NIH_Model_Code_Cheuk.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1c5ebe86-46f5-4784-b59c-005effee49e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(942, 1716)\n"
     ]
    }
   ],
   "source": [
    "import cv2 \n",
    "\n",
    "img = cv2.imread('WC25_LLEDGE2.tif',0) \n",
    "# the slash is folder name, and the name after the thing is \n",
    "# the number at the end here changes whether or not you have color. 1 is color; 0 is no color\n",
    "cv2.imshow(\"pic\",img)\n",
    "\n",
    "print(img.shape)\n",
    "\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f9a0d0f0-cbf5-437a-bf55-5dfae1966711",
   "metadata": {},
   "outputs": [],
   "source": [
    "pwd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "07ce7e30-2673-4a5d-8947-643a4660b42b",
   "metadata": {},
   "outputs": [],
   "source": [
    "cd Python_project\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c9fd602f-ba76-467e-85e0-49c3ca06bc07",
   "metadata": {},
   "outputs": [],
   "source": [
    "#16 - Understanding digital images for Python processing\n",
    "#https://www.youtube.com/watch?v=Ijc-9L2iXEc&list=PLZsOBAyNTZwYx-7GylDo3LSYpSompzsqW\n",
    "#6/24/2025-6/25/2024"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "afff74ed-b8d2-4e36-a169-f1df60fae16c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\anaconda3\\Lib\\site-packages\\paramiko\\pkey.py:82: CryptographyDeprecationWarning: TripleDES has been moved to cryptography.hazmat.decrepit.ciphers.algorithms.TripleDES and will be removed from this module in 48.0.0.\n",
      "  \"cipher\": algorithms.TripleDES,\n",
      "C:\\ProgramData\\anaconda3\\Lib\\site-packages\\paramiko\\transport.py:219: CryptographyDeprecationWarning: Blowfish has been moved to cryptography.hazmat.decrepit.ciphers.algorithms.Blowfish and will be removed from this module in 45.0.0.\n",
      "  \"class\": algorithms.Blowfish,\n",
      "C:\\ProgramData\\anaconda3\\Lib\\site-packages\\paramiko\\transport.py:243: CryptographyDeprecationWarning: TripleDES has been moved to cryptography.hazmat.decrepit.ciphers.algorithms.TripleDES and will be removed from this module in 48.0.0.\n",
      "  \"class\": algorithms.TripleDES,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(942, 1716)\n"
     ]
    }
   ],
   "source": [
    "import cv2 \n",
    "from skimage.filters import sobel\n",
    "\n",
    "\n",
    "img = cv2.imread(\"WC25_LLEDGE2.tif\",0) \n",
    "img2= sobel(img)\n",
    "# the slash is folder name, and the name after the thing is \n",
    "# the number at the end here changes whether or not you have color. 1 is color; 0 is no color\n",
    "cv2.imshow(\"abc\",img)\n",
    "cv2.imshow(\"edge\",img2)\n",
    "\n",
    "## in imshow(_name_,img) the _name_ is the name of the windows that pops out\n",
    "print(img.shape)\n",
    "\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c7a045b9-62bb-448d-835a-2e6f6a9179d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from skimage import io \n",
    "\n",
    "my_image = io.imread(\"WC25_LLEDGE2.tif\"),\n",
    "#that reads it from top to bottom in terms of code\n",
    "print(my_image) \n",
    "print(my_image.min(), my_image.max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3ccceda5-45c1-4f59-9046-373f362fdb79",
   "metadata": {},
   "outputs": [],
   "source": [
    "from skimage import io \n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from skimage import img_as_float\n",
    "\n",
    "my_image = io.imread(\"WC25_LLEDGE2.tif\"),\n",
    "#that reads it from top to bottom in terms of code\n",
    "print(my_image) \n",
    "# print(my_image.min(), my_image.max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c0feff63-f3d8-46c3-bccf-ac9a91208c4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from skimage import io \n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from skimage import img_as_float\n",
    "\n",
    "my_image = io.imread(\"WC25_LLEDGE2.tif\"),\n",
    "#that reads it from top to bottom in terms of code\n",
    "print(my_image) \n",
    "# print(my_image.min(), my_image.max())\n",
    "\n",
    "my_float_image = img_as_float(my_image)\n",
    "print(my_float_image.min(), my_float_image.max())\n",
    "\n",
    "\n",
    "# the first profile is the top left corner of the image\n",
    "random_image = np.random.random([500,500])\n",
    "plt.imshow(random_image)\n",
    "#this is an unsigned integer that shows the values between 0 and 1\n",
    "print(random_image.min(),random_image.max())\n",
    "\n",
    "\n",
    "# dtypes...\n",
    "# uint8 - 0 to 225\n",
    "# uint16 = 0 to 65536\n",
    "# uint 32 = 0 to ((2**32) -1)\n",
    "# float = -1 to 1 or 0 to 1 \n",
    "# int8 = -128 to 127\n",
    "# int16 = -32768 to 32767\n",
    "# int32 = -2**31 to 2**31 -1 \n",
    "\n",
    "# Functinos that convert images to desired dtype and properly rescale their values \n",
    "# img_as_float - convert to 64-bit floating point\n",
    "# img_as_ubyte - convert tt 8-bit uint. \n",
    "# img_as_uint - convert to 16-bit uint. \n",
    "# img_as_int - Convert to 16-bit int. \n",
    "print(random_image)\n",
    "dark_image = random_image*0.5\n",
    "print(dark_image)\n",
    "print(dark_image.max())\n",
    "\n",
    "# above is show how you can change the data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "140c3011-8dca-4976-a870-4b6a8aa22b9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "my_float_image = img_as_float(my_image)\n",
    "print(my_float_image.min(), my_float_image.max())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6e7c43c1-d3f8-4833-be4d-1d3a4f744ae0",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# the first profile is the top left corner of the image\n",
    "random_image = np.random.random([500,500])\n",
    "plt.imshow(random_image)\n",
    "#this is an unsigned integer that shows the values between 0 and 1\n",
    "print(random_image.min(),random_image.max())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6214663d-b042-490b-9806-86ab44cfc527",
   "metadata": {},
   "outputs": [],
   "source": [
    "pip install scikit-image numpy matplotlib\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1582b041-2909-495e-ae37-7e29bc43cb4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from skimage import io \n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from skimage import img_as_float\n",
    "# my_float_image = img_as_float(my_image)\n",
    "\n",
    "my_image = io.imread(\"WC25_LLEDGE2.tif\")\n",
    "#that reads it from top to bottom in terms of code\n",
    "print(my_image.min(), my_image.max()) \n",
    "\n",
    "my_image[10:200, 10:200, :] = [255, 200, 150]\n",
    "plt.imshow(my_image)\n",
    "# print(my_image)\n",
    "# print(my_image.max())\n",
    "\n",
    "# what is supposed to happen here is that a box would appear on the upper left corner of the image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "41ab2a17-24d1-49c5-b2f5-d6ccb9734450",
   "metadata": {},
   "outputs": [],
   "source": [
    "pip install scikit-image numpy matplotlib\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9a3f7789-8fb5-488f-ad80-583883036624",
   "metadata": {},
   "outputs": [],
   "source": [
    "pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c4a52fb2-b3e3-4799-8928-426bad8a6bf8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#35 - Cell Nuclei analysis in Python using watershed segmentation\n",
    "#https://www.youtube.com/watch?v=AsTvGxuiqKs\n",
    "# https://github.com/bnsreenu/python_for_microscopists/blob/master/035-Cell%20Nuclei%20analysis%20using%20watershed.py\n",
    "# 6/25/2024 - 6/26/2024"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "407a2b88-af42-48e6-a9fb-413e662e5646",
   "metadata": {},
   "outputs": [],
   "source": [
    "# #!/usr/bin/env python\n",
    "# __author__ = \"Sreenivas Bhattiprolu\"\n",
    "# __license__ = \"Feel free to copy, I appreciate if you acknowledge Python for Microscopists\"\n",
    "\n",
    "# # https://www.youtube.com/watch?v=AsTvGxuiqKs\n",
    "\n",
    "# \"\"\"\n",
    "# https://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_imgproc/py_watershed/py_watershed.html\n",
    "\n",
    "# This code performs cell counting and size distribution analysis and dumps results into a csv file.\n",
    "# It uses watershed segmentationfor better segmentation.\n",
    "# Similar to grain analysis except here we segment cells. \n",
    "# \"\"\"\n",
    "\n",
    "import cv2\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from scipy import ndimage\n",
    "from skimage import measure, color, io\n",
    "\n",
    "img = cv2.imread(r\"C:\\Users\\cheukjy\\osteosarcoma.tiff\")\n",
    "#Extract only blue channel as DAPI / nuclear (blue) staining is the best\n",
    "#channel to perform cell count.\n",
    "cells=img[:,:,0]  #Blue channel. Image equivalent to grey image.\n",
    "\n",
    "\n",
    "pixels_to_um = 0.454 # 1 pixel = 454 nm (got this from the metadata of original image)\n",
    "\n",
    "#Threshold image to binary using OTSU. ALl thresholded pixels will be set to 255\n",
    "ret1, thresh = cv2.threshold(cells, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)\n",
    "\n",
    "\n",
    "# Morphological operations to remove small noise - opening\n",
    "#To remove holes we can use closing\n",
    "kernel = np.ones((3,3),np.uint8)\n",
    "opening = cv2.morphologyEx(thresh,cv2.MORPH_OPEN,kernel, iterations = 2)\n",
    "\n",
    "from skimage.segmentation import clear_border\n",
    "opening = clear_border(opening) #Remove edge touching grains\n",
    "#Check the total regions found before and after applying this. \n",
    "\n",
    "#Now we know that the regions at the center of cells is for sure cells\n",
    "#The region far away is background.\n",
    "#We need to extract sure regions. For that we can use erode. \n",
    "#But we have cells touching, so erode alone will not work. \n",
    "#To separate touching objects, the best approach would be distance transform and then thresholding.\n",
    "\n",
    "# let us start by identifying sure background area\n",
    "# dilating pixes a few times increases cell boundary to background. \n",
    "# This way whatever is remaining for sure will be background. \n",
    "#The area in between sure background and foreground is our ambiguous area. \n",
    "#Watershed should find this area for us. \n",
    "sure_bg = cv2.dilate(opening,kernel,iterations=10)\n",
    "\n",
    "# Finding sure foreground area using distance transform and thresholding\n",
    "#intensities of the points inside the foreground regions are changed to \n",
    "#distance their respective distances from the closest 0 value (boundary).\n",
    "#https://www.tutorialspoint.com/opencv/opencv_distance_transformation.htm\n",
    "dist_transform = cv2.distanceTransform(opening,cv2.DIST_L2,5)\n",
    "\n",
    "\n",
    "#Let us threshold the dist transform by starting at 1/2 its max value.\n",
    "#print(dist_transform.max()) gives about 21.9\n",
    "ret2, sure_fg = cv2.threshold(dist_transform,0.5*dist_transform.max(),255,0)\n",
    "\n",
    "#Later you realize that 0.25* max value will not separate the cells well.\n",
    "#High value like 0.7 will not recognize some cells. 0.5 seems to be a good compromize\n",
    "\n",
    "# Unknown ambiguous region is nothing but bkground - foreground\n",
