/__pycache__/loadExpWDS.cpython-39.pyc and b/__pycache__/loadExpWDS.cpython-39.pyc differ
diff --git a/__pycache__/loadFer2013DS.cpython-39.pyc b/__pycache__/loadFer2013DS.cpython-39.pyc
index 7b64846ee29704e89d9ed9af4f85a8d33b13da94..4b5253827e702a1240f20c75bb12c7f9fec3f1d7 100644
Binary files a/__pycache__/loadFer2013DS.cpython-39.pyc and b/__pycache__/loadFer2013DS.cpython-39.pyc differ
diff --git a/__pycache__/utils.cpython-39.pyc b/__pycache__/utils.cpython-39.pyc
index eafd068f1ea8525212da57b96fa68b9204ff399c..a5344a291dabb4584bdf2026e912330768c9dce7 100644
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diff --git a/buildEmotionModel.ipynb b/buildEmotionModel.ipynb
index 6cd2e5b97811b2453ed71832dadbd9e9dec547da..b1a19e9c22b00ef69ae0e5289d71efcc72332843 100644
--- a/buildEmotionModel.ipynb
+++ b/buildEmotionModel.ipynb
@@ -28,13 +28,18 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 1,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
+ "INFO:tensorflow:Enabling eager execution\n",
+ "INFO:tensorflow:Enabling v2 tensorshape\n",
+ "INFO:tensorflow:Enabling resource variables\n",
+ "INFO:tensorflow:Enabling tensor equality\n",
+ "INFO:tensorflow:Enabling control flow v2\n",
"WARNING:tensorflow:SavedModel saved prior to TF 2.5 detected when loading Keras model. Please ensure that you are saving the model with model.save() or tf.keras.models.save_model(), *NOT* tf.saved_model.save(). To confirm, there should be a file named \"keras_metadata.pb\" in the SavedModel directory.\n",
"Model used: firstModel\n"
]
@@ -73,124 +78,465 @@
]
},
{
+ "source": [
+ "#CHargement des données\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])"
+ ],
"cell_type": "code",
- "execution_count": 15,
- "metadata": {},
+ "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",
+ "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",
+ "Traitement de 02-01-02-02-01-01-23.mp4, video 69/120\n",
+ "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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+ "Traitement de 02-01-04-02-02-02-23.mp4, video 88/120\n",
+ "Erreur pour la donnée : Aucun ou plusieurs visages détectés\n",
+ "Traitement de 02-01-05-01-01-01-23.mp4, video 89/120\n",
+ "Traitement de 02-01-05-01-01-02-23.mp4, video 90/120\n",
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+ "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 01-01-01-01-02-02-24.mp4, video 4/120\n",
+ "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",
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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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+ "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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+ "Traitement de 02-01-03-01-01-02-24.mp4, video 74/120\n",
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+ "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 02-01-05-01-02-02-24.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",
+ "Traitement de 02-01-06-01-02-01-24.mp4, video 99/120\n",
+ "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 100 DONNEES DEPUIS FER2013 ...\n",
- "100 données chargées depuis fer2013.\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",
- "CHARGEMENT DE 10 DONNEES DEPUIS RAVDESS...\n",
- "TRAITEMENT ACTEUR N°01\n",
- "Traitement de 01-01-01-01-01-01-01.mp4, video 1/120\n",
- "Traitement de 01-01-01-01-01-02-01.mp4, video 2/120\n",
- "Traitement de 01-01-01-01-02-01-01.mp4, video 3/120\n",
- "TRAITEMENT RAVDESS: traitement des 10 visages détectés sur les vidéos de Ravdess...\n",
- "10 données chargées depuis Ravdess.\n",
"\n",
- "CHARGEMENT DE 100 DONNEES DEPUIS EXPW...\n",
- "100 données chargées depuis expW (sur 100 données traités).\n",
"\n",
- "\n",
- "CHARGEMENT DE 10 DONNEES DEPUIS AFFWILD...\n",
+ "CHARGEMENT DE 10000000000 DONNEES DEPUIS AFFWILD...\n",
"Traitement de 1-30-1280x720.mp4, video 1/79\n",
- "TRAITEMENT AFFWILD: traitement des 10 visages détectés sur les vidéos de AffWild...\n",
