287 lines
1.0 MiB
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287 lines
1.0 MiB
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{
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
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"colab": {
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"provenance": []
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},
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3"
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},
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"language_info": {
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"name": "python"
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}
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},
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"cells": [
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{
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"cell_type": "code",
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"source": [
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"!pip install fer"
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],
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "nRoS8sphBgk-",
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"outputId": "9ef29a04-6522-4267-f80d-5122a4c7e247"
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},
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"execution_count": 1,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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"Collecting fer\n",
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" Downloading fer-22.5.0-py3-none-any.whl (1.5 MB)\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.5/1.5 MB\u001b[0m \u001b[31m17.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[?25hRequirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from fer) (3.7.1)\n",
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"Requirement already satisfied: opencv-contrib-python in /usr/local/lib/python3.10/dist-packages (from fer) (4.7.0.72)\n",
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"Requirement already satisfied: keras>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from fer) (2.12.0)\n",
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"Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from fer) (1.5.3)\n",
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"Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from fer) (2.27.1)\n",
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"Collecting facenet-pytorch (from fer)\n",
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" Downloading facenet_pytorch-2.5.3-py3-none-any.whl (1.9 MB)\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.9/1.9 MB\u001b[0m \u001b[31m68.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[?25hRequirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from fer) (4.65.0)\n",
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"Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from facenet-pytorch->fer) (1.22.4)\n",
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"Requirement already satisfied: torchvision in /usr/local/lib/python3.10/dist-packages (from facenet-pytorch->fer) (0.15.1+cu118)\n",
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"Requirement already satisfied: pillow in /usr/local/lib/python3.10/dist-packages (from facenet-pytorch->fer) (8.4.0)\n",
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"Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->fer) (1.0.7)\n",
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"Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->fer) (0.11.0)\n",
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"Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->fer) (4.39.3)\n",
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"Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->fer) (1.4.4)\n",
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"Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->fer) (23.1)\n",
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"Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->fer) (3.0.9)\n",
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"Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib->fer) (2.8.2)\n",
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"Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->fer) (2022.7.1)\n",
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"Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->fer) (1.26.15)\n",
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"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->fer) (2022.12.7)\n",
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"Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests->fer) (2.0.12)\n",
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"Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->fer) (3.4)\n",
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"Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib->fer) (1.16.0)\n",
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"Requirement already satisfied: torch==2.0.0 in /usr/local/lib/python3.10/dist-packages (from torchvision->facenet-pytorch->fer) (2.0.0+cu118)\n",
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"Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch==2.0.0->torchvision->facenet-pytorch->fer) (3.12.0)\n",
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"Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch==2.0.0->torchvision->facenet-pytorch->fer) (4.5.0)\n",
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"Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch==2.0.0->torchvision->facenet-pytorch->fer) (1.11.1)\n",
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"Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch==2.0.0->torchvision->facenet-pytorch->fer) (3.1)\n",
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"Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch==2.0.0->torchvision->facenet-pytorch->fer) (3.1.2)\n",
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"Requirement already satisfied: triton==2.0.0 in /usr/local/lib/python3.10/dist-packages (from torch==2.0.0->torchvision->facenet-pytorch->fer) (2.0.0)\n",
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"Requirement already satisfied: cmake in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch==2.0.0->torchvision->facenet-pytorch->fer) (3.25.2)\n",
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"Requirement already satisfied: lit in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch==2.0.0->torchvision->facenet-pytorch->fer) (16.0.3)\n",
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"Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch==2.0.0->torchvision->facenet-pytorch->fer) (2.1.2)\n",
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"Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch==2.0.0->torchvision->facenet-pytorch->fer) (1.3.0)\n",
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"Installing collected packages: facenet-pytorch, fer\n",
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"Successfully installed facenet-pytorch-2.5.3 fer-22.5.0\n"
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]
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}
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"id": "zwQsaihI9_wU"
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},
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"outputs": [],
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"source": [
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"# Przykładowy kod do rozpoznawania emocji za pomocą biblioteki fer, OpenCV i TensorFlow\n",