    "sure_fg = np.uint8(sure_fg)\n",
    "unknown = cv2.subtract(sure_bg,sure_fg)\n",
    "\n",
    "#Now we create a marker and label the regions inside. \n",
    "# For sure regions, both foreground and background will be labeled with positive numbers.\n",
    "# Unknown regions will be labeled 0. \n",
    "#For markers let us use ConnectedComponents. \n",
    "ret3, markers = cv2.connectedComponents(sure_fg)\n",
    "\n",
    "#One problem rightnow is that the entire background pixels is given value 0.\n",
    "#This means watershed considers this region as unknown.\n",
    "#So let us add 10 to all labels so that sure background is not 0, but 10\n",
    "markers = markers+10\n",
    "\n",
    "# Now, mark the region of unknown with zero\n",
    "markers[unknown==255] = 0\n",
    "#plt.imshow(markers, cmap='jet')   #Look at the 3 distinct regions.\n",
    "\n",
    "#Now we are ready for watershed filling. \n",
    "markers = cv2.watershed(img,markers)\n",
    "#The boundary region will be marked -1\n",
    "#https://docs.opencv.org/3.3.1/d7/d1b/group__imgproc__misc.html#ga3267243e4d3f95165d55a618c65ac6e1\n",
    "\n",
    "#Let us color boundaries in yellow. \n",
    "img[markers == -1] = [0,255,255]  \n",
    "\n",
    "img2 = color.label2rgb(markers, bg_label=0)\n",
    "\n",
    "#cv2.imshow('Overlay on original image', img)\n",
    "#cv2.imshow('Colored Grains', img2)\n",
    "#cv2.waitKey(0)\n",
    "\n",
    "#Now, time to extract properties of detected cells\n",
    "# regionprops function in skimage measure module calculates useful parameters for each object.\n",
    "regions = measure.regionprops(markers, intensity_image=cells)\n",
    "\n",
    "#Can print various parameters for all objects\n",
    "for prop in regions:\n",
    "    print('Label: {} Area: {}'.format(prop.label, prop.area))\n",
    "\n",
    "#Best way is to output all properties to a csv file\n",
    "#Let us pick which ones we want to export. \n",
    "\n",
    "propList = ['Area',\n",
    "            'equivalent_diameter', #Added... verify if it works\n",
    "            'orientation', #Added, verify if it works. Angle btwn x-axis and major axis.\n",
    "            'MajorAxisLength',\n",
    "            'MinorAxisLength',\n",
    "            'Perimeter',\n",
    "            'MinIntensity',\n",
    "            'MeanIntensity',\n",
    "            'MaxIntensity']    \n",
    "    \n",
    "\n",
    "output_file = open('cell_measurements.csv', 'w')\n",
    "output_file.write(',' + \",\".join(propList) + '\\n') #join strings in array by commas, leave first cell blank\n",
    "#First cell blank to leave room for header (column names)\n",
    "\n",
    "for region_props in regions:\n",
    "    #output cluster properties to the excel file\n",
    "    output_file.write(str(region_props['Label']))\n",
    "    for i,prop in enumerate(propList):\n",
    "        if(prop == 'Area'): \n",
    "            to_print = region_props[prop]*pixels_to_um**2   #Convert pixel square to um square\n",
    "        elif(prop == 'orientation'): \n",
    "            to_print = region_props[prop]*57.2958  #Convert to degrees from radians\n",
    "        elif(prop.find('Intensity') < 0):          # Any prop without Intensity in its name\n",
    "            to_print = region_props[prop]*pixels_to_um\n",
    "        else: \n",
    "            to_print = region_props[prop]     #Reamining props, basically the ones with Intensity in its name\n",
    "        output_file.write(',' + str(to_print))\n",
    "    output_file.write('\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "93d9ac52-6c1b-4bf1-9362-4e3b8ec1ae62",
   "metadata": {},
   "outputs": [],
   "source": [
    "###Cell nuclei analysis in python using watershed segmentation \n",
    "\n",
    "import cv2\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from scipy import ndimage\n",
    "from skimage import measure, color, io\n",
    "\n",
    "img1= cv2.imread(r\"C:\\Users\\cheukjy\\osteosarcoma.tiff\")\n",
    "img=img1[:,:,0]\n",
    "\n",
    "cv2.imshow(\"Blue image\",img)\n",
    "#imshow(\"name of tab opening\", file_that_will_be_opened)\n",
    "\n",
    "cv2.waitKey(0)\n",
    "\n",
    "pixels_to_um = 0,454 #1 pixel = 500 nm (got this from the metadata of the original image)\n",
    "\n",
    "#doing this only shows the blue in the image "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc1badcf-d658-4be0-8cba-ca364a6becd0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from scipy import ndimage\n",
    "from skimage import measure, color, io\n",
    "\n",
    "img1 = cv2.imread(r\"C:\\Users\\cheukjy\\osteosarcoma.tiff\")\n",
    "#Extract only blue channel as DAPI / nuclear (blue) staining is the best\n",
    "#channel to perform cell count.\n",
    "img=img1[:, :, 0]#Blue channel. Image equivalent to grey image.\n",
    "\n",
    "pixels_to_um = 0.454 # 1 pixel = 454 nm (got this from the metadata of original image)\n",
    "\n",
    "#Threshold image to binary using OTSU. ALl thresholded pixels will be set to 255\n",
    "ret1, thresh = cv2.threshold(img, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)\n",
    "\n",
    "# Morphological operations to remove small noise - opening\n",
    "#To remove holes we can use closing\n",
    "kernel = np.ones((3,3),np.uint8)\n",
    "opening = cv2.morphologyEx(thresh,cv2.MORPH_OPEN,kernel, iterations = 2)\n",
    "\n",
    "from skimage.segmentation import clear_border\n",
    "opening = clear_border(opening) #Remove edge touching grains\n",
    "#Check the total regions found before and after applying this. \n",
    "\n",
    "\n",
    "#Now we know that the regions at the center of cells is for sure cells\n",
    "#The region far away is background.\n",
    "#We need to extract sure regions. For that we can use erode. \n",
    "#But we have cells touching, so erode alone will not work. \n",
    "#To separate touching objects, the best approach would be distance transform and then thresholding.\n",
    "\n",
    "# let us start by identifying sure background area\n",
    "# dilating pixes a few times increases cell boundary to background. \n",
    "# This way whatever is remaining for sure will be background. \n",
    "#The area in between sure background and foreground is our ambiguous area. \n",
    "#Watershed should find this area for us.\n",
    "# - in other words, you're looking at the background. and seeing if the pixels \n",
    "#are too far awawy \n",
    "sure_bg = cv2.dilate(opening,kernel,iterations=2)\n",
    "cv2.imshow(\"Sure Background\", sure_bg)\n",
    "cv2.waitKey(0)\n",
    "\n",
    "# the result of this are larger pixelated dots that look different \n",
    "# now all the black area we know is just backgroudn... \n",
    "# the white cells are now just cells"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e8633974-68f4-499a-9b55-23f6162cc597",
   "metadata": {},
   "outputs": [],
   "source": [
    "#### this is the background - definite foregroudn###\n",
    "\n",
    "import cv2\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from scipy import ndimage\n",
    "from skimage import measure, color, io\n",
    "\n",
    "img1 = cv2.imread(r\"C:\\Users\\cheukjy\\osteosarcoma.tiff\")\n",
    "#Extract only blue channel as DAPI / nuclear (blue) staining is the best\n",
    "#channel to perform cell count.\n",
    "img=img1[:, :, 0]#Blue channel. Image equivalent to grey image.\n",
    "\n",
    "\n",
    "pixels_to_um = 0.454 # 1 pixel = 454 nm (got this from the metadata of original image)\n",
    "\n",
    "#Threshold image to binary using OTSU. ALl thresholded pixels will be set to 255\n",
    "ret1, thresh = cv2.threshold(img, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)\n",
    "\n",
    "\n",
    "# Morphological operations to remove small noise - opening\n",
    "#To remove holes we can use closing\n",
    "kernel = np.ones((3,3),np.uint8)\n",
    "opening = cv2.morphologyEx(thresh,cv2.MORPH_OPEN,kernel, iterations = 2)\n",
    "\n",
    "from skimage.segmentation import clear_border\n",
    "opening = clear_border(opening) #Remove edge touching grains\n",
    "#Check the total regions found before and after applying this. \n",
    "\n",
    "\n",
    "#Now we know that the regions at the center of cells is for sure cells\n",
    "#The region far away is background.\n",
    "#We need to extract sure regions. For that we can use erode. \n",
    "#But we have cells touching, so erode alone will not work. \n",
    "#To separate touching objects, the best approach would be distance transform and then thresholding.\n",
    "\n",
    "# let us start by identifying sure background area\n",
    "# dilating pixes a few times increases cell boundary to background. \n",
    "# This way whatever is remaining for sure will be background. \n",
    "#The area in between sure background and foreground is our ambiguous area. \n",
    "#Watershed should find this area for us.\n",
    "# - in other words, you're looking at the background. and seeing if the pixels \n",
    "#are too far awawy \n",
    "sure_bg = cv2.dilate(opening,kernel,iterations=2)\n",
    "\n",
    "# Finding sure foreground area using distance transform and thresholding\n",
    "#intensities of the points inside the foreground regions are changed to \n",
    "#distance their respective distances from the closest 0 value (boundary).\n",
    "#https://www.tutorialspoint.com/opencv/opencv_distance_transformation.htm\n",
    "## - what is the distance between each black area?\n",
    "dist_transform = cv2.distanceTransform(opening,cv2.DIST_L2,5)\n",
    "\n",
    "#Let us threshold the dist transform by starting at 1/2 its max value.\n",
    "#print(dist_transform.max()) gives about 21.9\n",
    "# Distance transform is when you are looking at how finely tuned you want to\n",
    "# filter only showing the white spaces on the image. The larger the number you put, the less white spots you see\n",
    "# this is helpful to only see cells \n",
    "ret2, sure_fg = cv2.threshold(dist_transform,0.5*dist_transform.max(),255,0)\n",
    "\n",
    "#Later you realize that 0.25* max value will not separate the cells well.\n",
    "#High value like 0.7 will not recognize some cells. 0.5 seems to be a good compromize\n",
    "\n",
    "# Unknown ambiguous region is nothing but bkground - foreground\n",
    "sure_fg = np.uint8(sure_fg)\n",
    "unknown = cv2.subtract(sure_bg,sure_fg)\n",
    "\n",
    "cv2.imshow(\"sure foreground image\", sure_fg)\n",
    "cv2.waitKey(0)\n",
    "\n",
    "# saw a problem with the tiny cells that seem to overlap "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f416e62e-1757-4509-a256-06cf0fdfa873",
   "metadata": {},
   "outputs": [],
   "source": [
    "### This is now about the markers and labels in the regions inside\n",
    "import cv2\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from scipy import ndimage\n",
    "from skimage import measure, color, io\n",
    "\n",
    "img1 = cv2.imread(r\"C:\\Users\\cheukjy\\osteosarcoma.tiff\")\n",
    "#Extract only blue channel as DAPI / nuclear (blue) staining is the best\n",
    "#channel to perform cell count.\n",
    "img=img1[:, :, 0]#Blue channel. Image equivalent to grey image.\n",
    "\n",
    "\n",
    "pixels_to_um = 0.454 # 1 pixel = 454 nm (got this from the metadata of original image)\n",
    "\n",
