[docker] Add docker with demo file

README.md

@@ -18,6 +18,16 @@ pip install -e .
 * To test on live video: `python face_alignment_test.py [-i webcam_index]`
 * To test on a video file: `python face_alignment_test.py [-i input_file] [-o output_file]`
 
+### Docker
+You can run a containerized demo using Docker. It will install the needed dependencies and allow you to test the FANPredictor on a sample image.
+
+Either run `bash demo/run.sh` or `docker build -t ibug-face_alignment -f ./demo/Dockerfile . && docker run -it --rm -p 8888:8888 ibug-face_alignment`
+You will automatically enter a tmux session and open jupter-lab, you can then open the notebook.ipynb. If you don't want this, you can either kill it immediately or provide "--entrypoint /bin/bash" to the docker run command.
+
+Please install Docker using [the official instructions](https://docs.docker.com/get-docker/)
+
+**NOTE** If you're running on Windows, running the container might fail, and you would need to replace `docker run` with `winpty docker run`. I also recommend running the commands from a Unix-shell, for example Git Bash, or Docker Quickstart Terminal. This is taken care of if you use the shell script.
+
 ## How to Use
 ```python
 # Import the libraries

demo/Dockerfile

@@ -0,0 +1,30 @@
+FROM continuumio/miniconda3:4.12.0
+
+RUN apt update && apt upgrade -y && \
+    apt install -y \
+        libgl1 \
+        lsof \ 
+        tmux \
+        vim
+
+WORKDIR /face_alignment
+
+COPY ../requirements.txt .
+RUN pip install -r requirements.txt && \
+    pip install scikit-image
+
+COPY ../setup.py .
+COPY ../ibug ibug 
+RUN pip install -e .
+
+COPY demo/main.py .
+
+RUN conda install jupyterlab matplotlib ipywidgets
+
+COPY demo/notebook.ipynb .
+
+CMD [ \
+    "tmux", \ 
+    "new-session", "/bin/bash", ";", \
+    "new-window", "jupyter-lab --ip=0.0.0.0 --no-browser --allow-root" \
+]

demo/main.py

@@ -0,0 +1,25 @@
+import os
+import cv2
+import torch
+from ibug.face_alignment import FANPredictor
+from skimage.data import astronaut
+import numpy as np
+
+image = astronaut()
+gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+face_cascade = cv2.CascadeClassifier(os.path.join(cv2.data.haarcascades, "haarcascade_frontalface_default.xml"))
+detections = face_cascade.detectMultiScale(gray)
+assert len(detections) == 1, "Please submit an image with exactly one clear frontal face"
+x, y, w, h = detections[0]
+detection = np.array([x, y, x+w, y+h])
+
+
+
+config = FANPredictor.create_config(gamma = 1.0, radius = 0.1, use_jit = False)
+
+device = "cpu"
+if torch.cuda.is_available():
+    device = torch.cuda.current_device()
+fan = FANPredictor(device=device, model=FANPredictor.get_model('2dfan2_alt'), config=config)
+
+landmarks, scores = fan(image, detection)

demo/notebook.ipynb

@@ -0,0 +1,145 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b3e3e628-e16a-474e-ad32-3edb91a82ba1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import time\n",
+    "import io\n",
+    "from typing import Callable\n",
+    "from contextlib import contextmanager\n",
+    "\n",
+    "import cv2\n",
+    "import torch\n",
+    "from skimage.data import astronaut\n",
+    "import numpy as np\n",
+    "from matplotlib import pyplot as plt\n",
+    "from PIL import Image\n",
+    "import ipywidgets as widgets\n",
+    "\n",
+    "from ibug.face_alignment import FANPredictor\n",
+    "from ibug.face_alignment.utils import plot_landmarks"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "eea2256f-2d81-406f-8d30-6753b026e8a4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def detect(image):\n",
+    "    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
+    "    face_cascade = cv2.CascadeClassifier(os.path.join(cv2.data.haarcascades, \"haarcascade_frontalface_default.xml\"))\n",
+    "    detections = face_cascade.detectMultiScale(gray)\n",
+    "    assert len(detections) == 1, \"Please submit an image with exactly one clear frontal face\"\n",
+    "    x, y, w, h = detections[0]\n",
+    "    return np.array([x, y, x+w, y+h])\n",
+    "\n",
+    "def resize(image: np.ndarray, longer_side: int) -> np.ndarray:\n",
+    "    width, height = image.shape[:2]\n",
+    "    largest = max(width, height)\n",
+    "    ratio = longer_side / largest\n",
+    "    return cv2.resize(image, (int(height * ratio), int(width * ratio)))\n",
+    "\n",
+    "@contextmanager\n",
+    "def time_tracking(callback: Callable[[float], None]):\n",
+    "    start = time.perf_counter()\n",
+    "    try:\n",
+    "        yield\n",
+    "    finally:\n",
+    "        end = time.perf_counter()\n",
+    "        callback(end - start)\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f065b832-3d29-473a-aa56-de236f5040e1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "file_upload = widgets.FileUpload()\n",
+    "\n",
+    "display(file_upload)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d18fc123-fcce-40a2-83be-0bf7fd430e99",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "image = astronaut()\n",
+    "\n",
+    "for filename, file_info in file_upload.value.items():\n",
+    "    image = np.array(Image.open(io.BytesIO(file_info['content'])))\n",
+    "    \n",
+    "image = resize(image, 400)\n",
+    "Image.fromarray(image)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5c15e1d3-2dd5-400d-85f6-46f7a1d88af5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with time_tracking(print):\n",
+    "    detection = detect(image)\n",
+    "print(f\"Detected face at {detection=}\")\n",
+    "\n",
+    "config = FANPredictor.create_config(gamma = 1.0, radius = 0.1, use_jit = False)\n",
+    "\n",
+    "device = \"cpu\"\n",
+    "if torch.cuda.is_available():\n",
+    "    device = torch.cuda.current_device()\n",
+    "\n",
+    "fan = FANPredictor(device=device, model=FANPredictor.get_model('2dfan2_alt'), config=config)\n",
+    "with time_tracking(print):\n",
+    "    landmarks, scores = fan(image, detection)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "afef425a-84d1-4ee2-b14b-8ee8bbbd21f6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vis = image.copy()\n",
+    "plot_landmarks(vis, landmarks[0])\n",
+    "\n",
+    "Image.fromarray(vis)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

demo/run.sh

@@ -0,0 +1,16 @@
+if ! [ "$(basename $PWD)" = "face_alignment" ]
+then
+	echo "Please run from the base directory of this repo."
+	exit 1
+fi
+
+docker build -t ibug-face_alignment -f ./demo/Dockerfile .
+
+prefix=""
+if [ $(uname | grep -iE "(mingw|cygwin)") ]
+then
+	prefix="winpty"
+fi
+
+echo $prefix
+$prefix docker run -it --rm -p 8888:8888 ibug-face_alignment