pytorch_facial_gan_multipleimgs_v1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python Platform: macOS-13.3.1-arm64-arm-64bit\n",
      "Python 3.9.16\n",
      "Pandas 2.0.1\n",
      "Scikit-Learn 1.2.2\n",
      "PyTorch Version: 2.1.0.dev20230507\n",
      "GPU is NOT AVAILABLE\n",
      "MPS (Apple Metal) is AVAILABLE\n",
      "Target device is mps\n"
     ]
    }
   ],
   "source": [
    "import platform\n",
    "import torch\n",
    "import pandas as pd\n",
    "import sklearn as sk\n",
    "\n",
    "# Get Python and package versions\n",
    "python_version = platform.python_version()\n",
    "pandas_version = pd.__version__\n",
    "sklearn_version = sk.__version__\n",
    "pytorch_version = torch.__version__\n",
    "\n",
    "# Check for GPU and MPS availability\n",
    "has_gpu = torch.cuda.is_available()\n",
    "has_mps = getattr(torch, 'has_mps', False)\n",
    "device = 'mps' if has_mps else 'gpu' if has_gpu else 'cpu'\n",
    "\n",
    "# Print the results\n",
    "print(f'Python Platform: {platform.platform()}')\n",
    "print(f'Python {python_version}')\n",
    "print(f'Pandas {pandas_version}')\n",
    "print(f'Scikit-Learn {sklearn_version}')\n",
    "print(f'PyTorch Version: {pytorch_version}')\n",
    "print(f'GPU is {\"available\" if has_gpu else \"NOT AVAILABLE\"}')\n",
    "print(f'MPS (Apple Metal) is {\"AVAILABLE\" if has_mps else \"NOT AVAILABLE\"}')\n",
    "print(f'Target device is {device}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python Platform: macOS-13.3.1-arm64-arm-64bit\n",
      "Python 3.9.16\n",
      "Pandas 2.0.1\n",
      "Scikit-Learn 1.2.2\n",
      "PyTorch Version: 2.1.0.dev20230507\n",
      "GPU is NOT AVAILABLE\n",
      "MPS (Apple Metal) is AVAILABLE\n",
      "Target device is mps\n"
     ]
    }
   ],
   "source": [
    "import platform\n",
    "import torch\n",
    "import pandas as pd\n",
    "import sklearn as sk\n",
    "\n",
    "# Get Python and package versions\n",
    "python_version = platform.python_version()\n",
    "pandas_version = pd.__version__\n",
    "sklearn_version = sk.__version__\n",
    "pytorch_version = torch.__version__\n",
    "\n",
    "# Check for GPU and MPS availability\n",
    "has_gpu = torch.cuda.is_available()\n",
    "has_mps = getattr(torch, 'has_mps', False)\n",
    "device = 'mps' if has_mps else 'gpu' if has_gpu else 'cpu'\n",
    "\n",
    "# Print the results\n",
    "print(f'Python Platform: {platform.platform()}')\n",
    "print(f'Python {python_version}')\n",
    "print(f'Pandas {pandas_version}')\n",
    "print(f'Scikit-Learn {sklearn_version}')\n",
    "print(f'PyTorch Version: {pytorch_version}')\n",
    "print(f'GPU is {\"available\" if has_gpu else \"NOT AVAILABLE\"}')\n",
    "print(f'MPS (Apple Metal) is {\"AVAILABLE\" if has_mps else \"NOT AVAILABLE\"}')\n",
    "print(f'Target device is {device}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "--2023-05-10 18:17:32--  http://dlib.net/files/shape_predictor_5_face_landmarks.dat.bz2\n",
      "Resolving dlib.net (dlib.net)... 107.180.26.78\n",
      "Connecting to dlib.net (dlib.net)|107.180.26.78|:80... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 5706710 (5.4M)\n",
      "Saving to: ‘shape_predictor_5_face_landmarks.dat.bz2’\n",
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      "\n",
      "2023-05-10 18:17:35 (2.53 MB/s) - ‘shape_predictor_5_face_landmarks.dat.bz2’ saved [5706710/5706710]\n",
      "\n",
      "bzip2: Output file shape_predictor_5_face_landmarks.dat already exists.\n",
