3_RetinaFace_and_HSEmotion.ipynb

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      "name": "python3",
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      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Video and Image Emotion Annotation\n",
        "\n",
        "This script facilitates the detection of faces and annotation of recognized emotions in both videos and images. It utilizes state-of-the-art deep learning models for face detection and emotion recognition, namely RetinaFace and HSEmotionRecognizer, respectively. The goal is to enhance media content understanding by automatically labeling facial expressions with emotional states.\n",
        "Components:\n",
        "\n",
        "## Face Detection using RetinaFace:\n",
        "     The detect_faces function leverages the RetinaFace model to identify faces within a given frame of video or image data. It retrieves facial bounding boxes, providing precise coordinates for subsequent processing.\n",
        "\n",
        "## Emotion Recognition with HSEmotionRecognizer:\n",
        "     The HSEmotionRecognizer model, initialized as recognizer, interprets emotional states from extracted face regions. It predicts emotions based on learned features from the provided face images.\n",
        "\n",
        "## Annotation and Visualization:\n",
        "     The annotate_frame function annotates each detected face with its recognized emotion. It draws bounding boxes around faces and labels them with the predicted emotional state, enhancing visual understanding of the content.\n",
        "\n",
        "## Processing Pipeline:\n",
        "        Video Processing:\n",
        "        process_video_frames: Iterates through frames of a video, applying face detection and emotion annotation. It saves the processed frames into a temporary video file.\n",
        "        add_audio_to_video: Incorporates audio from the original video back into the processed frames, creating a final annotated video output.\n",
        "        process_video: Integrates frame processing and audio addition into a cohesive function for video processing tasks.\n",
        "        Image Processing:\n",
        "        process_image: Handles single images by detecting faces, annotating emotions, and optionally combining input and annotated images for visualization.\n",
        "\n",
        "# Usage:\n",
        "\n",
        "    Video Processing: Provide paths to video files (*.mp4, *.avi, *.mov, *.mkv) to analyze and annotate facial expressions throughout the video duration.\n",
        "    Image Processing: For static images (*.jpg, *.jpeg, *.png), the script detects faces, predicts emotions, and optionally displays the original and annotated images side by side.\n"
      ],
      "metadata": {
        "id": "uVIVXD0L9CLi"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Setup\n",
        "install the required libraries:"
      ],
      "metadata": {
        "id": "H5vMPITJIVyT"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XfbBa45h4i-Q",
        "outputId": "897c0f6c-2622-4143-beff-0916c33b62cb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Building wheels for collected packages: hsemotion\n",
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            "Installing collected packages: nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, timm, retina-face, hsemotion\n",
            "Successfully installed hsemotion-0.3.0 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.20.5 nvidia-nvjitlink-cu12-12.5.40 nvidia-nvtx-cu12-12.1.105 retina-face-0.0.17 timm-1.0.3\n"
          ]
        }
      ],
      "source": [
        "! pip install retina-face hsemotion moviepy"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from moviepy.editor import VideoFileClip, concatenate_videoclips\n",
        "from retinaface import RetinaFace\n",
        "from hsemotion.facial_emotions import HSEmotionRecognizer\n",
        "import cv2\n",
        "import numpy as np\n",
        "import os\n",
        "from google.colab.patches import cv2_imshow  # Import cv2_imshow for Colab"
      ],
      "metadata": {
        "id": "Ab06qU5l4p5G"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## Initialize recognizer\n",
        "\n",
        "recognizer = HSEmotionRecognizer(model_name='enet_b0_8_best_vgaf', device='cpu')\n",
        "\n",
        "## Face Detection Function\n",
        "\n",
        "def detect_faces(frame):\n",
        "    \"\"\" Detect faces in the frame using RetinaFace \"\"\"\n",
        "    faces = RetinaFace.detect_faces(frame)\n",
        "    if isinstance(faces, dict):\n",
        "        face_list = []\n",
        "        for key in faces.keys():\n",
        "            face = faces[key]\n",
        "            facial_area = face['facial_area']\n",
        "            face_dict = {\n",
        "                'box': (facial_area[0], facial_area[1], facial_area[2] - facial_area[0], facial_area[3] - facial_area[1])\n",
        "            }\n",
        "            face_list.append(face_dict)\n",
        "        return face_list\n",
        "    return []\n",
        "\n",
        "## Annotation Function\n",
        "\n",
        "def annotate_frame(frame, faces):\n",
        "    \"\"\" Annotate the frame with recognized emotions using global recognizer \"\"\"\n",
        "    for face in faces:\n",
        "        x, y, w, h = face['box']\n",
        "        face_image = frame[y:y+h, x:x+w]  # Extract face region from frame\n",
        "        emotion = classify_emotions(face_image)\n",
        "        cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 0, 0), 2)\n",
        "        cv2.putText(frame, emotion, (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (255, 0, 0), 2)\n",
        "\n",
        "## Emotion Classification Function\n",
        "\n",
        "def classify_emotions(face_image):\n",
        "    \"\"\" Classify emotions for the given face image using global recognizer \"\"\"\n",
        "    results = recognizer.predict_emotions(face_image)\n",
        "    if results:\n",
        "        emotion = results[0]  # Get the most likely emotion\n",
        "    else:\n",
        "        emotion = 'Unknown'\n",
        "    return emotion\n",
        "\n",
        "## Process Video Frames\n",
