diff --git a/.gitignore b/.gitignore index 73c66b60..8d82d6b7 100644 --- a/.gitignore +++ b/.gitignore @@ -172,3 +172,6 @@ checkpoints # Mac .DS_Store + +# Downloaded Model files +*.pth diff --git a/Colab Scripts/Ratio_AV_SadTalker_modelv1.ipynb b/Colab Scripts/Ratio_AV_SadTalker_modelv1.ipynb new file mode 100644 index 00000000..4b91b028 --- /dev/null +++ b/Colab Scripts/Ratio_AV_SadTalker_modelv1.ipynb @@ -0,0 +1,1159 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "M74Gs_TjYl_B" + }, + "source": [ + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Winfredy/SadTalker/blob/main/quick_demo.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github" + }, + "source": [ + "### SadTalker：Learning Realistic 3D Motion Coefficients for Stylized Audio-Driven Single Image Talking Face Animation\n", + "\n", + "[arxiv](https://arxiv.org/abs/2211.12194) | [project](https://sadtalker.github.io) | [Github](https://github.com/Winfredy/SadTalker)\n", + "\n", + "Wenxuan Zhang, Xiaodong Cun, Xuan Wang, Yong Zhang, Xi Shen, Yu Guo, Ying Shan, Fei Wang.\n", + "\n", + "Xi'an Jiaotong University, Tencent AI Lab, Ant Group\n", + "\n", + "CVPR 2023\n", + "\n", + "TL;DR: A realistic and stylized talking head video generation method from a single image and audio\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kA89DV-sKS4i" + }, + "source": [ + "Installation (around 5 mins)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "qJ4CplXsYl_E", + "outputId": "9d7a606a-bff4-4f29-83d1-5118d10f7837" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Tesla T4, 15360 MiB, 15101 MiB\n" + ] + } + ], + "source": [ + "### make sure that CUDA is available in Edit -> Nootbook settings -> GPU\n", + "!nvidia-smi --query-gpu=name,memory.total,memory.free --format=csv,noheader" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Mdq6j4E5KQAR", + "outputId": "2ba7ee3e-ca48-4aea-9530-0f46ad2d1059" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "update-alternatives: error: alternative path /usr/bin/python3.8 doesn't exist\n", + "update-alternatives: error: alternative path /usr/bin/python3.9 doesn't exist\n", + "Reading package lists... 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face_alignment, typer-cli, tb-nightly, librosa, facexlib, altair, typer, basicsr, gfpgan, gradio\n", + " Attempting uninstall: numpy\n", + " Found existing installation: numpy 1.24.4\n", + " Uninstalling numpy-1.24.4:\n", + " Successfully uninstalled numpy-1.24.4\n", + "Successfully installed PyWavelets-1.4.1 absl-py-2.1.0 addict-2.4.0 aiofiles-23.2.1 altair-5.3.0 annotated-types-0.6.0 anyio-4.3.0 attrs-23.2.0 audioread-3.0.1 av-12.0.0 basicsr-1.4.2 cachetools-5.3.3 cffi-1.16.0 click-8.1.7 contourpy-1.1.1 cycler-0.12.1 decorator-5.1.1 exceptiongroup-1.2.0 face_alignment-1.3.5 facexlib-0.3.0 fastapi-0.110.1 ffmpy-0.3.2 filelock-3.13.3 filterpy-1.4.5 fonttools-4.50.0 fsspec-2024.3.1 future-1.0.0 gfpgan-1.3.8 google-auth-2.29.0 google-auth-oauthlib-1.0.0 gradio-4.25.0 gradio-client-0.15.0 grpcio-1.62.1 h11-0.14.0 httpcore-1.0.5 httpx-0.27.0 huggingface-hub-0.22.2 imageio-2.19.3 imageio-ffmpeg-0.4.7 importlib-metadata-7.1.0 importlib-resources-6.4.0 jinja2-3.1.3 joblib-1.1.0 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It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m" + ] + } + ], + "source": [ + "!update-alternatives --install /usr/local/bin/python3 python3 /usr/bin/python3.8 2\n", + "!update-alternatives --install /usr/local/bin/python3 python3 /usr/bin/python3.9 1\n", + "!sudo apt install python3.8\n", + "\n", + "!sudo apt-get install python3.8-distutils\n", + "\n", + "!python --version\n", + "\n", + "!apt-get update\n", + "\n", + "!apt install software-properties-common\n", + "\n", + "!sudo dpkg --remove --force-remove-reinstreq python3-pip python3-setuptools python3-wheel\n", + "\n", + "!apt-get install python3-pip\n", + "\n", + "print('Git clone project and install requirements...')\n", + "!git clone -b vikasdev https://github.com/malhotra-vikas/SadTalker.git SadTalker &> /dev/null\n", + "%cd SadTalker\n", + "!export PYTHONPATH=/content/SadTalker:$PYTHONPATH\n", + "!python3.8 -m pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 torchaudio==0.12.1 --extra-index-url https://download.pytorch.org/whl/cu113\n", + "!apt update\n", + "!apt install ffmpeg &> /dev/null\n", + "!python3.8 -m pip install -r requirements.txt" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "DddcKB_nKsnk" + }, + "source": [ + "Download models (1 mins)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "eDw3_UN8K2xa", + "outputId": "f062f119-1eca-4564-beba-69696e2dfc6f" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Download pre-trained models...\n", + "--2024-04-03 02:36:20-- https://github.com/OpenTalker/SadTalker/releases/download/v0.0.2-rc/mapping_00109-model.pth.tar\n", + "Resolving github.com (github.com)... 140.82.112.3\n", + "Connecting to github.com (github.com)|140.82.112.3|:443... connected.\n", + "HTTP request sent, awaiting response... 302 Found\n", + "Location: https://objects.githubusercontent.com/github-production-release-asset-2e65be/569518584/ccc415aa-c6f4-47ee-8250-b10bf440ba62?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAVCODYLSA53PQK4ZA%2F20240403%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20240403T023621Z&X-Amz-Expires=300&X-Amz-Signature=600b274796ff8d8ffbcc53f06c72361cd5f7d7cc8762abc013dd2c1441781fa4&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=569518584&response-content-disposition=attachment%3B%20filename%3Dmapping_00109-model.pth.tar&response-content-type=application%2Foctet-stream [following]\n", + "--2024-04-03 02:36:21-- https://objects.githubusercontent.com/github-production-release-asset-2e65be/569518584/ccc415aa-c6f4-47ee-8250-b10bf440ba62?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAVCODYLSA53PQK4ZA%2F20240403%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20240403T023621Z&X-Amz-Expires=300&X-Amz-Signature=600b274796ff8d8ffbcc53f06c72361cd5f7d7cc8762abc013dd2c1441781fa4&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=569518584&response-content-disposition=attachment%3B%20filename%3Dmapping_00109-model.pth.tar&response-content-type=application%2Foctet-stream\n", + "Resolving objects.githubusercontent.com (objects.githubusercontent.com)... 185.199.110.133, 185.199.108.133, 185.199.111.133, ...\n", + "Connecting to objects.githubusercontent.com (objects.githubusercontent.com)|185.199.110.133|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 155779231 (149M) [application/octet-stream]\n", + "Saving to: ‘./checkpoints/mapping_00109-model.pth.tar’\n", + "\n", + "./checkpoints/mappi 100%[===================>] 148.56M 164MB/s in 0.9s \n", + "\n", + "2024-04-03 02:36:22 (164 MB/s) - 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‘./gfpgan/weights/parsing_parsenet.pth’ saved [85331193/85331193]\n", + "\n" + ] + } + ], + "source": [ + "print('Download pre-trained models...')\n", + "!rm -rf checkpoints\n", + "!bash scripts/download_models.sh" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 844, + "referenced_widgets": [ + "c55abebf55dd4ebfb6b30cfcf2a76a43", + "08bce7b6dcbd433188f8efa5db9565da", + "0b2262b9c516489b93c37e78a9795342" + ] + }, + "id": "kK7DYeo7Yl_H", + "outputId": "bb885520-a9c5-49d4-de1b-66249718cd2d" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Choose the image name to animate: (saved in folder 'examples/')\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Dropdown(index=21, options=('art_0', 'art_1', 'art_10', 'art_11', 'art_12', 'art_13', 'art_14', 'art_15', 'art…" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "c55abebf55dd4ebfb6b30cfcf2a76a43" + } + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" 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\n" + }, + "metadata": {} + } + ], + "source": [ + "# borrow from makeittalk\n", + "import ipywidgets as widgets\n", + "import glob\n", + "import matplotlib.pyplot as plt\n", + "print(\"Choose the image name to animate: (saved in folder 'examples/')\")\n", + "img_list = glob.glob1('examples/source_image', '*.png')\n", + "img_list.sort()\n", + "img_list = [item.split('.')[0] for item in img_list]\n", + "default_head_name = widgets.Dropdown(options=img_list, value='full3')\n", + "def on_change(change):\n", + " if change['type'] == 'change' and change['name'] == 'value':\n", + " plt.imshow(plt.imread('examples/source_image/{}.png'.format(default_head_name.value)))\n", + " plt.axis('off')\n", + " plt.show()\n", + "default_head_name.observe(on_change)\n", + "display(default_head_name)\n", + "plt.imshow(plt.imread('examples/source_image/{}.png'.format(default_head_name.value)))\n", + "plt.axis('off')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-khNZcnGK4UK" + }, + "source": [ + "Animation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ToBlDusjK5sS" + }, + "outputs": [], + "source": [ + "# selected audio from exmaple/driven_audio\n", + "img = 'examples/source_image/{}.png'.format(default_head_name.value)\n", + "print(img)\n", + "!python3.8 inference.py --driven_audio ./examples/driven_audio/RD_Radio31_000.wav \\\n", + " --source_image {img} \\\n", + " --result_dir ./results --still" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 311 + }, + "id": "fAjwGmKKYl_I", + "outputId": "11611f93-0285-4d77-a5ab-b1fc1bbafe22" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Display animation: ./results/2024_04_03_02.37.45.mp4\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + " \n", + " " + ] + }, + "metadata": {} + } + ], + "source": [ + "# visualize code from makeittalk\n", + "from IPython.display import HTML\n", + "from base64 import b64encode\n", + "import os, sys\n", + "\n", + "# get the last from results\n", + "\n", + "results = sorted(os.listdir('./results/'))\n", + "\n", + "mp4_name = glob.glob('./results/*.mp4')[0]\n", + "\n", + "mp4 = open('{}'.format(mp4_name),'rb').read()\n", + "data_url = \"data:video/mp4;base64,\" + b64encode(mp4).decode()\n", + "\n", + "print('Display animation: {}'.format(mp4_name), file=sys.stderr)\n", + "display(HTML(\"\"\"\n", + " \n", + " \"\"\" % data_url))\n" + ] + }, + { + "cell_type": "code", + "source": [ + "#@title 5. Delete old code, samples & result files, so you can start over again.\n", + "%rm /content/sample_data/*\n", + "#%rm /content/SadfTalker/results/*\n", + "%rm /content/SadTalker\n", + "from IPython.display import clear_output\n", + "clear_output()\n", + "print(\"\\nDone! now press X\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "tbSpBUk7En3i", + "outputId": "cc5f887d-a6ad-4f00-aaad-7c1a7c77cf5a" + }, + "execution_count": 20, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "Done! now press X\n" + ] + } + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "gpuType": "T4", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.9.7" + }, + "vscode": { + "interpreter": { + "hash": "db5031b3636a3f037ea48eb287fd3d023feb9033aefc2a9652a92e470fb0851b" + } + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "c55abebf55dd4ebfb6b30cfcf2a76a43": { + "model_module": "@jupyter-widgets/controls", + "model_name": "DropdownModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DropdownModel", + "_options_labels": [ + "art_0", + "art_1", + "art_10", + "art_11", + "art_12", + "art_13", + "art_14", + "art_15", + "art_16", + "art_17", + "art_18", + "art_19", + "art_2", + "art_20", + "art_3", + "art_4", + "art_5", + "art_6", + "art_7", + "art_8", + "art_9", + "full3", + "full_body_1", + "full_body_2", + "happy", + "happy1", + "image_1", + "people_0", + "sad", + "sad1" + ], + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "DropdownView", + "description": "", + "description_tooltip": null, + "disabled": false, + "index": 26, + "layout": "IPY_MODEL_08bce7b6dcbd433188f8efa5db9565da", + "style": "IPY_MODEL_0b2262b9c516489b93c37e78a9795342" + } + }, + "08bce7b6dcbd433188f8efa5db9565da": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "0b2262b9c516489b93c37e78a9795342": { + "model_module": "@jupyter-widgets/controls", + "model_name": "DescriptionStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + } + } + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/Colab Scripts/Ratio_AV_Wav2Lip_modelv1.ipynb b/Colab Scripts/Ratio_AV_Wav2Lip_modelv1.ipynb new file mode 100644 index 00000000..9be1bd8b --- /dev/null +++ b/Colab Scripts/Ratio_AV_Wav2Lip_modelv1.ipynb @@ -0,0 +1,908 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "sE22UQtQ9YYi" + }, + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dT9AQwdf8sJK" + }, + "source": [ + "\n", + "\n", + "---\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "yJ5taGmPcWV-" + }, + "source": [ + "# **Get the code and models**" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "Qgo-oaI3JU2u", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "c429178e-18f6-4e1a-8ee8-422c47f3ab13" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "Done\n" + ] + } + ], + "source": [ + "#@title

