setup for emotion embeddings

.gitignore

@@ -1,2 +1,3 @@
 /images/
+/videos/
 embeddings.json

app.py

@@ -4,7 +4,7 @@ import os
 import json
 import numpy as np
 
-folderPath = "images"
+folderPath = "videos/rte-archive-football"
 output_file = "embeddings.json"
 
 embeddings = []
@@ -33,7 +33,7 @@ def processImage(image, idx):
         anaylse_obj = DeepFace.analyze(
             img_path=image,
             actions=['emotion'],
-            enforce_detection=False,
+            enforce_detection=True,
             detector_backend=backends[0]
         )
 
@@ -67,6 +67,9 @@ def setup():
     # Optional: Filter by specific image extensions (e.g., .jpg, .png)
     image_paths = [img for img in image_files if img.endswith(('.jpg', '.jpeg', '.png', '.bmp', '.tiff', '.gif'))]
 
+    # Sort the image paths by filename
+    image_paths.sort(key=lambda x: os.path.basename(x))
+
     return image_paths
 
 def l2_normalize(vector):

process_video.py

@@ -0,0 +1,82 @@
+import os
+import subprocess
+import re
+
+def get_frame_rate(video_path):
+    """
+    Get the frame rate of a video file using ffmpeg.
+    """
+    command = [
+        'ffmpeg',
+        '-i', video_path,
+        '-vcodec', 'png',
+        '-f', 'null',
+        '-'
+    ]
+    
+    result = subprocess.run(command, stderr=subprocess.PIPE, text=True)
+    output = result.stderr
+
+    match = re.search(r'(\d+(\.\d+)?) fps', output)
+    if match:
+        return float(match.group(1))
+    else:
+        raise ValueError("Frame rate not found in video information.")
+
+def extract_frames_from_video(video_path, output_folder):
+    """
+    Extract frames from a video file at 1 fps.
+    """
+    os.makedirs(output_folder, exist_ok=True)
+
+    command = [
+        'ffmpeg',
+        '-i', video_path,                 # Input file
+        '-vf', 'fps=1',                   # Extract 1 frame per second
+        '-vsync', 'vfr',                  # Variable frame rate
+        os.path.join(output_folder, 'frame_%04d.png')  # Output pattern
+    ]
+
+    subprocess.run(command, check=True)
+
+def rename_frames(output_folder, frame_rate):
+    """
+    Rename the extracted frames to match their original frame numbers.
+    """
+    frame_files = sorted(f for f in os.listdir(output_folder) if f.startswith('frame_') and f.endswith('.png'))
+    for i, filename in enumerate(frame_files):
+        # Calculate the original frame number
+        original_frame_number = int(i * frame_rate)
+        
+        # Create the new filename with the original frame number
+        new_filename = f"frame_{original_frame_number:05d}.png"
+        os.rename(
+            os.path.join(output_folder, filename),
+            os.path.join(output_folder, new_filename)
+        )
+
+def process_videos_in_folder(folder_path):
+    """
+    Process all video files in the specified folder and extract frames from each video.
+    """
+    for filename in os.listdir(folder_path):
+        if filename.lower().endswith(('.mp4', '.avi', '.mov', '.mkv', '.flv')):
+            video_path = os.path.join(folder_path, filename)
+            output_folder = os.path.join(folder_path, os.path.splitext(filename)[0])  # Create a folder for each video
+
+            print(f'Processing video: {video_path}')
+            
+            try:
+                frame_rate = get_frame_rate(video_path)
+                print(f'Original frame rate: {frame_rate} fps')
+                extract_frames_from_video(video_path, output_folder)
+                rename_frames(output_folder, frame_rate)
+                print(f'Frames saved and renamed to: {output_folder}')
+            except ValueError as e:
+                print(f'Error processing video {video_path}: {e}')
+
+if __name__ == "__main__":
+    # Path to the folder containing videos
+    folder_path = 'videos'
+    
+    process_videos_in_folder(folder_path)