      "fatal: destination path 'stylegan2-ada-pytorch' already exists and is not an empty directory.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: ninja in /Users/oscarwu_admin_1.0/miniconda3/envs/facial_detection_gan_pytorch/lib/python3.9/site-packages (1.11.1)\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "# No need to activate conda environment here\n",
    "# conda activate facial_detection_gan_pytorch\n",
    "\n",
    "# Download and extract shape_predictor_5_face_landmarks.dat\n",
    "wget http://dlib.net/files/shape_predictor_5_face_landmarks.dat.bz2\n",
    "bzip2 -d shape_predictor_5_face_landmarks.dat.bz2\n",
    "\n",
    "# Clone StyleGAN2-ada-pytorch repository and install requirements\n",
    "git clone https://github.com/NVlabs/stylegan2-ada-pytorch.git\n",
    "pip install ninja"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current working directory:\n",
      "/Users/oscarwu_admin_1.0/repos/facial_detection_gan_pytorch/notebooks\n",
      "Listing files in the current working directory:\n",
      "jen_1.jpg\n",
      "jen_3.jpeg\n",
      "pytorch_facial_gan_multipleimgs.ipynb\n",
      "pytorch_facial_gan_multipleimgs_v1.py\n",
      "shape_predictor_5_face_landmarks.dat\n",
      "shape_predictor_5_face_landmarks.dat.bz2\n",
      "\u001b[34mstylegan2-ada-pytorch\u001b[m\u001b[m\n",
      "Listing files in the StyleGAN2-ada-pytorch directory:\n",
      "\u001b[31mDockerfile\u001b[m\u001b[m\n",
      "\u001b[31mLICENSE.txt\u001b[m\u001b[m\n",
      "\u001b[31mREADME.md\u001b[m\u001b[m\n",
      "\u001b[31mcalc_metrics.py\u001b[m\u001b[m\n",
      "\u001b[31mdataset_tool.py\u001b[m\u001b[m\n",
      "\u001b[34mdnnlib\u001b[m\u001b[m\n",
      "\u001b[31mdocker_run.sh\u001b[m\u001b[m\n",
      "\u001b[34mdocs\u001b[m\u001b[m\n",
      "\u001b[31mgenerate.py\u001b[m\u001b[m\n",
      "\u001b[31mlegacy.py\u001b[m\u001b[m\n",
      "\u001b[34mmetrics\u001b[m\u001b[m\n",
      "\u001b[31mprojector.py\u001b[m\u001b[m\n",
      "\u001b[31mstyle_mixing.py\u001b[m\u001b[m\n",
      "\u001b[34mtorch_utils\u001b[m\u001b[m\n",
      "\u001b[31mtrain.py\u001b[m\u001b[m\n",
      "\u001b[34mtraining\u001b[m\u001b[m\n",
      "Finding the location of the ninja package:\n",
      "Name: ninja\n",
      "Version: 1.11.1\n",
      "Summary: Ninja is a small build system with a focus on speed\n",
      "Home-page: http://ninja-build.org/\n",
      "Author: Jean-Christophe Fillion-Robin\n",
      "Author-email: scikit-build@googlegroups.com\n",
      "License: Apache 2.0\n",
      "Location: /Users/oscarwu_admin_1.0/miniconda3/envs/facial_detection_gan_pytorch/lib/python3.9/site-packages\n",
      "Requires: \n",
      "Required-by: \n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "\n",
    "echo \"Current working directory:\"\n",
    "pwd\n",
    "\n",
    "echo \"Listing files in the current working directory:\"\n",
    "ls\n",
    "\n",
    "echo \"Listing files in the StyleGAN2-ada-pytorch directory:\"\n",
    "ls stylegan2-ada-pytorch\n",
    "\n",
    "echo \"Finding the location of the ninja package:\"\n",
    "pip show ninja"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import os\n",
    "# import sys\n",
    "# import cv2\n",
    "# import numpy as np\n",
    "# from PIL import Image\n",
    "# import dlib\n",
    "# from matplotlib import pyplot as plt\n",
    "# import torch\n",
    "# import dnnlib\n",
    "# import legacy\n",
    "# import PIL.Image\n",
    "# import numpy as np\n",
    "# import imageio\n",