    "#Threshold image to binary using OTSU. ALl thresholded pixels will be set to 255\n",
    "ret1, thresh = cv2.threshold(img, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)\n",
    "\n",
    "# Morphological operations to remove small noise - opening\n",
    "#To remove holes we can use closing\n",
    "kernel = np.ones((3,3),np.uint8)\n",
    "opening = cv2.morphologyEx(thresh,cv2.MORPH_OPEN,kernel, iterations = 2)\n",
    "\n",
    "from skimage.segmentation import clear_border\n",
    "opening = clear_border(opening) #Remove edge touching grains\n",
    "#Check the total regions found before and after applying this. \n",
    "\n",
    "#Now we know that the regions at the center of cells is for sure cells\n",
    "#The region far away is background.\n",
    "#We need to extract sure regions. For that we can use erode. \n",
    "#But we have cells touching, so erode alone will not work. \n",
    "#To separate touching objects, the best approach would be distance transform and then thresholding.\n",
    "\n",
    "# let us start by identifying sure background area\n",
    "# dilating pixes a few times increases cell boundary to background. \n",
    "# This way whatever is remaining for sure will be background. \n",
    "#The area in between sure background and foreground is our ambiguous area. \n",
    "#Watershed should find this area for us.\n",
    "# - in other words, you're looking at the background. and seeing if the pixels \n",
    "#are too far awawy \n",
    "sure_bg = cv2.dilate(opening,kernel,iterations=2)\n",
    "\n",
    "# Finding sure foreground area using distance transform and thresholding\n",
    "#intensities of the points inside the foreground regions are changed to \n",
    "#distance their respective distances from the closest 0 value (boundary).\n",
    "#https://www.tutorialspoint.com/opencv/opencv_distance_transformation.htm\n",
    "## - what is the distance between each black area?\n",
    "dist_transform = cv2.distanceTransform(opening,cv2.DIST_L2,5)\n",
    "\n",
    "#Let us threshold the dist transform by starting at 1/2 its max value.\n",
    "#print(dist_transform.max()) gives about 21.9\n",
    "# Distance transform is when you are looking at how finely tuned you want to\n",
    "# filter only showing the white spaces on the image. The larger the number you put, the less white spots you see\n",
    "# this is helpful to only see cells \n",
    "ret2, sure_fg = cv2.threshold(dist_transform,0.5*dist_transform.max(),255,0)\n",
    "\n",
    "#Later you realize that 0.25* max value will not separate the cells well.\n",
    "#High value like 0.7 will not recognize some cells. 0.5 seems to be a good compromize\n",
    "\n",
    "# Unknown ambiguous region is nothing but bkground - foreground\n",
    "sure_fg = np.uint8(sure_fg)\n",
    "unknown = cv2.subtract(sure_bg,sure_fg)\n",
    "\n",
    "#Now we create a marker and label the regions inside. \n",
    "# For sure regions, both foreground and background will be labeled with positive numbers.\n",
    "# Unknown regions will be labeled 0. \n",
    "#For markers let us use ConnectedComponents. \n",
    "ret3, markers = cv2.connectedComponents(sure_fg)\n",
    "\n",
    "#One problem rightnow is that the entire background pixels is given value 0.\n",
    "#This means watershed considers this region as unknown.\n",
    "#So let us add 10 to all labels so that sure background is not 0, but 10\n",
    "markers = markers+10\n",
    "\n",
    "# Now, mark the region of unknown with zero\n",
    "markers[unknown==255] = 0\n",
    "#plt.imshow(markers, cmap='jet')   #Look at the 3 distinct regions.\n",
    "\n",
    "#Now we are ready for watershed filling. \n",
    "markers = cv2.watershed(img1,markers)\n",
    "#The boundary region will be marked -1\n",
    "#https://docs.opencv.org/3.3.1/d7/d1b/group__imgproc__misc.html#ga3267243e4d3f95165d55a618c65ac6e1\n",
    "\n",
    "#Let us color boundaries in yellow. \n",
    "img1[markers == -1] = [0,255,255]  \n",
    "\n",
    "img2 = color.label2rgb(markers, bg_label=0)\n",
    "\n",
    "cv2.imshow('Overlay on original image', img1)\n",
    "cv2.imshow('Colored Grains', img2)\n",
    "cv2.waitKey(0)\n",
    "\n",
    "#Now, time to extract properties of detected cells\n",
    "# regionprops function in skimage measure module calculates useful parameters for each object.\n",
    "regions = measure.regionprops(markers, intensity_image=img1)\n",
    "\n",
    "#Can print various parameters for all objects\n",
    "for prop in regions:\n",
    "    print('Label: {} Area: {}'.format(prop.label, prop.area))\n",
    "\n",
    "#Best way is to output all properties to a csv file\n",
    "#Let us pick which ones we want to export. \n",
    "\n",
    "propList = ['Area',\n",
    "            'equivalent_diameter', #Added... verify if it works\n",
    "            'orientation', #Added, verify if it works. Angle btwn x-axis and major axis.\n",
    "            'MajorAxisLength',\n",
    "            'MinorAxisLength',\n",
    "            'Perimeter',\n",
    "            'MinIntensity',\n",
    "            'MeanIntensity',\n",
    "            'MaxIntensity']    \n",
    "    \n",
    "\n",
    "output_file = open('cell_measurements.csv', 'w')\n",
    "output_file.write('Cell #,' + \",\".join(propList) + '\\n') #join strings in array by commas, leave first cell blank\n",
    "#First cell blank to leave room for header (column names)\n",
    "\n",
    "# for region_props in regions:\n",
    "#     #output cluster properties to the excel file\n",
    "#     output_file.write(str(region_props['Label']))\n",
    "#     for i,prop in enumerate(propList):\n",
    "#         if(prop == 'Area'): \n",
    "#             to_print = region_props[prop]*pixels_to_um**2   #Convert pixel square to um square\n",
    "#         elif(prop == 'orientation'): \n",
    "#             to_print = region_props[prop]*57.2958  #Convert to degrees from radians\n",
    "#         elif(prop.find('Intensity') < 0):          # Any prop without Intensity in its name\n",
    "#             to_print = region_props[prop]*pixels_to_um\n",
    "#         else: \n",
    "#             to_print = region_props[prop]     #Reamining props, basically the ones with Intensity in its name\n",
    "#         output_file.write(',' + str(to_print))\n",
    "#     output_file.write('\\n')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "70e2d895-a0ae-41a8-acbc-e88ca898f924",
   "metadata": {},
   "outputs": [],
   "source": [
    "#https://scikit-image.org/docs/stable/auto_examples/edges/plot_edge_filter.html\n",
    "#https://docs.opencv.org/4.x/d0/de3/tutorial_py_intro.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9697d75a-b12b-4a10-aeb5-8b2c4e1ac816",
   "metadata": {},
   "outputs": [],
   "source": [
    "pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7cc8b2e5-0064-4706-aa73-30ee043bc17f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#266 - Openslide library for whole slide images\n",
    "#https://www.youtube.com/watch?v=QntLBvUZR5c\n",
    "# https://github.com/bnsreenu/python_for_microscopists/tree/master/266_openslide_for_whole_slide_images\n",
    "#6/27/2024"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4ba272d4-7689-4890-a526-3d7584006eaa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\cheukjy\n",
      "C:\\ProgramData\\anaconda3\\python311.zip\n",
      "C:\\ProgramData\\anaconda3\\DLLs\n",
      "C:\\ProgramData\\anaconda3\\Lib\n",
      "C:\\ProgramData\\anaconda3\n",
      "\n",
      "C:\\Users\\cheukjy\\AppData\\Roaming\\Python\\Python311\\site-packages\n",
      "C:\\ProgramData\\anaconda3\\Lib\\site-packages\n",
      "C:\\ProgramData\\anaconda3\\Lib\\site-packages\\win32\n",
      "C:\\ProgramData\\anaconda3\\Lib\\site-packages\\win32\\lib\n",
      "C:\\ProgramData\\anaconda3\\Lib\\site-packages\\Pythonwin\n"
     ]
    }
   ],
   "source": [
    "#266 - Openslide library for whole slide images\n",
    "\n",
    "import sys\n",
    "for p in sys.path:\n",
    "    print(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "92099d2b-e8e4-44e8-a0a2-82241828ca4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import division\n",
    "\n",
    "# import os \n",
    "# os.environ['PATH'] \n",
    "OPENSLIDE_PATH = r\"C:\\Users\\cheukjy\\AppData\\Roaming\\Python\\Python311\\site-packages\\openslide\\openslide-bin-4.0.0.3-windows-x64\\bin\"\n",
    "# https://openslide.org/api/python/#installing \n",
    "import os\n",
    "if hasattr(os, 'add_dll_directory'):\n",
    "    # Windows\n",
    "    with os.add_dll_directory(OPENSLIDE_PATH):\n",
    "        import openslide\n",
    "else:\n",
    "    import openslide"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cfda6d30-06b5-4e99-a628-1f33eff81e82",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<_PropertyMap {'aperio.AppMag': '20', 'aperio.Date': '08/22/18', 'aperio.DisplayColor': '0', 'aperio.Exposure Scale': '0.000001', 'aperio.Exposure Time': '109', 'aperio.Filename': '1015382', 'aperio.Focus Offset': '0.000000', 'aperio.ICC Profile': 'ScanScope v1', 'aperio.ImageID': '1015382', 'aperio.Left': '12.486486', 'aperio.LineAreaXOffset': '-0.000018', 'aperio.LineAreaYOffset': '-0.012135', 'aperio.LineCameraSkew': '0.000425', 'aperio.MPP': '0.4972', 'aperio.OriginalHeight': '80808', 'aperio.OriginalWidth': '89280', 'aperio.Parmset': '2x3 Support', 'aperio.ScanScope ID': 'SS5486', 'aperio.SessonMode': 'NR', 'aperio.StripeWidth': '992', 'aperio.Time': '09:20:45', 'aperio.Time Zone': 'GMT-04:00', 'aperio.Top': '47.058392', 'aperio.User': '11c2f5f1-03d8-414a-bb47-bd06f111f8ed', 'openslide.associated.macro.height': '1246', 'openslide.associated.macro.width': '1600', 'openslide.associated.thumbnail.height': '768', 'openslide.associated.thumbnail.width': '815', 'openslide.comment': 'Aperio Image Library v12.0.15 \\r\\n89280x80808 [0,100 85679x80708] (240x240) JPEG/RGB Q=30|AppMag = 20|StripeWidth = 992|ScanScope ID = SS5486|Filename = 1015382|Date = 08/22/18|Time = 09:20:45|Time Zone = GMT-04:00|User = 11c2f5f1-03d8-414a-bb47-bd06f111f8ed|Parmset = 2x3 Support|MPP = 0.4972|Left = 12.486486|Top = 47.058392|LineCameraSkew = 0.000425|LineAreaXOffset = -0.000018|LineAreaYOffset = -0.012135|Focus Offset = 0.000000|ImageID = 1015382|Exposure Time = 109|Exposure Scale = 0.000001|DisplayColor = 0|SessonMode = NR|OriginalWidth = 89280|OriginalHeight = 80808|ICC Profile = ScanScope v1', 'openslide.icc-size': '141992', 'openslide.level-count': '4', 'openslide.level[0].downsample': '1', 'openslide.level[0].height': '80708', 'openslide.level[0].tile-height': '240', 'openslide.level[0].tile-width': '240', 'openslide.level[0].width': '85679', 'openslide.level[1].downsample': '4.0000700312806385', 'openslide.level[1].height': '20177', 'openslide.level[1].tile-height': '240', 'openslide.level[1].tile-width': '240', 'openslide.level[1].width': '21419', 'openslide.level[2].downsample': '16.001797332521257', 'openslide.level[2].height': '5044', 'openslide.level[2].tile-height': '240', 'openslide.level[2].tile-width': '240', 'openslide.level[2].width': '5354', 'openslide.level[3].downsample': '32.003594665042513', 'openslide.level[3].height': '2522', 'openslide.level[3].tile-height': '240', 'openslide.level[3].tile-width': '240', 'openslide.level[3].width': '2677', 'openslide.mpp-x': '0.49719999999999998', 'openslide.mpp-y': '0.49719999999999998', 'openslide.objective-power': '20', 'openslide.quickhash-1': '8c83bc5b4d7cf7ba35013efe6ae70a3e884c83d196362ac94175aeaceca4777a', 'openslide.vendor': 'aperio', 'tiff.ImageDescription': 'Aperio Image Library v12.0.15 \\r\\n89280x80808 [0,100 85679x80708] (240x240) JPEG/RGB Q=30|AppMag = 20|StripeWidth = 992|ScanScope ID = SS5486|Filename = 1015382|Date = 08/22/18|Time = 09:20:45|Time Zone = GMT-04:00|User = 11c2f5f1-03d8-414a-bb47-bd06f111f8ed|Parmset = 2x3 Support|MPP = 0.4972|Left = 12.486486|Top = 47.058392|LineCameraSkew = 0.000425|LineAreaXOffset = -0.000018|LineAreaYOffset = -0.012135|Focus Offset = 0.000000|ImageID = 1015382|Exposure Time = 109|Exposure Scale = 0.000001|DisplayColor = 0|SessonMode = NR|OriginalWidth = 89280|OriginalHeight = 80808|ICC Profile = ScanScope v1', 'tiff.ResolutionUnit': 'inch'}>\n",