- "10 données chargées depuis AffWild.\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"
+ ]
+ },
+ {
+ "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: "
]
}
- ],
- "source": [
- "#CHargement des données\n",
- "Xf, Yf = loadFer2013Data(100)\n",
- "Xr, Yr = loadRavdessData(10)\n",
- "Xe, Ye = loadExpWData(100)\n",
- "Xa, Ya = loadAffwildData(10)\n",
- "\n",
- "X_train, Y_train, X_test, Y_test = mergeToDatabase([Xf, Xr, Xe, Xa], [Yf, Yr, Ye, Ya])"
]
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 12,
"metadata": {},
"outputs": [
{
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "Dataset: fer2013\nImages: (100, 48, 48, 1) Labels: (100,)\n"
- ]
- },
- {
- "output_type": "display_data",
- "data": {
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\n"
- },
- "metadata": {}
- },
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "Dataset: ravdess\nImages: (10, 48, 48, 1) Labels: (10,)\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-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: "
]
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- "name": "stdout",
- "text": [
- "Dataset: expW\nImages: (100, 48, 48, 1) Labels: (100,)\n"
- ]
- },
- {
- "output_type": "display_data",
- "data": {
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\n"
- },
- "metadata": {}
- },
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "Dataset: affwild\nImages: (10, 48, 48, 1) Labels: (10,)\n"
- ]
- },
- {
- "output_type": "display_data",
- "data": {
- "text/plain": "<Figure size 432x288 with 25 Axes>",
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\n"
- },
- "metadata": {}
}
],
+ "source": [
+ "\n",
+ "X_train = stackImages([Xa, Xr])\n",
+ "X_test = stackImages([Xe[25000:], Xf[35000:]])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": [
"#@title Visualisation de chaque dataset\n",
"for X, Y, name in zip([Xf, Xr, Xe, Xa], [Yf, Yr, Ye, Ya], [\"fer2013\", \"ravdess\", \"expW\", \"affwild\"]):\n",
" N=5\n",
" M=5\n",
" print(\"Dataset:\", name)\n",
- " print(\"Images:\", X.shape, \"Labels:\", Y.shape)\n",
+ " print(\"Images:\", X.shape, \"La bels:\", Y.shape)\n",
" plt.figure()\n",
" for i in range(N*M):\n",
" if X.shape[0] == 0: continue\n",
@@ -207,24 +553,19 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 5,
"metadata": {},
"outputs": [
{
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "X_train: (198, 48, 48, 1)\nY_train: (198,)\n\nX_test: (22, 48, 48, 1)\nY_test: (22,)\n"
+ "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"
]
- },
- {
- "output_type": "display_data",
- "data": {
- "text/plain": "<Figure size 432x288 with 25 Axes>",
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\n"
- },
- "metadata": {}
}
],
"source": [
@@ -252,19 +593,44 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"#@title Hyperparamètres\n",
- "epochs = 5\n",
+ "epochs = \n",
"batch_size = 128\n",
"validation_size = 0.1"
]
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 8,
+ "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"
+ ]
+ }
+ ],
+ "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",
+ "\n",
+ "print(\"\\nX_test_cat:\", X_test.shape)\n",
+ "print(\"Y_test_cat:\", Y_test_cat.shape)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
@@ -273,23 +639,24 @@
"\n",
" def __init__(self, input_shape):\n",
" super(MyModel, self).__init__()\n",
- " self.add(keras.layers.Conv2D(32, kernel_size = (3, 3), activation = 'relu', input_shape = input_shape))\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",
" 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",
" self.add(keras.layers.MaxPooling2D(pool_size = 2))\n",
" self.add(keras.layers.BatchNormalization())\n",
+ "\n",
" self.add(keras.layers.Flatten())\n",
+ "\n",
" self.add(keras.layers.Dense(64, activation = 'relu'))\n",
" self.add(keras.layers.BatchNormalization())\n",
+ " \n",
" self.add(keras.layers.Dense(7, activation = 'softmax'))\n",
- "\n",
- " self.conv2D2 = keras.layers.Conv2D(64, kernel_size = (3, 3), activation = 'relu')\n",