    "# from tqdm import tqdm\n",
    "\n",
    "import os\n",
    "import sys\n",
    "\n",
    "# Set working directory\n",
    "# project_root = \"/Users/oscarwu_admin_1.0/repos/facial_detection_gan_pytorch\"\n",
    "project_root = \"./\"\n",
    "os.chdir(project_root)\n",
    "\n",
    "# Update sys.path\n",
    "sys.path.insert(0, os.path.join(project_root, \"stylegan2-ada-pytorch\"))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Constants\n",
    "NETWORK = \"https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/ffhq.pkl\"\n",
    "STEPS = 150\n",
    "FPS = 30\n",
    "FREEZE_STEPS = 30\n",
    "\n",
    "# Read image files\n",
    "img_list = []\n",
    "for file in os.listdir(project_root):\n",
    "    if file.endswith((\".jpg\", \".jpeg\", \".png\", \".bmp\")):\n",
    "        img_list.append(file)\n",
    "\n",
    "if len(img_list) < 2:\n",
    "    print(\"Upload at least 2 images for morphing.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The images have been read.\n"
     ]
    }
   ],
   "source": [
    "if len(img_list) > 0:\n",
    "    print(\"The images have been read.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000,
     "referenced_widgets": [
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      "8348e9ff26cc4e4696cd4d928ec0d824",
      "07d6393119fe4347b4c33f29b72fa524",
      "69ddb3c51515480b8d8ec95098026cf6",
      "3d2ece81a48543d6a19297c0a7a45baa",
      "925a81279bd643b2af5abcb56ce719e3",
      "eb9e883842ee4308bf2a364fae9485ee",
      "0e187e7892ac4220b61446549796983a",
      "bd93a1dc84b942dfb4c256e82faa111b",
      "d31e65ffeb014dc88e2d84bb55177f69",
      "f93c650d4ce145aca2d3e541589180fb",
      "86460ac590c04ec89b4db3b13ba7c2cc",
      "9a9c8fa37f504a4ca3938ec95765b4b8"
     ]
    },
    "id": "dlHshgB1K4-z",
    "outputId": "2f6563d4-ffe3-4029-80c8-99566f2d7374"
   },
   "outputs": [],
   "source": [
    "# # stylegan2 yielded better results than stylegan3 for feature vectors of selfies, so I'll use v2\n",
    "# # 150 imgs for imgs b/w 2 imgs uploaded\n",
    "# # 30 images @ beginning and end since otherwise it's just jumping sequences\n",
    "\n",
    "# NETWORK = \"https://nvlabs-fi-cdn.nvidia.com/\"\\\n",
    "#   \"stylegan2-ada-pytorch/pretrained/ffhq.pkl\"\n",
    "# STEPS = 150\n",
    "# FPS = 30\n",
    "# FREEZE_STEPS = 30\n",
    "\n",
    "# # HIDE OUTPUT\n",
    "# import os\n",
    "# from google.colab import files\n",
    "\n",
    "# uploaded_files = files.upload()\n",
    "\n",
    "# img_list = []\n",
    "# for k, v in uploaded_files.items():\n",
    "#     _, ext = os.path.splitext(k)\n",
    "#     os.remove(k)\n",
    "#     image_name = f\"{k}{ext}\"\n",
    "#     open(image_name, 'wb').write(v)\n",
    "#     img_list.append(image_name)\n",
    "\n",
    "# if len(img_list) < 2:\n",
    "#   print(\"Upload at least 2 images for morphing.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current sys.path:\n",
      "['./stylegan2-ada-pytorch', '/Users/oscarwu_admin_1.0/repos/facial_detection_gan_pytorch/notebooks/stylegan2-ada-pytorch', '/Users/oscarwu_admin_1.0/repos/facial_detection_gan_pytorch/notebooks', '/Users/oscarwu_admin_1.0/repos/facial_detection_gan_pytorch/notebooks/stylegan2-ada-pytorch', '/Users/oscarwu_admin_1.0/repos/facial_detection_gan_pytorch/notebooks', '/Users/oscarwu_admin_1.0/repos/facial_detection_gan_pytorch/notebooks/stylegan2-ada-pytorch', 'stylegan2-ada-pytorch', './stylegan2-ada-pytorch', '/Users/oscarwu_admin_1.0/repos/facial_detection_gan_pytorch/notebooks', '/Users/oscarwu_admin_1.0/miniconda3/envs/facial_detection_gan_pytorch/lib/python39.zip', '/Users/oscarwu_admin_1.0/miniconda3/envs/facial_detection_gan_pytorch/lib/python3.9', '/Users/oscarwu_admin_1.0/miniconda3/envs/facial_detection_gan_pytorch/lib/python3.9/lib-dynload', '', '/Users/oscarwu_admin_1.0/.local/lib/python3.9/site-packages', '/Users/oscarwu_admin_1.0/miniconda3/envs/facial_detection_gan_pytorch/lib/python3.9/site-packages']\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "\n",
