05b_quality_assurance/visual_comparison.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a3d1485b-8b39-4453-b6da-6f61c11e6170",
   "metadata": {},
   "source": [
    "# Visual labeling comparison\n",
    "\n",
    "A first important step when comparing segmentation algorithms is to visualize the results of the individual algorithms and showing differences between them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "689b4b03-fb4c-4854-b5dd-5c2d45bfe3fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from skimage.io import imread\n",
    "from pyclesperanto_prototype import imshow\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1d1b6fa-6077-482d-aa5c-a22f544523da",
   "metadata": {},
   "source": [
    "From just visualizing the images, we can see that none pair of the four compared algorithms produce the same label image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "29d71c2d-3007-47ad-b23b-e8b6d03edce5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data/blobs_labels_imagej.tif\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data/blobs_labels_skimage.tif\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data/blobs_labels_clesperanto_python.tif\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data/blobs_labels_clesperanto_imagej.tif\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "filenames = [\n",
    "    \"data/blobs_labels_imagej.tif\",\n",
    "    \"data/blobs_labels_skimage.tif\",\n",
    "    \"data/blobs_labels_clesperanto_python.tif\",\n",
    "    \"data/blobs_labels_clesperanto_imagej.tif\"\n",
    "]\n",
    "\n",
    "for filename in filenames:\n",
    "    print(filename)\n",
    "    label_image = imread(filename)\n",
    "    imshow(label_image, labels=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9efb3697-2eb1-4281-a8cd-00ea37ad667f",
   "metadata": {},
   "source": [
    "As corresponding objects have different colours in this visualization, we can conclude that at least the labels of the objects are differnt. We cannot conclude yet if Otsu's thresholding method delivers different results in the given implementations.\n",
    "\n",
    "## Difference images\n",
    "To see the overlap between the different images, we will produce binary images from the label images and visualize the difference between these binary images.\n",
    "\n",
    "We first compare ImageJ's result and scikit-image's result to demonstrate the procedure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0cc576eb-a489-4cae-8635-8237ec3cb6af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "binary_blobs_imagej = imread(filenames[0]) > 0\n",
    "binary_blobs_skimage = imread(filenames[1]) > 0\n",
    "imshow(binary_blobs_imagej)\n",
    "imshow(binary_blobs_skimage)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9681d99f-4ad4-4fd5-b4a3-1c45ec841786",
   "metadata": {},
   "source": [
    "When looking at these two images carefully, one can already see that objects are a tiny bit larger in the second image.\n",
    "\n",
    "To produce the `difference` image, we will use [numpy's `logical_xor` method](https://numpy.org/doc/stable/reference/generated/numpy.logical_xor.html). It will set all pixels in the `difference` image to 1, where either of the two input binary images are `1`, but not both. It is called the [exclusive-OR operator](https://en.wikipedia.org/wiki/Exclusive_or)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6c5a0f85-a235-4c5c-b5b1-be508c483408",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1ba442633a0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "difference = np.logical_xor(binary_blobs_imagej, binary_blobs_skimage)\n",
    "\n",
    "fig, axs = plt.subplots(figsize=(10,10))\n",
    "axs.imshow(difference)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dae39c45-acec-4fd6-add6-45fbca363c06",
   "metadata": {},
   "source": [
    "From this binary image, we can count the number of pixels that are different between these two approaches:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e8964848-b976-4a77-b93f-a45afd3a239e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "830"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(difference)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fb99618-a082-4ae9-87d8-c562aa3b09d7",
   "metadata": {},
   "source": [
    "## A matrix of difference-pixel counts\n",
    "We will now program two for-loops that iterate over pairs of label images and compare them using the same approach. The result will be a 4x4 matrix with the number of pixels that are different when comparing the corresponding algorithm implementations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "87551a55-a324-4f0c-b82a-3d5726de0073",
   "metadata": {},
   "outputs": [],
   "source": [
    "matrix = {\"Algorithms\": filenames}\n",
    "\n",
    "for filename_A in filenames:\n",
    "    image_A = imread(filename_A)\n",
    "    measurements = []\n",
    "    for filename_B in filenames:\n",
    "        image_B = imread(filename_B)\n",
    "        \n",
    "        difference = np.logical_xor(image_A, image_B)\n",
    "        number_of_different_pixels = np.sum(difference)\n",
    "        measurements.append(number_of_different_pixels)\n",
    "    \n",
    "    matrix[filename_A] = measurements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4c9f6a32-e9c5-499a-aefd-cf980b02be7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>Algorithms</th>\n",
       "      <th>data/blobs_labels_imagej.tif</th>\n",
       "      <th>data/blobs_labels_skimage.tif</th>\n",
       "      <th>data/blobs_labels_clesperanto_python.tif</th>\n",
       "      <th>data/blobs_labels_clesperanto_imagej.tif</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>data/blobs_labels_imagej.tif</td>\n",
       "      <td>0</td>\n",
       "      <td>830</td>\n",
       "      <td>830</td>\n",
       "      <td>830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>data/blobs_labels_skimage.tif</td>\n",
       "      <td>830</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>data/blobs_labels_clesperanto_python.tif</td>\n",
       "      <td>830</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>data/blobs_labels_clesperanto_imagej.tif</td>\n",
       "      <td>830</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Algorithms  data/blobs_labels_imagej.tif  \\\n",
       "0              data/blobs_labels_imagej.tif                             0   \n",
       "1             data/blobs_labels_skimage.tif                           830   \n",
       "2  data/blobs_labels_clesperanto_python.tif                           830   \n",
       "3  data/blobs_labels_clesperanto_imagej.tif                           830   \n",
       "\n",
       "   data/blobs_labels_skimage.tif  data/blobs_labels_clesperanto_python.tif  \\\n",
       "0                            830                                       830   \n",
       "1                              0                                         0   \n",
       "2                              0                                         0   \n",
       "3                              0                                         0   \n",
       "\n",
       "   data/blobs_labels_clesperanto_imagej.tif  \n",
       "0                                       830  \n",
       "1                                         0  \n",
       "2                                         0  \n",
       "3                                         0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a3c7e44-e60d-49b8-b98c-5d2d3740f514",
   "metadata": {},
   "source": [
    "From this matrix, we can conclude that only the ImageJ result is different from the other three implementations.\n",
    "\n",
    "## Exercise\n",
    "Apply the two algorithms `cle.voronoi_otsu_labeling` and `nsbatwm.voronoi_otsu_labeling` to `blobs.tif` (see exercise last week). Compare the segmentation results visually."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0a5e66cf-0b0b-4e21-bbf3-515f0bbbfcdf",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.9.19"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}