Step1: Setup Wav2Lip

\n", + "#@markdown * Install dependency\n", + "#@markdown * Download pretrained model\n", + "!rm -rf /content/sample_data\n", + "!mkdir /content/sample_data\n", + "\n", + "!git clone https://github.com/malhotra-vikas/Wav2Lip\n", + "\n", + "#download the pretrained model\n", + "!wget 'https://iiitaphyd-my.sharepoint.com/personal/radrabha_m_research_iiit_ac_in/_layouts/15/download.aspx?share=EdjI7bZlgApMqsVoEUUXpLsBxqXbn5z8VTmoxp55YNDcIA' -O '/content/Wav2Lip/checkpoints/wav2lip_gan.pth'\n", + "a = !pip install https://raw.githubusercontent.com/AwaleSajil/ghc/master/ghc-1.0-py3-none-any.whl\n", + "\n", + "# !pip uninstall tensorflow tensorflow-gpu\n", + "!cd Wav2Lip && pip install -r requirements.txt\n", + "\n", + "#download pretrained model for face detection\n", + "!wget \"https://www.adrianbulat.com/downloads/python-fan/s3fd-619a316812.pth\" -O \"/content/Wav2Lip/face_detection/detection/sfd/s3fd.pth\"\n", + "\n", + "!pip install -q youtube-dl\n", + "!pip install ffmpeg-python\n", + "!pip install librosa==0.9.1\n", + "\n", + "#this code for recording audio\n", + "\"\"\"\n", + "To write this piece of code I took inspiration/code from a lot of places.\n", + "It was late night, so I'm not sure how much I created or just copied o.O\n", + "Here are some of the possible references:\n", + "https://blog.addpipe.com/recording-audio-in-the-browser-using-pure-html5-and-minimal-javascript/\n", + "https://stackoverflow.com/a/18650249\n", + "https://hacks.mozilla.org/2014/06/easy-audio-capture-with-the-mediarecorder-api/\n", + "https://air.ghost.io/recording-to-an-audio-file-using-html5-and-js/\n", + "https://stackoverflow.com/a/49019356\n", + "\"\"\"\n", + "from IPython.display import HTML, Audio\n", + "from google.colab.output import eval_js\n", + "from base64 import b64decode\n", + "import numpy as np\n", + "from scipy.io.wavfile import read as wav_read\n", + "import io\n", + "import ffmpeg\n", + "\n", + "from IPython.display import clear_output\n", + "clear_output()\n", + "print(\"\\nDone\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vzokJMO19IyY" + }, + "source": [ + "\n", + "\n", + "---\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "RgjaWJFs8B38" + }, + "source": [ + "# **Quick guide**\n", + "1. Create video file named input_video.mp4 - audio track removed. We will call this this Seed Video for the character\n", + "2. Create audio file named input_audio.wav. This is avaiable pre-generated to this service for doing Video Overlay\n", + "3. Both files have to be exact same length\n", + "4. Target face in the input_video.mp4, must be \"detectable\" in ALL videoframes (So no black or blurry frames etc)\n", + "5. Make sure u use correct file extensions\n", + "6. wav2lip does not like very long and high res clips (1080p/30seconds max)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qdIQfY2Kswcb" + }, + "source": [ + "# **Now lets try!**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "9bDRYsfXTzXD" + }, + "source": [ + "\n", + "\n", + "---\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "vsphzJawLF-f", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 160 + }, + "outputId": "95f80c9e-b115-49df-cbe1-d82db4f1de16" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[Errno 2] No such file or directory: 'sample_data/'\n", + "/content/sample_data\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + " \n", + " \n", + " Upload widget is only available when the cell has been executed in the\n", + " current browser session. Please rerun this cell to enable.\n", + " \n", + " " + ] + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saving image_1.jpg to image_1.jpg\n", + "Saving input_audio.wav to input_audio.wav\n", + "/content\n" + ] + } + ], + "source": [ + "#@title 1.Upload input_video.mp4 & input_audio.wav files\n", + "%cd sample_data/\n", + "from google.colab import files\n", + "uploaded = files.upload()\n", + "%cd .." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "jR5utmDMcSZY" + }, + "outputs": [], + "source": [ + "#@title 2.Create Wav2Lip video (using wav2lip_gan.pth) GAN\n", + "!cd Wav2Lip && python inference.py --checkpoint_path checkpoints/wav2lip_gan.pth --face \"/content/sample_data/image_1.jpg\" --audio \"/content/sample_data/input_audio.wav\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "form", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 644 + }, + "id": "WxbzXZvLliiA", + "outputId": "fb987312-dca0-48a2-8e6d-0a9d0c76e4eb" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "" + ] + }, + "metadata": {}, + "execution_count": 37 + } + ], + "source": [ + "#@title 3.Play result video - 50% scaling\n", + "from IPython.display import HTML\n", + "from base64 import b64encode\n", + "mp4 = open('/content/Wav2Lip/results/result_voice.mp4','rb').read()\n", + "data_url = \"data:video/mp4;base64,\" + b64encode(mp4).decode()\n", + "HTML(f\"\"\"\n", + "\"\"\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "1kt-krsEbz5j", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 17 + }, + "outputId": "c838a780-b90d-4a8b-cb72-858aa7611c77" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "application/javascript": [ + "\n", + " async function download(id, filename, size) {\n", + " if (!google.colab.kernel.accessAllowed) {\n", + " return;\n", + " }\n", + " const div = document.createElement('div');\n", + " const label = document.createElement('label');\n", + " label.textContent = `Downloading \"${filename}\": `;\n", + " div.appendChild(label);\n", + " const progress = document.createElement('progress');\n", + " progress.max = size;\n", + " div.appendChild(progress);\n", + " document.body.appendChild(div);\n", + "\n", + " const buffers = [];\n", + " let downloaded = 0;\n", + "\n", + " const channel = await google.colab.kernel.comms.open(id);\n", + " // Send a message to notify the kernel that we're ready.\n", + " channel.send({})\n", + "\n", + " for await (const message of channel.messages) {\n", + " // Send a message to notify the kernel that we're ready.\n", + " channel.send({})\n", + " if (message.buffers) {\n", + " for (const buffer of message.buffers) {\n", + " buffers.push(buffer);\n", + " downloaded += buffer.byteLength;\n", + " progress.value = downloaded;\n", + " }\n", + " }\n", + " }\n", + " const blob = new Blob(buffers, {type: 'application/binary'});\n", + " const a = document.createElement('a');\n", + " a.href = window.URL.createObjectURL(blob);\n", + " a.download = filename;\n", + " div.appendChild(a);\n", + " a.click();\n", + " div.remove();\n", + " }\n", + " " + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "application/javascript": [ + "download(\"download_f5be07e5-ec4e-48d7-94a6-dae7a9253217\", \"result_voice.mp4\", 257065)" + ] + }, + "metadata": {} + } + ], + "source": [ + "#@title 4.Download Result.mp4 to your computer\n", + "from google.colab import files\n", + "files.download('/content/Wav2Lip/results/result_voice.mp4')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "fT8njpBCJ7gD", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "aa4921b1-31ee-4fd6-dea8-dc9d3b6b365f" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "Done! now press X\n" + ] + } + ], + "source": [ + "#@title 5. Delete old uploaded samples & result files, so you can start over again.\n", + "%rm /content/sample_data/*\n", + "%rm /content/Wav2Lip/results/*\n", + "from IPython.display import clear_output\n", + "clear_output()\n", + "print(\"\\nDone! now press X\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "BgMkHOFqT2fK" + }, + "source": [ + "\n", + "\n", + "---\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "form", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 447 + }, + "id": "2OhT0w2uT4rQ", + "outputId": "9d3f37e3-4efe-4ab0-b888-c554ce12c2dc" + }, + "outputs": [ + { + "metadata": { + "tags": null + }, + "name": "stdout", + "output_type": "stream", + "text": [ + "/content/sample_data\n", + "rm: cannot remove 'input_audio.wav': No such file or directory\n", + "rm: cannot remove 'input_video.mp4': No such file or directory\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " Upload widget is only available when the cell has been executed in the\n", + " current browser session. Please rerun this cell to enable.\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "ename": "TypeError", + "evalue": "'NoneType' object is not subscriptable", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'rm'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'input_video.mp4'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mgoogle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolab\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mfiles\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0muploaded\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfiles\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'cd'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'..'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msystem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'cd Wav2Lip && python inference.py --checkpoint_path checkpoints/wav2lip_gan.pth --face \"/content/sample_data/input_video.mp4\" --audio \"/content/sample_data/input_audio.wav\"'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/google/colab/files.py\u001b[0m in \u001b[0;36mupload\u001b[0;34m()\u001b[0m\n\u001b[1;32m 67\u001b[0m \"\"\"\n\u001b[1;32m 68\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 69\u001b[0;31m \u001b[0muploaded_files\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_upload_files\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmultiple\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 70\u001b[0m \u001b[0;31m# Mapping from original filename to filename as saved locally.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 71\u001b[0m \u001b[0mlocal_filenames\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/google/colab/files.py\u001b[0m in \u001b[0;36m_upload_files\u001b[0;34m(multiple)\u001b[0m\n\u001b[1;32m 161\u001b[0m \u001b[0mfiles\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_collections\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdefaultdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbytes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 162\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 163\u001b[0;31m \u001b[0;32mwhile\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'action'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m'complete'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 164\u001b[0m result = _output.eval_js(\n\u001b[1;32m 165\u001b[0m 'google.colab._files._uploadFilesContinue(\"{output_id}\")'.format(\n", + "\u001b[0;31mTypeError\u001b[0m: 'NoneType' object is not subscriptable" + ] + } + ], + "source": [ + "#@title 1-4. Batch processing - Upload -> process -> download -> play result\n", + "%cd sample_data/\n", + "%rm input_audio.wav\n", + "%rm input_video.mp4\n", + "from google.colab import files\n", + "uploaded = files.upload()\n", + "%cd ..