    "print(\"Current sys.path:\")\n",
    "print(sys.path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/oscarwu_admin_1.0/repos/facial_detection_gan_pytorch/notebooks\n"
     ]
    }
   ],
   "source": [
    "!pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Updated sys.path:\n",
      "['stylegan2-ada-pytorch', './stylegan2-ada-pytorch', '/Users/oscarwu_admin_1.0/repos/facial_detection_gan_pytorch/notebooks/stylegan2-ada-pytorch', '/Users/oscarwu_admin_1.0/repos/facial_detection_gan_pytorch/notebooks', '/Users/oscarwu_admin_1.0/repos/facial_detection_gan_pytorch/notebooks/stylegan2-ada-pytorch', '/Users/oscarwu_admin_1.0/repos/facial_detection_gan_pytorch/notebooks', '/Users/oscarwu_admin_1.0/repos/facial_detection_gan_pytorch/notebooks/stylegan2-ada-pytorch', 'stylegan2-ada-pytorch', './stylegan2-ada-pytorch', '/Users/oscarwu_admin_1.0/repos/facial_detection_gan_pytorch/notebooks', '/Users/oscarwu_admin_1.0/miniconda3/envs/facial_detection_gan_pytorch/lib/python39.zip', '/Users/oscarwu_admin_1.0/miniconda3/envs/facial_detection_gan_pytorch/lib/python3.9', '/Users/oscarwu_admin_1.0/miniconda3/envs/facial_detection_gan_pytorch/lib/python3.9/lib-dynload', '', '/Users/oscarwu_admin_1.0/.local/lib/python3.9/site-packages', '/Users/oscarwu_admin_1.0/miniconda3/envs/facial_detection_gan_pytorch/lib/python3.9/site-packages']\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# HIDE OUTPUT\n",
    "# 5 facial landmark predictor - base of mouth and nose \n",
    "# !wget http://dlib.net/files/shape_predictor_5_face_landmarks.dat.bz2\n",
    "# !bzip2 -d shape_predictor_5_face_landmarks.dat.bz2\n",
    "\n",
    "# # HIDE OUTPUT\n",
    "# import sys\n",
    "# !git clone https://github.com/NVlabs/stylegan2-ada-pytorch.git\n",
    "# !pip install ninja\n",
    "# sys.path.insert(0, \"/content/stylegan2-ada-pytorch\")\n",
    "\n",
    "stylegan2_ada_pytorch_dir = os.path.join(\"stylegan2-ada-pytorch\")\n",
    "sys.path.insert(0, stylegan2_ada_pytorch_dir)\n",
    "\n",
    "print(\"Updated sys.path:\")\n",
    "print(sys.path)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Updated sys.path:\n",
      "['/Users/oscarwu_admin_1.0/repos/facial_detection_gan_pytorch/notebooks/stylegan2-ada-pytorch', 'stylegan2-ada-pytorch', './stylegan2-ada-pytorch', '/Users/oscarwu_admin_1.0/repos/facial_detection_gan_pytorch/notebooks/stylegan2-ada-pytorch', '/Users/oscarwu_admin_1.0/repos/facial_detection_gan_pytorch/notebooks', '/Users/oscarwu_admin_1.0/repos/facial_detection_gan_pytorch/notebooks/stylegan2-ada-pytorch', '/Users/oscarwu_admin_1.0/repos/facial_detection_gan_pytorch/notebooks', '/Users/oscarwu_admin_1.0/repos/facial_detection_gan_pytorch/notebooks/stylegan2-ada-pytorch', 'stylegan2-ada-pytorch', './stylegan2-ada-pytorch', '/Users/oscarwu_admin_1.0/repos/facial_detection_gan_pytorch/notebooks', '/Users/oscarwu_admin_1.0/miniconda3/envs/facial_detection_gan_pytorch/lib/python39.zip', '/Users/oscarwu_admin_1.0/miniconda3/envs/facial_detection_gan_pytorch/lib/python3.9', '/Users/oscarwu_admin_1.0/miniconda3/envs/facial_detection_gan_pytorch/lib/python3.9/lib-dynload', '', '/Users/oscarwu_admin_1.0/.local/lib/python3.9/site-packages', '/Users/oscarwu_admin_1.0/miniconda3/envs/facial_detection_gan_pytorch/lib/python3.9/site-packages']\n"
