final deployment

face_emotion_pipeline.py

@@ -4,8 +4,9 @@ import numpy as np
 from moviepy.editor import VideoFileClip
 from retinaface import RetinaFace
 from hsemotion.facial_emotions import HSEmotionRecognizer
-from torchvision.transforms import ToTensor
-device='cpu'
+
+
+# Initialize recognizer
 recognizer = HSEmotionRecognizer(model_name='enet_b0_8_best_vgaf', device='cpu')
  
 # Face Detection Function
@@ -37,8 +38,6 @@ def annotate_frame(frame, faces):
 # Emotion Classification Function
 def classify_emotions(face_image):
     """ Classify emotions for the given face image using global recognizer """
-    face_image = cv2.cvtColor(face_image, cv2.COLOR_BGR2RGB)
-    face_image = ToTensor()(face_image).unsqueeze(0).to(device)
     results = recognizer.predict_emotions(face_image)
     if results:
         emotion = results[0]  # Get the most likely emotion
@@ -63,7 +62,7 @@ def process_video_frames(video_path, temp_output_path, frame_skip=5):
             frame = np.copy(frame)  # Create a writable copy of the frame
             faces = detect_faces(frame)
             annotate_frame(frame, faces)
-        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)  # Convert RGB to BGR for OpenCV
+        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)  # Convert RGB to BGR for OpenCV RGB2BGR
         out.write(frame)
         frame_count += 1
  
@@ -128,140 +127,6 @@ def process_image(input_path, output_path):
     cv2.imwrite(combined_output_path, combined_image)
 
 
-
-
-
-
-
-# import cv2
-# import os
-# import numpy as np
-# from moviepy.editor import VideoFileClip
-# from retinaface import RetinaFace
-# from hsemotion.facial_emotions import HSEmotionRecognizer
-
-
-# # Initialize recognizer
-# recognizer = HSEmotionRecognizer(model_name='enet_b0_8_best_vgaf', device='cpu')
- 
-# # Face Detection Function
-# def detect_faces(frame):
-#     """ Detect faces in the frame using RetinaFace """
-#     faces = RetinaFace.detect_faces(frame)
-#     if isinstance(faces, dict):
-#         face_list = []
-#         for key in faces.keys():
-#             face = faces[key]
-#             facial_area = face['facial_area']
-#             face_dict = {
-#                 'box': (facial_area[0], facial_area[1], facial_area[2] - facial_area[0], facial_area[3] - facial_area[1])
-#             }
-#             face_list.append(face_dict)
-#         return face_list
-#     return []
- 
-# # Annotation Function
-# def annotate_frame(frame, faces):
-#     """ Annotate the frame with recognized emotions using global recognizer """
-#     for face in faces:
-#         x, y, w, h = face['box']
-#         face_image = frame[y:y+h, x:x+w]  # Extract face region from frame
-#         emotion = classify_emotions(face_image)
-#         cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 0, 0), 2)
-#         cv2.putText(frame, emotion, (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (255, 0, 0), 2)
- 
-# # Emotion Classification Function
-# def classify_emotions(face_image):
-#     """ Classify emotions for the given face image using global recognizer """
-#     results = recognizer.predict_emotions(face_image)
-#     if results:
-#         emotion = results[0]  # Get the most likely emotion
-#         print("=====>",emotion)
-#     else:
-#         emotion = 'Unknown'
-#     return emotion
- 
-# # Process Video Frames
-# def process_video_frames(video_path, temp_output_path, frame_skip=5):
-#     # Load the video
-#     video_clip = VideoFileClip(video_path)
-#     fps = video_clip.fps
- 
-#     # Initialize output video writer
-#     out = cv2.VideoWriter(temp_output_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (int(video_clip.size[0]), int(video_clip.size[1])))
- 
-#     # Iterate through frames, detect faces, and annotate emotions
-#     frame_count = 0
-#     for frame in video_clip.iter_frames():
-#         if frame_count % frame_skip == 0:  # Process every nth frame
-#             frame = np.copy(frame)  # Create a writable copy of the frame
-#             faces = detect_faces(frame)
-#             annotate_frame(frame, faces)
-#         frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)  # Convert RGB to BGR for OpenCV RGB2BGR
-#         out.write(frame)
-#         frame_count += 1
- 
-#     # Release resources and cleanup
-#     out.release()
-#     cv2.destroyAllWindows()
-#     video_clip.close()
- 
-# # Add Audio to Processed Video
-# def add_audio_to_video(original_video_path, processed_video_path, output_path):
-#     try:
-#         original_clip = VideoFileClip(original_video_path)
-#         processed_clip = VideoFileClip(processed_video_path)
-#         final_clip = processed_clip.set_audio(original_clip.audio)
-#         final_clip.write_videofile(output_path, codec='libx264', audio_codec='aac')
-#     except Exception as e:
-#         print(f"Error while combining with audio: {e}")
-#     finally:
-#         original_clip.close()
-#         processed_clip.close()
- 
-# # Process Video
-# def process_video(video_path, output_path , skip = 1  , add_audio = True):
-#     temp_output_path = 'temp_output_video.mp4'
- 
-#     # Process video frames and save to a temporary file
-#     process_video_frames(video_path, temp_output_path, frame_skip=skip)  # Adjust frame_skip as needed
- 
-#     # Add audio to the processed video
-#     if add_audio:
-#         add_audio_to_video(video_path, temp_output_path, output_path)
-#     else:
-#         os.rename(temp_output_path, output_path)  # Rename the temporary file if audio is not needed
- 
-# # Process Image
-# def process_image(input_path, output_path):
-#     # Ensure output path has a valid extension
-#     if not output_path.lower().endswith(('.jpg', '.jpeg', '.png','.heic')):
-#         output_path += '.jpg'  # Default to .jpg if no valid extension is found
- 
-#     # Step 1: Read input image
-#     image = cv2.imread(input_path)
-#     if image is None:
-#         print(f"Error: Unable to read image at '{input_path}'")
-#         return
- 
-#     # Step 2: Detect faces and annotate emotions
-#     faces = detect_faces(image)
-#     annotate_frame(image, faces)
- 
-#     # Step 3: Write annotated image to output path
-#     cv2.imwrite(output_path, image)
- 
-#     # Step 4: Combine input and output images horizontally
-#     input_image = cv2.imread(input_path)
-#     combined_image = cv2.hconcat([input_image, image])
-#     combined_image = cv2.cvtColor(combined_image, cv2.COLOR_BGR2RGB)
- 
-#     # Step 5: Save the combined image
-#     combined_output_path = os.path.splitext(output_path)[0] + '_combined.jpg'
-    
-#     cv2.imwrite(combined_output_path, combined_image)
-
-
     ###########################
     
 # recognizer = HSEmotionRecognizer(model_name='enet_b0_8_best_vgaf', device='cpu')

requirements.txt

@@ -3,5 +3,3 @@ moviepy==1.0.3
 hsemotion==0.3.0
 retina-face==0.0.17
 tf_keras==2.15.1
-torch==2.2.0
-torchvision==0.17.0