- " self.conv2D3 = keras.layers.Conv2D(128, kernel_size = (3, 3), activation = 'relu')\n",
- " self.maxPooling = keras.layers.MaxPooling2D(pool_size = 2)\n",
- "\n",
" \n",
" def predir(self, monImage):\n",
" return self.predict(np.array([monImage]))[0,:]\n",
@@ -301,6 +668,78 @@
"myModel.compile_o()"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "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>",
+ "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\r\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\r\n<svg height=\"250.052344pt\" version=\"1.1\" viewBox=\"0 0 251.565 250.052344\" width=\"251.565pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\r\n <metadata>\r\n <rdf:RDF xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\r\n <cc:Work>\r\n <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\r\n <dc:date>2021-05-04T23:17:39.951349</dc:date>\r\n <dc:format>image/svg+xml</dc:format>\r\n <dc:creator>\r\n <cc:Agent>\r\n <dc:title>Matplotlib v3.4.1, https://matplotlib.org/</dc:title>\r\n </cc:Agent>\r\n </dc:creator>\r\n </cc:Work>\r\n </rdf:RDF>\r\n </metadata>\r\n <defs>\r\n <style 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\n"
+ },
+ "metadata": {
+ "needs_background": "light"
+ }
+ }
+ ],
+ "source": [
+ "theImage = X_train[5]\n",
+ "afficher(theImage)\n",
+ "print(predir(myModel, theImage))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Epoch 1/5\n",
+ " 880/2532 [=========>....................] - ETA: 1:01 - loss: 0.8081 - accuracy: 0.7104"
+ ]
+ }
+ ],
+ "source": [
+ "history = myModel.fit(X_train, Y_train_cat, epochs=epochs, validation_data=(X_test, Y_test_cat))\n",
+ "\n",
+ "#Affichage de l'historique de l'apprentissage\n",
+ "plt.plot(history.history['accuracy'], label='accuracy')\n",
+ "plt.plot(history.history['val_accuracy'], label='val_accuracy')\n",
+ "plt.legend()\n",
+ "plt.ylim([min(history.history['val_accuracy']+history.history['accuracy']), 1])\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "INFO:tensorflow:Assets written to: exp901\\assets\n"
+ ]
+ }
+ ],
+ "source": [
+ "myModel.save('exp901')"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
diff --git a/buildEmotionModel2.ipynb b/buildEmotionModel2.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..b1a19e9c22b00ef69ae0e5289d71efcc72332843
--- /dev/null
+++ b/buildEmotionModel2.ipynb
@@ -0,0 +1,751 @@
+{
+ "metadata": {
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.4-final"
+ },
+ "orig_nbformat": 2,
+ "kernelspec": {
+ "name": "python394jvsc74a57bd0d55a872fb12b64c3eb6a530d12935ddebcb38da0925d2cc3bd9c2ebc1d370b0d",
+ "display_name": "Python 3.9.4 64-bit"
+ },
+ "metadata": {
+ "interpreter": {
+ "hash": "d55a872fb12b64c3eb6a530d12935ddebcb38da0925d2cc3bd9c2ebc1d370b0d"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "INFO:tensorflow:Enabling eager execution\n",
+ "INFO:tensorflow:Enabling v2 tensorshape\n",
+ "INFO:tensorflow:Enabling resource variables\n",
+ "INFO:tensorflow:Enabling tensor equality\n",
+ "INFO:tensorflow:Enabling control flow v2\n",
+ "WARNING:tensorflow:SavedModel saved prior to TF 2.5 detected when loading Keras model. Please ensure that you are saving the model with model.save() or tf.keras.models.save_model(), *NOT* tf.saved_model.save(). To confirm, there should be a file named \"keras_metadata.pb\" in the SavedModel directory.\n",
+ "Model used: firstModel\n"
+ ]
+ }
+ ],
+ "source": [
+ "#@title Imports\n",
+ "#%load_ext autoreload #Need to uncomment for import sometime, dont understand\n",
+ "\n",
+ "#Tensorflow :\n",
+ "import tensorflow as tf\n",
+ "from tensorflow import keras\n",
+ "from tensorflow.keras import datasets, layers, models, losses\n",
+ "import tensorflow_datasets as tfds\n",
+ "#from google.colab import files\n",
+ "\n",
+ "#Others :\n",
+ "from matplotlib import image\n",
+ "import os\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "import matplotlib\n",
+ "import random as rd\n",
+ "import cv2\n",
+ "import csv\n",
+ "\n",
+ "#Data loaders :\n",