     ]
    }
   ],
   "source": [
    "sys.path.insert(0, \"/Users/oscarwu_admin_1.0/repos/facial_detection_gan_pytorch/notebooks/stylegan2-ada-pytorch\")\n",
    "\n",
    "print(\"Updated sys.path:\")\n",
    "print(sys.path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['/Users/oscarwu_admin_1.0/repos/facial_detection_gan_pytorch/notebooks/stylegan2-ada-pytorch', 'stylegan2-ada-pytorch', './stylegan2-ada-pytorch', '/Users/oscarwu_admin_1.0/repos/facial_detection_gan_pytorch/notebooks/stylegan2-ada-pytorch', '/Users/oscarwu_admin_1.0/repos/facial_detection_gan_pytorch/notebooks', '/Users/oscarwu_admin_1.0/repos/facial_detection_gan_pytorch/notebooks/stylegan2-ada-pytorch', '/Users/oscarwu_admin_1.0/repos/facial_detection_gan_pytorch/notebooks', '/Users/oscarwu_admin_1.0/repos/facial_detection_gan_pytorch/notebooks/stylegan2-ada-pytorch', 'stylegan2-ada-pytorch', './stylegan2-ada-pytorch', '/Users/oscarwu_admin_1.0/repos/facial_detection_gan_pytorch/notebooks', '/Users/oscarwu_admin_1.0/miniconda3/envs/facial_detection_gan_pytorch/lib/python39.zip', '/Users/oscarwu_admin_1.0/miniconda3/envs/facial_detection_gan_pytorch/lib/python3.9', '/Users/oscarwu_admin_1.0/miniconda3/envs/facial_detection_gan_pytorch/lib/python3.9/lib-dynload', '', '/Users/oscarwu_admin_1.0/.local/lib/python3.9/site-packages', '/Users/oscarwu_admin_1.0/miniconda3/envs/facial_detection_gan_pytorch/lib/python3.9/site-packages']\n"
     ]
    }
   ],
   "source": [
    "print(sys.path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['/Users/oscarwu_admin_1.0/repos/facial_detection_gan_pytorch/notebooks/stylegan2-ada-pytorch', '/Users/oscarwu_admin_1.0/repos/facial_detection_gan_pytorch/notebooks', '/Users/oscarwu_admin_1.0/repos/facial_detection_gan_pytorch/notebooks/stylegan2-ada-pytorch', 'stylegan2-ada-pytorch', './stylegan2-ada-pytorch', '/Users/oscarwu_admin_1.0/repos/facial_detection_gan_pytorch/notebooks/stylegan2-ada-pytorch', '/Users/oscarwu_admin_1.0/repos/facial_detection_gan_pytorch/notebooks', '/Users/oscarwu_admin_1.0/repos/facial_detection_gan_pytorch/notebooks/stylegan2-ada-pytorch', '/Users/oscarwu_admin_1.0/repos/facial_detection_gan_pytorch/notebooks', '/Users/oscarwu_admin_1.0/repos/facial_detection_gan_pytorch/notebooks/stylegan2-ada-pytorch', 'stylegan2-ada-pytorch', './stylegan2-ada-pytorch', '/Users/oscarwu_admin_1.0/repos/facial_detection_gan_pytorch/notebooks', '/Users/oscarwu_admin_1.0/miniconda3/envs/facial_detection_gan_pytorch/lib/python39.zip', '/Users/oscarwu_admin_1.0/miniconda3/envs/facial_detection_gan_pytorch/lib/python3.9', '/Users/oscarwu_admin_1.0/miniconda3/envs/facial_detection_gan_pytorch/lib/python3.9/lib-dynload', '', '/Users/oscarwu_admin_1.0/.local/lib/python3.9/site-packages', '/Users/oscarwu_admin_1.0/miniconda3/envs/facial_detection_gan_pytorch/lib/python3.9/site-packages']\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "import os\n",
    "\n",
    "project_directory = \"/Users/oscarwu_admin_1.0/repos/facial_detection_gan_pytorch/notebooks\"\n",
    "\n",
    "# Insert the stylegan2-ada-pytorch directory\n",
    "sys.path.insert(0, os.path.join(project_directory, \"stylegan2-ada-pytorch\"))\n",
    "\n",
    "# Insert the project_directory\n",
    "sys.path.insert(1, project_directory)\n",
