cilia_segmentation_1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import torch\n",
    "from torch.utils import data\n",
    "from torch.utils.data import Dataset\n",
    "from skimage import io\n",
    "from skimage.io import imread\n",
    "from torchvision import transforms\n",
    "from PIL import Image\n",
    "import matplotlib.pyplot as plt\n",
    "from skimage import io, transform\n",
    "import torchvision.transforms.functional as TF\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CiliaDataSet(Dataset):\n",
    "    def __init__(self, train_txt, root_dir):\n",
    "        self.annotations = pd.read_csv(train_txt)\n",
    "        self.root_dir = root_dir\n",
    "        self.inputs_dtype = torch.float32\n",
    "        self.masks_dtype = torch.long\n",
    "    def __len__(self):\n",
    "        return len(self.annotations)\n",
    "    \n",
    "    def transform(self, image, mask):\n",
    "        # Resize\n",
    "        resize = transforms.Resize(size=(520, 520))\n",
    "        image = resize(image)\n",
    "        mask = resize(mask)\n",
    "\n",
    "        # Random crop\n",
    "        i, j, h, w = transforms.RandomCrop.get_params(\n",
    "            image, output_size=(512, 512))\n",
    "        image = TF.crop(image, i, j, h, w)\n",
    "        mask = TF.crop(mask, i, j, h, w)\n",
    "\n",
    "        # Random horizontal flipping\n",
    "        if random.random() > 0.5:\n",
    "            image = TF.hflip(image)\n",
    "            mask = TF.hflip(mask)\n",
    "\n",
    "        # Random vertical flipping\n",
    "        if random.random() > 0.5:\n",
    "            image = TF.vflip(image)\n",
    "            mask = TF.vflip(mask)\n",
    "\n",
    "        # Transform to tensor\n",
    "        image = TF.to_tensor(image)\n",
    "        mask = TF.to_tensor(mask)\n",
    "        return image, mask\n",
    "\n",
    "    \n",
    "    def __getitem__(self,index: int):\n",
    "        \n",
    "        img_path = os.path.join(self.root_dir, \"data\",self.annotations.iloc[index,0], \"data\",self.annotations.iloc[index,0],\"frame0000.png\")\n",
    "        mask_path = os.path.join(self.root_dir, \"masks\",self.annotations.iloc[index,0]+\".png\")\n",
    "        image = Image.open(img_path)\n",
    "        label = Image.open(mask_path)\n",
    "        image, label = self.transform(image, label)\n",
    "        #image, label = torch.from_numpy(image).type(self.inputs_dtype), torch.from_numpy(label).type(self.masks_dtype)\n",
    "        return image,label\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "data_transform = transforms.Compose([\n",
    "        transforms.RandomSizedCrop(224),\n",
    "        transforms.RandomHorizontalFlip(),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize(mean=[0.485, 0.456, 0.406],\n",
    "                             std=[0.229, 0.224, 0.225])\n",
    "    ])\n",
    "cilia_dataset = CiliaDataSet(train_txt= 'D:/MS/DataSciencePracticum/P3/train.txt', root_dir = 'D:/MS/DataSciencePracticum/P3' ) \n",
    "                                                          \n",
    "                               \n",
    "                              \n",
    "                            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "#temp_img, temp_mask = ciliaDataObject[5]\n",
    "#plt.imshow(temp_img.numpy().transpose((1, 2, 0)))\n",
    "#plt.axis('off')\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x = shape: torch.Size([2, 1, 512, 512]); type: torch.float32\n",
      "x = min: 0.22745098173618317; max: 0.9882352948188782\n",
      "y = shape: torch.Size([2, 1, 512, 512]); class: tensor([0.0000, 0.0039, 0.0078]); type: torch.float32\n"
     ]
    }
   ],
   "source": [
    "dataset_loader = data.DataLoader(dataset = cilia_dataset, batch_size=2, shuffle=True)\n",
    "x, y = next(iter(dataset_loader))\n",
    "\n",
    "print(f'x = shape: {x.shape}; type: {x.dtype}')\n",
    "print(f'x = min: {x.min()}; max: {x.max()}')\n",
    "print(f'y = shape: {y.shape}; class: {y.unique()}; type: {y.dtype}')\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1aecbf89dc0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(temp_mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "__call__() takes 2 positional arguments but 3 were given",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-14-cf6fcb3f4520>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mdata_loader\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataLoader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mciliaDataObject\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnext\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata_loader\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf'x =shape: {x.shape};'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\torch\\utils\\data\\dataloader.py\u001b[0m in \u001b[0;36m__next__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    515\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_sampler_iter\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    516\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 517\u001b[1;33m             \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_next_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    518\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_num_yielded\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    519\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dataset_kind\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0m_DatasetKind\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mIterable\u001b[0m \u001b[1;32mand\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\torch\\utils\\data\\dataloader.py\u001b[0m in \u001b[0;36m_next_data\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    555\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_next_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    556\u001b[0m         \u001b[0mindex\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_next_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# may raise StopIteration\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 557\u001b[1;33m         \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dataset_fetcher\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfetch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# may raise StopIteration\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    558\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_pin_memory\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    559\u001b[0m             \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_utils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\torch\\utils\\data\\_utils\\fetch.py\u001b[0m in \u001b[0;36mfetch\u001b[1;34m(self, possibly_batched_index)\u001b[0m\n\u001b[0;32m     42\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mfetch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpossibly_batched_index\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     43\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mauto_collation\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 44\u001b[1;33m             \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0midx\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mpossibly_batched_index\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     45\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     46\u001b[0m             \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\torch\\utils\\data\\_utils\\fetch.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m     42\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mfetch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpossibly_batched_index\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     43\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mauto_collation\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 44\u001b[1;33m             \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0midx\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mpossibly_batched_index\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     45\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     46\u001b[0m             \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-12-014053c84f0d>\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, index)\u001b[0m\n\u001b[0;32m     14\u001b[0m         \u001b[0mlabel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mImage\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmask_path\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     15\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 16\u001b[1;33m             \u001b[0mimage\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimage\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     17\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mimage\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     18\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: __call__() takes 2 positional arguments but 3 were given"
     ]
    }
   ],
   "source": [
    "data_loader = data.DataLoader(dataset=ciliaDataObject, batch_size=2,shuffle=True)\n",
    "\n",
    "x, y = next(iter(data_loader))\n",
    "\n",
    "print(f'x =shape: {x.shape};')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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