+ "from loadFer2013DS import *\n",
+ "from loadRavdessDS import *\n",
+ "from loadExpWDS import *\n",
+ "from loadAffwildDS import *\n",
+ "\n",
+ "#Others\n",
+ "from utils import *\n",
+ "from config import *"
+ ]
+ },
+ {
+ "source": [
+ "#CHargement des données\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])"
+ ],
+ "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",
+ "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",
+ "Traitement de 02-01-02-02-01-01-23.mp4, video 69/120\n",
+ "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",
+ "Traitement de 02-01-03-01-02-01-23.mp4, video 75/120\n",
+ "Traitement de 02-01-03-01-02-02-23.mp4, video 76/120\n",
+ "Traitement de 02-01-03-02-01-01-23.mp4, video 77/120\n",
+ "Traitement de 02-01-03-02-01-02-23.mp4, video 78/120\n",
+ "Traitement de 02-01-03-02-02-01-23.mp4, video 79/120\n",
+ "Traitement de 02-01-03-02-02-02-23.mp4, video 80/120\n",
+ "Traitement de 02-01-04-01-01-01-23.mp4, video 81/120\n",
+ "Traitement de 02-01-04-01-01-02-23.mp4, video 82/120\n",
+ "Traitement de 02-01-04-01-02-01-23.mp4, video 83/120\n",
+ "Traitement de 02-01-04-01-02-02-23.mp4, video 84/120\n",
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+ "Traitement de 02-01-04-02-02-01-23.mp4, video 87/120\n",
+ "Traitement de 02-01-04-02-02-02-23.mp4, video 88/120\n",
+ "Erreur pour la donnée : Aucun ou plusieurs visages détectés\n",
+ "Traitement de 02-01-05-01-01-01-23.mp4, video 89/120\n",
+ "Traitement de 02-01-05-01-01-02-23.mp4, video 90/120\n",
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+ "Traitement de 02-01-06-01-02-02-23.mp4, video 100/120\n",
+ "Traitement de 02-01-06-02-01-01-23.mp4, video 101/120\n",
+ "Traitement de 02-01-06-02-01-02-23.mp4, video 102/120\n",
+ "Traitement de 02-01-06-02-02-01-23.mp4, video 103/120\n",
+ "Traitement de 02-01-06-02-02-02-23.mp4, video 104/120\n",
+ "Traitement de 02-01-07-01-01-01-23.mp4, video 105/120\n",
+ "Traitement de 02-01-07-01-01-02-23.mp4, video 106/120\n",
+ "Traitement de 02-01-07-01-02-01-23.mp4, video 107/120\n",
+ "Traitement de 02-01-07-01-02-02-23.mp4, video 108/120\n",
+ "Traitement de 02-01-07-02-01-01-23.mp4, video 109/120\n",
+ "Traitement de 02-01-07-02-01-02-23.mp4, video 110/120\n",
+ "Traitement de 02-01-07-02-02-01-23.mp4, video 111/120\n",
+ "Traitement de 02-01-07-02-02-02-23.mp4, video 112/120\n",
+ "Traitement de 02-01-08-01-01-01-23.mp4, video 113/120\n",
+ "Traitement de 02-01-08-01-01-02-23.mp4, video 114/120\n",
+ "Traitement de 02-01-08-01-02-01-23.mp4, video 115/120\n",
+ "Traitement de 02-01-08-01-02-02-23.mp4, video 116/120\n",
+ "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",
+ "Traitement de 01-01-01-01-02-01-24.mp4, video 3/120\n",
+ "Traitement de 01-01-01-01-02-02-24.mp4, video 4/120\n",
+ "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",
+ "Traitement de 01-01-02-01-02-01-24.mp4, video 7/120\n",
+ "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",
+ "Traitement de 01-01-02-02-02-02-24.mp4, video 12/120\n",
+ "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",
+ "Traitement de 01-01-03-01-02-01-24.mp4, video 15/120\n",
+ "Traitement de 01-01-03-01-02-02-24.mp4, video 16/120\n",
+ "Traitement de 01-01-03-02-01-01-24.mp4, video 17/120\n",
+ "Traitement de 01-01-03-02-01-02-24.mp4, video 18/120\n",
+ "Traitement de 01-01-03-02-02-01-24.mp4, video 19/120\n",
+ "Traitement de 01-01-03-02-02-02-24.mp4, video 20/120\n",
+ "Traitement de 01-01-04-01-01-01-24.mp4, video 21/120\n",
+ "Traitement de 01-01-04-01-01-02-24.mp4, video 22/120\n",
+ "Traitement de 01-01-04-01-02-01-24.mp4, video 23/120\n",
+ "Traitement de 01-01-04-01-02-02-24.mp4, video 24/120\n",
+ "Traitement de 01-01-04-02-01-01-24.mp4, video 25/120\n",
+ "Traitement de 01-01-04-02-01-02-24.mp4, video 26/120\n",
+ "Traitement de 01-01-04-02-02-01-24.mp4, video 27/120\n",
+ "Traitement de 01-01-04-02-02-02-24.mp4, video 28/120\n",
+ "Traitement de 01-01-05-01-01-01-24.mp4, video 29/120\n",
+ "Traitement de 01-01-05-01-01-02-24.mp4, video 30/120\n",