\n", + "!cd Wav2Lip && python inference.py --checkpoint_path checkpoints/wav2lip_gan.pth --face \"/content/sample_data/input_video.mp4\" --audio \"/content/sample_data/input_audio.wav\"\n", + "from google.colab import files\n", + "files.download('/content/Wav2Lip/results/result_voice.mp4')\n", + "from IPython.display import HTML\n", + "from base64 import b64encode\n", + "mp4 = open('/content/Wav2Lip/results/result_voice.mp4','rb').read()\n", + "data_url = \"data:video/mp4;base64,\" + b64encode(mp4).decode()\n", + "HTML(f\"\"\"\n", + "\"\"\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "q9NvwrJ3vRcs" + }, + "source": [ + "\n", + "\n", + "---\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "d7zgfrQqbKom" + }, + "source": [ + "# **Variations to try**\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "form", + "id": "xw0xFtZ2bsx8" + }, + "outputs": [], + "source": [ + "#@title 2.Use resize_factor to reduce the video resolution, as there is a change you might get better results for lower resolution videos. Why? Because the model was trained on low resolution faces.\n", + "!cd Wav2Lip && python inference.py --checkpoint_path checkpoints/wav2lip_gan.pth --face \"/content/sample_data/input_video.mp4\" --audio \"/content/sample_data/input_audio.wav\" --resize_factor 2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "45XW4SZAzIz5" + }, + "outputs": [], + "source": [ + "#@title 3.Use more padding to include the chin region (u can manually edit pads dimensions viewing and changing the code)\n", + "!cd Wav2Lip && python inference.py --checkpoint_path checkpoints/wav2lip_gan.pth --face \"/content/sample_data/input_video.mp4\" --audio \"/content/sample_data/input_audio.wav\" --pads 0 20 0 0" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "form", + "colab": { + "background_save": true + }, + "id": "X1Z0zRdZR5BZ" + }, + "outputs": [], + "source": [ + "#@title 4.Play result video - 50% scaling\n", + "from IPython.display import HTML\n", + "from base64 import b64encode\n", + "mp4 = open('/content/Wav2Lip/results/result_voice.mp4','rb').read()\n", + "data_url = \"data:video/mp4;base64,\" + b64encode(mp4).decode()\n", + "HTML(f\"\"\"\n", + "\"\"\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "form", + "id": "gfXFOpAmR_dh" + }, + "outputs": [], + "source": [ + "#@title 5.Download Result.mp4 to your computer\n", + "from google.colab import files\n", + "files.download('/content/Wav2Lip/results/result_voice.mp4')\n" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "provenance": [], + "gpuType": "T4" + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/animateCharacters.py b/animateCharacters.py new file mode 100644 index 00000000..1878fdcb --- /dev/null +++ b/animateCharacters.py @@ -0,0 +1,323 @@ +import subprocess + +from glob import glob +import shutil +import torch +from time import strftime +import os, sys, time +from argparse import ArgumentParser + +from src.utils.preprocess import CropAndExtract +from src.test_audio2coeff import Audio2Coeff +from src.facerender.animate import AnimateFromCoeff +from src.generate_batch import get_data +from src.generate_facerender_batch import get_facerender_data +from src.utils.init_path import init_path +import inferGen + +# Global Variables Initialization +global_vars = { + "global_pic_path":"", + "global_audio_path":"", + "global_save_dir":"", + "global_pose_style":"", + "global_device":"", + "global_batch_size":"", + "global_input_yaw_list":"", + "global_input_pitch_list":"", + "global_input_roll_list":"", + "global_ref_eyeblink":"", + "global_ref_pose":"", + "global_pre_process":"", + "global_arg_size":"", + "global_args_still":"", + "global_args_face3d":"", + "global_args":"", + "global_crop_pic_path":"", + "global_crop_info":"", + + "global_current_root_path":"", + "global_sadtalker_paths":"", + + "global_preprocess_model":"", + "global_audio_to_coeff":"", + "global_animate_from_coeff":"", + + "first_coeff_path": "", + "ref_eyeblink_coeff_path": "", + "ref_pose_coeff_path": "", + "data_for_AVRender": "", # Placeholder for data generated in ONE_AVtoCoeff +} + +def set_global_var(var_name, value): + global_vars[var_name] = value + +def get_global_var(var_name): + return global_vars[var_name] + +def extractPaths(args): + set_global_var("global_args", args) + set_global_var("global_pic_path", args.source_image) + set_global_var("global_audio_path",args.driven_audio) + set_global_var("global_save_dir", os.path.join(args.result_dir, strftime("%Y_%m_%d_%H.%M.%S"))) + + os.makedirs(get_global_var("global_save_dir"), exist_ok=True) + set_global_var("global_pose_style", args.pose_style) + set_global_var("global_device", args.device) + set_global_var("global_batch_size", args.batch_size) + set_global_var("global_input_yaw_list", args.input_yaw) + set_global_var("global_input_pitch_list", args.input_pitch) + set_global_var("global_input_roll_list", args.input_roll) + set_global_var("global_ref_eyeblink", args.ref_eyeblink) + set_global_var("global_ref_pose", args.ref_pose) + set_global_var("global_pre_process", args.preprocess) + set_global_var("global_arg_size", args.size) + set_global_var("global_args_still", args.still) + set_global_var("global_args_face3d", args.face3dvis) + + set_global_var("global_current_root_path", os.path.split(sys.argv[0])[0]) + + set_global_var("global_sadtalker_paths", init_path(args.checkpoint_dir, os.path.join(get_global_var("global_current_root_path"), 'src/config'), get_global_var("global_arg_size"), args.old_version, get_global_var("global_pre_process"))) + +def ZERO_startProcess(): + start_time = time.time() # Record the start time + + ''' + #parser = ArgumentParser() + parser.add_argument("--driven_audio", default='./examples/driven_audio/bus_chinese.wav', help="path to driven audio") + parser.add_argument("--source_image", default='./examples/source_image/full_body_1.png', help="path to source image") + parser.add_argument("--ref_eyeblink", default=None, help="path to reference video providing eye blinking") + parser.add_argument("--ref_pose", default=None, help="path to reference video providing pose") + parser.add_argument("--checkpoint_dir", default='./checkpoints', help="path to output") + parser.add_argument("--result_dir", default='./results', help="path to output") + parser.add_argument("--pose_style", type=int, default=0, help="input pose style from [0, 46)") + parser.add_argument("--batch_size", type=int, default=2, help="the batch size of facerender") + parser.add_argument("--size", type=int, default=256, help="the image size of the facerender") + parser.add_argument("--expression_scale", type=float, default=1., help="the batch size of facerender") + parser.add_argument('--input_yaw', nargs='+', type=int, default=None, help="the input yaw degree of the user ") + parser.add_argument('--input_pitch', nargs='+', type=int, default=None, help="the input pitch degree of the user") + parser.add_argument('--input_roll', nargs='+', type=int, default=None, help="the input roll degree of the user") + parser.add_argument('--enhancer', type=str, default=None, help="Face enhancer, [gfpgan, RestoreFormer]") + parser.add_argument('--background_enhancer', type=str, default=None, help="background enhancer, [realesrgan]") + parser.add_argument("--cpu", dest="cpu", action="store_true") + parser.add_argument("--face3dvis", action="store_true", help="generate 3d face and 3d landmarks") + parser.add_argument("--still", action="store_true", help="can crop back to the original videos for the full body aniamtion") + parser.add_argument("--preprocess", default='crop', choices=['crop', 'extcrop', 'resize', 'full', 'extfull'], help="how to preprocess the images" ) + parser.add_argument("--verbose",action="store_true", help="saving the intermedia output or not" ) + parser.add_argument("--old_version",action="store_true", help="use the pth other than safetensor version" ) + + print(f"args.face3dvis: {get_global_var('global_args_face3d')} ") + + + # net structure and parameters + parser.add_argument('--net_recon', type=str, default='resnet50', choices=['resnet18', 'resnet34', 'resnet50'], help='useless') + parser.add_argument('--init_path', type=str, default=None, help='Useless') + parser.add_argument('--use_last_fc',default=False, help='zero initialize the last fc') + parser.add_argument('--bfm_folder', type=str, default='./checkpoints/BFM_Fitting/') + parser.add_argument('--bfm_model', type=str, default='BFM_model_front.mat', help='bfm model') + + + # default renderer parameters + parser.add_argument('--focal', type=float, default=1015.) + parser.add_argument('--center', type=float, default=112.) + parser.add_argument('--camera_d', type=float, default=10.) + parser.add_argument('--z_near', type=float, default=5.) + parser.add_argument('--z_far', type=float, default=15.) + + args = parser.parse_args() + + if torch.cuda.is_available() and not args.cpu: + args.device = "cuda" + else: + args.device = "cpu" + + extractPaths(args) + ''' + + initializeModel() + + first_coeff_path, ref_eyeblink_coeff_path, ref_pose_coeff_path = extract3DimmFromImage(get_global_var("global_preprocess_model")) + + event_marker = time.time() # Record the marker time after the code execution + print(f"startProcess took: {event_marker - start_time} seconds") + + set_global_var("first_coeff_path", first_coeff_path) + set_global_var("ref_eyeblink_coeff_path", ref_eyeblink_coeff_path) + set_global_var("ref_pose_coeff_path", ref_pose_coeff_path) + + print("first_coeff_path", first_coeff_path) + + first_coeff_path = get_global_var("first_coeff_path") + print("fetched in initilize first_coeff_path", first_coeff_path) + + print("After setting in ZERO_startProcess:", global_vars) + + ONE_AVtoCoeff() + + #return first_coeff_path, ref_eyeblink_coeff_path, ref_pose_coeff_path + + +def ONE_AVtoCoeff(): + start_time = time.time() # Record the start timer + + first_coeff_path = get_global_var("first_coeff_path") + print(" ONE_AVtoCoeff first_coeff_path", first_coeff_path) + + + coeff_path = audio2Coeff(first_coeff_path, get_global_var("ref_eyeblink_coeff_path"), get_global_var("ref_pose_coeff_path")) + + data = video2Coeff(coeff_path, get_global_var("first_coeff_path")) + + event_marker = time.time() # Record the marker time after the code execution + print(f"AVtoCoeff took: {event_marker - start_time} seconds") + + set_global_var("data_for_AVRender", data) + + #return data + +def TWO_AVRender(): + start_time = time.time() # Record the start timer + + generateAudioOverlay(get_global_var("data_for_AVRender")) + + event_marker = time.time() # Record the marker time after the code execution + print(f"AVRender took: {event_marker - start_time} seconds") + + + +def initializeModel(): + #init model + preprocess_model = CropAndExtract(get_global_var("global_sadtalker_paths"), get_global_var("global_device")) + + audio_to_coeff = Audio2Coeff(get_global_var("global_sadtalker_paths"), get_global_var("global_device")) + + animate_from_coeff = AnimateFromCoeff(get_global_var("global_sadtalker_paths"), get_global_var("global_device")) + + # Use set_global_var to update the global_vars dictionary + set_global_var("global_preprocess_model", preprocess_model) + set_global_var("global_audio_to_coeff", audio_to_coeff) + set_global_var("global_animate_from_coeff", animate_from_coeff) + + +def extract3DimmFromImage(global_preprocess_model): + #crop image and extract 3dmm from image + first_frame_dir = os.path.join(get_global_var("global_save_dir"), 'first_frame_dir') + os.makedirs(first_frame_dir, exist_ok=True) + + first_coeff_path, global_crop_pic_path, global_crop_info = global_preprocess_model.generate(get_global_var("global_pic_path"), first_frame_dir, get_global_var("global_pre_process"),\ + source_image_flag=True, pic_size=get_global_var("global_arg_size")) + if first_coeff_path is None: + print("Can't get the coeffs of the input") + return + + if get_global_var("global_ref_eyeblink") is not None: + ref_eyeblink_videoname = os.path.splitext(os.path.split(get_global_var("global_ref_eyeblink"))[-1])[0] + ref_eyeblink_frame_dir = os.path.join(get_global_var("global_save_dir"), ref_eyeblink_videoname) + os.makedirs(ref_eyeblink_frame_dir, exist_ok=True) + + + ref_eyeblink_coeff_path, _, _ = global_preprocess_model.generate(get_global_var("global_ref_eyeblink"), ref_eyeblink_frame_dir, get_global_var("global_pre_process"), source_image_flag=False) + else: + ref_eyeblink_coeff_path=None + + if get_global_var("global_ref_pose") is not None: + if