    "\n",
    "print(sys.path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fatal: destination path 'stylegan2-ada-pytorch' already exists and is not an empty directory.\n"
     ]
    }
   ],
   "source": [
    "!git clone https://github.com/NVlabs/stylegan2-ada-pytorch.git"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Traceback (most recent call last):\n",
      "  File \"<string>\", line 1, in <module>\n",
      "ModuleNotFoundError: No module named 'cv2'\n"
     ]
    }
   ],
   "source": [
    "!python -c \"import cv2; print(cv2.__file__)\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: ninja in /Users/oscarwu_admin_1.0/miniconda3/envs/facial_detection_gan_pytorch/lib/python3.9/site-packages (1.11.1)\n"
     ]
    }
   ],
   "source": [
    "!pip install ninja"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'cv2'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[30], line 6\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[39m# import cv2\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[39m# import numpy as np\u001b[39;00m\n\u001b[1;32m      3\u001b[0m \u001b[39m# from PIL import Image\u001b[39;00m\n\u001b[1;32m      4\u001b[0m \u001b[39m# import dlib\u001b[39;00m\n\u001b[1;32m      5\u001b[0m \u001b[39m# from matplotlib import pyplot as plt\u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mcv2\u001b[39;00m\n\u001b[1;32m      7\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mnumpy\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mnp\u001b[39;00m\n\u001b[1;32m      8\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mPIL\u001b[39;00m \u001b[39mimport\u001b[39;00m Image\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'cv2'"
     ]
    }
   ],
   "source": [
    "\n",
    "# import cv2\n",
    "# import numpy as np\n",
    "# from PIL import Image\n",
    "# import dlib\n",
    "# from matplotlib import pyplot as plt\n",
    "import cv2\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "import dlib\n",
    "from matplotlib import pyplot as plt\n",
    "import torch\n",
    "import dnnlib\n",
    "import legacy\n",
    "import PIL.Image\n",
    "import numpy as np\n",
    "import imageio\n",
    "from tqdm import tqdm\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "detector = dlib.get_frontal_face_detector()\n",
    "predictor = dlib.shape_predictor('shape_predictor_5_face_landmarks.dat')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def crop_stylegan(img):\n",
    "    dets = detector(img, 1)\n",
    "    if len(dets) == 0:\n",
    "        raise ValueError(\"No face detected\")\n",
    "\n",
    "    d = dets[0]\n",
    "    shape = predictor(img, d)\n",
    "\n",
    "    x1, y1 = shape.part(0).x, shape.part(0).y\n",
    "    x2, y2 = shape.part(2).x, shape.part(2).y\n",
    "    x3, y3 = shape.part(4).x, shape.part(4).y\n",
    "\n",
    "    center = dlib.point((x1 + x2) // 2, (y1 + y2) // 2)\n",
    "    width = np.linalg.norm(np.array([x1, y1]) - np.array([x3, y3]))\n",
    "\n",
    "    size = int(width * 2.2)\n",
    "    half_size = size // 2\n",
    "    left, top = center.x - half_size, center.y - half_size\n",
    "    right, bottom = left + size, top + size\n",
    "\n",
    "    cropped_img = img[top:bottom, left:right]\n",
    "    cropped_img = cv2.resize(cropped_img, (1024, 1024))\n",
    "    return cropped_img\n",
    "\n",
    "def process_images(img_list):\n",
    "    cropped_images = []\n",
    "    for img_name in img_list:\n",
    "        img = cv2.imread(img_name)\n",
