Add mpiiface dataset pre-processing and visualization notebook

notebooks/mpiiface_view_dataset.ipynb

@@ -0,0 +1,117 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e5b2fe72-599d-46ed-8285-25e9e0d31ffc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import cv2\n",
+    "import pickle\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from pathlib import Path\n",
+    "\n",
+    "from utils import *\n",
+    "\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1275092e-02f5-4538-b43d-33295e8d1151",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# data set base dir\n",
+    "# path_base = Path('/storage/nfs2/evangelosv/databases/EyeReconstruction/MPIIFaceGaze/')\n",
+    "path_base = Path('datasets/MPIIFaceGaze')\n",
+    "path_images = path_base / 'images'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "06b70cc3-7c91-473a-942c-ac18e27872b8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(str(path_base / 'data_for_model_2/all_gaze_eyes3D_face68.pkl'), 'rb') as f:\n",
+    "    data = pickle.load(f)\n",
+    "data = {d['name']: d for d in data}\n",
+    "paths = list(data.keys())\n",
+    "print(f\"Number of samples in dataset: {len(data)}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "07baab32-872f-4b1e-8bde-3448e9e5049c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Randomly loop through the data\n",
+    "k = np.random.randint(len(paths))\n",
+    "path = paths[k]\n",
+    "\n",
+    "res = data[path]\n",
+    "image = cv2.imread(str(path_images / path))\n",
+    "lms68 = res['face']['xyz68'].astype(np.float32)\n",
+    "eyes = {\n",
+    "    'left' : (res['eyes']['left']['P'] @ eyel_template_homo.T).T,\n",
+    "    'right': (res['eyes']['right']['P'] @ eyer_template_homo.T).T\n",
+    "}\n",
+    "gaze_vector = res['gaze']['face']['vector']\n",
+    "print(f\"Gaze direction: {gaze_vector}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cfcb10b6-1736-41e7-8aa1-94aa60e081d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# visualize gaze direction\n",
+    "image_gaze = draw_gaze_from_vector(image.copy(), lms68, gaze_vector, colour=[255, 0, 0])\n",
+    "plt.imshow(image_gaze[:, :, [2, 1, 0]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bb35015f-3e74-4f6b-a812-4424e03fd6eb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# visualize 3D eyes\n",
+    "image_eyes = draw_eyes(image.copy(), lms68, eyes, colour=[178, 255, 102])\n",
+    "plt.imshow(image_eyes[:, :, [2, 1, 0]])"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

tools/gaze360_preprocess.py

@@ -34,16 +34,16 @@ def get_path(i):
 print(f"Total number of samples with headpose <= 90 degrees: train: {len(paths_train)}, test: {len(paths_test)}, all: {len(paths)}")
 
 # Lms and Headpose ------------------------------------------------------------------------------
-
-# tracked with insightface
+print('Loading face and iris lms...')
+# face lms
 with open(str(path_base / 'lms68_3D_wbb.pkl'), 'rb') as f:
     lms68_wbb = pickle.load(f)
 with open(str(path_base / 'lms68_3D_wobb.pkl'), 'rb') as f:
     lms68_wobb = pickle.load(f)
 lms68_all = lms68_wbb.copy()
 lms68_all.update(lms68_wobb)
 print(f"face lms of images with given bboxes: {len(lms68_wbb)}, without given bboxes: {len(lms68_wobb)}, all: {len(lms68_all)}")
-
+# iris lms
 with open(str(path_base / 'lms8_all.pkl'), 'rb') as f:
     lms8_all = pickle.load(f)