        "\n",
        "def process_video_frames(video_path, temp_output_path, frame_skip=5):\n",
        "    # Load the video\n",
        "    video_clip = VideoFileClip(video_path)\n",
        "    fps = video_clip.fps\n",
        "\n",
        "    # Initialize output video writer\n",
        "    out = cv2.VideoWriter(temp_output_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (int(video_clip.size[0]), int(video_clip.size[1])))\n",
        "\n",
        "    # Iterate through frames, detect faces, and annotate emotions\n",
        "    frame_count = 0\n",
        "    for frame in video_clip.iter_frames():\n",
        "        if frame_count % frame_skip == 0:  # Process every nth frame\n",
        "            faces = detect_faces(frame)\n",
        "            annotate_frame(frame, faces)\n",
        "        frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)  # Convert RGB to BGR for OpenCV\n",
        "        out.write(frame)\n",
        "        frame_count += 1\n",
        "\n",
        "    # Release resources and cleanup\n",
        "    out.release()\n",
        "    cv2.destroyAllWindows()\n",
        "    video_clip.close()\n",
        "\n",
        "## Add Audio to Processed Video\n",
        "\n",
        "def add_audio_to_video(original_video_path, processed_video_path, output_path):\n",
        "    try:\n",
        "        original_clip = VideoFileClip(original_video_path)\n",
        "        processed_clip = VideoFileClip(processed_video_path)\n",
        "        final_clip = processed_clip.set_audio(original_clip.audio)\n",
        "        final_clip.write_videofile(output_path, codec='libx264', audio_codec='aac')\n",
        "    except Exception as e:\n",
        "        print(f\"Error while combining with audio: {e}\")\n",
        "    finally:\n",
        "        original_clip.close()\n",
        "        processed_clip.close()\n",
        "\n",
        "## Process Video\n",
        "\n",
        "def process_video(video_path, output_path):\n",
        "    temp_output_path = 'temp_output_video.mp4'\n",
        "\n",
        "    # Process video frames and save to a temporary file\n",
        "    process_video_frames(video_path, temp_output_path, frame_skip=5)  # Adjust frame_skip as needed\n",
        "\n",
        "    # Add audio to the processed video\n",
        "    add_audio_to_video(video_path, temp_output_path, output_path)\n",
        "\n",
        "## Process Image\n",
        "\n",
        "def process_image(input_path, output_path):\n",
        "    # Step 1: Read input image\n",
        "    image = cv2.imread(input_path)\n",
        "    if image is None:\n",
        "        print(f\"Error: Unable to read image at '{input_path}'\")\n",
        "        return\n",
        "\n",
        "    # Step 2: Detect faces and annotate emotions\n",
        "    faces = detect_faces(image)\n",
        "    annotate_frame(image, faces)\n",
        "\n",
        "    # Step 3: Write annotated image to output path\n",
        "    cv2.imwrite(output_path, image)\n",
        "\n",
        "    # Step 4: Combine input and output images horizontally\n",
        "    input_image = cv2.imread(input_path)\n",
        "    combined_image = cv2.hconcat([input_image, image])\n",
        "\n",
        "    # Step 5: Save or display the combined image\n",
        "    cv2.imwrite(output_path, combined_image)\n",
        "    cv2_imshow(combined_image)  # Display combined image in Colab\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eSjUuQBG7Opw",
        "outputId": "bb32e267-67f0-4797-f5ad-72e6ff5ef869"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/root/.hsemotion/enet_b0_8_best_vgaf.pt Compose(\n",
            "    Resize(size=(224, 224), interpolation=bilinear, max_size=None, antialias=True)\n",
            "    ToTensor()\n",
            "    Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
            ")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Time to process the video or image\n",
        "**NOTE : You can use your own data by changing the path**"
      ],
      "metadata": {
        "id": "fQ77JezWHh9y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "if __name__ == \"__main__\":\n",
        "    input_path = '/content/رياكشن عبلة كامل تبكي.mp4'  # Update with your video or image path\n",
        "    output_path = '/content/رياكشن عبلة كامل تبكي out.mp4'  # Update with the desired output path\n",
        "\n",
        "    if input_path.lower().endswith(('.mp4', '.avi', '.mov', '.mkv')):\n",
        "        process_video(input_path, output_path)\n",
        "    elif input_path.lower().endswith(('.jpg', '.jpeg', '.png')):\n",
        "        process_image(input_path, output_path)\n",
        "    else:\n",
        "        print(\"Unsupported file format. Please provide a video or image file.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HiWL0XT5AONd",
        "outputId": "060281bb-0269-4369-ecce-32f472a3cf0a"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Moviepy - Building video /content/رياكشن عبلة كامل تبكي out.mp4.\n",
            "MoviePy - Writing audio in رياكشن عبلة كامل تبكي outTEMP_MPY_wvf_snd.mp4\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MoviePy - Done.\n",
            "Moviepy - Writing video /content/رياكشن عبلة كامل تبكي out.mp4\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Moviepy - Done !\n",
            "Moviepy - video ready /content/رياكشن عبلة كامل تبكي out.mp4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "if __name__ == \"__main__\":\n",
        "    input_path = '/content/mn (2).jpeg'  # Update with your video or image path\n",
        "    output_path = '/content/mn (2)-out.jpeg'  # Update with the desired output path\n",
        "\n",
        "    if input_path.lower().endswith(('.mp4', '.avi', '.mov', '.mkv')):\n",
        "        process_video(input_path, output_path)\n",
        "    elif input_path.lower().endswith(('.jpg', '.jpeg', '.png')):\n",
        "        process_image(input_path, output_path)\n",
        "    else:\n",
        "        print(\"Unsupported file format. Please provide a video or image file.\")"
      ],
      "metadata": {
        "id": "mdllZ7085ZlK"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}