      "Vondor is: aperio\n",
      "Pixel size of X is um is: 0.49719999999999998\n",
      "pixel size of Y is um is: 0.49719999999999998\n",
      "The objective power is:  20.0\n",
      "(85679, 80708)\n",
      "number of levels in this image are: 4\n",
      "Dimensions of various levels in this image are: ((85679, 80708), (21419, 20177), (5354, 5044), (2677, 2522))\n",
      "each Level is downsampled by an amount of: (1.0, 4.0000700312806385, 16.001797332521257, 32.00359466504251)\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "###### how to import your .svs image into the python and then make sure you can scale the image to its proper proportions #####\n",
    "\n",
    "from openslide import open_slide\n",
    "import openslide\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "#Load the slide file (svs) into an object.\n",
    "\n",
    "slide = open_slide(\"WC25_LLEDGE2.svs\")\n",
    "\n",
    "slide_props = slide.properties\n",
    "print(slide_props)\n",
    "\n",
    "print(\"Vondor is:\", slide_props['openslide.vendor'])\n",
    "print(\"Pixel size of X is um is:\", slide_props['openslide.mpp-x'])\n",
    "print('pixel size of Y is um is:', slide_props['openslide.mpp-y'])\n",
    "\n",
    "#Objective used to capture the image\n",
    "objective = float(slide.properties[openslide.PROPERTY_NAME_OBJECTIVE_POWER])\n",
    "print(\"The objective power is: \", objective)\n",
    "\n",
    "# get slide dimensions for the level 0 - max resolution level\n",
    "slide_dims = slide.dimensions\n",
    "print(slide_dims)\n",
    "\n",
    "#Get a thimbnail of the image and visualize \n",
    "slide_thumb_600 = slide.get_thumbnail(size=(600,600))\n",
    "slide_thumb_600.show()\n",
    "\n",
    "slide_thumb_600_np = np.array(slide_thumb_600)\n",
    "plt.figure(figsize =(8,8))\n",
    "plt.imshow(slide_thumb_600_np)\n",
    "\n",
    "#Get slide dims at each Level. Remember that whole slide images store iformation\n",
    "# as pytamids at various levels\n",
    "dims = slide.level_dimensions\n",
    "\n",
    "num_levels = len(dims)\n",
    "print(\"number of levels in this image are:\", num_levels)\n",
    "print(\"Dimensions of various levels in this image are:\", dims)\n",
    "\n",
    "#by how much are levels downsampled from the original image?\n",
    "factors = slide.level_downsamples\n",
    "print(\"each Level is downsampled by an amount of:\", factors)\n",
    "\n",
    "#copy an image from a level\n",
    "level3_dim =dims[2]\n",
    "# give pixel coordinates (top left pixel in the original large image)\n",
    "# also give the level number (for level 3 we are providing a value of 2) \n",
    "# size of your output image \n",
    "#remember that the output would be a RGBA image (not,RGB) \n",
    "level3_img = slide.read_region((0,0),2,level3_dim) #Pillow object, mode = RGBA\n",
    "# the levels show the type of resolution you can do. Extract a region from your slide... \n",
    "# the image is in your local hard drive  \n",
    "# level 2 is 2k by 2.4 k \n",
    "# above in the read_region(), its saying to extract an image from the top left corner, that is by 2k by 2.4k resolution, and its height and witdth is \n",
    "# defined by level3_dim. \n",
    "\n",
    "# level3_dim: This argument should be a tuple representing the dimensions of the desired region at level 3. It likely looks like (width, height).\n",
    "\n",
    "#Convert the image to RGB\n",
    "level3_img_RGB = level3_img.convert('RGB')\n",
    "level3_img_RGB.show()\n",
    "\n",
    "#Convert the image into numpy arrat for processing \n",
    "level3_img_np = np.array(level3_img_RGB)\n",
    "plt.imshow(level3_img_np)\n",
    "# here, you just increased the quality of the image\n",
    "#in np.array the scale will change.\n",
    "\n",
    "\n",
    "\n",
    "# convert the image into numpy array for processing \n",
    "level3_img_RGB = level3_img.convert('RGB')\n",
    "plt.imshow(level3_img_np)\n",
    "\n",
    "#Return the best level for displaying the given downsample \n",
    "SCALE_FACTOR = 32\n",
    "# what does the scale factor mean? \n",
    "best_level = slide.get_best_level_for_downsample(SCALE_FACTOR)\n",
    "# the get_best_level_for_downsample, you just change the scale factor to what the computer thinks the best level is. \n",
    "#Here it returns the best level to be 2 (third level)\n",
    "#If you change the scale factor to 2, it will suggest the best level to be 0 (our 1st level)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0125f3ca-adc3-4066-a875-b347b2cb8b66",
   "metadata": {},
   "outputs": [],
   "source": [
    "####### generating tiles for deep learning #######\n",
    "\n",
    "from openslide import open_slide\n",
    "import openslide\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "#Load the slide file (svs) into an object.\n",
    "\n",
    "slide = open_slide(\"WC25_LLEDGE2.svs\")\n",
    "\n",
    "slide_props = slide.properties\n",
    "print(slide_props)\n",
    "\n",
    "print(\"Vondor is:\", slide_props['openslide.vendor'])\n",
    "print(\"Pixel size of X is um is:\", slide_props['openslide.mpp-x'])\n",
    "print('pixel size of Y is um is:', slide_props['openslide.mpp-y'])\n",
    "\n",
    "#Objective used to capture the image\n",
    "objective = float(slide.properties[openslide.PROPERTY_NAME_OBJECTIVE_POWER])\n",
    "print(\"The objective power is: \", objective)\n",
    "\n",
    "# get slide dimensions for the level 0 - max resolution level\n",
    "slide_dims = slide.dimensions\n",
    "print(slide_dims)\n",
    "\n",
    "#Get a thimbnail of the image and visualize \n",
    "slide_thumb_600 = slide.get_thumbnail(size=(600,600))\n",
    "slide_thumb_600.show()\n",
    "\n",
    "slide_thumb_600_np = np.array(slide_thumb_600)\n",
    "plt.figure(figsize =(8,8))\n",
    "plt.imshow(slide_thumb_600_np)\n",
    "\n",
    "#Get slide dims at each Level. Remember that whole slide images store iformation\n",
    "# as pytamids at various levels\n",
    "dims = slide.level_dimensions\n",
    "\n",
    "num_levels = len(dims)\n",
    "print(\"number of levels in this image are:\", num_levels)\n",
    "print(\"Dimensions of various levels in this image are:\", dims)\n",
    "\n",
    "#by how much are levels downsampled from the original image?\n",
    "factors = slide.level_downsamples\n",
    "print(\"each Level is downsampled by an amount of:\", factors)\n",
    "\n",
    "#copy an image from a level\n",
    "level3_dim =dims[2]\n",
    "# give pixel coordinates (top left pixel in the original large image)\n",
    "# also give the level number (for level 3 we are providing a value of 2) \n",
    "# size of your output image \n",
    "#remember that the output would be a RGBA image (not,RGB) \n",
    "level3_img = slide.read_region((0,0),2,level3_dim) #Pillow object, mode = RGBA\n",
    "# the levels show the type of resolution you can do. Extract a region from your slide... \n",
    "# the image is in your local hard drive  \n",
    "# level 2 is 2k by 2.4 k \n",
    "# above in the read_region(), its saying to extract an image from the top left corner, that is by 2k by 2.4k resolution, and its height and witdth is \n",
    "# defined by level3_dim. \n",
    "\n",
    "# level3_dim: This argument should be a tuple representing the dimensions of the desired region at level 3. It likely looks like (width, height).\n",
    "\n",
    "#Convert the image to RGB\n",
    "level3_img_RGB = level3_img.convert('RGB')\n",
    "level3_img_RGB.show()\n",
    "\n",
    "#Convert the image into numpy arrat for processing \n",
    "level3_img_np = np.array(level3_img_RGB)\n",
    "plt.imshow(level3_img_np)\n",
    "# here, you just increased the quality of the image\n",
    "#in np.array the scale will change.\n",
    "\n",
    "# convert the image into numpy array for processing \n",
    "level3_img_RGB = level3_img.convert('RGB')\n",
    "plt.imshow(level3_img_np)\n",
    "\n",
    "#Return the best level for displaying the given downsample \n",
    "SCALE_FACTOR = 32\n",
    "# what does the scale factor mean? \n",
    "best_level = slide.get_best_level_for_downsample(SCALE_FACTOR)\n",
    "# the get_best_level_for_downsample, you just change the scale factor to what the computer thinks the best level is. \n",
    "#Here it returns the best level to be 2 (third level)\n",
    "#If you change the scale factor to 2, it will suggest the best level to be 0 (our 1st level)\n",
    "\n",
    "#########################################################\n",
    "#Generating tiles for deep learning training or other processing purposes\n",
    "#We can use read_region function and slide over the large image to extract tiles\n",
    "#but an easier approach would be to use DeepZoom based generator.\n",
    "# https://openslide.org/api/python/\n",
    "\n",
    "from openslide.deepzoom import DeepZoomGenerator\n",
    "\n",
    "#Generate object for tiles using the DeepZoomGenerator\n",
    "tiles = DeepZoomGenerator(slide, tile_size=256, overlap=0, limit_bounds=False)\n",
    "#Here, we have divided our svs into tiles of size 256 with no overlap. \n",
    "# THe documentation is to have different tiles. \n",
    "\n",
    "print(\"The number of levels in the Tiles objects are: \", tiles.level_count)\n",
    "print(\"The dimensions of data in each level are: \", tiles.level_dimensions)\n",
    "\n",
    "# total number of tiles in the tiles object\n",
    "print(\"total number of tiles =: \", tiles.tile_count)\n",
    "\n",
    "#How many tiles at one specific level?\n",
    "level_num = 11\n",
    "print(\"Tiles shape at level \", level_num, \" is: \", tiles.level_tiles[level_num])\n",
    "print(\"This means there are \", tiles.level_tiles[level_num][0]*tiles.level_tiles[level_num][1], \" total tiles in this level\")\n",
    "\n",
    "print(tiles.level_tiles[level_num])\n",
    "\n",
    "#Dimensions of the tile (tile size) for a specific tile from a specific layer\n",
    "tile_dims = tiles.get_tile_dimensions(11, (5,4)) #Provide deep zoom level and address (column, row)\n",
    "# You put the tile shape there and you make sure subtract one from the total tile count \n",
    "# because you start from zero. ex. if you have 4,5 tile dimensions, you will put in 3,4\n",
    "\n",
    "# #Tile count at the highest resolution level (level 16 in our tiles)\n",
    "tile_count_in_large_image = tiles.level_tiles[16] #126 x 151 (32001/256 = 126 with no overlap pixels)\n",
    "# \n",
    "\n",
    "#Check tile size for some random tile\n",
