examples/1x/local/scc_local.ipynb

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    "## Initialize Session"
   ]
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    "##### 1. First step, go to \"Runtime\" -> \"Change runtime type\", under \"Hardware Accelerator, select \"GPU\""
   ]
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    "# please note that tensorflow version 1.0 is selected since original model was created in 1.0\n",
    "\n",
    "%tensorflow_version 1.x\n",
    "import numpy as np\n",
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "import pandas as pd\n",
    "import math\n",
    "import os\n",
    "from tqdm.notebook import tqdm\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import load_model\n",
    "from tensorflow.keras.utils import get_file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "##### 2. Read in functions and load model\n",
    "##### after `!cd`, enter in the path to your Desktop"
   ]
  },
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   "source": [
    "!cd PATH\\TO\\DESKTOP\n",
    "!git clone https://github.com/aneeshbal/SimpylCellCounter.git\n",
    "!cd SimpylCellCounter\\scc\\1.1x"
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "from scc import scc\n",
    "model = load_model('model_save.h5')"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
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   "source": [
    "## Run SCC"
   ]
  },
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   "source": [
    "##### 3. For the `image_path` variable, type in the appropriate folder name that contains your images"
   ]
  },
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   "source": [
    "image_path = 'PATH\\TO\\IMAGES' # replace 'sample_images' with appropriate folder name"
   ]
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    "roots = []\n",
    "[roots.append(root) for root, dirs, files in os.walk(image_path)]\n",
    "roots.pop(0)\n",
    "roots \n",
    "## ensure that the printed output looks correct in terms of where the images are located within the images folder"
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    "## rememeber, scc parameters are as follows:\n",
    "## scc(read, model, set_th_value=150, radius=10, circularity_parameter=0.77)\n",
    "## 'read' = image to be read, don't change this!\n",
    "## 'model' = CNN model to-be-used, don't change this!\n",
    "## 'set_th_value' = binary threshold value set at default to 150, can be changed to any value!\n",
    "## 'radius' = radius for cells set at default to 10, can be changed to any value!\n",
    "## 'circularity_parameter' = how circular cells should be set at default to 0.77, can be changed to any value! The lower the value, ...\n",
    "## the less circular cells will be tolerated\n",
    "\n",
    "\n",
    "output = []\n",
    "img_track = []\n",
    "for p in tqdm(roots):\n",
    "    images = os.listdir(p)\n",
    "    for im in images:   \n",
    "        img_path = os.path.join(p, im)\n",
    "        img = cv2.imread(img_path, cv2.IMREAD_GRAYSCALE)\n",
    "        output.append(scc(read=img, model=model, set_th_value=115, circularity_parameter=0.7))\n",
    "        img_track.append(im)"
   ]
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   "source": [
    "## Save Output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
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   "outputs": [],
   "source": [
    "from time import gmtime, strftime\n",
    "save_output = pd.DataFrame({'image':img_track,'counts':output})\n",
    "save_output.set_index('image')\n",
    "save_output.to_csv('scc_output{}.csv'.format(strftime(\"%Y-%m-%d %H:%M:%S\", gmtime())))\n",
    "\n",
    "# output should be saved on Desktop as 'scc_output' followed by the date and time"
   ]
  }
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