From 3bcce61a462340d9782f238018fcf69381a9d19e Mon Sep 17 00:00:00 2001 From: Jakub Adamski Date: Wed, 14 Jun 2023 10:59:08 +0200 Subject: [PATCH] SE8 fix --- .gitignore | 4 +- SE8.ipynb | 291 +++++++++++++++++++++++++++++++++++++++++++++++------ data.csv | 81 +++++++++++++++ 3 files changed, 343 insertions(+), 33 deletions(-) create mode 100644 data.csv diff --git a/.gitignore b/.gitignore index 4d4a299..ca1dcf4 100644 --- a/.gitignore +++ b/.gitignore @@ -1 +1,3 @@ -.conda/ \ No newline at end of file +.conda/ +output/ +*.mp4 \ No newline at end of file diff --git a/SE8.ipynb b/SE8.ipynb index d13903b..7ef0845 100644 --- a/SE8.ipynb +++ b/SE8.ipynb @@ -1,6 +1,7 @@ { "cells": [ { + "attachments": {}, "cell_type": "markdown", "metadata": { "slideshow": { @@ -32,6 +33,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -50,6 +52,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -78,6 +81,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -125,6 +129,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -150,6 +155,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -163,6 +169,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -172,6 +179,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -179,6 +187,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -195,6 +204,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -211,6 +221,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -218,6 +229,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -257,6 +269,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -281,6 +294,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -433,6 +447,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -440,6 +455,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -447,11 +463,17 @@ ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 1, "metadata": {}, - "source": [] + "outputs": [], + "source": [ + "from fer import FER, Video\n", + "import cv2" + ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -459,11 +481,17 @@ ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 2, "metadata": {}, - "source": [] + "outputs": [], + "source": [ + "input_video_path = \"face.mp4\"\n", + "output_video_path = \"out.mp4\"" + ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -471,11 +499,16 @@ ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 3, "metadata": {}, - "source": [] + "outputs": [], + "source": [ + "video = Video(input_video_path)" + ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -483,11 +516,31 @@ ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 5, "metadata": {}, - "source": [] + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:fer:30.00 fps, 80 frames, 2.67 seconds\n", + "INFO:fer:Making directories at output\n", + "100%|██████████| 80/80 [00:06<00:00, 11.67frames/s]\n", + "INFO:fer:Completed analysis: saved to output\\face_output.mp4\n", + "INFO:fer:Starting to Zip\n", + "INFO:fer:Compressing: 62%\n", + "INFO:fer:Zip has finished\n" + ] + } + ], + "source": [ + "detector = FER(mtcnn=True)\n", + "analysis = video.analyze(detector, display=False)" + ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -495,11 +548,24 @@ ] }, { - "cell_type": "markdown", + 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'surprise0': 0.0, 'neutral0': 0.0}, {'box0': [66, 174, 209, 269], 'angry0': 0.0, 'disgust0': 0.0, 'fear0': 0.0, 'happy0': 0.98, 'sad0': 0.0, 'surprise0': 0.0, 'neutral0': 0.02}, {'box0': [70, 178, 211, 264], 'angry0': 0.0, 'disgust0': 0.0, 'fear0': 0.0, 'happy0': 0.93, 'sad0': 0.0, 'surprise0': 0.0, 'neutral0': 0.07}, {'box0': [71, 181, 209, 266], 'angry0': 0.01, 'disgust0': 0.0, 'fear0': 0.0, 'happy0': 0.58, 'sad0': 0.0, 'surprise0': 0.0, 'neutral0': 0.4}, {'box0': [73, 186, 198, 251], 'angry0': 0.03, 'disgust0': 0.0, 'fear0': 0.0, 'happy0': 0.06, 'sad0': 0.01, 'surprise0': 0.0, 'neutral0': 0.89}]\n" + ] + } + ], + "source": [ + "print(analysis)" + ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -507,6 +573,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -516,11 +583,22 @@ ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 7, "metadata": {}, - "source": [] + "outputs": [], + "source": [ + "import os\n", + "import soundfile\n", + "import numpy as np\n", + "import librosa\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.neural_network import MLPClassifier\n", + "from sklearn.metrics import accuracy_score" + ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -528,11 +606,32 @@ ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 14, "metadata": {}, - "source": [] + "outputs": [], + "source": [ + "def extract_features(file_name, mfcc, chroma, mel):\n", + " with soundfile.SoundFile(file_name) as sound_file:\n", + " X = sound_file.read(dtype=\"float32\")\n", + " sample_rate = sound_file.samplerate\n", + " if chroma:\n", + " stft = np.abs(librosa.stft(X))\n", + " result = np.array([])\n", + " if mfcc:\n", + " mfccs = np.mean(librosa.feature.mfcc(y=X, sr=sample_rate, n_mfcc=40).T, axis=0)\n", + " result = np.hstack((result, mfccs))\n", + " if chroma:\n", + " chroma = np.mean(librosa.feature.chroma_stft(S=stft, sr=sample_rate).T, axis=0)\n", + " result = np.hstack((result, chroma))\n", + " if mel:\n", + " mel = np.mean(librosa.feature.melspectrogram(y=X, sr=sample_rate).T, axis=0)\n", + " result = np.hstack((result, mel))\n", + " return result\n" + ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -540,11 +639,20 @@ ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 9, "metadata": {}, - "source": [] + "outputs": [], + "source": [ + "emotions = {\n", + " '03': 'happy',\n", + " '04': 'sad',\n", + " '05': 'angry'\n", + "}" + ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -552,11 +660,44 @@ ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 12, "metadata": {}, - "source": [] + "outputs": [], + "source": [ + "def load_data(test_size=0.2):\n", + " x, y = [], []\n", + " main_data_folder = \"C:\\\\Users\\\\jadamski\\\\Downloads\\\\speech-emotion-recognition-ravdess-data\"\n", + " \n", + "\n", + " # Iterate through each actor's folder\n", + " for actor_folder in os.listdir(main_data_folder):\n", + " actor_folder_path = os.path.join(main_data_folder, actor_folder)\n", + "\n", + " # Ensure it's a directory\n", + " if os.path.isdir(actor_folder_path):\n", + " \n", + " # Iterate through each file inside the actor's folder\n", + " for file in os.listdir(actor_folder_path):\n", + " emotion_code = file.split(\"-\")[2]\n", + " \n", + " # Process only files with the emotions you're interested in\n", + " if emotion_code in emotions:\n", + " file_name = os.path.join(actor_folder_path, file)\n", + " emotion = emotions[emotion_code]\n", + " \n", + " # Extract features from the audio file\n", + " feature = extract_features(file_name, mfcc=True, chroma=True, mel=True)\n", + " \n", + " # Append features and emotion to the lists\n", + " x.append(feature)\n", + " y.append(emotion)\n", + " \n", + " return train_test_split(np.array(x), y, test_size=test_size, random_state=9)\n" + ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -564,11 +705,16 @@ ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 15, "metadata": {}, - "source": [] + "outputs": [], + "source": [ + "x_train, x_test, y_train, y_test = load_data(test_size=0.25)" + ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -576,11 +722,24 @@ ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 16, "metadata": {}, - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Treningowe: 432, Testowe: 144\n" + ] + } + ], + "source": [ + "print(f'Treningowe: {x_train.shape[0]}, Testowe: {x_test.shape[0]}')" + ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -588,11 +747,24 @@ ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 17, "metadata": {}, - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Liczba cech: 180\n" + ] + } + ], + "source": [ + "print(f'Liczba cech: {x_train.shape[1]}')\n" + ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -600,11 +772,16 @@ ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 18, "metadata": {}, - "source": [] + "outputs": [], + "source": [ + "model = MLPClassifier(alpha=0.01, batch_size=256, epsilon=1e-08, hidden_layer_sizes=(300,), learning_rate='adaptive', max_iter=500)" + ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -612,11 +789,42 @@ ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 19, "metadata": {}, - "source": [] + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:py.warnings:c:\\Users\\jadamski\\.conda\\envs\\empatia\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.\n", + " warnings.warn(\n", + "\n" + ] + }, + { + "data": { + "text/html": [ + "
MLPClassifier(alpha=0.01, batch_size=256, hidden_layer_sizes=(300,),\n",
+       "              learning_rate='adaptive', max_iter=500)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "MLPClassifier(alpha=0.01, batch_size=256, hidden_layer_sizes=(300,),\n", + " learning_rate='adaptive', max_iter=500)" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.fit(x_train, y_train)" + ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -624,11 +832,16 @@ ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 20, "metadata": {}, - "source": [] + "outputs": [], + "source": [ + "y_pred = model.predict(x_test)\n" + ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -636,11 +849,25 @@ ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 21, "metadata": {}, - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dokładność: 80.56%\n" + ] + } + ], + "source": [ + "accuracy = accuracy_score(y_true=y_test, y_pred=y_pred)\n", + "print(\"Dokładność: {:.2f}%\".format(accuracy * 100))\n" + ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ diff --git a/data.csv b/data.csv new file mode 100644 index 0000000..bc3b1ad --- /dev/null +++ b/data.csv @@ -0,0 +1,81 @@ +angry0,box0,disgust0,fear0,happy0,neutral0,sad0,surprise0 +0.19,"[72, 192, 207, 269]",0.0,0.06,0.0,0.58,0.17,0.0 +0.14,"[72, 192, 208, 269]",0.0,0.04,0.0,0.68,0.14,0.0 +0.2,"[71, 191, 208, 271]",0.0,0.02,0.0,0.62,0.15,0.0 +0.24,"[71, 192, 208, 267]",0.0,0.02,0.0,0.59,0.15,0.0 +0.28,"[72, 193, 206, 266]",0.0,0.02,0.0,0.56,0.13,0.0 +0.33,"[69, 189, 209, 265]",0.0,0.04,0.0,0.51,0.11,0.0 +0.3,"[70, 191, 205, 262]",0.0,0.02,0.0,0.56,0.12,0.0 +0.25,"[69, 190, 206, 263]",0.0,0.02,0.0,0.62,0.11,0.0 +0.22,"[69, 190, 206, 262]",0.0,0.02,0.0,0.62,0.14,0.0 +0.17,"[73, 189, 203, 264]",0.0,0.02,0.0,0.67,0.14,0.0 +0.17,"[72, 188, 203, 262]",0.0,0.02,0.0,0.7,0.1,0.0 +0.16,"[72, 187, 202, 263]",0.0,0.02,0.0,0.7,0.11,0.0 +0.12,"[72, 187, 204, 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