    "tile_dims = tiles.get_tile_dimensions(16, (120,140))\n",
    "\n",
    "\n",
    "#Last tiles may not have full 256x256 dimensions as our large image is not exactly divisible by 256\n",
    "tile_dims = tiles.get_tile_dimensions(16, (125,150))\n",
    "# this is typically one pixel by 74 pixels, so they are the ones at the edge. \n",
    "\n",
    "single_tile = tiles.get_tile(16, (62,70)) #provide deep zoom level and address (column, row)\n",
    "single_tile_RGB = single_tile.convert('RGB')\n",
    "single_tile_RGB.show()\n",
    "# After doing this, the picture is a bit blurry when you zoom in, but you do focus on a \n",
    "#small part of the cell\n",
    "# will show tile in 256 x 256 resolution\n",
    "\n",
    "###### what if you want to learn deep learning and save the file on your local drive\n",
    "###### Saving each tile to local directory\n",
    "\n",
    "cols, rows = tiles.level_tiles[16]\n",
    "\n",
    "import os\n",
    "tile_dir = \"openslide_test_images_06.27.2024\"\n",
    "for row in range(rows):\n",
    "    for col in range(cols):\n",
    "        tile_name = os.path.join(tile_dir, '%d_%d' % (col, row))\n",
    "        # you iterate through each column and then you see what you can do.\n",
    "        print(\"Now saving tile with title: \", tile_name)\n",
    "        temp_tile = tiles.get_tile(16, (col, row))\n",
    "        # extract tile from specific location \n",
    "        temp_tile_RGB = temp_tile.convert('RGB')\n",
    "        temp_tile_np = np.array(temp_tile_RGB)\n",
    "        plt.imsave(tile_name + \".tif\", temp_tile_np)\n",
    "        # in the line of code directly above, the tutorial says to put it in a .png file, but remember to convert it into a .tiff file because you may need to \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a05b2702-a605-4dfb-b3ff-2f4352903ab4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#267 - Processing whole slide images (as tiles)\n",
    "# https://www.youtube.com/watch?v=tNfcvgPKgyU \n",
    "# https://github.com/bnsreenu/python_for_microscopists/tree/master/267_processing_whole_slide_images# 6/28/2024\n",
    "# 6/28/2024\n",
    "\n",
    "#122 - Normalizing H&E images and digitally separating Hematoxylin and Eosin components\n",
    "#https://www.youtube.com/watch?v=yUrwEYgZUsA \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9547edf8-73e4-403a-9ea7-dddcd1dcfb08",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loop through all files in the directory\n",
    "for filename in os.listdir(png_folder):\n",
    "  # Check if file is a PNG\n",
    "  if filename.endswith(\".png\"):\n",
    "    # Construct full path for PNG file\n",
    "    png_path = os.path.join(png_folder, filename)\n",
    "    \n",
    "    # Generate output TIFF filename (optional: modify for specific naming)\n",
    "    tiff_filename = os.path.splitext(filename)[0] + \".tiff\"  # Remove extension and add .tiff\n",
    "    tiff_path = os.path.join(png_folder, tiff_filename)\n",
    "\n",
    "    # Open the PNG image\n",
    "    image = Image.open(png_path)\n",
    "\n",
    "    # Save the image as TIFF\n",
    "    image.save(tiff_path)\n",
    "\n",
    "    print(f\"Converted {png_path} to {tiff_path}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f36f9c87-aa7b-488e-b7ca-7f2b4714028c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "for p in sys.path:\n",
    "    print(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "630e16a5-ef57-4560-97be-43aa73e189d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import division\n",
    "\n",
    "# import os \n",
    "# os.environ['PATH'] \n",
    "OPENSLIDE_PATH = r\"C:\\Users\\cheukjy\\AppData\\Roaming\\Python\\Python311\\site-packages\\openslide\\openslide-bin-4.0.0.3-windows-x64\\bin\"\n",
    "# https://openslide.org/api/python/#installing \n",
    "import os\n",
    "if hasattr(os, 'add_dll_directory'):\n",
    "    # Windows\n",
    "    with os.add_dll_directory(OPENSLIDE_PATH):\n",
    "        import openslide\n",
    "else:\n",
    "    import openslide"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a5d22e7c-f43c-495f-80d0-827e1f149c1a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "306f61df-af7d-4d25-ad13-110eeba7c012",
   "metadata": {},
   "outputs": [],
   "source": [
    "from openslide import open_slide \n",
    "from PIL import Image\n",
    "import numpy as np \n",
    "from matplotlib import pyplot as plt \n",
    "import tifffile as tiff\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "289a3ee9-8823-4fa9-b027-2da5328c103e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#how to install pyvips"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ca1c6de7-441e-487e-b81d-84241fd03b0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import os\n",
    "# vipsbin = r'c:\\vips-dev-8.13\\bin'\n",
    "# add_dll_dir = getattr(os, 'add_dll_directory', None)\n",
    "# if callable(add_dll_dir):\n",
    "#     add_dll_dir(vipsbin)\n",
    "# else:\n",
    "#     os.environ['PATH'] = os.pathsep.join((vipsbin, os.environ['PATH']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "209c5df4-4d2a-400e-b4b9-c1749bc11489",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import os\n",
    "# vipsbin = r'C:\\Users\\cheukjy\\AppData\\Roaming\\Python\\Python311\\site-packages\\pyvips\\vips-dev-arm32-web-8.15.2-static\\vips-dev-8.15\\bin'\n",
    "# add_dll_dir = getattr(os, 'add_dll_directory', None)\n",
    "# if callable(add_dll_dir):\n",
    "#     add_dll_dir(vipsbin)\n",
    "# else:\n",
    "#     os.environ['PATH'] = os.pathsep.join((vipsbin, os.environ['PATH']))\n",
    "\n",
    "# C:\\Users\\cheukjy\\AppData\\Roaming\\Python\\Python311\\site-packages\\pyvips\\vips-dev-arm32-web-8.15.2-static\\vips-dev-8.15\\bin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "52604818-0faa-4629-9efb-f51e0a76f2f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# would use it to install and make an image pyramid said in the importimg libraries chapter in the video\n",
    "# import pyvips"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "65a62bb8-5619-491f-93ab-b98668860e8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://youtu.be/tNfcvgPKgyU\n",
    "\n",
    "# Original video on H&E topic: https://youtu.be/yUrwEYgZUsA\n",
    "# \"\"\"\n",
    "# This code normalizes staining appearance of H&E stained images.\n",
    "# It also separates the hematoxylin and eosing stains in to different images. \n",
    "\n",
    "# Workflow based on the following papers:\n",
    "# A method for normalizing histology slides for quantitative analysis. \n",
    "# M. Macenko et al., ISBI 2009\n",
    "#     http://wwwx.cs.unc.edu/~mn/sites/default/files/macenko2009.pdf\n",
    "\n",
    "# Efficient nucleus detector in histopathology images. J.P. Vink et al., J Microscopy, 2013\n",
    "\n",
    "# Original MATLAB code:\n",
    "#     https://github.com/mitkovetta/staining-normalization/blob/master/normalizeStaining.m\n",
    " \n",
    "# Other useful references:\n",
    "#     https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5226799/\n",
    "#     https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0169875\n",
    "\n",
    "# PROPOSED WORKFLOW:  \n",
    "    \n",
    "# Input: RGB image\n",
    "# Step 1: Convert RGB to OD (optical density)\n",
    "# Step 2: Remove data with OD intensity less than β\n",
    "# Step 3: Calculate  singular value decomposition (SVD) on the OD tuples\n",
    "# Step 4: Create plane from the SVD directions corresponding to the\n",
    "# two largest singular values\n",
    "# Step 5: Project data onto the plane, and normalize to unit length\n",
    "# Step 6: Calculate angle of each point wrt the first SVD direction\n",
    "# Step 7: Find robust extremes (αth and (100−α)th 7 percentiles) of the\n",
    "# angle\n",
    "# Step 8: Convert extreme values back to OD space\n",
    "\n",
    "# Output: Optimal Stain Vectors\n",
    "\n",
    "# \"\"\"\n",
    "\n",
    "import numpy as np\n",
    "import cv2\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "############### INPUT RGB IMAGE #######################\n",
    "#Using opencv to read images may bemore robust compared to using skimage\n",
    "#but need to remember to convert BGR to RGB.\n",
    "#Also, convert to float later on and normalize to between 0 and 1.\n",
    "\n",
    "#Image downloaded from:\n",
    "#https://pbs.twimg.com/media/C1MkrgQWQAASbdz.jpg\n",
    "# img=cv2.imread('images/HnE_Image.jpg', 1)\n",
    "# img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
    "\n",
    "# Io = 240 # Transmitted light intensity, Normalizing factor for image intensities\n",
    "# alpha = 1  #As recommend in the paper. tolerance for the pseudo-min and pseudo-max (default: 1)\n",
    "# beta = 0.15 #As recommended in the paper. OD threshold for transparent pixels (default: 0.15)\n",
    "\n",
    "\n",
    "def norm_HnE(img, Io=240, alpha=1, beta=0.15):\n",
    "\n",
    "\n",
    "    ######## Step 1: Convert RGB to OD ###################\n",
    "    ## reference H&E OD matrix.\n",
    "    #Can be updated if you know the best values for your image. \n",
    "    #Otherwise use the following default values. \n",
    "    #Read the above referenced papers on this topic. \n",
    "    HERef = np.array([[0.5626, 0.2159],\n",
    "                      [0.7201, 0.8012],\n",
    "                      [0.4062, 0.5581]])\n",
    "    ### reference maximum stain concentrations for H&E\n",
    "    maxCRef = np.array([1.9705, 1.0308])\n",
    "    \n",
    "    \n",
    "    # extract the height, width and num of channels of image\n",
    "    h, w, c = img.shape\n",
    "    \n",
    "    # reshape image to multiple rows and 3 columns.\n",
    "    #Num of rows depends on the image size (wxh)\n",
    "    img = img.reshape((-1,3))\n",
    "    \n",
    "    # calculate optical density\n",
    "    # OD = −log10(I)  \n",
    "    #OD = -np.log10(img+0.004)  #Use this when reading images with skimage\n",
    "    #Adding 0.004 just to avoid log of zero. \n",
    "    \n",
    "    OD = -np.log10((img.astype(float)+1)/Io) #Use this for opencv imread\n",
    "    #Add 1 in case any pixels in the image have a value of 0 (log 0 is indeterminate)\n",
    "    \n",
    "    \n",
    "    ############ Step 2: Remove data with OD intensity less than β ############\n",
    "    # remove transparent pixels (clear region with no tissue)\n",
    "    ODhat = OD[~np.any(OD < beta, axis=1)] #Returns an array where OD values are above beta\n",
    "    #Check by printing ODhat.min()\n",
    "    \n",
    "    ############# Step 3: Calculate SVD on the OD tuples ######################\n",
    "    #Estimate covariance matrix of ODhat (transposed)\n",
    "    # and then compute eigen values & eigenvectors.\n",
    "    eigvals, eigvecs = np.linalg.eigh(np.cov(ODhat.T))\n",
    "    \n",
    "    \n",
    "    ######## Step 4: Create plane from the SVD directions with two largest values ######\n",
    "    #project on the plane spanned by the eigenvectors corresponding to the two \n",
    "    # largest eigenvalues    \n",
    "    That = ODhat.dot(eigvecs[:,1:3]) #Dot product\n",
    "    \n",
    "    ############### Step 5: Project data onto the plane, and normalize to unit length ###########\n",
