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SVMexec_modeltesting113.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9d74e83d3a13350c461631313d215a466465db8fcb64db2a89c530c7a38e2d78
+size 71814547

app.py

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+import os
+import numpy as np
+import cv2
+import librosa
+import joblib
+from deepface import DeepFace
+import streamlit as st
+from collections import Counter
+from moviepy.editor import VideoFileClip
+
+
+emotion_map = {
+    'angry': 0,
+    'disgust': 1,
+    'fear': 2,
+    'happy': 3,
+    'neutral': 4,
+    'sad': 5
+}
+
+
+def split_video_into_frames_and_analyze_emotions(video_path, frame_rate=1):
+    cap = cv2.VideoCapture(video_path)
+    if not cap.isOpened():
+        st.error("Error: Could not open video.")
+        return
+
+    frame_count = 0
+    success, frame = cap.read()
+
+    emotion_counter = Counter()
+
+    while success:
+        if frame_count % frame_rate == 0:
+            try:
+                analysis = DeepFace.analyze(frame, actions=['emotion'])
+                if isinstance(analysis, list):
+                    for result in analysis:
+                        dominant_emotion = result['dominant_emotion']
+                        emotion_counter[dominant_emotion] += 1
+                else:
+                    dominant_emotion = analysis['dominant_emotion']
+                    emotion_counter[dominant_emotion] += 1
+            except Exception as e:
+                pass
+
+        success, frame = cap.read()
+        frame_count += 1
+
+    cap.release()
+
+    if emotion_counter:
+        highest_occurring_emotion = emotion_counter.most_common(1)[0][0]
+    else:
+        highest_occurring_emotion = None
+
+    return highest_occurring_emotion
+
+def extract_audio_from_video(video_path):
+    video_clip = VideoFileClip(video_path)
+    audio_path = "temp_audio.wav"
+    video_clip.audio.write_audiofile(audio_path)
+    audio_array, sr = librosa.load(audio_path, sr=None)
+    os.remove(audio_path)
+    return audio_array, sr
+
+def extract_features(audio_array, sr, max_length=100):
+    try:
+        mfccs = librosa.feature.mfcc(y=audio_array, sr=sr, n_mfcc=13)
+        chroma = librosa.feature.chroma_stft(y=audio_array, sr=sr)
+        spectral_contrast = librosa.feature.spectral_contrast(y=audio_array, sr=sr)
+
+        features = np.vstack([mfccs, chroma, spectral_contrast])
+        if features.shape[1] < max_length:
+            features = np.pad(features, ((0, 0), (0, max_length - features.shape[1])), mode='constant')
+        elif features.shape[1] > max_length:
+            features = features[:, :max_length]
+        return features.T
+    except Exception as e:
+        st.error(f"Error extracting features from audio: {str(e)}")
+        return None
+
+def main():
+    with open("style.css") as f:
+      st.markdown(f"<style>{f.read()}</style>", unsafe_allow_html=True)
+    st.title("Emotion Detection from Video")
+    
+    uploaded_file = st.file_uploader("Upload a video", type=["mp4"])
+    if uploaded_file is not None:
+        video_path = "uploaded_video.mp4"
+        with open(video_path, "wb") as f:
+            f.write(uploaded_file.read())
+        
+        st.write("Processing video...please wait")
+        highest_emotion = split_video_into_frames_and_analyze_emotions(video_path)
+        audio_array, sr = extract_audio_from_video(video_path)
+
+        model_path = "SVMexec_modeltesting113.pkl"
+        svm_model = joblib.load(model_path)
+        scaler = joblib.load('scaler.pkl')
+
+        features = extract_features(audio_array, sr)
+        if features is not None:
+            features_2d = features.reshape(1, -1)
+            features_normalized = scaler.transform(features_2d)
+
+            predicted_class = svm_model.predict(features_normalized)[0]
+            emotion_labels = ['angry', 'disgust', 'fear', 'happy', 'neutral', 'sad']
+            predicted_emotion = emotion_labels[predicted_class]
+            
+            if highest_emotion == predicted_emotion:
+                st.write(f"The person in the video is {predicted_emotion}.")
+            else:
+                st.write(f"The emotions from the frames and audio do not match, but the facial expression seems to be {highest_emotion}, while the audio emotion seems to be {predicted_emotion}.")
+        else:
+            st.write("Failed to extract features from the audio file.")
+
+if __name__ == "__main__":
+    main()

packages.txt

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+libgl1

requirements.txt

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+numpy<2
+librosa
+joblib
+torch
+moviepy
+scikit-learn
+opencv-python-headless
+streamlit
+Pillow
+deepface
+tensorflow
+tf-keras
+pydub
+imageio
+ffmpeg-python
+

scaler.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d373f3b7e456bf96fec1d03b56b42e99ab43ff10e4623f8e6970ca63bbba27dd
+size 77415

style.css

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+.stApp {
+    background-image: url('https://i.postimg.cc/2yFrwJWM/Blue-And-Pink-Aesthetic-Desktop-Wallpaper.png'); 
+    background-size: cover;
+    background-position: center;
+    background-repeat: no-repeat;
+    
+    
+}