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main.py

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+import warnings
+warnings.filterwarnings('ignore', category=UserWarning, module='tensorflow')
+import os 
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
+os.environ['TF_ENABLE_ONEDNN_OPTS'] = '0'
+import logging
+logging.getLogger('absl').setLevel(logging.ERROR)
+from functions.models import models_dict
+from functions.helper import extract_faces_from_frames
+from functions.video import eyebrow,blinks,detect_yawn,detect_smiles
+from functions.valence_arousal import va_predict
+from functions.fer import fer_predict,plot_graph
+from moviepy.editor import VideoFileClip
+import json 
+import pandas as pd 
+from typing import Callable
+from functions.audio import extract_audio_features
+asrmodel=models_dict['asrmodel']
+asrproc=models_dict['asrproc']
+sentipipe=models_dict['sentipipe']
+valence_arousal_model=models_dict['valence_fer'][1]
+val_ar_feat_model=models_dict['valence_fer'][0]
+fer_model=models_dict['fer']
+smile_cascade=models_dict['smile_cascade']
+dnn_net=models_dict['face'][0]
+predictor=models_dict['face'][1]
+fps=30
+session_data={}
+
+def analyze_live_video(video_path: str, uid: str, user_id: str, count: int, final: bool, log: Callable[[str], None]):
+    try:
+        global session_data
+        if uid not in session_data:
+            session_data[uid]={
+                "vcount":[],
+				"duration":[],
+                    
+                "audio":[],
+
+                "blinks":[],
+                "yawn":[],
+                "smile":[],
+                "eyebrow":[],
+
+                "fer": [],
+				"valence":[],
+				"arousal":[],
+				"stress":[],
+            }
+        print(f"UID: {uid}, User ID: {user_id}, Count: {count}, Final: {final}, Video: {video_path}")
+        print(f"analysing video for question - {count}")
+
+        output_dir = os.path.join('output', uid)
+        os.makedirs(output_dir,exist_ok=True)
+        
+        folder_path=os.path.join(output_dir,f'{count}')
+        os.makedirs(folder_path,exist_ok=True)
+        meta_data_path=os.path.join(folder_path,'metadata.json')
+        valence_plot=os.path.join(folder_path,"vas.png")
+        df_path=os.path.join(folder_path,'data.csv')
+
+        video_clip=VideoFileClip(video_path)
+        video_clip=video_clip.set_fps(fps)
+        duration=video_clip.duration
+        print(duration)
+        audio=video_clip.audio
+        audio_path = os.path.join(folder_path,'extracted_audio.wav')
+        print(audio_path)
+        audio.write_audiofile(audio_path)
+        video_frames=[frame for frame in video_clip.iter_frames()]
+        faces, landmarks, sizes=extract_faces_from_frames(video_frames,dnn_net,predictor)
+
+
+        # faces=[extract_face(frame) for frame in tqdm(video_frames)]
+        af=extract_audio_features(audio_path,asrmodel,asrproc,sentipipe)
+
+
+        fer_emotions,class_wise_frame_count,em_tensors=fer_predict(faces,fps,fer_model)
+        valence_list,arousal_list,stress_list=va_predict(valence_arousal_model,val_ar_feat_model,faces,list(em_tensors))
+        timestamps=[j/fps for j in range(len(valence_list))]
+
+        eyebrow_dist=eyebrow(landmarks,sizes)
+        print('eyebrow done')
+        blink_durations,total_blinks=blinks(landmarks,sizes,fps)
+        print('blinks done')
+        smiles,smile_count=detect_smiles(faces,smile_cascade)
+        print('smiles done')
+        yawn,normalized_lip_distances,yawn_count=detect_yawn(landmarks,sizes)
+        print('ywan done')
+
+
+        y_vals = [valence_list, arousal_list, stress_list,eyebrow_dist]
+        labels = ['Valence', 'Arousal', 'Stress',"EyeBrowDistance"]
+        plot_graph(timestamps, y_vals, labels, valence_plot)
+        print('graph_plotted')
+        meta_data={}
+        meta_data['facial_emotion_recognition'] = {
+			"class_wise_frame_count": class_wise_frame_count,
+		}
+        meta_data['audio']=af
+
+        meta_data['blinks']={
+            'blink_durations':blink_durations,
+            'total_blinks':total_blinks
+        }
+        meta_data['smile']=smile_count
+        meta_data['yawn']=yawn_count
+        with open(meta_data_path, 'w') as json_file:
+            json.dump(meta_data, json_file, indent=4)
+        df=pd.DataFrame(
+            {
+                'timestamps':timestamps,
+                'fer': fer_emotions,
+                'valence': valence_list,
+                'arousal': arousal_list,
+                'stress': stress_list,
+                'eyebrow':eyebrow_dist,
+            }
+        )
+        df.to_csv(df_path,index=False)
+    except Exception as e:
+            print("Error analyzing video...: ", e)
+            
+# analyze_live_video('s1.mp4','1',1,1,True,print)
+