    "    ############## Step 6: Calculate angle of each point wrt the first SVD direction ########\n",
    "    #find the min and max vectors and project back to OD space\n",
    "    phi = np.arctan2(That[:,1],That[:,0])\n",
    "    \n",
    "    minPhi = np.percentile(phi, alpha)\n",
    "    maxPhi = np.percentile(phi, 100-alpha)\n",
    "    \n",
    "    vMin = eigvecs[:,1:3].dot(np.array([(np.cos(minPhi), np.sin(minPhi))]).T)\n",
    "    vMax = eigvecs[:,1:3].dot(np.array([(np.cos(maxPhi), np.sin(maxPhi))]).T)\n",
    "    \n",
    "    \n",
    "    # a heuristic to make the vector corresponding to hematoxylin first and the \n",
    "    # one corresponding to eosin second\n",
    "    if vMin[0] > vMax[0]:    \n",
    "        HE = np.array((vMin[:,0], vMax[:,0])).T\n",
    "        \n",
    "    else:\n",
    "        HE = np.array((vMax[:,0], vMin[:,0])).T\n",
    "    \n",
    "    \n",
    "    # rows correspond to channels (RGB), columns to OD values\n",
    "    Y = np.reshape(OD, (-1, 3)).T\n",
    "    \n",
    "    # determine concentrations of the individual stains\n",
    "    C = np.linalg.lstsq(HE,Y, rcond=None)[0]\n",
    "    \n",
    "    # normalize stain concentrations\n",
    "    maxC = np.array([np.percentile(C[0,:], 99), np.percentile(C[1,:],99)])\n",
    "    tmp = np.divide(maxC,maxCRef)\n",
    "    C2 = np.divide(C,tmp[:, np.newaxis])\n",
    "    \n",
    "    ###### Step 8: Convert extreme values back to OD space\n",
    "    # recreate the normalized image using reference mixing matrix \n",
    "    \n",
    "    Inorm = np.multiply(Io, np.exp(-HERef.dot(C2)))\n",
    "    Inorm[Inorm>255] = 254\n",
    "    Inorm = np.reshape(Inorm.T, (h, w, 3)).astype(np.uint8)  \n",
    "    \n",
    "    # Separating H and E components\n",
    "    \n",
    "    H = np.multiply(Io, np.exp(np.expand_dims(-HERef[:,0], axis=1).dot(np.expand_dims(C2[0,:], axis=0))))\n",
    "    H[H>255] = 254\n",
    "    H = np.reshape(H.T, (h, w, 3)).astype(np.uint8)\n",
    "    \n",
    "    E = np.multiply(Io, np.exp(np.expand_dims(-HERef[:,1], axis=1).dot(np.expand_dims(C2[1,:], axis=0))))\n",
    "    E[E>255] = 254\n",
    "    E = np.reshape(E.T, (h, w, 3)).astype(np.uint8)\n",
    "    \n",
    "    return (Inorm, H, E)\n",
    "\n",
    "# img=cv2.imread('images/HnE_Image.jpg', 1)\n",
    "# img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
    "\n",
    "# norm_img, H_img, E_img = norm_HnE(img, Io=240, alpha=1, beta=0.15)\n",
    "\n",
    "\n",
    "# plt.imsave(\"images/HnE_normalized.jpg\", Inorm)\n",
    "# plt.imsave(\"images/HnE_separated_H.jpg\", H)\n",
    "# plt.imsave(\"images/HnE_separated_E.jpg\", E)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aa2d4069-725e-4edb-aa9a-a2499cefb98b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# H&E is the combination of two histological stains: hematoxylin and eosin \n",
    "# has a paper the code is based on. \n",
    "\n",
    "\n",
    "slide = open_slide(\"WC25_LLEDGE2.svs\")\n",
    "# If I need to watch the normalize_HnE thing, here is the video, for now, I'm just not going to use it\n",
    "#https://www.youtube.com/watch?v=yUrwEYgZUsA\n",
    "# the video assumes the class bleow can't be imported, so you've gotta do this:\n",
    "# from normalize_HnE import norm_HnE \n",
    "\n",
    "# below is code to extract a small region from the large file at level 0 \n",
    "# let us extract a region from somewhere in the middle  at coordinates 16k, 16k \n",
    "# extract 1024, 1024 region \n",
    "smaller_region = slide.read_region((50000,40000) #remember to check the pixels. sometimes if you pick a number that's out of range of the cells it'll be white, if it's too small the output will be a black screen,0,(1024,1024))\n",
    "# ((the top left of the image's position),the level of the image you're viewing the image at , (the size of the image)) \n",
    "# there are levels to the image. I think he's trying to say that there are different levels of zooming into the image\n",
    "smaller_region_RGB = smaller_region.convert('RGB')\n",
    "smaller_region_np = np.array(smaller_region_RGB) \n",
    "\n",
    "plt.axis('on')\n",
    "plt.imshow(smaller_region_np)\n",
    "smaller_region_RGB.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fd94924b-af0d-405a-a572-cf4de69bb7cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Extracting a small region and processing it\n",
    "from openslide import open_slide\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import tifffile as tiff\n",
    "#Load a level image, normalize the image and digitally extract H and E images\n",
    "#As described in video 122: https://www.youtube.com/watch?v=yUrwEYgZUsA\n",
    "# from normalize_HnE import norm_HnE\n",
    "slide = open_slide(\"WC25_LLEDGE2.svs\")\n",
    "\n",
    "def norm_HnE(img, Io=240, alpha=1, beta=0.15):\n",
    "\n",
    "\n",
    "    ######## Step 1: Convert RGB to OD ###################\n",
    "    ## reference H&E OD matrix.\n",
    "    #Can be updated if you know the best values for your image. \n",
    "    #Otherwise use the following default values. \n",
    "    #Read the above referenced papers on this topic. \n",
    "    HERef = np.array([[0.5626, 0.2159],\n",
    "                      [0.7201, 0.8012],\n",
    "                      [0.4062, 0.5581]])\n",
    "    ### reference maximum stain concentrations for H&E\n",
    "    maxCRef = np.array([1.9705, 1.0308])\n",
    "    \n",
    "    \n",
    "    # extract the height, width and num of channels of image\n",
    "    h, w, c = img.shape\n",
    "    \n",
    "    # reshape image to multiple rows and 3 columns.\n",
    "    #Num of rows depends on the image size (wxh)\n",
    "    img = img.reshape((-1,3))\n",
    "    \n",
    "    # calculate optical density\n",
    "    # OD = −log10(I)  \n",
    "    #OD = -np.log10(img+0.004)  #Use this when reading images with skimage\n",
    "    #Adding 0.004 just to avoid log of zero. \n",
    "    \n",
    "    OD = -np.log10((img.astype(float)+1)/Io) #Use this for opencv imread\n",
    "    #Add 1 in case any pixels in the image have a value of 0 (log 0 is indeterminate)\n",
    "    \n",
    "    \n",
    "    ############ Step 2: Remove data with OD intensity less than β ############\n",
    "    # remove transparent pixels (clear region with no tissue)\n",
    "    ODhat = OD[~np.any(OD < beta, axis=1)] #Returns an array where OD values are above beta\n",
    "    #Check by printing ODhat.min()\n",
    "    \n",
    "    ############# Step 3: Calculate SVD on the OD tuples ######################\n",
    "    #Estimate covariance matrix of ODhat (transposed)\n",
    "    # and then compute eigen values & eigenvectors.\n",
    "    eigvals, eigvecs = np.linalg.eigh(np.cov(ODhat.T))\n",
    "    \n",
    "    \n",
    "    ######## Step 4: Create plane from the SVD directions with two largest values ######\n",
    "    #project on the plane spanned by the eigenvectors corresponding to the two \n",
    "    # largest eigenvalues    \n",
    "    That = ODhat.dot(eigvecs[:,1:3]) #Dot product\n",
    "    \n",
    "    ############### Step 5: Project data onto the plane, and normalize to unit length ###########\n",
    "    ############## Step 6: Calculate angle of each point wrt the first SVD direction ########\n",
    "    #find the min and max vectors and project back to OD space\n",
    "    phi = np.arctan2(That[:,1],That[:,0])\n",
    "    \n",
    "    minPhi = np.percentile(phi, alpha)\n",
    "    maxPhi = np.percentile(phi, 100-alpha)\n",
    "    \n",
    "    vMin = eigvecs[:,1:3].dot(np.array([(np.cos(minPhi), np.sin(minPhi))]).T)\n",
    "    vMax = eigvecs[:,1:3].dot(np.array([(np.cos(maxPhi), np.sin(maxPhi))]).T)\n",
    "    \n",
    "    \n",
    "    # a heuristic to make the vector corresponding to hematoxylin first and the \n",
    "    # one corresponding to eosin second\n",
    "    if vMin[0] > vMax[0]:    \n",
    "        HE = np.array((vMin[:,0], vMax[:,0])).T\n",
    "        \n",
    "    else:\n",
    "        HE = np.array((vMax[:,0], vMin[:,0])).T\n",
    "    \n",
    "    \n",
    "    # rows correspond to channels (RGB), columns to OD values\n",
    "    Y = np.reshape(OD, (-1, 3)).T\n",
    "    \n",
    "    # determine concentrations of the individual stains\n",
    "    C = np.linalg.lstsq(HE,Y, rcond=None)[0]\n",
    "    \n",
    "    # normalize stain concentrations\n",
    "    maxC = np.array([np.percentile(C[0,:], 99), np.percentile(C[1,:],99)])\n",
    "    tmp = np.divide(maxC,maxCRef)\n",
    "    C2 = np.divide(C,tmp[:, np.newaxis])\n",
    "    \n",
    "    ###### Step 8: Convert extreme values back to OD space\n",
    "    # recreate the normalized image using reference mixing matrix \n",
    "    \n",
    "    Inorm = np.multiply(Io, np.exp(-HERef.dot(C2)))\n",
    "    Inorm[Inorm>255] = 254\n",
    "    Inorm = np.reshape(Inorm.T, (h, w, 3)).astype(np.uint8)  \n",
    "    \n",
    "    # Separating H and E components\n",
    "    \n",
    "    H = np.multiply(Io, np.exp(np.expand_dims(-HERef[:,0], axis=1).dot(np.expand_dims(C2[0,:], axis=0))))\n",
    "    H[H>255] = 254\n",
    "    H = np.reshape(H.T, (h, w, 3)).astype(np.uint8)\n",
    "    \n",
    "    E = np.multiply(Io, np.exp(np.expand_dims(-HERef[:,1], axis=1).dot(np.expand_dims(C2[1,:], axis=0))))\n",
    "    E[E>255] = 254\n",
    "    E = np.reshape(E.T, (h, w, 3)).astype(np.uint8)\n",
    "    \n",
    "    return (Inorm, H, E)\n",
    "\n",
    "#Extract a small region from the large file (level 0)\n",
    "#Let us extract a region from somewhere in the middle - coords 16k, 16k\n",
    "#Extract 1024,1024 region\n",
    "smaller_region = slide.read_region((40000,50000), 0, (1024,1024))\n",
    "smaller_region_RGB = smaller_region.convert('RGB')\n",
    "smaller_region_np = np.array(smaller_region_RGB)\n",
    "\n",
    "plt.axis('off')\n",
    "plt.imshow(smaller_region_np)\n",
    "\n",
    "norm_img, H_img, E_img = norm_HnE(smaller_region_np, Io=240, alpha=1, beta=0.15)\n",
    "\n",
    "\n",
    "\n",
    "plt.figure(figsize=(12, 12))\n",
    "plt.subplot(221)\n",
    "plt.title('Original Image')\n",
    "plt.imshow(smaller_region_np)\n",
    "plt.subplot(222)\n",
    "plt.title('Normalized Image')\n",
    "plt.imshow(norm_img)\n",
    "plt.subplot(223)\n",
    "plt.title('H image')\n",
    "plt.imshow(H_img)\n",
    "plt.subplot(224)\n",
    "plt.title('E image')\n",
    "plt.imshow(E_img)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "baaf61c1-fb05-4c03-956c-4f9d67c18db7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# next step is to manually separate the blank slides, partial slides, and full slides \n",
    "# full slides are slides that have cells that make up the half of the image \n",
    "# "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1a93705b-346e-4d3e-9bb3-25fa372916af",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Convert .png files to .tiff files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4df76dff-4aae-411d-abda-9adeff5aec2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "###converting .png to .tiff\n",
    "!pip install pillow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d892378-5ba5-4fff-b630-6f3dad81c43b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "# Open the PNG image\n",