+ "Traitement de 01-01-05-01-02-01-24.mp4, video 31/120\n",
+ "Traitement de 01-01-05-01-02-02-24.mp4, video 32/120\n",
+ "Traitement de 01-01-05-02-01-01-24.mp4, video 33/120\n",
+ "Traitement de 01-01-05-02-01-02-24.mp4, video 34/120\n",
+ "Traitement de 01-01-05-02-02-01-24.mp4, video 35/120\n",
+ "Traitement de 01-01-05-02-02-02-24.mp4, video 36/120\n",
+ "Traitement de 01-01-06-01-01-01-24.mp4, video 37/120\n",
+ "Traitement de 01-01-06-01-01-02-24.mp4, video 38/120\n",
+ "Traitement de 01-01-06-01-02-01-24.mp4, video 39/120\n",
+ "Traitement de 01-01-06-01-02-02-24.mp4, video 40/120\n",
+ "Traitement de 01-01-06-02-01-01-24.mp4, video 41/120\n",
+ "Traitement de 01-01-06-02-01-02-24.mp4, video 42/120\n",
+ "Traitement de 01-01-06-02-02-01-24.mp4, video 43/120\n",
+ "Traitement de 01-01-06-02-02-02-24.mp4, video 44/120\n",
+ "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",
+ "Traitement de 01-01-07-02-01-01-24.mp4, video 49/120\n",
+ "Traitement de 01-01-07-02-01-02-24.mp4, video 50/120\n",
+ "Traitement de 01-01-07-02-02-01-24.mp4, video 51/120\n",
+ "Traitement de 01-01-07-02-02-02-24.mp4, video 52/120\n",
+ "Traitement de 01-01-08-01-01-01-24.mp4, video 53/120\n",
+ "Traitement de 01-01-08-01-01-02-24.mp4, video 54/120\n",
+ "Traitement de 01-01-08-01-02-01-24.mp4, video 55/120\n",
+ "Traitement de 01-01-08-01-02-02-24.mp4, video 56/120\n",
+ "Traitement de 01-01-08-02-01-01-24.mp4, video 57/120\n",
+ "Traitement de 01-01-08-02-01-02-24.mp4, video 58/120\n",
+ "Traitement de 01-01-08-02-02-01-24.mp4, video 59/120\n",
+ "Traitement de 01-01-08-02-02-02-24.mp4, video 60/120\n",
+ "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",
+ "Traitement de 02-01-04-01-02-02-24.mp4, video 84/120\n",
+ "Traitement de 02-01-04-02-01-01-24.mp4, video 85/120\n",
+ "Traitement de 02-01-04-02-01-02-24.mp4, video 86/120\n",
+ "Traitement de 02-01-04-02-02-01-24.mp4, video 87/120\n",
+ "Traitement de 02-01-04-02-02-02-24.mp4, video 88/120\n",
+ "Traitement de 02-01-05-01-01-01-24.mp4, video 89/120\n",
+ "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",
+ "Traitement de 02-01-06-01-02-01-24.mp4, video 99/120\n",
+ "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"
+ ]
+ },
+ {
+ "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",
+ "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: "
+ ]
+ }
+ ],
+ "source": [
+ "\n",
+ "X_train = stackImages([Xa, Xr])\n",
+ "X_test = stackImages([Xe[25000:], Xf[35000:]])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#@title Visualisation de chaque dataset\n",
+ "for X, Y, name in zip([Xf, Xr, Xe, Xa], [Yf, Yr, Ye, Ya], [\"fer2013\", \"ravdess\", \"expW\", \"affwild\"]):\n",
+ " N=5\n",
+ " M=5\n",
+ " print(\"Dataset:\", name)\n",
+ " print(\"Images:\", X.shape, \"La bels:\", Y.shape)\n",
+ " plt.figure()\n",
+ " for i in range(N*M):\n",
+ " if X.shape[0] == 0: continue\n",
+ " k = rd.randrange(X.shape[0])\n",
+ " plt.subplot(N, M, i+1)\n",
+ " plt.xticks([])\n",
+ " plt.yticks([])\n",
+ " plt.grid(False)\n",
+ "\n",
+ " afficher(X[k])\n",
+ " plt.title(emotions[int(Y[k])])\n",
+ " plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "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"
+ ]
+ }
+ ],
+ "source": [
+ "#Visualisation du dataset global\n",
+ "print(\"X_train:\", X_train.shape)\n",
+ "print(\"Y_train:\", Y_train.shape)\n",
+ "\n",
+ "print(\"\\nX_test:\", X_test.shape)\n",
+ "print(\"Y_test:\", Y_test.shape)\n",
+ "\n",
+ "N=5\n",
+ "M=5\n",
+ "plt.figure()\n",
+ "for i in range(N*M):\n",
+ " k = rd.randrange(X_train.shape[0])\n",
+ " plt.subplot(N, M, i+1)\n",
+ " plt.xticks([])\n",
+ " plt.yticks([])\n",
+ " plt.grid(False)\n",
+ "\n",
+ " afficher(X_train[k])\n",
+ " plt.title(emotions[int(Y_train[k])])\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#@title Hyperparamètres\n",
+ "epochs = \n",
+ "batch_size = 128\n",
+ "validation_size = 0.1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "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"