get_global_var("global_ref_pose") == get_global_var("global_ref_eyeblink"): + ref_pose_coeff_path = ref_eyeblink_coeff_path + else: + ref_pose_videoname = os.path.splitext(os.path.split(get_global_var("global_ref_pose"))[-1])[0] + ref_pose_frame_dir = os.path.join(get_global_var("global_save_dir"), ref_pose_videoname) + os.makedirs(ref_pose_frame_dir, exist_ok=True) + + ref_pose_coeff_path, _, _ = global_preprocess_model.generate(get_global_var("global_ref_pose"), ref_pose_frame_dir, get_global_var("global_pre_process"), source_image_flag=False) + else: + ref_pose_coeff_path=None + + return first_coeff_path, ref_eyeblink_coeff_path, ref_pose_coeff_path + + +#audio2ceoff. We will run this for each audio file that needs to be added to the Video +def audio2Coeff(first_coeff_path, ref_eyeblink_coeff_path, ref_pose_coeff_path): + #audio2ceoff + batch = get_data(first_coeff_path, get_global_var("global_audio_path"), get_global_var("global_device"), ref_eyeblink_coeff_path, still=get_global_var("global_args_still")) + + coeff_path = get_global_var("global_audio_to_coeff").generate(batch, get_global_var("global_save_dir"), get_global_var("global_pose_style"), ref_pose_coeff_path) + + print(f"args.face3dvis: {get_global_var('global_args_face3d')} ") + + # 3dface render + if get_global_var("global_args_face3d"): + from src.face3d.visualize import gen_composed_video + gen_composed_video(get_global_var("global_args"), get_global_var("global_device"), first_coeff_path, coeff_path, get_global_var("global_audio_path"), os.path.join(get_global_var("global_save_dir"), '3dface.mp4')) + + return coeff_path + +#coeff2video. We will run this for each audio file that needs to be added to the Video +def video2Coeff(coeff_path, first_coeff_path): + data = get_facerender_data(coeff_path, get_global_var("global_crop_pic_path"), first_coeff_path, get_global_var("global_audio_path"), + get_global_var("global_batch_size"), get_global_var("global_input_yaw_list"), get_global_var("global_input_pitch_list"), get_global_var("global_input_roll_list"), + expression_scale=get_global_var("global_args").expression_scale, still_mode=get_global_var("global_args_still"), preprocess=get_global_var("global_pre_process"), size=get_global_var("global_arg_size")) + + return data + +# This is the most expensive step in the Rendering TODO +def generateAudioOverlay(data): + result = get_global_var("global_animate_from_coeff").generate(data, get_global_var("global_save_dir"), get_global_var("global_pic_path"), get_global_var("global_crop_info"), \ + enhancer=get_global_var("global_args").enhancer, background_enhancer=get_global_var("global_args").background_enhancer, preprocess=get_global_var("global_pre_process"), img_size=get_global_var("global_arg_size")) + + + shutil.move(result, get_global_var("global_save_dir")+'.mp4') + + print('The generated video is named:', get_global_var("global_save_dir")+'.mp4') + + if not get_global_var("global_args").verbose: + shutil.rmtree(get_global_var("global_save_dir")) + + +if __name__ == '__main__': + + parser = ArgumentParser() + parser.add_argument("--driven_audio", default='./examples/driven_audio/bus_chinese.wav', help="path to driven audio") + parser.add_argument("--source_image", default='./examples/source_image/full_body_1.png', help="path to source image") + parser.add_argument("--ref_eyeblink", default=None, help="path to reference video providing eye blinking") + parser.add_argument("--ref_pose", default=None, help="path to reference video providing pose") + parser.add_argument("--checkpoint_dir", default='./checkpoints', help="path to output") + parser.add_argument("--result_dir", default='./results', help="path to output") + parser.add_argument("--pose_style", type=int, default=0, help="input pose style from [0, 46)") + parser.add_argument("--batch_size", type=int, default=2, help="the batch size of facerender") + parser.add_argument("--size", type=int, default=256, help="the image size of the facerender") + parser.add_argument("--expression_scale", type=float, default=1., help="the batch size of facerender") + parser.add_argument('--input_yaw', nargs='+', type=int, default=None, help="the input yaw degree of the user ") + parser.add_argument('--input_pitch', nargs='+', type=int, default=None, help="the input pitch degree of the user") + parser.add_argument('--input_roll', nargs='+', type=int, default=None, help="the input roll degree of the user") + parser.add_argument('--enhancer', type=str, default=None, help="Face enhancer, [gfpgan, RestoreFormer]") + parser.add_argument('--background_enhancer', type=str, default=None, help="background enhancer, [realesrgan]") + parser.add_argument("--cpu", dest="cpu", action="store_true") + parser.add_argument("--face3dvis", action="store_true", help="generate 3d face and 3d landmarks") + parser.add_argument("--still", action="store_true", help="can crop back to the original videos for the full body aniamtion") + parser.add_argument("--preprocess", default='crop', choices=['crop', 'extcrop', 'resize', 'full', 'extfull'], help="how to preprocess the images" ) + parser.add_argument("--verbose",action="store_true", help="saving the intermedia output or not" ) + parser.add_argument("--old_version",action="store_true", help="use the pth other than safetensor version" ) + + + # net structure and parameters + parser.add_argument('--net_recon', type=str, default='resnet50', choices=['resnet18', 'resnet34', 'resnet50'], help='useless') + parser.add_argument('--init_path', type=str, default=None, help='Useless') + parser.add_argument('--use_last_fc',default=False, help='zero initialize the last fc') + parser.add_argument('--bfm_folder', type=str, default='./checkpoints/BFM_Fitting/') + parser.add_argument('--bfm_model', type=str, default='BFM_model_front.mat', help='bfm model') + + # default renderer parameters + parser.add_argument('--focal', type=float, default=1015.) + parser.add_argument('--center', type=float, default=112.) + parser.add_argument('--camera_d', type=float, default=10.) + parser.add_argument('--z_near', type=float, default=5.) + parser.add_argument('--z_far', type=float, default=15.) + + args = parser.parse_args() + + if torch.cuda.is_available() and not args.cpu: + args.device = "cuda" + else: + args.device = "cpu" + + inferGen.main(args) \ No newline at end of file diff --git a/examples/source_image/jesus.png b/examples/source_image/jesus.png new file mode 100644 index 00000000..0b1dbe13 Binary files /dev/null and b/examples/source_image/jesus.png differ diff --git a/inferGen.py b/inferGen.py new file mode 100644 index 00000000..fdee133b --- /dev/null +++ b/inferGen.py @@ -0,0 +1,182 @@ +from glob import glob +import shutil +import torch +from time import strftime +import os, sys, time +from argparse import ArgumentParser +from functools import lru_cache + +from src.utils.preprocess import CropAndExtract +from src.test_audio2coeff import Audio2Coeff +from src.facerender.animate import AnimateFromCoeff +from src.generate_batch import get_data +from src.generate_facerender_batch import get_facerender_data +from src.utils.init_path import init_path + +def main(args): + #torch.backends.cudnn.enabled = False + start_time = time.time() # Record the start time + + seed_video_path = args.seed_video + pic_path = args.source_image + audio_path = args.driven_audio + save_dir = os.path.join(args.result_dir, strftime("%Y_%m_%d_%H.%M.%S")) + os.makedirs(save_dir, exist_ok=True) + pose_style = args.pose_style + device = args.device + batch_size = args.batch_size + input_yaw_list = args.input_yaw + input_pitch_list = args.input_pitch + input_roll_list = args.input_roll + ref_eyeblink = args.ref_eyeblink + ref_pose = args.ref_pose + + current_root_path = os.path.split(sys.argv[0])[0] + + sadtalker_paths = init_path(args.checkpoint_dir, os.path.join(current_root_path, 'src/config'), args.size, args.old_version, args.preprocess) + + #init model + preprocess_model = CropAndExtract(sadtalker_paths, device) + + audio_to_coeff = Audio2Coeff(sadtalker_paths, device) + + animate_from_coeff = AnimateFromCoeff(sadtalker_paths, device) + + #crop image and extract 3dmm from image + first_frame_dir = os.path.join(save_dir, 'first_frame_dir') + os.makedirs(first_frame_dir, exist_ok=True) + + event_marker = time.time() # Record the marker time after the code execution + print(f"3DMM Extraction for source image: {event_marker - start_time} seconds") + + first_coeff_path, crop_pic_path, crop_info = preprocess_model.generate(pic_path, first_frame_dir, args.preprocess,\ + source_image_flag=True, pic_size=args.size) + if first_coeff_path is None: + print("Can't get the coeffs of the input") + return + + if ref_eyeblink is not None: + ref_eyeblink_videoname = os.path.splitext(os.path.split(ref_eyeblink)[-1])[0] + ref_eyeblink_frame_dir = os.path.join(save_dir, ref_eyeblink_videoname) + os.makedirs(ref_eyeblink_frame_dir, exist_ok=True) + + event_marker = time.time() # Record the marker time after the code execution + print(f"3DMM Extraction for the reference video providing eye blinking: {event_marker - start_time} seconds") + + ref_eyeblink_coeff_path, _, _ = preprocess_model.generate(ref_eyeblink, ref_eyeblink_frame_dir, args.preprocess, source_image_flag=False) + else: + ref_eyeblink_coeff_path=None + + if ref_pose is not None: + if ref_pose == ref_eyeblink: + ref_pose_coeff_path = ref_eyeblink_coeff_path + else: + ref_pose_videoname = os.path.splitext(os.path.split(ref_pose)[-1])[0] + ref_pose_frame_dir = os.path.join(save_dir, ref_pose_videoname) + os.makedirs(ref_pose_frame_dir, exist_ok=True) + + event_marker = time.time() # Record the marker time after the code execution + print(f"3DMM Extraction for the reference video providing pose: {event_marker - start_time} seconds") + + ref_pose_coeff_path, _, _ = preprocess_model.generate(ref_pose, ref_pose_frame_dir, args.preprocess, source_image_flag=False) + else: + ref_pose_coeff_path=None + + #audio2ceoff + batch = get_data(first_coeff_path, audio_path, device, ref_eyeblink_coeff_path, still=args.still) + + event_marker = time.time() # Record the marker time after the code execution + print(f"Audio Data batched in: {event_marker - start_time} seconds") + + coeff_path = audio_to_coeff.generate(batch, save_dir, pose_style, ref_pose_coeff_path) + + event_marker = time.time() # Record the marker time after the code execution + print(f"coeff_path done in: {event_marker - start_time} seconds") + + print(f"args.face3dvis: {args.face3dvis} ") + + # 3dface render + if args.face3dvis: + from src.face3d.visualize import gen_composed_video + gen_composed_video(args, device, first_coeff_path, coeff_path, audio_path, os.path.join(save_dir, '3dface.mp4')) + + event_marker = time.time() # Record the marker time after the code execution + print(f"Before Face Render: {event_marker - start_time} seconds") + + #coeff2video + data = get_facerender_data(coeff_path, crop_pic_path, first_coeff_path, audio_path, + batch_size, input_yaw_list, input_pitch_list, input_roll_list, + expression_scale=args.expression_scale, still_mode=args.still, preprocess=args.preprocess, size=args.size) + + event_marker = time.time() # Record the marker time after the code execution + print(f"After Face Render: {event_marker - start_time} seconds") + + # This is the most expensive step in the Rendering TODO + result = animate_from_coeff.generate(data, save_dir, pic_path, crop_info, seed_video_path, \ + enhancer=args.enhancer, background_enhancer=args.background_enhancer, preprocess=args.preprocess, img_size=args.size) + + event_marker = time.time() # Record the marker time after the code execution + print(f"After Generate steps: {event_marker - start_time} seconds") + + shutil.move(result, save_dir+'.mp4') + + event_marker = time.time() # Record the marker time after the code execution + print(f"The generated video is available: {event_marker - start_time} seconds") + + print('The generated video is named:', save_dir+'.mp4') + + if not args.verbose: + shutil.rmtree(save_dir) + + +if __name__ == '__main__': + + parser = ArgumentParser() + parser.add_argument("--driven_audio", default='./examples/driven_audio/bus_chinese.wav', help="path