    "img = Image.open(r\"C:\\Users\\cheukjy\\WC25_LLEDGE2_test\\Blank_Copy\\0_2.png\")\n",
    "# Save the image as TIFF\n",
    "img.save(r\"C:\\Users\\cheukjy\\WC25_LLEDGE2_test\\Blank_Copy_tiff_convert\\0_2.tiff\")\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4dfc01d4-1a8c-46df-8a6b-2dde29533979",
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "import os\n",
    "\n",
    "directory = r'C:\\Users\\cheukjy\\WC25_LLEDGE2_test\\Blank_Copy'\n",
    "c=1\n",
    "for filename in os.listdir(directory):\n",
    "    if filename.endswith(\".png\"):\n",
    "        im = Image.open(filename)\n",
    "        name='img'+str(c)+'.tiff'\n",
    "        rgb_im = im.convert('RGB')\n",
    "        rgb_im.save(name)\n",
    "        c+=1\n",
    "        print(os.path.join(directory, filename))\n",
    "        continue\n",
    "    else:\n",
    "        continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ab3b4213-a775-47d2-99d4-71f510c81932",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ##Converting the a whole foldewr from a .png image to a .tiff image\n",
    "# import os \n",
    "# from PIL import Image \n",
    "\n",
    "# directory = r\"C:\\Users\\cheukjy\\WC25_LLEDGE2_test\\Blank_Copy\"\n",
    "# end_directory = r\"C:\\Users\\cheukjy\\WC25_LLEDGE2_test\\Blank_Copy_tiff_convert\"\n",
    "\n",
    "# #looping through all the files \n",
    "# for filename in os.listdir(directory):\n",
    "#   # Check if file is a PNG\n",
    "#   if filename.endswith(\".png\"):\n",
    "#     # Construct full path for PNG file\n",
    "#     im = Image.open(filename)\n",
    "#     rgb_im = im.convert('RGB')\n",
    "#     name = im + '.tiff'\n",
    "#     name.save(name in end_directory)\n",
    "#     print(os.path.join(directory, filename))\n",
    "#     continue\n",
    "#   else: \n",
    "#     continue \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6033eba9-5d57-4bc4-80e2-0d29d398a04f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from PIL import Image\n",
    "\n",
    "# Define directory containing PNG files\n",
    "directory = r\"C:\\Users\\cheukjy\\WC25_LLEDGE2_test\\Blank_Copy\"\n",
    "\n",
    "# Define output directory for converted TIFF files (create it if it doesn't exist)\n",
    "end_directory = r\"C:\\Users\\cheukjy\\WC25_LLEDGE2_test\\Blank_Copy_tiff_convert\"\n",
    "os.makedirs(end_directory, exist_ok=True)  # Create output directory if needed\n",
    "\n",
    "# Loop through all files in the directory\n",
    "for filename in os.listdir(directory):\n",
    "    # Check if file is a PNG\n",
    "    if filename.endswith(\".png\"):\n",
    "        # Construct full path for PNG file (use os.path.join)\n",
    "        png_path = os.path.join(directory, filename)\n",
    "\n",
    "        try:\n",
    "            # Open the PNG image\n",
    "            im = Image.open(png_path)\n",
    "\n",
    "            # Generate output TIFF filename with original name (minus extension)\n",
    "            tiff_filename = os.path.splitext(filename)[0] + \".tiff\"  # Remove extension and add .tiff\n",
    "\n",
    "            # Construct full path for output TIFF file\n",
    "            tiff_path = os.path.join(end_directory, tiff_filename)\n",
    "\n",
    "            # Convert to RGB and save as TIFF (optional, remove comment if needed)\n",
    "            rgb_im = im.convert('RGB')\n",
    "            rgb_im.save(tiff_path)\n",
    "\n",
    "            print(f\"Converted {png_path} to {tiff_path}\")  # Clearer output message\n",
    "        except FileNotFoundError:\n",
    "            print(f\"File not found: {png_path}\")  # Handle potential missing files\n",
    "            continue  # Skip to the next iteration\n",
    "\n",
    "    else:\n",
    "        continue  # Skip non-PNG files\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cae646e2-3be5-44e2-bbc1-ab3b912ffc64",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from PIL import Image\n",
    "\n",
    "# Define directory containing PNG files\n",
    "partial_directory = r\"C:\\Users\\cheukjy\\WC25_LLEDGE2_test\\Partial_Copy\"\n",
    "\n",
    "# Define output directory for converted TIFF files (create it if it doesn't exist)\n",
    "partial_end_directory = r\"C:\\Users\\cheukjy\\WC25_LLEDGE2_test\\Partial_Copy_tiff_convert\"\n",
    "os.makedirs(partial_end_directory, exist_ok=True)  # Create output directory if needed\n",
    "\n",
    "# Loop through all files in the directory\n",
    "for filename in os.listdir(partial_directory):\n",
    "    # Check if file is a PNG\n",
    "    if filename.endswith(\".png\"):\n",
    "        # Construct full path for PNG file (use os.path.join)\n",
    "        png_path = os.path.join(partial_directory, filename)\n",
    "\n",
    "        try:\n",
    "            # Open the PNG image\n",
    "            im = Image.open(png_path)\n",
    "\n",
    "            # Generate output TIFF filename with original name (minus extension)\n",
    "            tiff_filename = os.path.splitext(filename)[0] + \".tiff\"  # Remove extension and add .tiff\n",
    "\n",
    "            # Construct full path for output TIFF file\n",
    "            tiff_path = os.path.join(partial_end_directory, tiff_filename)\n",
    "\n",
    "            # Convert to RGB and save as TIFF (optional, remove comment if needed)\n",
    "            rgb_im = im.convert('RGB')\n",
    "            rgb_im.save(tiff_path)\n",
    "\n",
    "            print(f\"Converted {png_path} to {tiff_path}\")  # Clearer output message\n",
    "        except FileNotFoundError:\n",
    "            print(f\"File not found: {png_path}\")  # Handle potential missing files\n",
    "            continue  # Skip to the next iteration\n",
    "\n",
    "    else:\n",
    "        continue  # Skip non-PNG files\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f97f4964-df58-47ca-92b3-c71738780004",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from PIL import Image\n",
    "\n",
    "good_directory = r\"C:\\Users\\cheukjy\\WC25_LLEDGE2_test\\Good_Copy\"\n",
    "\n",
    "# Define output directory for converted TIFF files (create it if it doesn't exist)\n",
    "good_end_directory = r\"C:\\Users\\cheukjy\\WC25_LLEDGE2_test\\Good_Copy_Tiff_convert\"\n",
    "os.makedirs(good_end_directory, exist_ok=True)  # Create output directory if needed\n",
    "\n",
    "# Loop through all files in the directory\n",
    "for filename in os.listdir(good_directory):\n",
    "    # Check if file is a PNG\n",
    "    if filename.endswith(\".png\"):\n",
    "        # Construct full path for PNG file (use os.path.join)\n",
    "        png_path = os.path.join(good_directory, filename)\n",
    "\n",
    "        try:\n",
    "            # Open the PNG image\n",
    "            im = Image.open(png_path)\n",
    "\n",
    "            # Generate output TIFF filename with original name (minus extension)\n",
    "            tiff_filename = os.path.splitext(filename)[0] + \".tiff\"  # Remove extension and add .tiff\n",
    "\n",
    "            # Construct full path for output TIFF file\n",
    "            tiff_path = os.path.join(good_end_directory, tiff_filename)\n",
    "\n",
    "            # Convert to RGB and save as TIFF (optional, remove comment if needed)\n",
    "            rgb_im = im.convert('RGB')\n",
    "            rgb_im.save(tiff_path)\n",
    "\n",
    "            print(f\"Converted {png_path} to {tiff_path}\")  # Clearer output message\n",
    "        except FileNotFoundError:\n",
    "            print(f\"File not found: {png_path}\")  # Handle potential missing files\n",
    "            continue  # Skip to the next iteration\n",
    "\n",
    "    else: to es\n",
    "        continue  # Skip non-PNG files\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "077f5b20-d482-4492-b5c6-5de3ef7957c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip openslide"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "257bc493-1a02-4771-b39d-2a331cdaa307",
   "metadata": {},
   "outputs": [],
   "source": [
    "###########################################################################################\n",
    "\n",
    "#The way the HnE normalization code is written, it does not work for blank images.\n",
    "#Also, it does not do a good job with very little regions.\n",
    "\n",
    "#A few tiles were already saved and in the following exercise we will load them \n",
    "#to understand the mean and std. dev. in their pixel values. \n",
    "#We can then handle blank tiles and tiles with low sample region separately.\n",
    "\n",
    "################################################################\n",
    "\n",
    "import os\n",
    "from openslide import open_slide\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import tifffile as tiff\n",
    "################################################################\n",
    "#For blank it throws an Eigenvalues error.\n",
    "##The purpose of this is to show how blanks work with the library \n",
    "blank = tiff.imread(\"WC25_LLEDGE2_test/Blank_Copy_tiff_convert/0_0.tiff\")\n",
    "norm_img, H_img, E_img = norm_HnE(blank, Io=240, alpha=1, beta=0.15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2dd5f460-f39f-4fb2-b0c9-5ca8ddb5d279",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Let us define a function to detect blank tiles and tiles with very minimal information\n",
    "#This function can be used to identify these tiles so we can make a decision on what to do with them. \n",
    "#Here, the function calculates mean and std dev of pixel values in a tile. \n",
    "import numpy as np\n",
    "def find_mean_std_pixel_value(img_list):\n",
    "        avg_pixel_value = []\n",
    "    stddev_pixel_value= []\n",
    "    for file in img_list:\n",
    "        image = tiff.imread(file)\n",
    "        avg = image.mean()\n",
    "        std = image.std()\n",
    "        avg_pixel_value.append(avg)\n",
    "        stddev_pixel_value.append(std)\n",
    "        \n",
    "    avg_pixel_value = np.array(avg_pixel_value)  \n",
    "    stddev_pixel_value=np.array(stddev_pixel_value)\n",
    "        \n",
    "    print(\"Average pixel value for all images is:\", avg_pixel_value.mean())\n",
    "    print(\"Average std dev of pixel value for all images is:\", stddev_pixel_value.mean())\n",
    "    \n",
    "    return(avg_pixel_value, stddev_pixel_value)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "39c4db46-c250-4208-8797-2eeefa301c85",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Let us read some blank tiles, some partial tiles and some good ones to find out\n",
    "#the mean and std dev of pixel values. \n",
    "#These numbers can be used to identify 'problematic' slides that we can bypass from our processing. \n",
    "\n",
    "import glob\n",
    "# orig_tile_dir_name = r\"C:\\Users\\cheukjy\\WC25_LLEDGE2_test\"\n",
    "\n",
    "blank_img_list=(glob.glob(orig_tile_dir_name + r\"\\Blank_Copy_tiff_convert\\*.tiff\"))\n",
    "partial_img_list=(glob.glob(orig_tile_dir_name + r\"\\Partial_Copy_Tiff_convert\\*.tiff\"))\n",
    "good_img_list=(glob.glob(orig_tile_dir_name + r\"\\Good_Copy_Tiff_convert\\*.tiff\"))\n",
    "\n",
    "blank_img_stats = find_mean_std_pixel_value(blank_img_list)\n",
    "partial_img_stats = find_mean_std_pixel_value(partial_img_list)\n",
    "good_img_stats = find_mean_std_pixel_value(good_img_list)\n",