    "        if img is None:\n",
    "            raise ValueError(f\"{img_name} not found\")\n",
    "\n",
    "        cropped_img = crop_stylegan(img)\n",
    "        cropped_images.append(cropped_img)\n",
    "\n",
    "        img_rgb = cv2.cvtColor(cropped_img, cv2.COLOR_BGR2RGB)\n",
    "        plt.imshow(img_rgb)\n",
    "        plt.title(f'cropped {img_name}')\n",
    "        plt.show()\n",
    "    return cropped_images\n",
    "\n",
    "cropped_images = process_images(img_list)\n",
    "\n",
    "# Generate GAN images and latent vectors for each input image\n",
    "gan_images = []\n",
    "latent_vectors = []\n",
    "\n",
    "for i, cropped_img in enumerate(cropped_images):\n",
    "    cv2.imwrite(f\"cropped_{i}.png\", cropped_img)\n",
    "\n",
    "    # HIDE OUTPUT\n",
    "    cmd = f\"python /content/stylegan2-ada-pytorch/projector.py \"\\\n",
    "      f\"--save-video 0 --num-steps 1000 --outdir=out_{i} \"\\\n",
    "      f\"--target=cropped_{i}.png --network={NETWORK}\"\n",
    "    !{cmd}\n",
    "\n",
    "    img_gan = cv2.imread(f'/content/out_{i}/proj.png')\n",
    "    img_rgb = cv2.cvtColor(img_gan, cv2.COLOR_BGR2RGB)\n",
    "    plt.imshow(img_rgb)\n",
    "    plt.title(f'gan-image-{i}')\n",
    "    plt.show()\n",
    "\n",
    "    latent_vector = np.load(f'/content/out_{i}/projected_w.npz')['w']\n",
    "    gan_images.append(img_gan)\n",
    "    latent_vectors.append(latent_vector)\n",
    "\n",
    "# Create morph video with all images\n",
    "import torch\n",
    "import dnnlib\n",
    "import legacy\n",
    "import PIL.Image\n",
    "import numpy as np\n",
    "import imageio\n",
    "from tqdm.notebook import tqdm\n",
    "\n",
    "network_pkl = \"https://nvlabs-fi-cdn.nvidia.com/stylegan2\"\\\n",
    "  \"-ada-pytorch/pretrained/ffhq.pkl\"\n",
    "device = torch.device('cuda')\n",
    "with dnnlib.util.open_url(network_pkl) as fp:\n",
    "    G = legacy.load_network_pkl(fp)['G_ema']\\\n",
    "      .requires_grad_(False).to(device)\n",
    "\n",
    "video = imageio.get_writer('/content/movie.mp4', mode='I', fps=FPS, codec='libx264', bitrate='16M')\n",
    "\n",
    "for idx in range(len(latent_vectors) - 1):\n",
    "    lvec1 = latent_vectors[idx]\n",
    "    lvec2 = latent_vectors[idx + 1]\n",
    "\n",
    "    diff = lvec2 - lvec1\n",
    "    step = diff / STEPS\n",
    "    current = lvec1.copy()\n",
    "\n",
    "    for j in tqdm(range(STEPS)):\n",
    "        z = torch.from_numpy(current).to(device)\n",
    "        synth_image = G.synthesis(z, noise_mode='const')\n",
    "        synth_image = (synth_image + 1) * (255/2)\n",
    "        synth_image = synth_image.permute(0, 2, 3, 1).clamp(0, 255)\\\n",
    "          .to(torch.uint8)[0].cpu().numpy()\n",
    "\n",
    "        repeat = FREEZE_STEPS if j == 0 or j == (STEPS - 1) else 1\n",
    "        for i in range(repeat):\n",
    "            video.append_data(synth_image)\n",
    "        current = current + step\n",
    "\n",
    "video.close()\n",
    "\n",
    "# HIDE OUTPUT\n",
    "from google.colab import files\n",
    "files.download(\"movie.mp4\")"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "gpuType": "T4",
   "machine_shape": "hm",
   "provenance": []
  },
  "gpuClass": "standard",
  "kernelspec": {
   "display_name": "facial_detection_gan_pytorch-3.9",
   "language": "python",
   "name": "facial_detection_gan_pytorch"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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