to driven audio") + parser.add_argument("--source_image", default='./examples/source_image/full_body_1.png', help="path to source image") + parser.add_argument("--ref_eyeblink", default=None, help="path to reference video providing eye blinking") + parser.add_argument("--ref_pose", default=None, help="path to reference video providing pose") + parser.add_argument("--checkpoint_dir", default='./checkpoints', help="path to output") + parser.add_argument("--result_dir", default='./results', help="path to output") + parser.add_argument("--seed_video", default='XXX', help="path to pre processed Animated Video") + parser.add_argument("--character", default='XXX', help="Name of Animated Character") + parser.add_argument("--pose_style", type=int, default=0, help="input pose style from [0, 46)") + parser.add_argument("--batch_size", type=int, default=2, help="the batch size of facerender") + parser.add_argument("--size", type=int, default=256, help="the image size of the facerender") + parser.add_argument("--expression_scale", type=float, default=1., help="the batch size of facerender") + parser.add_argument('--input_yaw', nargs='+', type=int, default=None, help="the input yaw degree of the user ") + parser.add_argument('--input_pitch', nargs='+', type=int, default=None, help="the input pitch degree of the user") + parser.add_argument('--input_roll', nargs='+', type=int, default=None, help="the input roll degree of the user") + parser.add_argument('--enhancer', type=str, default=None, help="Face enhancer, [gfpgan, RestoreFormer]") + parser.add_argument('--background_enhancer', type=str, default=None, help="background enhancer, [realesrgan]") + parser.add_argument("--cpu", dest="cpu", action="store_true") + parser.add_argument("--face3dvis", action="store_true", help="generate 3d face and 3d landmarks") + parser.add_argument("--still", action="store_true", help="can crop back to the original videos for the full body aniamtion") + parser.add_argument("--preprocess", default='crop', choices=['crop', 'extcrop', 'resize', 'full', 'extfull'], help="how to preprocess the images" ) + parser.add_argument("--verbose",action="store_true", help="saving the intermedia output or not" ) + parser.add_argument("--old_version",action="store_true", help="use the pth other than safetensor version" ) + + + # net structure and parameters + parser.add_argument('--net_recon', type=str, default='resnet50', choices=['resnet18', 'resnet34', 'resnet50'], help='useless') + parser.add_argument('--init_path', type=str, default=None, help='Useless') + parser.add_argument('--use_last_fc',default=False, help='zero initialize the last fc') + parser.add_argument('--bfm_folder', type=str, default='./checkpoints/BFM_Fitting/') + parser.add_argument('--bfm_model', type=str, default='BFM_model_front.mat', help='bfm model') + + # default renderer parameters + parser.add_argument('--focal', type=float, default=1015.) + parser.add_argument('--center', type=float, default=112.) + parser.add_argument('--camera_d', type=float, default=10.) + parser.add_argument('--z_near', type=float, default=5.) + parser.add_argument('--z_far', type=float, default=15.) + + args = parser.parse_args() + + if torch.cuda.is_available() and not args.cpu: + args.device = "cuda" + else: + args.device = "cpu" + + main(args) + diff --git a/inference.py b/inference.py index a0b00790..cea35d07 100644 --- a/inference.py +++ b/inference.py @@ -4,6 +4,7 @@ from time import strftime import os, sys, time from argparse import ArgumentParser +from functools import lru_cache from src.utils.preprocess import CropAndExtract from src.test_audio2coeff import Audio2Coeff @@ -14,6 +15,7 @@ def main(args): #torch.backends.cudnn.enabled = False + start_time = time.time() # Record the start time pic_path = args.source_image audio_path = args.driven_audio @@ -42,7 +44,10 @@ def main(args): #crop image and extract 3dmm from image first_frame_dir = os.path.join(save_dir, 'first_frame_dir') os.makedirs(first_frame_dir, exist_ok=True) - print('3DMM Extraction for source image') + + event_marker = time.time() # Record the marker time after the code execution + print(f"3DMM Extraction for source image: {event_marker - start_time} seconds") + first_coeff_path, crop_pic_path, crop_info = preprocess_model.generate(pic_path, first_frame_dir, args.preprocess,\ source_image_flag=True, pic_size=args.size) if first_coeff_path is None: @@ -53,7 +58,10 @@ def main(args): ref_eyeblink_videoname = os.path.splitext(os.path.split(ref_eyeblink)[-1])[0] ref_eyeblink_frame_dir = os.path.join(save_dir, ref_eyeblink_videoname) os.makedirs(ref_eyeblink_frame_dir, exist_ok=True) - print('3DMM Extraction for the reference video providing eye blinking') + + event_marker = time.time() # Record the marker time after the code execution + print(f"3DMM Extraction for the reference video providing eye blinking: {event_marker - start_time} seconds") + ref_eyeblink_coeff_path, _, _ = preprocess_model.generate(ref_eyeblink, ref_eyeblink_frame_dir, args.preprocess, source_image_flag=False) else: ref_eyeblink_coeff_path=None @@ -65,29 +73,55 @@ def main(args): ref_pose_videoname = os.path.splitext(os.path.split(ref_pose)[-1])[0] ref_pose_frame_dir = os.path.join(save_dir, ref_pose_videoname) os.makedirs(ref_pose_frame_dir, exist_ok=True) - print('3DMM Extraction for the reference video providing pose') + + event_marker = time.time() # Record the marker time after the code execution + print(f"3DMM Extraction for the reference video providing pose: {event_marker - start_time} seconds") + ref_pose_coeff_path, _, _ = preprocess_model.generate(ref_pose, ref_pose_frame_dir, args.preprocess, source_image_flag=False) else: ref_pose_coeff_path=None #audio2ceoff batch = get_data(first_coeff_path, audio_path, device, ref_eyeblink_coeff_path, still=args.still) + + event_marker = time.time() # Record the marker time after the code execution + print(f"Audio Data batched in: {event_marker - start_time} seconds") + coeff_path = audio_to_coeff.generate(batch, save_dir, pose_style, ref_pose_coeff_path) + event_marker = time.time() # Record the marker time after the code execution + print(f"coeff_path done in: {event_marker - start_time} seconds") + + print(f"args.face3dvis: {args.face3dvis} ") + # 3dface render if args.face3dvis: from src.face3d.visualize import gen_composed_video gen_composed_video(args, device, first_coeff_path, coeff_path, audio_path, os.path.join(save_dir, '3dface.mp4')) + event_marker = time.time() # Record the marker time after the code execution + print(f"Before Face Render: {event_marker - start_time} seconds") + #coeff2video data = get_facerender_data(coeff_path, crop_pic_path, first_coeff_path, audio_path, batch_size, input_yaw_list, input_pitch_list, input_roll_list, expression_scale=args.expression_scale, still_mode=args.still, preprocess=args.preprocess, size=args.size) + event_marker = time.time() # Record the marker time after the code execution + print(f"After Face Render: {event_marker - start_time} seconds") + + # This is the most expensive step in the Rendering TODO result = animate_from_coeff.generate(data, save_dir, pic_path, crop_info, \ enhancer=args.enhancer, background_enhancer=args.background_enhancer, preprocess=args.preprocess, img_size=args.size) + event_marker = time.time() # Record the marker time after the code execution + print(f"After Generate steps: {event_marker - start_time} seconds") + shutil.move(result, save_dir+'.mp4') + + event_marker = time.time() # Record the marker time after the code execution + print(f"The generated video is available: {event_marker - start_time} seconds") + print('The generated video is named:', save_dir+'.mp4') if not args.verbose: diff --git a/initilizeAnimateCharacter.py b/initilizeAnimateCharacter.py new file mode 100644 index 00000000..c5b1cdf2 --- /dev/null +++ b/initilizeAnimateCharacter.py @@ -0,0 +1,364 @@ +import subprocess + +from glob import glob +import shutil +import torch +from time import strftime +import os, sys, time +from argparse import ArgumentParser + +from src.utils.preprocess import CropAndExtract +from src.test_audio2coeff import Audio2Coeff +from src.facerender.animate import AnimateFromCoeff +from src.generate_batch import get_data +from src.generate_facerender_batch import get_facerender_data +from src.utils.init_path import init_path + +# Global Variables Initialization +global_vars = { + "global_pic_path":"", + "global_audio_path":"", + "global_save_dir":"", + "global_pose_style":"", + "global_device":"", + "global_batch_size":"", + "global_input_yaw_list":"", + "global_input_pitch_list":"", + "global_input_roll_list":"", + "global_ref_eyeblink":"", + "global_ref_pose":"", + "global_pre_process":"", + "global_arg_size":"", + "global_args_still":"", + "global_args_face3d":"", + "global_args":"", + "global_crop_pic_path":"", + "global_crop_info":"", + + "global_current_root_path":"", + "global_sadtalker_paths":"", + + "global_preprocess_model":"", + "global_audio_to_coeff":"", + "global_animate_from_coeff":"", + + "first_coeff_path": "", + "ref_eyeblink_coeff_path": "", + "ref_pose_coeff_path": "", + "data_for_AVRender": "", # Placeholder for data generated in ONE_AVtoCoeff +} + +def set_global_var(var_name, value): + global_vars[var_name] = value + +def get_global_var(var_name): + return global_vars[var_name] + +def extractPaths(args): + set_global_var("global_args", args) + set_global_var("global_pic_path", args.source_image) + set_global_var("global_audio_path",args.driven_audio) + set_global_var("global_save_dir", os.path.join(args.result_dir, strftime("%Y_%m_%d_%H.%M.%S"))) + + os.makedirs(get_global_var("global_save_dir"), exist_ok=True) + set_global_var("global_pose_style", args.pose_style) + set_global_var("global_device", args.device) + set_global_var("global_batch_size", args.batch_size) + set_global_var("global_input_yaw_list", args.input_yaw) + set_global_var("global_input_pitch_list", args.input_pitch) + set_global_var("global_input_roll_list", args.input_roll) + set_global_var("global_ref_eyeblink", args.ref_eyeblink) + set_global_var("global_ref_pose", args.ref_pose) + set_global_var("global_pre_process", args.preprocess) + set_global_var("global_arg_size", args.size) + set_global_var("global_args_still", args.still) + set_global_var("global_args_face3d", args.face3dvis) + + set_global_var("global_current_root_path", os.path.split(sys.argv[0])[0]) + + set_global_var("global_sadtalker_paths", init_path(args.checkpoint_dir, os.path.join(get_global_var("global_current_root_path"), 'src/config'), get_global_var("global_arg_size"), args.old_version, get_global_var("global_pre_process"))) + +def ZERO_startProcess(): + start_time = time.time() # Record the start time + + ''' + #parser = ArgumentParser() + parser.add_argument("--driven_audio", default='./examples/driven_audio/bus_chinese.wav', help="path to driven audio") + parser.add_argument("--source_image", default='./examples/source_image/full_body_1.png', help="path to source image") + parser.add_argument("--ref_eyeblink", default=None, help="path to reference video providing eye blinking") + parser.add_argument("--ref_pose", default=None, help="path to reference video providing pose") + parser.add_argument("--checkpoint_dir", default='./checkpoints', help="path to output") + parser.add_argument("--result_dir", default='./results', help="path to output") + parser.add_argument("--pose_style", type=int, default=0, help="input pose style from [0, 46)") + parser.add_argument("--batch_size", type=int, default=2, help="the batch size of facerender") + parser.add_argument("--size", type=int, default=256, help="the image size of the facerender") + parser.add_argument("--expression_scale", type=float, default=1., help="the batch size of facerender") + parser.add_argument('--input_yaw', nargs='+', type=int, default=None, help="the input yaw degree of the user ") + parser.add_argument('--input_pitch', nargs='+', type=int, default=None, help="the input pitch degree of the user") + parser.add_argument('--input_roll', nargs='+', type=int, default=None, help="the input roll degree of