+ ]
+ }
+ ],
+ "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",
+ "\n",
+ "print(\"\\nX_test_cat:\", X_test.shape)\n",
+ "print(\"Y_test_cat:\", Y_test_cat.shape)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#MODELE\n",
+ "class MyModel(keras.Sequential):\n",
+ "\n",
+ " def __init__(self, input_shape):\n",
+ " super(MyModel, self).__init__()\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",
+ " 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",
+ " self.add(keras.layers.MaxPooling2D(pool_size = 2))\n",
+ " self.add(keras.layers.BatchNormalization())\n",
+ "\n",
+ " self.add(keras.layers.Flatten())\n",
+ "\n",
+ " self.add(keras.layers.Dense(64, activation = 'relu'))\n",
+ " self.add(keras.layers.BatchNormalization())\n",
+ " \n",
+ " self.add(keras.layers.Dense(7, activation = 'softmax'))\n",
+ " \n",
+ " def predir(self, monImage):\n",
+ " return self.predict(np.array([monImage]))[0,:]\n",
+ "\n",
+ " def compile_o(self):\n",
+ " self.compile(optimizer = 'adam', loss=losses.categorical_crossentropy, metrics = ['accuracy'])\n",
+ "\n",
+ "myModel = MyModel(input_shape)\n",
+ "myModel.compile_o()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "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>",
+ "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\r\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\r\n<svg height=\"250.052344pt\" version=\"1.1\" viewBox=\"0 0 251.565 250.052344\" width=\"251.565pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\r\n <metadata>\r\n <rdf:RDF xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\r\n <cc:Work>\r\n <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\r\n <dc:date>2021-05-04T23:17:39.951349</dc:date>\r\n <dc:format>image/svg+xml</dc:format>\r\n <dc:creator>\r\n <cc:Agent>\r\n <dc:title>Matplotlib v3.4.1, https://matplotlib.org/</dc:title>\r\n </cc:Agent>\r\n </dc:creator>\r\n </cc:Work>\r\n </rdf:RDF>\r\n </metadata>\r\n <defs>\r\n <style 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\n"
+ },
+ "metadata": {
+ "needs_background": "light"
+ }
+ }
+ ],
+ "source": [
+ "theImage = X_train[5]\n",
+ "afficher(theImage)\n",
+ "print(predir(myModel, theImage))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Epoch 1/5\n",
+ " 880/2532 [=========>....................] - ETA: 1:01 - loss: 0.8081 - accuracy: 0.7104"
+ ]
+ }
+ ],
+ "source": [
+ "history = myModel.fit(X_train, Y_train_cat, epochs=epochs, validation_data=(X_test, Y_test_cat))\n",
+ "\n",
+ "#Affichage de l'historique de l'apprentissage\n",
+ "plt.plot(history.history['accuracy'], label='accuracy')\n",
+ "plt.plot(history.history['val_accuracy'], label='val_accuracy')\n",
+ "plt.legend()\n",
+ "plt.ylim([min(history.history['val_accuracy']+history.history['accuracy']), 1])\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "INFO:tensorflow:Assets written to: exp901\\assets\n"
+ ]
+ }
+ ],
+ "source": [
+ "myModel.save('exp901')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ]
+}
\ No newline at end of file
diff --git a/config.py b/config.py
index 7fd6a4d5521d365c4abb414768bac616b01f6733..39a9b19015377b70329585670715abf52a932d7a 100644
--- a/config.py
+++ b/config.py
@@ -6,3 +6,4 @@ emotions = ["Angry", "Disgust", "Fear", "Happy", "Sad", "Suprise", "Neutral"]
# Shape of input of the model
input_shape = (48, 48, 1)
+# input_shape = (64,64,1)
diff --git a/exp902/keras_metadata.pb b/exp902/keras_metadata.pb
new file mode 100644
index 0000000000000000000000000000000000000000..0507b3b724cf70df1505d98b9ce361005093d447
--- /dev/null
+++ b/exp902/keras_metadata.pb
@@ -0,0 +1,20 @@
+
