Remove shape features

Hopefully it'll be added again in the future.

requirements.txt

@@ -12,4 +12,3 @@ scipy~=1.9.1
 scikit-learn~=1.2.1
 tabulate~=0.9.0
 tqdm~=4.64.1
-trimesh~=3.22.0

tutorials/segmentation.ipynb

@@ -230,94 +230,6 @@
      "start_time": "2023-06-29T15:26:33.555928800Z"
     }
    }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## Shape Features\n",
-    "To further characterise the kidney, a number of shape features can be calculated. These can be calculated from a labeled\n",
-    "image i.e. each region/tissue is represented by a different integer such as the kidney variable above or from a binary mask\n",
-    "of 0s and 1s such as the mask variable above. If a binary mask is used, the labeled image is generated internally by\n",
-    "assuming each unconnected component is a different region/tissue. If the labeled image (be it supplied or internally\n",
-    "generated) has background plus two labels, it is assumed the first label is the left kidney and the second label is the\n",
-    "right kidney."
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "       volume surface_area volume_bbox volume_convex volume_filled   n_vox  \\\nL  118.193529   148.056898  360.854707    170.527361    118.193529  9551.0   \nR  121.807026   154.516434  263.735786    150.368503    121.807026  9843.0   \n\n   long_axis short_axis compactness euler_number  solidity  \nL   11.79375   4.347013    0.078666          2.0  0.693106  \nR  12.317681   3.643008    0.076906          2.0  0.810057  ",
-      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>volume</th>\n      <th>surface_area</th>\n      <th>volume_bbox</th>\n      <th>volume_convex</th>\n      <th>volume_filled</th>\n      <th>n_vox</th>\n      <th>long_axis</th>\n      <th>short_axis</th>\n      <th>compactness</th>\n      <th>euler_number</th>\n      <th>solidity</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>L</th>\n      <td>118.193529</td>\n      <td>148.056898</td>\n      <td>360.854707</td>\n      <td>170.527361</td>\n      <td>118.193529</td>\n      <td>9551.0</td>\n      <td>11.79375</td>\n      <td>4.347013</td>\n      <td>0.078666</td>\n      <td>2.0</td>\n      <td>0.693106</td>\n    </tr>\n    <tr>\n      <th>R</th>\n      <td>121.807026</td>\n      <td>154.516434</td>\n      <td>263.735786</td>\n      <td>150.368503</td>\n      <td>121.807026</td>\n      <td>9843.0</td>\n      <td>12.317681</td>\n      <td>3.643008</td>\n      <td>0.076906</td>\n      <td>2.0</td>\n      <td>0.810057</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from ukat.segmentation.shape_features import ShapeFeatures\n",
-    "\n",
-    "shape_features = ShapeFeatures(kidneys, affine)\n",
-    "\n",
-    "# Save the shape features as a csv.\n",
-    "shape_features.save_features_csv(os.path.join(OUTPUT_DIR, 'shape_features.csv'))\n",
-    "\n",
-    "# Print shape features.\n",
-    "shape_features.get_features()"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-06-29T15:26:34.994777600Z",
-     "start_time": "2023-06-29T15:26:33.647876800Z"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "By default, if there are two labels in the image, they are assumed to be the left and right kidney. This can be overridden\n",
-    "using the `region_labels` argument. This can also be used to update the row index if there are more than two labels e.g. the\n",
-    " kidneys, liver and spleen are all segmented."
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "           volume surface_area volume_bbox volume_convex volume_filled  \\\nLeft   118.193529   148.056898  360.854707    170.527361    118.193529   \nRight  121.807026   154.516434  263.735786    150.368503    121.807026   \n\n        n_vox  long_axis short_axis compactness euler_number  solidity  \nLeft   9551.0   11.79375   4.347013    0.078666          2.0  0.693106  \nRight  9843.0  12.317681   3.643008    0.076906          2.0  0.810057  ",
-      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>volume</th>\n      <th>surface_area</th>\n      <th>volume_bbox</th>\n      <th>volume_convex</th>\n      <th>volume_filled</th>\n      <th>n_vox</th>\n      <th>long_axis</th>\n      <th>short_axis</th>\n      <th>compactness</th>\n      <th>euler_number</th>\n      <th>solidity</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Left</th>\n      <td>118.193529</td>\n      <td>148.056898</td>\n      <td>360.854707</td>\n      <td>170.527361</td>\n      <td>118.193529</td>\n      <td>9551.0</td>\n      <td>11.79375</td>\n      <td>4.347013</td>\n      <td>0.078666</td>\n      <td>2.0</td>\n      <td>0.693106</td>\n    </tr>\n    <tr>\n      <th>Right</th>\n      <td>121.807026</td>\n      <td>154.516434</td>\n      <td>263.735786</td>\n      <td>150.368503</td>\n      <td>121.807026</td>\n      <td>9843.0</td>\n      <td>12.317681</td>\n      <td>3.643008</td>\n      <td>0.076906</td>\n      <td>2.0</td>\n      <td>0.810057</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Add custom index labels, here we'll just expand L and R to Left and Right as that's what's in the data we're using, but\n",
-    "# these could be cortex and medulla.\n",
-    "shape_features = ShapeFeatures(kidneys, affine, region_labels=['Left', 'Right'])\n",
-    "\n",
-    "shape_features.get_features()"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-06-29T15:26:37.158651300Z",
-     "start_time": "2023-06-29T15:26:34.999775400Z"
-    }
-   }
   }
  ],
  "metadata": {

ukat/segmentation/__init__.py

@@ -1 +1 @@
-from . import shape_features, whole_kidney
+from . import whole_kidney

ukat/segmentation/tests/__init__.py

@@ -1 +1 @@
-from . import test_shape_features, test_whole_kidney
+from . import test_whole_kidney