the user") + parser.add_argument('--enhancer', type=str, default=None, help="Face enhancer, [gfpgan, RestoreFormer]") + parser.add_argument('--background_enhancer', type=str, default=None, help="background enhancer, [realesrgan]") + parser.add_argument("--cpu", dest="cpu", action="store_true") + parser.add_argument("--face3dvis", action="store_true", help="generate 3d face and 3d landmarks") + parser.add_argument("--still", action="store_true", help="can crop back to the original videos for the full body aniamtion") + parser.add_argument("--preprocess", default='crop', choices=['crop', 'extcrop', 'resize', 'full', 'extfull'], help="how to preprocess the images" ) + parser.add_argument("--verbose",action="store_true", help="saving the intermedia output or not" ) + parser.add_argument("--old_version",action="store_true", help="use the pth other than safetensor version" ) + + print(f"args.face3dvis: {get_global_var('global_args_face3d')} ") + + + # net structure and parameters + parser.add_argument('--net_recon', type=str, default='resnet50', choices=['resnet18', 'resnet34', 'resnet50'], help='useless') + parser.add_argument('--init_path', type=str, default=None, help='Useless') + parser.add_argument('--use_last_fc',default=False, help='zero initialize the last fc') + parser.add_argument('--bfm_folder', type=str, default='./checkpoints/BFM_Fitting/') + parser.add_argument('--bfm_model', type=str, default='BFM_model_front.mat', help='bfm model') + + + # default renderer parameters + parser.add_argument('--focal', type=float, default=1015.) + parser.add_argument('--center', type=float, default=112.) + parser.add_argument('--camera_d', type=float, default=10.) + parser.add_argument('--z_near', type=float, default=5.) + parser.add_argument('--z_far', type=float, default=15.) + + args = parser.parse_args() + + if torch.cuda.is_available() and not args.cpu: + args.device = "cuda" + else: + args.device = "cpu" + + extractPaths(args) + ''' + + initializeModel() + + first_coeff_path, ref_eyeblink_coeff_path, ref_pose_coeff_path = extract3DimmFromImage(get_global_var("global_preprocess_model")) + + event_marker = time.time() # Record the marker time after the code execution + print(f"startProcess took: {event_marker - start_time} seconds") + + set_global_var("first_coeff_path", first_coeff_path) + set_global_var("ref_eyeblink_coeff_path", ref_eyeblink_coeff_path) + set_global_var("ref_pose_coeff_path", ref_pose_coeff_path) + + print("first_coeff_path", first_coeff_path) + + first_coeff_path = get_global_var("first_coeff_path") + print("fetched in initilize first_coeff_path", first_coeff_path) + + print("After setting in ZERO_startProcess:", global_vars) + + ONE_AVtoCoeff() + + #return first_coeff_path, ref_eyeblink_coeff_path, ref_pose_coeff_path + + +def ONE_AVtoCoeff(): + start_time = time.time() # Record the start timer + + first_coeff_path = get_global_var("first_coeff_path") + print(" ONE_AVtoCoeff first_coeff_path", first_coeff_path) + + + coeff_path = audio2Coeff(first_coeff_path, get_global_var("ref_eyeblink_coeff_path"), get_global_var("ref_pose_coeff_path")) + + data = video2Coeff(coeff_path, get_global_var("first_coeff_path")) + + event_marker = time.time() # Record the marker time after the code execution + print(f"AVtoCoeff took: {event_marker - start_time} seconds") + + set_global_var("data_for_AVRender", data) + + #return data + +def TWO_AVRender(): + start_time = time.time() # Record the start timer + + generateAudioOverlay(get_global_var("data_for_AVRender")) + + event_marker = time.time() # Record the marker time after the code execution + print(f"AVRender took: {event_marker - start_time} seconds") + + + +def initializeModel(): + #init model + preprocess_model = CropAndExtract(get_global_var("global_sadtalker_paths"), get_global_var("global_device")) + + audio_to_coeff = Audio2Coeff(get_global_var("global_sadtalker_paths"), get_global_var("global_device")) + + animate_from_coeff = AnimateFromCoeff(get_global_var("global_sadtalker_paths"), get_global_var("global_device")) + + # Use set_global_var to update the global_vars dictionary + set_global_var("global_preprocess_model", preprocess_model) + set_global_var("global_audio_to_coeff", audio_to_coeff) + set_global_var("global_animate_from_coeff", animate_from_coeff) + + +def extract3DimmFromImage(global_preprocess_model): + #crop image and extract 3dmm from image + first_frame_dir = os.path.join(get_global_var("global_save_dir"), 'first_frame_dir') + os.makedirs(first_frame_dir, exist_ok=True) + + first_coeff_path, global_crop_pic_path, global_crop_info = global_preprocess_model.generate(get_global_var("global_pic_path"), first_frame_dir, get_global_var("global_pre_process"),\ + source_image_flag=True, pic_size=get_global_var("global_arg_size")) + if first_coeff_path is None: + print("Can't get the coeffs of the input") + return + + if get_global_var("global_ref_eyeblink") is not None: + ref_eyeblink_videoname = os.path.splitext(os.path.split(get_global_var("global_ref_eyeblink"))[-1])[0] + ref_eyeblink_frame_dir = os.path.join(get_global_var("global_save_dir"), ref_eyeblink_videoname) + os.makedirs(ref_eyeblink_frame_dir, exist_ok=True) + + + ref_eyeblink_coeff_path, _, _ = global_preprocess_model.generate(get_global_var("global_ref_eyeblink"), ref_eyeblink_frame_dir, get_global_var("global_pre_process"), source_image_flag=False) + else: + ref_eyeblink_coeff_path=None + + if get_global_var("global_ref_pose") is not None: + if get_global_var("global_ref_pose") == get_global_var("global_ref_eyeblink"): + ref_pose_coeff_path = ref_eyeblink_coeff_path + else: + ref_pose_videoname = os.path.splitext(os.path.split(get_global_var("global_ref_pose"))[-1])[0] + ref_pose_frame_dir = os.path.join(get_global_var("global_save_dir"), ref_pose_videoname) + os.makedirs(ref_pose_frame_dir, exist_ok=True) + + ref_pose_coeff_path, _, _ = global_preprocess_model.generate(get_global_var("global_ref_pose"), ref_pose_frame_dir, get_global_var("global_pre_process"), source_image_flag=False) + else: + ref_pose_coeff_path=None + + return first_coeff_path, ref_eyeblink_coeff_path, ref_pose_coeff_path + + +#audio2ceoff. We will run this for each audio file that needs to be added to the Video +def audio2Coeff(first_coeff_path, ref_eyeblink_coeff_path, ref_pose_coeff_path): + #audio2ceoff + batch = get_data(first_coeff_path, get_global_var("global_audio_path"), get_global_var("global_device"), ref_eyeblink_coeff_path, still=get_global_var("global_args_still")) + + coeff_path = get_global_var("global_audio_to_coeff").generate(batch, get_global_var("global_save_dir"), get_global_var("global_pose_style"), ref_pose_coeff_path) + + print(f"args.face3dvis: {get_global_var('global_args_face3d')} ") + + # 3dface render + if get_global_var("global_args_face3d"): + from src.face3d.visualize import gen_composed_video + gen_composed_video(get_global_var("global_args"), get_global_var("global_device"), first_coeff_path, coeff_path, get_global_var("global_audio_path"), os.path.join(get_global_var("global_save_dir"), '3dface.mp4')) + + return coeff_path + +#coeff2video. We will run this for each audio file that needs to be added to the Video +def video2Coeff(coeff_path, first_coeff_path): + data = get_facerender_data(coeff_path, get_global_var("global_crop_pic_path"), first_coeff_path, get_global_var("global_audio_path"), + get_global_var("global_batch_size"), get_global_var("global_input_yaw_list"), get_global_var("global_input_pitch_list"), get_global_var("global_input_roll_list"), + expression_scale=get_global_var("global_args").expression_scale, still_mode=get_global_var("global_args_still"), preprocess=get_global_var("global_pre_process"), size=get_global_var("global_arg_size")) + + return data + +# This is the most expensive step in the Rendering TODO +def generateAudioOverlay(data): + result = get_global_var("global_animate_from_coeff").generate(data, get_global_var("global_save_dir"), get_global_var("global_pic_path"), get_global_var("global_crop_info"), \ + enhancer=get_global_var("global_args").enhancer, background_enhancer=get_global_var("global_args").background_enhancer, preprocess=get_global_var("global_pre_process"), img_size=get_global_var("global_arg_size")) + + + shutil.move(result, get_global_var("global_save_dir")+'.mp4') + + print('The generated video is named:', get_global_var("global_save_dir")+'.mp4') + + if not get_global_var("global_args").verbose: + shutil.rmtree(get_global_var("global_save_dir")) + + +if __name__ == "__main__": + parser = ArgumentParser() + parser.add_argument("--step", choices=['initilize', 'stage', 'render'], required=True, help="Specify which step to execute.") + + #parser = ArgumentParser() + parser.add_argument("--driven_audio", default='./examples/driven_audio/bus_chinese.wav', help="path to driven audio") + parser.add_argument("--source_image", default='./examples/source_image/full_body_1.png', help="path to source image") + parser.add_argument("--ref_eyeblink", default=None, help="path to reference video providing eye blinking") + parser.add_argument("--ref_pose", default=None, help="path to reference video providing pose") + parser.add_argument("--checkpoint_dir", default='./checkpoints', help="path to output") + parser.add_argument("--result_dir", default='./results', help="path to output") + parser.add_argument("--pose_style", type=int, default=0, help="input pose style from [0, 46)") + parser.add_argument("--batch_size", type=int, default=2, help="the batch size of facerender") + parser.add_argument("--size", type=int, default=256, help="the image size of the facerender") + parser.add_argument("--expression_scale", type=float, default=1., help="the batch size of facerender") + parser.add_argument('--input_yaw', nargs='+', type=int, default=None, help="the input yaw degree of the user ") + parser.add_argument('--input_pitch', nargs='+', type=int, default=None, help="the input pitch degree of the user") + parser.add_argument('--input_roll', nargs='+', type=int, default=None, help="the input roll degree of the user") + parser.add_argument('--enhancer', type=str, default=None, help="Face enhancer, [gfpgan, RestoreFormer]") + parser.add_argument('--background_enhancer', type=str, default=None, help="background enhancer, [realesrgan]") + parser.add_argument("--cpu", dest="cpu", action="store_true") + parser.add_argument("--face3dvis", action="store_true", help="generate 3d face and 3d landmarks") + parser.add_argument("--still", action="store_true", help="can crop back to the original videos for the full body aniamtion") + parser.add_argument("--preprocess", default='crop', choices=['crop', 'extcrop', 'resize', 'full', 'extfull'], help="how to preprocess the images" ) + parser.add_argument("--verbose",action="store_true", help="saving the intermedia output or not" ) + parser.add_argument("--old_version",action="store_true", help="use the pth other than safetensor version" ) + + print(f"args.face3dvis: {get_global_var('global_args_face3d')} ") + + + # net structure and parameters + parser.add_argument('--net_recon', type=str, default='resnet50', choices=['resnet18', 'resnet34', 'resnet50'], help='useless') + parser.add_argument('--init_path', type=str, default=None, help='Useless') + parser.add_argument('--use_last_fc',default=False, help='zero initialize the last fc') + parser.add_argument('--bfm_folder', type=str, default='./checkpoints/BFM_Fitting/') + parser.add_argument('--bfm_model', type=str, default='BFM_model_front.mat', help='bfm model') + + + # default renderer parameters + parser.add_argument('--focal', type=float, default=1015.) + parser.add_argument('--center', type=float, default=112.) + parser.add_argument('--camera_d', type=float, default=10.) + parser.add_argument('--z_near', type=float, default=5.) + parser.add_argument('--z_far', type=float, default=15.) + + args = parser.parse_args() + + if torch.cuda.is_available() and not args.cpu: + args.device = "cuda" + else: + args.device = "cpu" + + extractPaths(args) + + print("args are", args) + print("step is", args.step) + + if args.step == 'initilize': + ZERO_startProcess() + elif args.step == 