gender nb

1 files changed, 187 insertions, 0 deletions

diff --git a/megapixels/notebooks/face_analysis/age_gender.ipynb b/megapixels/notebooks/face_analysis/age_gender.ipynb
index 98a18fc6..4f5b5cc7 100644
--- a/megapixels/notebooks/face_analysis/age_gender.ipynb
+++ b/megapixels/notebooks/face_analysis/age_gender.ipynb
@@ -248,6 +248,193 @@
   },
   {
    "cell_type": "code",
+   "execution_count": 319,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pywavefront"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 320,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pywavefront\n",
+    "\n",
+    "fp_obj = '/home/adam/Downloads/faces/Hillary_Clinton_official_Secretary_of_State_portrait_crop_0.obj'\n",
+    "scene = pywavefront.Wavefront(fp_obj, strict=True, encoding=\"iso-8859-1\", parse=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 336,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "None [<pywavefront.material.Material object at 0x7fb2aae12c88>]\n"
+     ]
+    }
+   ],
+   "source": [
+    "for k, v in scene.meshes.items():\n",
+    "  print(k, v.materials)\n",
+    "  break"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 332,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "C3F_V3F\n",
+      "133.0\n"
+     ]
+    },
+    {
+     "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<ipython-input-332-0a9430508b22>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0;31m# Contains the vertex list of floats in the format described above\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmaterial\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvertices\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmaterial\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtexture\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m10\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      9\u001b[0m     \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0;31m# Material properties\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mTypeError\u001b[0m: 'NoneType' object is not subscriptable"
+     ]
+    }
+   ],
+   "source": [
+    "# Iterate vertex data collected in each material\n",
+    "for name, material in scene.materials.items():\n",
+    "    # Contains the vertex format (string) such as \"T2F_N3F_V3F\"\n",
+    "    # T2F, C3F, N3F and V3F may appear in this string\n",
+    "    print(material.vertex_format)\n",
+    "    # Contains the vertex list of floats in the format described above\n",
+    "    print(material.vertices[0])\n",
+    "    print(material.texture[0:10])\n",
+    "    break\n",
+    "    # Material properties\n",
+    "    material.diffuse\n",
+    "    material.ambient\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 356,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = np.array([[1,100,10], [2,2,12], [3,3,13]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 358,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "b = np.array([np.min(a[:, 0]), np.min(a[:, 1]), np.min(a[:, 2])]) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 373,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(5, 3)\n",
+      "['b' 'g' 'r']\n",
+      "(5, 3)\n",
+      "['r' 'g' 'b']\n"
+     ]
+    }
+   ],
+   "source": [
+    "colors = np.array([['b','g','r'], ['b','g','r'], ['b','g','r'], ['b','g','r'], ['b','g','r']])\n",
+    "print(colors.shape)\n",
+    "print(colors[0])\n",
+    "colors = np.array([c[::-1] for c in colors])\n",
+    "print(colors.shape)\n",
+    "print(colors[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 348,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[  1., 100.,   1.],\n",
+       "       [  2.,   2.,   2.],\n",
+       "       [  3.,   3.,   3.]])"
+      ]
+     },
+     "execution_count": 348,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 374,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 375,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PosixPath('/media/adam/ah8tb/work/megapixels_dev/megapixels/notebooks/face_analysis')"
+      ]
+     },
+     "execution_count": 375,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "\n",
+    "Path.cwd()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 377,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = '/path/to/file.mp3'\n",
+    "fpp_a = Path(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],