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"\n",
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"import cv2\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"from fer import FER\n",
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"\n",
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"# Wczytanie obrazu\n",
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"def get_emotions(image_path):\n",
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" image_path\n",
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" image = cv2.imread(image_path)\n",
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"\n",
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" # Inicjalizacja detektora emocji\n",
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" detector = FER()\n",
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"\n",
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" # Wykrycie twarzy na obrazie\n",
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" faces = detector.detect_emotions(image)\n",
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"\n",
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" # Wyświetlenie wyników\n",
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" plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))\n",
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" plt.axis(\"off\")\n",
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"\n",
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" for face in faces:\n",
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" x, y, w, h = face[\"box\"]\n",
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" emotions = face[\"emotions\"]\n",
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"\n",
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" # Wyświetlenie wykrytych emocji\n",
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" for emotion, score in emotions.items():\n",
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" text = f\"{emotion}: {score:.2f}\"\n",
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" plt.text(x, y, text, backgroundcolor=\"white\")\n",
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" y += 20\n",
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"\n",
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" plt.show()\n"
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]
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},
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{
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"cell_type": "code",
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"source": [
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"get_emotions(\"/content/happy.png\")"
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],
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 406
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},
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"id": "aUFoiR2KC7MJ",
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"outputId": "4c1f483e-227d-4b90-f17d-b1d49e5872d9"
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},
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"execution_count": 6,
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"outputs": [
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{
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"output_type": "display_data",
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"data": {
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"text/plain": [
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"<Figure size 640x480 with 1 Axes>"
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],
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"image/png": "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},
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"metadata": {}
|
||
|
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}
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|
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]
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|
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},
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{
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"cell_type": "code",
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"source": [
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"get_emotions(\"/content/angry.png\")"
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],
|
||
|
|
"metadata": {
|
||
|
|
"colab": {
|
||
|
|
"base_uri": "https://localhost:8080/",
|
||
|
|
"height": 406
|
||
|
|
},
|
||
|
|
"id": "py3WfR1eC_xR",
|
||
|
|
"outputId": "6a5e088a-85f0-434d-98cd-f2284b9d637c"
|
||
|
|
},
|
||
|
|
"execution_count": 7,
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"output_type": "display_data",
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
],
|
||
|
|
"image/png": "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
|
||
|
|
},
|
||
|
|
"metadata": {}
|
||
|
|
}
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"source": [
|
||
|
|
"get_emotions(\"/content/scared.png\")"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"colab": {
|
||
|
|
"base_uri": "https://localhost:8080/",
|
||
|
|
"height": 406
|
||
|
|
},
|
||
|
|
"id": "YXO5EtRyDCwC",
|
||
|
|
"outputId": "2e73c9a4-cea7-43e1-e53b-034b5bfdaf78"
|
||
|
|
},
|
||
|
|
"execution_count": 8,
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"output_type": "display_data",
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
],
|
||
|
|
"image/png": "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
|
||
|
|
},
|
||
|
|
"metadata": {}
|
||
|
|
}
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"source": [
|
||
|
|
"get_emotions(\"/content/sad.png\")"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"colab": {
|
||
|
|
"base_uri": "https://localhost:8080/",
|
||
|
|
"height": 406
|
||
|
|
},
|
||
|
|
"id": "efy7oOLsDSOS",
|
||
|
|
"outputId": "597bb6cb-2a54-4694-e286-2ab6786386e0"
|
||
|
|
},
|
||
|
|
"execution_count": 9,
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"output_type": "display_data",
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
],
|
||
|
|
"image/png": "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
|
||
|
|
},
|
||
|
|
"metadata": {}
|
||
|
|
}
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"source": [
|
||
|
|
"get_emotions(\"/content/neutral.png\")"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"colab": {
|
||
|
|
"base_uri": "https://localhost:8080/",
|
||
|
|
"height": 406
|
||
|
|
},
|
||
|
|
"id": "00_JqZqWDtWR",
|
||
|
|
"outputId": "d0c70155-2062-4afb-9f4b-9e32ad70bae9"
|
||
|
|
},
|
||
|
|
"execution_count": 15,
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"output_type": "display_data",
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
],
|
||
|
|
"image/png": "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
|
||
|
|
},
|
||
|
|
"metadata": {}
|
||
|
|
}
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"source": [
|
||
|
|
"get_emotions(\"/content/suprise.png\")"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"colab": {
|
||
|
|
"base_uri": "https://localhost:8080/",
|
||
|
|
"height": 406
|
||
|
|
},
|
||
|
|
"id": "NsgnGqiyEZjs",
|
||
|
|
"outputId": "f88c6144-8c78-4ef6-b45e-fc15db8a8255"
|
||
|
|
},
|
||
|
|
"execution_count": 12,
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"output_type": "display_data",
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
],
|
||
|
|
"image/png": "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
|
||
|
|
},
|
||
|
|
"metadata": {}
|
||
|
|
}
|
||
|
|
]
|
||
|
|
}
|
||
|
|
]
|
||
|
|
}
|