+�uroot"_tf_keras_sequential*�u{"name": "my_model_1", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "must_restore_from_config": false, "class_name": "MyModel", "config": {"name": "my_model_1", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": {"class_name": "__tuple__", "items": [null, 48, 48, 1]}, "dtype": "float32", "sparse": false, "ragged": false, "name": "conv2d_3_input"}}, {"class_name": "Conv2D", "config": {"name": "conv2d_3", "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": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "MaxPooling2D", "config": {"name": "max_pooling2d_3", "trainable": true, "dtype": "float32", "pool_size": {"class_name": "__tuple__", "items": [2, 2]}, "padding": "valid", "strides": {"class_name": "__tuple__", "items": [2, 2]}, "data_format": "channels_last"}}, {"class_name": "BatchNormalization", "config": {"name": "batch_normalization_4", "trainable": true, "dtype": "float32", "axis": [3], "momentum": 0.99, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}}, "gamma_initializer": {"class_name": "Ones", "config": {}}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}}, "moving_variance_initializer": {"class_name": "Ones", "config": {}}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}}, {"class_name": "Conv2D", "config": {"name": "conv2d_4", "trainable": true, "dtype": "float32", "filters": 64, "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": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "MaxPooling2D", "config": {"name": "max_pooling2d_4", "trainable": true, "dtype": "float32", "pool_size": {"class_name": "__tuple__", "items": [2, 2]}, "padding": "valid", "strides": {"class_name": "__tuple__", "items": [2, 2]}, "data_format": "channels_last"}}, {"class_name": "BatchNormalization", "config": {"name": "batch_normalization_5", "trainable": true, "dtype": "float32", "axis": [3], "momentum": 0.99, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}}, "gamma_initializer": {"class_name": "Ones", "config": {}}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}}, "moving_variance_initializer": {"class_name": "Ones", "config": {}}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}}, {"class_name": "Conv2D", "config": {"name": "conv2d_5", "trainable": true, "dtype": "float32", "filters": 96, "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": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "MaxPooling2D", "config": {"name": "max_pooling2d_5", "trainable": true, "dtype": "float32", "pool_size": {"class_name": "__tuple__", "items": [2, 2]}, "padding": "valid", "strides": {"class_name": "__tuple__", "items": [2, 2]}, "data_format": "channels_last"}}, {"class_name": "BatchNormalization", "config": {"name": "batch_normalization_6", "trainable": true, "dtype": "float32", "axis": [3], "momentum": 0.99, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}}, "gamma_initializer": {"class_name": "Ones", "config": {}}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}}, "moving_variance_initializer": {"class_name": "Ones", "config": {}}, 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\ No newline at end of file
diff --git a/exp902/saved_model.pb b/exp902/saved_model.pb
new file mode 100644
index 0000000000000000000000000000000000000000..288cd9b7e1c865d9d3f379d529a03cfb4ded54f8
Binary files /dev/null and b/exp902/saved_model.pb differ
diff --git a/exp902/variables/variables.data-00000-of-00001 b/exp902/variables/variables.data-00000-of-00001
new file mode 100644
index 0000000000000000000000000000000000000000..24d580afe7eedaae9086fc6edacd94a9e562bc76
Binary files /dev/null and b/exp902/variables/variables.data-00000-of-00001 differ
diff --git a/exp902/variables/variables.index b/exp902/variables/variables.index
new file mode 100644
index 0000000000000000000000000000000000000000..9eb361c80f3c4a624f5de2666a5924ec82f48eaa
Binary files /dev/null and b/exp902/variables/variables.index differ
diff --git a/loadExpWDS.py b/loadExpWDS.py
index 87f20bc9019fddb7c5c08420ea54b925dcdb552e..1e530fd57b494d59c56ae420df11743cc3852733 100644
--- a/loadExpWDS.py
+++ b/loadExpWDS.py
@@ -5,7 +5,7 @@ import imageProcess as ip
import numpy as np
-def loadExpWData(nbrMaxImages=float('inf'), onlyDetected=False, detectedFace=False):
+def loadExpWData(nbrMaxImages=float('inf'), onlyDetected=False, detectedFace=False, count=False):
print(f"\nCHARGEMENT DE {nbrMaxImages} DONNEES DEPUIS EXPW...")