    "\n",
    "###his results###\n",
    "# Average pixel value for all blank images is: 244.45962306699482\n",
    "# Average std dev of pixel value for all blank images is: 0.9214953206879862\n",
    "\n",
    "# Average pixel value for all partial images is: 242.93900954932494\n",
    "# Average std dev of pixel value for all partial images is: 10.427143587023263\n",
    "\n",
    "# Average pixel value for all good images is: 208.8701055190142\n",
    "# Average std dev of pixel value for all good images is: 37.36282416278772\n",
    "\n",
    "# pixel value is how bright a pixel is . Thus, the pixel with the highest on a 0 to 255 scale \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc996ef2-fcab-48d3-9516-ae2eca17686a",
   "metadata": {},
   "outputs": [],
   "source": [
    "###### this is the ersults about the slide\n",
    "\n",
    "#NOT PRESENT\n",
    "#Average pixel value for all images is: 242.28689065621583\n",
    "# Average std dev of pixel value for all images is: 1.3686457222497377\n",
    "\n",
    "# PARTIAL\n",
    "# Average pixel value for all images is: 237.855339822777\n",
    "# Average std dev of pixel value for all images is: 15.29509339495871\n",
    "\n",
    "# GOOD \n",
    "# Average pixel value for all images is: 164.11229821957025\n",
    "# Average std dev of pixel value for all images is: 48.29158780693436\n",
    "\n",
    "\n",
    "## THE STANDARD DEVIATION FOR THE GOOD "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8de59cd7-e706-4178-a74d-4c3ef644b7cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "###############################################\n",
    "#Generating tiles and processing\n",
    "#We can use read_region function and slide over the large image to extract tiles\n",
    "#but an easier approach would be to use DeepZoom based generator.\n",
    "# https://openslide.org/api/python/\n",
    "from openslide.deepzoom import DeepZoomGenerator\n",
    "\n",
    "#Generate object for tiles using the DeepZoomGenerator\n",
    "tiles = DeepZoomGenerator(slide, tile_size=256, overlap=0, limit_bounds=False)\n",
    "#Here, we have divided our svs into tiles of size 256 with no overlap. \n",
    "\n",
    "#The tiles object also contains data at many levels. \n",
    "#To check the number of levels\n",
    "print(\"The number of levels in the tiles object are: \", tiles.level_count)\n",
    "print(\"The dimensions of data in each level are: \", tiles.level_dimensions)\n",
    "#Total number of tiles in the tiles object\n",
    "print(\"Total number of tiles = : \", tiles.tile_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4660cdb4-2ba3-4aa9-9f34-451c02e28f3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "cols, rows = tiles.level_tiles[17]\n",
    "\n",
    "print(cols,rows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "15366e4a-9dda-49dd-a9ba-4b3af329bfc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "###### processing and saving each tile to local directory\n",
    "cols, rows = tiles.level_tiles[17]\n",
    "\n",
    "orig_tile_dir_name_1 = r\"C:\\Users\\cheukjy\\WC25_LLEDGE2_test\",\"Test_2_start_directory_original_files\" # read images from here\n",
    "orig_tile_end_dir_name_1 = os.path.join(r\"C:\\Users\\cheukjy\\WC25_LLEDGE2_test\",\"Test_2_end_directory_original_files\") # store the original to here\n",
    "norm_tile_dir_name = os.path.join( r\"C:\\Users\\cheukjy\\WC25_LLEDGE2_test\",\"Test_2_Normalized_tiles\") # store image\n",
    "H_tile_dir_name = os.path.join(r\"C:\\Users\\cheukjy\\WC25_LLEDGE2_test\",\"Test_2_H_tiles\") #store image \n",
    "E_tile_dir_name = os.path.join(r\"C:\\Users\\cheukjy\\WC25_LLEDGE2_test\",\"Test_2_E_tiles\")# store image\n",
    "\n",
    "for row in range(rows):\n",
    "    for col in range(cols):\n",
    "        tile_name = str(col) + \"_\" + str(row)\n",
    "        # tile_name = os.path.join(norm_tile_dir_name, '%d_%d' % (col, row))\n",
    "        # print(\"Now processing tile with title: \", tile_name)\n",
    "        temp_tile = tiles.get_tile(17, (col, row))\n",
    "        temp_tile_RGB = temp_tile.convert('RGB')\n",
    "        temp_tile_np = np.array(temp_tile_RGB)\n",
    "        #Save original tile\n",
    "        orig_img_path = os.path.join(orig_tile_end_dir_name_1, f\"{tile_name}_orig_test_2.tif\")\n",
    "        \n",
    "        tiff.imwrite(orig_img_path, temp_tile_np)\n",
    "        # print(orig_img_path)\n",
    "      # removed saving the original file because I alreaedy have a lot of copies of the same file... \n",
    "        if temp_tile_np.mean() < 200 and temp_tile_np.std() > 18:\n",
    "            # you're processing a lot of other; base this on the different slides you've got and the averages that exist\n",
    "            print(\"SUCCESSFULLY Processing tile number:\", tile_name) \n",
    "            norm_img, H_img, E_img = norm_HnE(temp_tile_np, Io=240, alpha=1, beta=0.15)\n",
    "        #Save the norm tile, H and E tiles      \n",
    "            norm_img_path = os.path.join(norm_tile_dir_name, f\"{tile_name}_norm_test_2.tif\")\n",
    "            H_img_path = os.path.join(H_tile_dir_name, f\"{tile_name}_H_test_2.tif\")\n",
    "            E_img_path = os.path.join(E_tile_dir_name, f\"{tile_name}_E_test_2.tif\")\n",
    "            \n",
    "            tiff.imwrite(norm_img_path, norm_img)\n",
    "            tiff.imwrite(H_img_path, H_img)\n",
    "            tiff.imwrite(E_img_path, E_img)\n",
    "\n",
    "        \n",
    "        else:\n",
    "            print(\"NOT PROCESSING TILE:\", tile_name)\n",
    "            # this only processes the tile number \n",
    "            \n",
    "            \n",
    "        # come back to this https://www.youtube.com/watch?v=tNfcvgPKgyU @ 25:17. maybe you can add code that allows you to count the number of tiles processed here...\n",
    "# print(len(\"SUCCESSFULLY PROCESSING tile number\"))\n",
    "# print(len(\"NOT PROCESSING TILE\"))\n",
    "\n",
    "\n",
    "##Is there a way to keep the name of the original file on there cuz it now just \n",
    "\n",
    "        \n",
    "#################### The purpose of this step is to make a smaller, zoomed in image of the tiles I put in.#######################\n",
    "################Would I need to figure out where in the existing tiles is this from?#############################\n",
    "###################OK i'm getting a lot more tiles that expected, did I accidentally put too many levels?######################\n",
    "#################### Find a way to locate the original, original file it came from############"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d768cc3f-925b-4e09-b789-74216e9fe50f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from PIL import Image\n",
    "\n",
    "# Define directory containing PNG files\n",
    "directory = r\"C:\\Users\\cheukjy\\WC25_LLEDGE2_test\"\n",
    "\n",
    "# Define output directory for converted TIFF files (create it if it doesn't exist)\n",
    "end_directory = r\"C:\\Users\\cheukjy\\WC25_LLEDGE2_test\\SAVE_TEST_Original\"\n",
    "os.makedirs(end_directory, exist_ok=True)  # Create output directory if needed\n",
    "\n",
    "# Loop through all files in the directory\n",
    "for filename in os.listdir(directory):\n",
    "    # Check if file is an original file\n",
    "    if filename.endswith(\"original.tif\"):\n",
    "        # Construct full path for PNG file (use os.path.join)\n",
    "        png_path = os.path.join(directory, filename)\n",
    "\n",
    "        try:\n",
    "            # open the files name original.tif\n",
    "            im = Image.open(png_path)\n",
    "\n",
    "            # Generate output TIFF filename with original name (minus extension)\n",
    "            tiff_filename = os.path.splitext(filename)[0] + \".tif\"  # Remove extension and add .tiff\n",
    "\n",
    "            # Construct full path for output TIFF file\n",
    "            tiff_path = os.path.join(end_directory, tiff_filename)\n",
    "\n",
    "            # Convert to RGB and save as TIFF (optional, remove comment if needed)\n",
    "            rgb_im = im.convert('RGB')\n",
    "            rgb_im.save(tiff_path)\n",
    "\n",
    "            print(f\"Converted {png_path} to {tiff_path}\")  # Clearer output message\n",
    "        except FileNotFoundError:\n",
    "            print(f\"File not found: {png_path}\")  # Handle potential missing files\n",
    "            continue  # Skip to the next iteration\n",
    "\n",
    "    else:\n",
    "        continue  # Skip non-PNG files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2483dbc5-7e9b-409c-b9d9-1ee5d93924fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "### 281 Setmenting Whole Slide Images\n",
    "# https://www.youtube.com/watch?v=nCMDZEScF-4 \n",
    "# https://github.com/bnsreenu/python_for_microscopists/blob/master/281_Segmenting_WSI_using_StarDist.ipynb\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "696b20ae-1444-48b4-879c-36860f4f609e",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install stardist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "21428ff9-33f6-4265-962b-16cb7339ea82",
   "metadata": {},
   "outputs": [],
   "source": [
    "# To read Tiff files on Colab we need to install imagecodecs library\n",
    "# !pip install imagecodecs  \n",
    "# not on colab so we skip this step "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1fa05ac5-5376-4eb2-8ef5-fa7b80162d95",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install tensorflow\n",
    "# must install Tensorflow manually to activate csbdeep.utils.tf \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "da125e58-185a-4ada-935a-7cf22393196a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "from tifffile import imread, imsave\n",
    "from csbdeep.utils import Path, normalize\n",
    "from csbdeep.utils.tf import keras_import\n",
    "keras = keras_import()\n",
    "\n",
    "from stardist import export_imagej_rois, random_label_cmap\n",
    "from stardist.models import StarDist2D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e6b6dd70-2cda-4a9f-b9c0-05962cde057e",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(0)\n",
    "cmap = random_label_cmap()\n",
    "\n",
    "def show_image(img, **kwargs):\n",
    "    \"\"\"Plot large image at different resolutions.\"\"\"\n",
    "    fig, ax = plt.subplots(2,4, figsize=(16,8))\n",
    "    mid = [s//2 + 600 for s in img.shape[:2]]\n",
    "    for a,t,u in zip(ax.ravel(),[1,2,4,8,16,32,64,128],[16,8,4,2,1,1,1,1]):\n",
    "        sl = tuple(slice(c - s//t//2, c + s//t//2, u) for s,c in zip(img.shape[:2],mid))\n",
    "        a.imshow(img[sl], **kwargs)\n",
    "        a.axis('off')\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "     \n",
    "img = imread('/content/drive/MyDrive/Colab Notebooks/data/WSI/whole_slide_image.svs')\n",
    "     \n",
    "img.shape\n",
    "     \n",
    "(38474, 32001, 3)\n",
    "\n",
    "show_image(img)\n",
    "\n",
    "\n",
    "# says to look at video \"280 - Custom object segmentation using StarDist library in python\" to know what StarDist Library is or to just read their github \n",
    "# link to Stardist library: https://github.com/stardist/stardist \n",
    "# "
   ]
  }
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