'stage': + ONE_AVtoCoeff() + elif args.step == 'render': + TWO_AVRender() + + +def runCharacterAnimation(driven_audio, source_image, result_dir, still="still"): + """ + Executes an audio-driven animation command with the given parameters. + + Parameters: + - driven_audio: Path to the audio file to drive the animation. + - source_image: Path to the source image to animate. + - result_dir: Directory to store the resulting animation. + - still: Optional; specify "still" to run in still mode. Default is "still". + """ + command = [ + "python3.8", + "inference.py", + "--driven_audio", driven_audio, + "--source_image", source_image, + "--result_dir", result_dir, + "--still", still + ] + + try: + subprocess.run(command, check=True) + print("Command executed successfully.") + except subprocess.CalledProcessError as e: + print(f"Error executing command: {e}") + diff --git a/src/character-bot/run_inference_on_character.py b/src/character-bot/run_inference_on_character.py new file mode 100644 index 00000000..6f2b769f --- /dev/null +++ b/src/character-bot/run_inference_on_character.py @@ -0,0 +1,309 @@ +import subprocess + +from glob import glob +import shutil +import torch +from time import strftime +import os, sys, time +from argparse import ArgumentParser + +from src.utils.preprocess import CropAndExtract +from src.test_audio2coeff import Audio2Coeff +from src.facerender.animate import AnimateFromCoeff +from src.generate_batch import get_data +from src.generate_facerender_batch import get_facerender_data +from src.utils.init_path import init_path + +# Global Variables Initialization +global_vars = { + "global_pic_path":"", + "global_audio_path":"", + "global_save_dir":"", + "global_pose_style":"", + "global_device":"", + "global_batch_size":"", + "global_input_yaw_list":"", + "global_input_pitch_list":"", + "global_input_roll_list":"", + "global_ref_eyeblink":"", + "global_ref_pose":"", + "global_pre_process":"", + "global_arg_size":"", + "global_args_still":"", + "global_args_face3d":"", + "global_args":"", + "global_crop_pic_path":"", + "global_crop_info":"", + + "global_current_root_path":"", + "global_sadtalker_paths":"", + + "global_preprocess_model":"", + "global_audio_to_coeff":"", + "global_animate_from_coeff":"" +} + +# Intermediatories Initialization + +intermediate_data = { + 'first_coeff_path': None, + 'ref_eyeblink_coeff_path': None, + 'ref_pose_coeff_path': None, + 'data_for_AVRender': None, # Placeholder for data generated in ONE_AVtoCoeff +} + +def set_global_var(var_name, value): + global_vars[var_name] = value + +def get_global_var(var_name): + return global_vars[var_name] + +def set_intermediatory_var(var_name, value): + intermediate_data[var_name] = value + +def get_intermediatory_var(var_name): + return intermediate_data[var_name] + + +def extractPaths(args): + set_global_var("global_args", args) + set_global_var("global_pic_path", args.source_image) + set_global_var("global_audio_path",args.driven_audio) + set_global_var("global_save_dir", os.path.join(args.result_dir, strftime("%Y_%m_%d_%H.%M.%S"))) + + os.makedirs(get_global_var("global_save_dir"), exist_ok=True) + set_global_var("global_pose_style", args.pose_style) + set_global_var("global_device", args.device) + set_global_var("global_batch_size", args.batch_size) + set_global_var("global_input_yaw_list", args.input_yaw) + set_global_var("global_input_pitch_list", args.input_pitch) + set_global_var("global_input_roll_list", args.input_roll) + set_global_var("global_ref_eyeblink", args.ref_eyeblink) + set_global_var("global_ref_pose", args.ref_pose) + set_global_var("global_pre_process", args.preprocess) + set_global_var("global_arg_size", args.size) + set_global_var("global_args_still", args.still) + set_global_var("global_args_face3d", args.face3dvis) + + set_global_var("global_current_root_path", os.path.split(sys.argv[0])[0]) + + set_global_var("global_sadtalker_paths", init_path(args.checkpoint_dir, os.path.join(get_global_var("global_current_root_path"), 'src/config'), get_global_var("global_arg_size"), args.old_version, get_global_var("global_pre_process"))) + +def ZERO_startProcess(parser): + start_time = time.time() # Record the start time + + #parser = ArgumentParser() + parser.add_argument("--driven_audio", default='./examples/driven_audio/bus_chinese.wav', help="path to driven audio") + parser.add_argument("--source_image", default='./examples/source_image/full_body_1.png', help="path to source image") + parser.add_argument("--ref_eyeblink", default=None, help="path to reference video providing eye blinking") + parser.add_argument("--ref_pose", default=None, help="path to reference video providing pose") + parser.add_argument("--checkpoint_dir", default='./checkpoints', help="path to output") + parser.add_argument("--result_dir", default='./results', help="path to output") + parser.add_argument("--pose_style", type=int, default=0, help="input pose style from [0, 46)") + parser.add_argument("--batch_size", type=int, default=2, help="the batch size of facerender") + parser.add_argument("--size", type=int, default=256, help="the image size of the facerender") + parser.add_argument("--expression_scale", type=float, default=1., help="the batch size of facerender") + parser.add_argument('--input_yaw', nargs='+', type=int, default=None, help="the input yaw degree of the user ") + parser.add_argument('--input_pitch', nargs='+', type=int, default=None, help="the input pitch degree of the user") + parser.add_argument('--input_roll', nargs='+', type=int, default=None, help="the input roll degree of the user") + parser.add_argument('--enhancer', type=str, default=None, help="Face enhancer, [gfpgan, RestoreFormer]") + parser.add_argument('--background_enhancer', type=str, default=None, help="background enhancer, [realesrgan]") + parser.add_argument("--cpu", dest="cpu", action="store_true") + parser.add_argument("--face3dvis", action="store_true", help="generate 3d face and 3d landmarks") + parser.add_argument("--still", action="store_true", help="can crop back to the original videos for the full body aniamtion") + parser.add_argument("--preprocess", default='crop', choices=['crop', 'extcrop', 'resize', 'full', 'extfull'], help="how to preprocess the images" ) + parser.add_argument("--verbose",action="store_true", help="saving the intermedia output or not" ) + parser.add_argument("--old_version",action="store_true", help="use the pth other than safetensor version" ) + + print(f"args.face3dvis: {get_global_var('global_args_face3d')} ") + + + # net structure and parameters + parser.add_argument('--net_recon', type=str, default='resnet50', choices=['resnet18', 'resnet34', 'resnet50'], help='useless') + parser.add_argument('--init_path', type=str, default=None, help='Useless') + parser.add_argument('--use_last_fc',default=False, help='zero initialize the last fc') + parser.add_argument('--bfm_folder', type=str, default='./checkpoints/BFM_Fitting/') + parser.add_argument('--bfm_model', type=str, default='BFM_model_front.mat', help='bfm model') + + + # default renderer parameters + parser.add_argument('--focal', type=float, default=1015.) + parser.add_argument('--center', type=float, default=112.) + parser.add_argument('--camera_d', type=float, default=10.) + parser.add_argument('--z_near', type=float, default=5.) + parser.add_argument('--z_far', type=float, default=15.) + + args = parser.parse_args() + + if torch.cuda.is_available() and not args.cpu: + args.device = "cuda" + else: + args.device = "cpu" + + extractPaths(args) + + initializeModel() + + first_coeff_path, ref_eyeblink_coeff_path, ref_pose_coeff_path = extract3DimmFromImage(get_global_var("global_preprocess_model")) + + event_marker = time.time() # Record the marker time after the code execution + print(f"startProcess took: {event_marker - start_time} seconds") + + set_intermediatory_var("first_coeff_path", first_coeff_path) + set_intermediatory_var("ref_eyeblink_coeff_path", ref_eyeblink_coeff_path) + set_intermediatory_var("ref_pose_coeff_path", first_coeff_path) + + + #return first_coeff_path, ref_eyeblink_coeff_path, ref_pose_coeff_path + + +def ONE_AVtoCoeff(): + start_time = time.time() # Record the start timer + + coeff_path = audio2Coeff(get_intermediatory_var("first_coeff_path"), get_intermediatory_var("ref_eyeblink_coeff_path"), get_intermediatory_var("ref_pose_coeff_path")) + + data = video2Coeff(coeff_path, get_intermediatory_var("first_coeff_path")) + + event_marker = time.time() # Record the marker time after the code execution + print(f"AVtoCoeff took: {event_marker - start_time} seconds") + + set_intermediatory_var("data_for_AVRender", data) + + #return data + +def TWO_AVRender(): + start_time = time.time() # Record the start timer + + generateAudioOverlay(get_intermediatory_var("data_for_AVRender")) + + event_marker = time.time() # Record the marker time after the code execution + print(f"AVRender took: {event_marker - start_time} seconds") + + + +def initializeModel(): + #init model + preprocess_model = CropAndExtract(get_global_var("global_sadtalker_paths"), get_global_var("global_device")) + + audio_to_coeff = Audio2Coeff(get_global_var("global_sadtalker_paths"), get_global_var("global_device")) + + animate_from_coeff = AnimateFromCoeff(get_global_var("global_sadtalker_paths"), get_global_var("global_device")) + + # Use set_global_var to update the global_vars dictionary + set_global_var("global_preprocess_model", preprocess_model) + set_global_var("global_audio_to_coeff", audio_to_coeff) + set_global_var("global_animate_from_coeff", animate_from_coeff) + + +def extract3DimmFromImage(global_preprocess_model): + #crop image and extract 3dmm from image + first_frame_dir = os.path.join(get_global_var("global_save_dir"), 'first_frame_dir') + os.makedirs(first_frame_dir, exist_ok=True) + + first_coeff_path, global_crop_pic_path, global_crop_info = global_preprocess_model.generate(get_global_var("global_pic_path"), first_frame_dir, get_global_var("global_pre_process"),\ + source_image_flag=True, pic_size=get_global_var("global_arg_size")) + if first_coeff_path is None: + print("Can't get the coeffs of the input") + return + + if get_global_var("global_ref_eyeblink") is not None: + ref_eyeblink_videoname = os.path.splitext(os.path.split(get_global_var("global_ref_eyeblink"))[-1])[0] + ref_eyeblink_frame_dir = os.path.join(get_global_var("global_save_dir"), ref_eyeblink_videoname) + os.makedirs(ref_eyeblink_frame_dir, exist_ok=True) + + + ref_eyeblink_coeff_path, _, _ = global_preprocess_model.generate(get_global_var("global_ref_eyeblink"), ref_eyeblink_frame_dir, get_global_var("global_pre_process"), source_image_flag=False) + else: + ref_eyeblink_coeff_path=None + + if get_global_var("global_ref_pose") is not None: + if get_global_var("global_ref_pose") == get_global_var("global_ref_eyeblink"): + ref_pose_coeff_path = ref_eyeblink_coeff_path + else: + ref_pose_videoname = os.path.splitext(os.path.split(get_global_var("global_ref_pose"))[-1])[0] + ref_pose_frame_dir = os.path.join(get_global_var("global_save_dir"), ref_pose_videoname) + os.makedirs(ref_pose_frame_dir, exist_ok=True) + + ref_pose_coeff_path, _, _ = global_preprocess_model.generate(get_global_var("global_ref_pose"), ref_pose_frame_dir, get_global_var("global_pre_process"), source_image_flag=False) + else: + ref_pose_coeff_path=None + + return first_coeff_path, ref_eyeblink_coeff_path, ref_pose_coeff_path + + +#audio2ceoff. We will run this for each audio file that needs to be added to the Video +def audio2Coeff(first_coeff_path, ref_eyeblink_coeff_path, ref_pose_coeff_path): + #audio2ceoff + batch = get_data(first_coeff_path, get_global_var("global_audio_path"), get_global_var("global_device"), ref_eyeblink_coeff_path, still=get_global_var("global_args_still")) + + coeff_path = get_global_var("global_audio_to_coeff").generate(batch, get_global_var("global_save_dir"), get_global_var("global_pose_style"), ref_pose_coeff_path) + + print(f"args.face3dvis: {get_global_var('global_args_face3d')} ") + + # 3dface render + if get_global_var("global_args_face3d"): + from src.face3d.visualize import gen_composed_video + gen_composed_video(get_global_var("global_args"), get_global_var("global_device"), first_coeff_path, coeff_path, get_global_var("global_audio_path"), os.path.join(get_global_var("global_save_dir"), '3dface.mp4')) + + return coeff_path + +#coeff2video. We will run this for each audio file that needs to be added to the Video +def video2Coeff(coeff_path, first_coeff_path): + data = get_facerender_data(coeff_path, get_global_var("global_crop_pic_path"), first_coeff_path, get_global_var("global_audio_path"), + get_global_var("global_batch_size"), get_global_var("global_input_yaw_list"), get_global_var("global_input_pitch_list"), get_global_var("global_input_roll_list"), + expression_scale=get_global_var("global_args").expression_scale, still_mode=get_global_var("global_args_still"), preprocess=get_global_var("global_pre_process"), size=get_global_var("global_arg_size")) + + return data + +# This is the most expensive step in the Rendering TODO +def generateAudioOverlay(data): + result = get_global_var("global_animate_from_coeff").generate(data, get_global_var("global_save_dir"), get_global_var("global_pic_path"), get_global_var("global_crop_info"), \ + enhancer=get_global_var("global_args").enhancer, background_enhancer=get_global_var("global_args").background_enhancer, preprocess=get_global_var("global_pre_process"), img_size=get_global_var("global_arg_size")) + + + shutil.move(result, get_global_var("global_save_dir")+'.mp4') + + print('The generated video is named:', get_global_var("global_save_dir")+'.mp4') + + if not get_global_var("global_args").verbose: + shutil.rmtree(get_global_var("global_save_dir")) + + +if __name__ == "__main__": + parser = ArgumentParser() + parser.add_argument("--step", choices=['initilize', 'stage', 'render'], required=True, help="Specify which step to execute.") + args = parser.parse_args() + + if args.step == 'initilize': + ZERO_startProcess(parser) + elif args.step == 'stage': + ONE_AVtoCoeff() + elif args.step == 'render': + TWO_AVRender() + + +def runCharacterAnimation(driven_audio, source_image, result_dir, still="still"): + """ + Executes an audio-driven animation command with the given parameters. + + Parameters: + - driven_audio: Path to the audio file to drive the animation. + - source_image: Path to the source image to animate. + - result_dir: Directory to store the resulting animation. + - still: Optional; specify "still" to run in still mode. Default is "still". + """ + command = [ + "python3.8", + "inference.py", + "--driven_audio", driven_audio, + "--source_image", source_image, + "--result_dir", result_dir, + "--still", still + ] + + try: + subprocess.run(command, check=True) + print("Command executed successfully.") + except subprocess.CalledProcessError as e: + print(f"Error executing command: {e}") + diff --git a/src/facerender/animate.py b/src/facerender/animate.py index 781f5a33..836c5584 100644 --- a/src/facerender/animate.py +++ b/src/facerender/animate.py @@ -7,7 +7,7 @@ import safetensors import safetensors.torch warnings.filterwarnings('ignore') - +import time import imageio import torch @@ -24,6 +24,8 @@ from src.utils.paste_pic import paste_pic from src.utils.videoio import save_video_with_watermark +from functools import lru_cache + try: import webui # in webui in_webui = True @@ -154,7 +156,10 @@ def load_cpk_mapping(self, checkpoint_path, mapping=None, discriminator=None, return checkpoint['epoch'] - def generate(self, x, video_save_dir, pic_path, crop_info, enhancer=None, background_enhancer=None, preprocess='crop', img_size=256): + def generate(self, x, video_save_dir, pic_path, crop_info, seed_video_path, enhancer=None, background_enhancer=None, preprocess='crop', img_size=256): + print(f"Seed Video Path is {seed_video_path} ") + + start_t = time.time() source_image=x['source_image'].type(torch.FloatTensor) source_semantics=x['source_semantics'].type(torch.FloatTensor) @@ -162,6 +167,10 @@ def generate(self, x, video_save_dir, pic_path, crop_info, enhancer=None, backgr source_image=source_image.to(self.device) source_semantics=source_semantics.to(self.device) target_semantics=target_semantics.to(self.device) + + tracker_t = time.time() + print(f"Generating video - Step-1 {tracker_t - start_t} seconds") + if 'yaw_c_seq' in x: yaw_c_seq = x['yaw_c_seq'].type(torch.FloatTensor) yaw_c_seq = x['yaw_c_seq'].to(self.device) @@ -180,33 +189,44 @@ def generate(self, x, video_save_dir, pic_path, crop_info, enhancer=None, backgr frame_num = x['frame_num'] - predictions_video = make_animation(source_image, source_semantics, target_semantics, - self.generator, self.kp_extractor, self.he_estimator, self.mapping, - yaw_c_seq, pitch_c_seq, roll_c_seq, use_exp = True) + if seed_video_path != 'XXX': + print("No need to make animation") + video_save_dir = seed_video_path + else: + print("We need to make animation") + predictions_video = make_animation(source_image, source_semantics, target_semantics, + self.generator, self.kp_extractor, self.he_estimator, self.mapping, + yaw_c_seq, pitch_c_seq, roll_c_seq, use_exp = True) + + tracker_t = time.time() + print(f"Generating video - Step-3 {tracker_t - start_t} seconds") + + predictions_video = predictions_video.reshape((-1,)+predictions_video.shape[2:]) + predictions_video = predictions_video[:frame_num] + + video = [] + for idx in range(predictions_video.shape[0]): + image = predictions_video[idx] + image = np.transpose(image.data.cpu().numpy(), [1, 2, 0]).astype(np.float32) + video.append(image) + result = img_as_ubyte(video) + + ### the generated video is 256x256, so we keep the aspect ratio, + original_size = crop_info[0] + if original_size: + result = [ cv2.resize(result_i,(img_size, int(img_size * original_size[1]/original_size[0]) )) for result_i in result ] + + video_name = x['video_name'] + '.mp4' + path = os.path.join(video_save_dir, 'temp_'+video_name) + + imageio.mimsave(path, result, fps=float(25)) - predictions_video = predictions_video.reshape((-1,)+predictions_video.shape[2:]) - predictions_video = predictions_video[:frame_num] + av_path = os.path.join(video_save_dir, video_name) + return_path = av_path + print(f"After Step 3 Video Name is {video_name} and Video Path is {video_save_dir} ") + print(f"After Step 3 av_path is {av_path} ") - video = [] - for idx in range(predictions_video.shape[0]): - image = predictions_video[idx] - image = np.transpose(image.data.cpu().numpy(), [1, 2, 0]).astype(np.float32) - video.append(image) - result = img_as_ubyte(video) - ### the generated video is 256x256, so we keep the aspect ratio, - original_size = crop_info[0] - if original_size: - result = [ cv2.resize(result_i,(img_size, int(img_size * original_size[1]/original_size[0]) )) for result_i in result ] - - video_name = x['video_name'] + '.mp4' - path = os.path.join(video_save_dir, 'temp_'+video_name) - - imageio.mimsave(path, result, fps=float(25)) - - av_path = os.path.join(video_save_dir, video_name) - return_path = av_path - audio_path = x['audio_path'] audio_name = os.path.splitext(os.path.split(audio_path)[-1])[0] new_audio_path = os.path.join(video_save_dir, audio_name+'.wav') @@ -219,9 +239,13 @@ def generate(self, x, video_save_dir, pic_path, crop_info, enhancer=None, backgr word = word1[start_time:end_time] word.export(new_audio_path, format="wav") + tracker_t = time.time() + print(f"Generating video - Step-4 {tracker_t - start_t} seconds") + save_video_with_watermark(path, new_audio_path, av_path, watermark= False) print(f'The generated video is named {video_save_dir}/{video_name}') + if 'full' in preprocess.lower(): # only add watermark to the full image. video_name_full = x['video_name'] + '_full.mp4' @@ -232,6 +256,9 @@ def generate(self, x, video_save_dir, pic_path, crop_info, enhancer=None, backgr else: full_video_path = av_path + tracker_t = time.time() + print(f"Generating video - Step-5 {tracker_t - start_t} seconds") + #### paste back then enhancers if enhancer: video_name_enhancer = x['video_name'] + '_enhanced.mp4' @@ -239,19 +266,28 @@ def generate(self, x, video_save_dir, pic_path, crop_info, enhancer=None, backgr av_path_enhancer = os.path.join(video_save_dir, video_name_enhancer) return_path = av_path_enhancer + tracker_t = time.time() + print(f"Generating video - Step-13 {tracker_t - start_t} seconds") + try: enhanced_images_gen_with_len = enhancer_generator_with_len(full_video_path, method=enhancer, bg_upsampler=background_enhancer) imageio.mimsave(enhanced_path, enhanced_images_gen_with_len, fps=float(25)) except: enhanced_images_gen_with_len = enhancer_list(full_video_path, method=enhancer, bg_upsampler=background_enhancer) imageio.mimsave(enhanced_path, enhanced_images_gen_with_len, fps=float(25)) + + tracker_t = time.time() + print(f"Generating video - Step-14 {tracker_t - start_t} seconds") save_video_with_watermark(enhanced_path, new_audio_path, av_path_enhancer, watermark= False) print(f'The generated video is named {video_save_dir}/{video_name_enhancer}') - os.remove(enhanced_path) + #os.remove(enhanced_path) + + tracker_t = time.time() + print(f"Generating video - Step-15 {tracker_t - start_t} seconds") - os.remove(path) - os.remove(new_audio_path) + #os.remove(path) + #os.remove(new_audio_path) return return_path diff --git a/src/facerender/modules/make_animation.py b/src/facerender/modules/make_animation.py index 3360c535..172916b2 100644 --- a/src/facerender/modules/make_animation.py +++ b/src/facerender/modules/make_animation.py @@ -98,6 +98,17 @@ def keypoint_transformation(kp_canonical, he, wo_exp=False): return {'value': kp_transformed} +def make_kp_detector_ready(source_image, source_semantics, kp_detector, mapping): + with torch.no_grad(): + kp_canonical = kp_detector(source_image) + return kp_canonical + + +def make_kp_detector_ready(source_semantics, mapping): + with torch.no_grad(): + he_source = mapping(source_semantics) + return he_source + def make_animation(source_image, source_semantics, target_semantics, generator, kp_detector, he_estimator, mapping, @@ -112,7 +123,8 @@ def make_animation(source_image, source_semantics, target_semantics, for frame_idx in tqdm(range(target_semantics.shape[1]), 'Face Renderer:'): # still check the dimension - # print(target_semantics.shape, source_semantics.shape) + print(target_semantics.shape, source_semantics.shape) + #print("frame_idx - ", frame_idx) target_semantics_frame = target_semantics[:, frame_idx] he_driving = mapping(target_semantics_frame) if yaw_c_seq is not None: diff --git a/src/test_audio2coeff.py b/src/test_audio2coeff.py index bbf19f49..e163e1b6 100644 --- a/src/test_audio2coeff.py +++ b/src/test_audio2coeff.py @@ -5,6 +5,8 @@ from yacs.config import CfgNode as CN from scipy.signal import savgol_filter +from functools import lru_cache + import safetensors import safetensors.torch diff --git a/src/utils/paste_pic.py b/src/utils/paste_pic.py index f9989e21..8e8dff20 100644 --- a/src/utils/paste_pic.py +++ b/src/utils/paste_pic.py @@ -66,4 +66,4 @@ def paste_pic(video_path, pic_path, crop_info, new_audio_path, full_video_path, out_tmp.release() save_video_with_watermark(tmp_path, new_audio_path, full_video_path, watermark=False) - os.remove(tmp_path) + #os.remove(tmp_path) diff --git a/src/utils/preprocess.py b/src/utils/preprocess.py index 0f784e6c..705a2173 100644 --- a/src/utils/preprocess.py +++ b/src/utils/preprocess.py @@ -13,6 +13,8 @@ from scipy.io import loadmat, savemat from src.utils.croper import Preprocesser +from functools import lru_cache + import warnings @@ -42,7 +44,6 @@ def split_coeff(coeffs): 'trans': translations } - class CropAndExtract(): def __init__(self, sadtalker_path, device): diff --git a/src/utils/videoio.py b/src/utils/videoio.py index 08bfbdd7..e94877d9 100644 --- a/src/utils/videoio.py +++ b/src/utils/videoio.py @@ -38,4 +38,4 @@ def save_video_with_watermark(video, audio, save_path, watermark=False): cmd = r'ffmpeg -y -hide_banner -loglevel error -i "%s" -i "%s" -filter_complex "[1]scale=100:-1[wm];[0][wm]overlay=(main_w-overlay_w)-10:10" "%s"' % (temp_file, watarmark_path, save_path) os.system(cmd) - os.remove(temp_file) \ No newline at end of file + #os.remove(temp_file) \ No newline at end of file