folderImages = 'data/expW/images/'
fileLabels = 'data/expW/labels.lst'
@@ -20,11 +20,10 @@ def loadExpWData(nbrMaxImages=float('inf'), onlyDetected=False, detectedFace=Fal
if nbrImages>=nbrMaxImages: break
k+= 1
- #Face extraction, according to the dataset annotations AND the face detector (cascade)
+ #Face extraction, according to the dataset annotations
imageName, Id, top, left, right, bottom, cofidence, label = line.strip().split(' ')
image = cv2.imread(folderImages+imageName)
faceAccordingToDS = image[int(top):int(bottom), int(left):int(right)]
- facesDetected = ip.imageProcess(faceAccordingToDS, writeEmotion=False, writeRectangle=False)
#Suivi visuel (facultatif, fait un peu peur sans attendre 1000ms entre deux images...)
if False:
@@ -35,9 +34,13 @@ def loadExpWData(nbrMaxImages=float('inf'), onlyDetected=False, detectedFace=Fal
#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 in priority image detected by detector
- if len(facesDetected) != 0 and detectedFace:
- face = facesDetected[0]
+ #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
@@ -49,7 +52,8 @@ def loadExpWData(nbrMaxImages=float('inf'), onlyDetected=False, detectedFace=Fal
nbrImages += 1
- print(f"{nbrImages} données chargées depuis expW (sur {k} données traités).", end='\r')
+ #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/loadFer2013ds.py b/loadFer2013ds.py
index 12932b4e797f0544c333c14f02681472c26bae8a..bd243fc95726ae55d75ec028484f580da632d88c 100644
--- a/loadFer2013ds.py
+++ b/loadFer2013ds.py
@@ -25,7 +25,7 @@ def strToArray(string): # Fer2013 provides images as string so it needs to be t
A.append(int(nbr))
A = np.array(A)
- A = np.reshape(A, input_shape)
+ A = np.reshape(A, (48,48,1))
return A
diff --git a/models/firstModel/exp901/keras_metadata.pb b/models/firstModel/exp901/keras_metadata.pb
new file mode 100644
index 0000000000000000000000000000000000000000..5cd67b742ca67067180589c552e4999ebf641b1b
--- /dev/null
+++ b/models/firstModel/exp901/keras_metadata.pb
@@ -0,0 +1,20 @@
+
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+�
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+�
root.layer_with_weights-8"_tf_keras_layer*�{"name": "dense_1", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "dtype": "float32", "units": 7, "activation": "softmax", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 37}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 38}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 39, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 64}}, "shared_object_id": 54}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 64]}}2
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\ No newline at end of file
diff --git a/models/firstModel/exp901/saved_model.pb b/models/firstModel/exp901/saved_model.pb
new file mode 100644
index 0000000000000000000000000000000000000000..a471215f868056c7dec6bef7a7fdc015c84d040a
Binary files /dev/null and b/models/firstModel/exp901/saved_model.pb differ
diff --git a/models/firstModel/exp901/variables/variables.data-00000-of-00001 b/models/firstModel/exp901/variables/variables.data-00000-of-00001
new file mode 100644
index 0000000000000000000000000000000000000000..00d0929294fe04ee510a8352fc30e8a9567e890d
Binary files /dev/null and b/models/firstModel/exp901/variables/variables.data-00000-of-00001 differ
diff --git a/models/firstModel/exp901/variables/variables.index b/models/firstModel/exp901/variables/variables.index
new file mode 100644
index 0000000000000000000000000000000000000000..dde295ee0ac05cabaea11ee2a3132bd0ea27e18f
Binary files /dev/null and b/models/firstModel/exp901/variables/variables.index differ
diff --git a/utils.py b/utils.py
index 3cd7a4acf69265034249c3b7323e7dbbe7e5047f..1df51ee384a9e31f88ed2d1211f5f0f48fa0e5ab 100644
--- a/utils.py
+++ b/utils.py
@@ -25,7 +25,8 @@ def normAndResize(image, input_shape):
# For an array image of shape (a,b,c) or (a,b), transform it into (h,l,p). Also normalize it.
h, l, p = input_shape
- # resize for h and l #
+ # resize for h and l
+ # print(image.shape) #
image = cv2.resize(image, dsize=(h, l), interpolation=cv2.INTER_CUBIC)
# if we want (h,l,3) -> (h,l,1) , we first transform it in to (h,l) (grey the image)
if len(image.shape) == 3 and p == 1 and image.shape[2] != 1: