Inference_Seg.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YW0SVNhPKn4O"
      },
      "source": [
        "#Sahil's space"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eNxzg2gnLQKC"
      },
      "source": [
        "## Imports and data "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WCaujT1xfxQH",
        "outputId": "f19694ae-d4cf-4d9d-e7cd-9e7e223579de"
      },
      "source": [
        "!pip install segmentation-models-pytorch"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting segmentation-models-pytorch\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/65/54/8953f9f7ee9d451b0f3be8d635aa3a654579abf898d17502a090efe1155a/segmentation_models_pytorch-0.1.3-py3-none-any.whl (66kB)\n",
            "\r\u001b[K     |█████                           | 10kB 14.1MB/s eta 0:00:01\r\u001b[K     |██████████                      | 20kB 2.0MB/s eta 0:00:01\r\u001b[K     |██████████████▉                 | 30kB 2.9MB/s eta 0:00:01\r\u001b[K     |███████████████████▉            | 40kB 3.8MB/s eta 0:00:01\r\u001b[K     |████████████████████████▉       | 51kB 4.6MB/s eta 0:00:01\r\u001b[K     |█████████████████████████████▊  | 61kB 5.4MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 71kB 3.8MB/s \n",
            "\u001b[?25hCollecting efficientnet-pytorch==0.6.3\n",
            "  Downloading https://files.pythonhosted.org/packages/b8/cb/0309a6e3d404862ae4bc017f89645cf150ac94c14c88ef81d215c8e52925/efficientnet_pytorch-0.6.3.tar.gz\n",
            "Requirement already satisfied: torchvision>=0.3.0 in /usr/local/lib/python3.7/dist-packages (from segmentation-models-pytorch) (0.9.1+cu101)\n",
            "Collecting pretrainedmodels==0.7.4\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/84/0e/be6a0e58447ac16c938799d49bfb5fb7a80ac35e137547fc6cee2c08c4cf/pretrainedmodels-0.7.4.tar.gz (58kB)\n",
            "\u001b[K     |████████████████████████████████| 61kB 6.9MB/s \n",
            "\u001b[?25hCollecting timm==0.3.2\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/51/2d/39ecc56fbb202e1891c317e8e44667299bc3b0762ea2ed6aaaa2c2f6613c/timm-0.3.2-py3-none-any.whl (244kB)\n",
            "\u001b[K     |████████████████████████████████| 245kB 33.1MB/s \n",
            "\u001b[?25hRequirement already satisfied: torch in /usr/local/lib/python3.7/dist-packages (from efficientnet-pytorch==0.6.3->segmentation-models-pytorch) (1.8.1+cu101)\n",
            "Requirement already satisfied: pillow>=4.1.1 in /usr/local/lib/python3.7/dist-packages (from torchvision>=0.3.0->segmentation-models-pytorch) (7.1.2)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from torchvision>=0.3.0->segmentation-models-pytorch) (1.19.5)\n",
            "Collecting munch\n",
            "  Downloading https://files.pythonhosted.org/packages/cc/ab/85d8da5c9a45e072301beb37ad7f833cd344e04c817d97e0cc75681d248f/munch-2.5.0-py2.py3-none-any.whl\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from pretrainedmodels==0.7.4->segmentation-models-pytorch) (4.41.1)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch->efficientnet-pytorch==0.6.3->segmentation-models-pytorch) (3.7.4.3)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from munch->pretrainedmodels==0.7.4->segmentation-models-pytorch) (1.15.0)\n",
            "Building wheels for collected packages: efficientnet-pytorch, pretrainedmodels\n",
            "  Building wheel for efficientnet-pytorch (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for efficientnet-pytorch: filename=efficientnet_pytorch-0.6.3-cp37-none-any.whl size=12420 sha256=fb56d408d73e0d1f078166f5197d7119d247fecfc1c097d25c9bb9c1973ce500\n",
            "  Stored in directory: /root/.cache/pip/wheels/42/1e/a9/2a578ba9ad04e776e80bf0f70d8a7f4c29ec0718b92d8f6ccd\n",
            "  Building wheel for pretrainedmodels (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pretrainedmodels: filename=pretrainedmodels-0.7.4-cp37-none-any.whl size=60963 sha256=c99027260db500e2f46930ab6704bada8e411e9a5c31dace60b874c4de0a7e0c\n",
            "  Stored in directory: /root/.cache/pip/wheels/69/df/63/62583c096289713f22db605aa2334de5b591d59861a02c2ecd\n",
            "Successfully built efficientnet-pytorch pretrainedmodels\n",
            "Installing collected packages: efficientnet-pytorch, munch, pretrainedmodels, timm, segmentation-models-pytorch\n",
            "Successfully installed efficientnet-pytorch-0.6.3 munch-2.5.0 pretrainedmodels-0.7.4 segmentation-models-pytorch-0.1.3 timm-0.3.2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dfRTZPY7LMS4"
      },
      "source": [
        "################# IMPORT PACKAGES #############\n",
        "%matplotlib inline\n",
        "import os\n",
        "import sys\n",
        "import cv2\n",
        "import random\n",
        "import numpy as np\n",
        "from pathlib import Path\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib import image\n",
        "from tqdm import tqdm\n",
        "import math\n",
        "from PIL import Image\n",
        "from google.colab.patches import cv2_imshow\n",
        "import sklearn\n",
        "from sklearn import model_selection\n",
        "from sklearn import metrics\n",
        "\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "from torch.optim import lr_scheduler\n",
        "from torch.utils.data import DataLoader\n",
        "import torch.nn as nn\n",
        "from collections import defaultdict\n",
        "import torchvision\n",
        "from torchvision import transforms\n",
        "import torch.nn.functional as F\n",
        "from tqdm.auto import tqdm\n",
        "from torch.utils.tensorboard import SummaryWriter\n",
        "\n",
        "import segmentation_models_pytorch \n",
        "\n",
        "import albumentations as A\n",
        "#from albumentations.pytorch import ToTensorV2\n",
        "from albumentations.pytorch.transforms import ToTensor"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "--lXT06qLXIZ"
      },
      "source": [
        "##To get the data:<br>\n",
        "Track 1: https://drive.google.com/drive/folders/1sZZMJkbqJNbHgebKvHzcXYZHJd6ss4tH <br>\n",
        "Track 2: https://drive.google.com/drive/folders/1g1r419bWBe4GEF-7si5DqWCjxiC8ErnY <br>\n",
        "Open and add shortcut to MyDrive"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fi9Orq4A7w1q",
        "outputId": "993f8491-3360-4855-a5d7-cb25cc6f2035"
      },
      "source": [
        "################ MOUNTING DRIVE ##################\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S8ubA5U16FeE",
        "outputId": "6628952d-2372-4fbd-dbb1-457999478ae7"
      },
      "source": [
        "!unzip /content/drive/MyDrive/512_Images/512_Images.zip"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  /content/drive/MyDrive/512_Images/512_Images.zip\n",
            "   creating: 512_Images/\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dSWm-lXz6dlN",
        "outputId": "a6a0520c-03f5-400d-ea8b-35d724c5fed8"
      },
      "source": [
        "%cd 512_Images"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/512_Images\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4DoyUgyNOiHm",
        "outputId": "89028920-5aee-4557-fbed-44868de8e95d"
      },
      "source": [
        "!ls Train/Labeled"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Flooded  Non-Flooded\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oddkSivmB9iY"
      },
      "source": [
        "#Saving Masks"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ewNLbu_NlBmB",
        "outputId": "0de9ea73-fbb3-4d33-be00-7ddea81787cd"
      },
      "source": [
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
        "print(device)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "cuda:0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mh0gGIurPMK2",
        "outputId": "852a8f8f-1837-44f6-f12e-10d91446f64f"
      },
      "source": [
        "!ls Train/Labeled\n",
        "print(len(os.listdir('/content/512_Images/Train/Labeled/Flooded/image')))\n",
        "print(len(os.listdir('/content/512_Images/Train/Labeled/Non-Flooded/image')))\n",
        "print(len(os.listdir('/content/512_Images/Train/Labeled/Flooded/mask')))\n",
        "print(len(os.listdir('/content/512_Images/Train/Labeled/Non-Flooded/mask')))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "ls: cannot access 'Train/Labeled': No such file or directory\n",
            "51\n",
            "347\n",
            "51\n",
            "347\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hrGeMTTSVBTi",
        "outputId": "fbb1a30d-b940-4eaa-b0fd-293c41a3399a"
      },
      "source": [
        "imgs = [os.path.join(\"Train/Labeled/Flooded/image\", img_n) for img_n in os.listdir('/content/512_Images/Train/Labeled/Flooded/image')]\n",
        "imgs += [os.path.join(\"Train/Labeled/Non-Flooded/image\", img_n) for img_n in os.listdir('/content/512_Images/Train/Labeled/Non-Flooded/image')]\n",
        "imgs = sorted(imgs)\n",
        "print(len(imgs), imgs[0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "398 Train/Labeled/Flooded/image/10165.jpg\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "gTW40_lcVuuN",
        "outputId": "e5758c01-e09f-4965-ddf5-89d197a37ef2"
      },
      "source": [
        "imgs[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Train/Labeled/Flooded/image/10165.jpg'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JkGpoSjKREyg",
        "outputId": "2e715bb2-0ea1-4dcb-d36d-e1de7b50363e"
      },
      "source": [
        "347 + 51"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "398"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v5oUe53wR5gf"
      },
      "source": [
        "DIR_PATH='/content/512_Images'\n",
        "MODEL_PATH_UNET='/content/drive/MyDrive/models/_Unet_anki_final40.pt'\n",
        "MODEL_PATH_PSP= '/content/drive/MyDrive/models/final-PSPNet_2-resnet101-ep_101-0.001-24.pt'\n",
        "MODEL_PATH_DL='/content/drive/MyDrive/models/abhi-pretrained_preproc_1-deeplabv3+-ep_79-0.001-8.pt'\n",
        "MODEL_PATH_DLPS= '/content/drive/MyDrive/models/abhi_sudo-pretrained_preproc_1-deeplabv3+-ep_20-0.001-8.pt'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9B3U6eCnEW7h"
      },
      "source": [
        "val_transform = transforms.Compose([\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
        "    ])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "isvXV3rGkmoF"
      },
      "source": [
        "def reverse_transform_mask(inp):\n",
        "    inp=inp.transpose((1, 2, 0))\n",
        "    t_mask=np.argmax(inp,axis=2).astype('float32') \n",
        "    t_mask=cv2.resize(t_mask, dsize=(4000, 3000))\n",
        "    return t_mask"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "CzJd1WvaU1ec",
        "outputId": "4b6bc5d9-6d6e-462f-84be-2b05a5b230d0"
      },
      "source": [
        "imgs[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'10165.jpg'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dD3vFFUjD02B"
      },
      "source": [
        "class ValDataset:\n",
        "    def __init__(self, dir_path, imgs, val_trans, img_dim):\n",
        "        self.dir_path=dir_path\n",
        "        self.imgs = imgs\n",
        "        self.img_dim = img_dim\n",
        "        self.val_trans=val_trans\n",
        "\n",
        "    def __len__(self):\n",
        "      return len(self.imgs)\n",
        "\n",
        "    def __getitem__(self, index):\n",
        "      # image = cv2.resize(cv2.imread(os.path.join(self.dir_path,self.imgs[index])), dsize=(self.img_dim, self.img_dim))\n",
        "      image = cv2.imread(os.path.join(self.dir_path,self.imgs[index]))\n",
        "      if self.val_trans:\n",
        "        image=self.val_trans(image)\n",
        "      return image, index"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sX_hLTbECCtO"
      },
      "source": [
        "LOAD_SIZE= 10\n",
        "val_set = ValDataset(DIR_PATH,imgs, val_transform , img_dim=IMG_DIM)\n",
        "val_dataloader = torch.utils.data.DataLoader(val_set, batch_size=LOAD_SIZE, shuffle=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5ffuUyyVIPrN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 188,
          "referenced_widgets": [
            "458cec8dd3994166a6cc7d5db6ebd47b",
            "b0f4ee03b40d41bab7e734c928bb8869",
            "414e3654c69b4243ac857e97d11e3bd4",
            "7447cec6d12247289056621cb1cc96f9",
            "433cd70ef9844b7c932ac412c4ae1971",
            "32aac742ba4e4f0ea3beadb9d56e573e",
            "6f93d295ef044c73addc0e621aca4f90",
            "509aaffe9e5e4f2a941cac1f33dabeb9",
            "d63bbbad3a8c45fab9e7ba0065664f75",
            "6f4a5d7ee3a44fdcbefdd0c0a2564b31",
            "20a23eb93f534f9e881d825614165620",
            "f5dc1f550f83485e98d4c64b63726305",
            "1914c7a2404b4370b3152c8fe651cdc1",
            "00b7391194db4a51a89296fa32aca762",
            "593115c263c74a96a5440955696c244d",
            "4f003c21fad940bc94faa8067d257c0d"
          ]
        },
        "outputId": "0523d467-f133-4253-eb1c-cbdd8dcbd594"
      },
      "source": [
        "# model_name = \"UNET\" # DV3S PSP DV3 UNET\n",
        "# SAVE_PATH=f'/content/drive/MyDrive/512_Images/inferences-{model_name}'\n",
        "# os.makedirs(SAVE_PATH, exist_ok=True)\n",
        "# if model_name == \"DV3S\":\n",
        "#   model = segmentation_models_pytorch.DeepLabV3Plus(encoder_name='efficientnet-b3', encoder_depth=5, \n",
        "#                                                   decoder_channels=256,classes=10)\n",
        "#   model.load_state_dict(torch.load(MODEL_PATH_DLPS))\n",
        "# elif model_name == \"PSP\":\n",
        "#   model = segmentation_models_pytorch.PSPNet(encoder_name='resnet101', encoder_weights='imagenet', encoder_depth=3, \n",
        "#                                                  psp_out_channels=512, psp_use_batchnorm=True, psp_dropout=0.2, in_channels=3, \n",
        "#                                                  classes=10, activation=None, upsampling=8, aux_params=None)\n",
        "#   model.load_state_dict(torch.load(MODEL_PATH_PSP))\n",
        "# elif model_name == \"DV3\":\n",
        "#   model = segmentation_models_pytorch.DeepLabV3Plus(encoder_name='efficientnet-b3', encoder_depth=5, \n",
        "#                                                   decoder_channels=256,classes=10)\n",
        "#   model.load_state_dict(torch.load(MODEL_PATH_DL))\n",
        "# elif model_name == \"UNET\":\n",
        "#   model = segmentation_models_pytorch.Unet(encoder_name='resnet34', encoder_depth=5, \n",
        "#                                          decoder_channels=(256, 128, 64, 32, 16), classes=10)\n",
        "#   model.load_state_dict(torch.load(MODEL_PATH_UNET))\n",
        "\n",
        "# model = model.to(device)\n",
        "# model.eval()\n",
        "# for inputs, index in tqdm(val_dataloader):\n",
        "#   inputs = inputs.to(device)\n",
        "#   preds = model(inputs)\n",
        "#   preds = F.sigmoid(preds)\n",
        "#   preds = preds.data.cpu().numpy()\n",
        "#   for i in range(min(LOAD_SIZE, len(index))):\n",
        "#     f_mask=reverse_transform_mask(preds[i])\n",
        "#     cv2.imwrite(os.path.join(SAVE_PATH,str(imgs[index[i]].split(\"/\")[-1].replace(\"jpg\", \"png\"))),f_mask)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading: \"https://download.pytorch.org/models/resnet34-333f7ec4.pth\" to /root/.cache/torch/hub/checkpoints/resnet34-333f7ec4.pth\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "458cec8dd3994166a6cc7d5db6ebd47b",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=87306240.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d63bbbad3a8c45fab9e7ba0065664f75",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=40.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:1709: UserWarning: nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\n",
            "  warnings.warn(\"nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\")\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 167
        },
        "id": "tPGTFuCNYeuS",
        "outputId": "385afa4b-6dc4-4f6c-8de0-d601d7bfd256"
      },
      "source": [
        "len(os.listdir(SAVE_PATH)) # 398"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-10-ae7770efc149>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlistdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mSAVE_PATH\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# 398\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'SAVE_PATH' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oExk6BqcajlH",
        "outputId": "e72f4cc9-0e04-467b-f0ab-2848faf6c2e3"
      },
      "source": [
        "masks = [os.path.join(\"/content/512_Images/Train/Labeled/Flooded/mask\", img_n) for img_n in os.listdir('/content/512_Images/Train/Labeled/Flooded/mask')]\n",
        "masks += [os.path.join(\"/content/512_Images/Train/Labeled/Non-Flooded/mask\", img_n) for img_n in os.listdir('/content/512_Images/Train/Labeled/Non-Flooded/mask')]\n",
        "masks = sorted(masks)\n",
        "print(len(masks), masks[0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "398 /content/512_Images/Train/Labeled/Flooded/mask/10165_lab.png\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-QDnozAqg7RG",
        "outputId": "ea9e7d4c-ca5e-441a-d9d5-a0be24045fe1"
      },
      "source": [
        "# models = [\"DV3S\", \"PSP\", \"DV3\", \"UNET\",]\n",
        "imgs = sorted([os.path.join('/content/512_Images', i) for i in imgs])\n",
        "\n",
        "unet_m = sorted(os.listdir(f'/content/drive/MyDrive/512_Images/inferences-UNET'))\n",
        "unet_m = sorted([os.path.join('/content/drive/MyDrive/512_Images/inferences-UNET', i) for i in unet_m])\n",
        "\n",
        "psp_m = sorted(os.listdir(f'/content/drive/MyDrive/512_Images/inferences-PSP'))\n",
        "psp_m = sorted([os.path.join('/content/drive/MyDrive/512_Images/inferences-PSP', i) for i in psp_m])\n",
        "\n",
        "dv3_m = sorted(os.listdir(f'/content/drive/MyDrive/512_Images/inferences-DV3'))\n",
        "dv3_m = sorted([os.path.join('/content/drive/MyDrive/512_Images/inferences-DV3', i) for i in dv3_m])\n",
        "\n",
        "dv3s_m = sorted(os.listdir(f'/content/drive/MyDrive/512_Images/inferences-DV3S'))\n",
        "dv3s_m = sorted([os.path.join('/content/drive/MyDrive/512_Images/inferences-DV3S', i) for i in dv3s_m])\n",
        "\n",
        "grn_t = sorted(masks)\n",
        "\n",
        "print(len(imgs), imgs[0])\n",
        "print(len(unet_m), unet_m[0])\n",
        "print(len(psp_m), psp_m[0])\n",
        "print(len(dv3_m), dv3_m[0])\n",
        "print(len(dv3s_m), dv3s_m[0])\n",
        "print(len(grn_t), grn_t[0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "398 /content/512_Images/Train/Labeled/Flooded/image/10165.jpg\n",
            "398 /content/drive/MyDrive/512_Images/inferences-UNET/10165.png\n",
            "398 /content/drive/MyDrive/512_Images/inferences-PSP/10165.png\n",
            "398 /content/drive/MyDrive/512_Images/inferences-DV3/10165.png\n",
            "398 /content/drive/MyDrive/512_Images/inferences-DV3S/10165.png\n",
            "398 /content/512_Images/Train/Labeled/Flooded/mask/10165_lab.png\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CTw6UQylOsUO",
        "outputId": "2eb0bdd5-9ac5-4a96-e0dc-5a39240799d4"
      },
      "source": [
        "count = 0\n",
        "for im, un, ps, dv, ds, g in zip(imgs, unet_m, psp_m, dv3_m, dv3s_m, grn_t):\n",
        "  im = im.split(\"/\")[-1].split(\".\")[0]\n",
        "  un = f\"/content/drive/MyDrive/512_Images/inferences-UNET/{im}.png\"\n",
        "  un = un.split(\"/\")[-1].split(\".\")[0]\n",
        "  ps = f\"/content/drive/MyDrive/512_Images/inferences-PSP/{im}.png\"\n",
        "  ps = ps.split(\"/\")[-1].split(\".\")[0]\n",
        "  dv = f\"/content/drive/MyDrive/512_Images/inferences-DV3/{im}.png\"\n",
        "  dv = dv.split(\"/\")[-1].split(\".\")[0]\n",
        "  ds = f\"/content/drive/MyDrive/512_Images/inferences-DV3S/{im}.png\"\n",
        "  ds = ds.split(\"/\")[-1].split(\".\")[0]\n",
        "  g = f\"/content/512_Images/Train/Labeled/Flooded/mask/{im}_lab.png\"\n",
        "  g = g.split(\"/\")[-1].split(\".\")[0].replace(\"_lab\", \"\")\n",
        "  if not (im == un == ps == dv == ds == g):\n",
        "    # print(im)\n",
        "    count += 1 \n",
        "print(count)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hSkm7T7ggVf9"
      },
      "source": [
        "num_imgs = 2"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RGFmXpcxjtzL",
        "outputId": "9f98bd9f-7930-40d6-d1ed-68598423e464"
      },
      "source": [
        "len(im_paths)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "18"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4YgaArAPpllu",
        "outputId": "7a0aed61-0f96-49dc-8501-5187298fedac"
      },
      "source": [
        "\n",
        "math.ceil(398/3)\n",
        "print(len(imgs))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "398\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qvDJ5H3bqBAn",
        "outputId": "c040b6f4-3ae4-4a9c-fb29-8babc9c44b3a"
      },
      "source": [
        "threes = 0\n",
        "twos = 0\n",
        "for i in range(math.ceil(len(imgs) / 3)):\n",
        "  num_imgs = min(3, len(imgs[i*3:i*3+3]))\n",
        "  if num_imgs == 2:\n",
        "    twos += 1\n",
        "  elif num_imgs == 3:\n",
        "    threes += 1\n",
        "print(twos, threes)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1 132\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "6323d3463575421faa6e79216fbb8c41",
            "d22462c1db424e24a3135477d92ea4d7",
            "ab20371bef6544f791375b30d9904b68",
            "0d3ab8a609c14703af4529b5651f69ea",
            "8d827860a362414a88d08d2af5b73ad7",
            "7e2d917dd1104320be17f31b8bb076b9",
            "163ef05081b94bbf91f7905950173e01",
            "e9639514670b43539c99fc68d8574746"
          ]
        },
        "id": "YWOXEO7hakHv",
        "outputId": "be9cc092-51a2-4f62-d6d0-3f00e023b878"
      },
      "source": [
        "# total images = 398\n",
        "# 3 images per plt\n",
        "os.makedirs('/content/drive/MyDrive/512_Images/plt_inference/', exist_ok=True)\n",
        "for i in tqdm(range(math.ceil(len(imgs) / 3))):\n",
        "  num_imgs = min(3, len(imgs[i*3:i*3+3]))\n",
        "  ims = imgs[i*3:i*3+3]\n",
        "  uns = []\n",
        "  pss = []\n",
        "  dvs = []\n",
        "  dss = []\n",
        "  gs = []\n",
        "  for im1 in ims:\n",
        "    im = im1.split(\"/\")[-1].split(\".\")[0]\n",
        "    uns.append(f\"/content/drive/MyDrive/512_Images/inferences-UNET/{im}.png\")\n",
        "    pss.append(f\"/content/drive/MyDrive/512_Images/inferences-PSP/{im}.png\")\n",
        "    dvs.append(f\"/content/drive/MyDrive/512_Images/inferences-DV3/{im}.png\")\n",
        "    dss.append(f\"/content/drive/MyDrive/512_Images/inferences-DV3S/{im}.png\")\n",
        "    gs.append(im1.replace(\"/image/\", \"/mask/\").replace(\".jpg\", \"_lab.png\"))\n",
        "\n",
        "  im_paths = []\n",
        "  for n in range(num_imgs):\n",
        "    im_paths.extend([ims[n], uns[n], pss[n], dvs[n], dss[n], gs[n]])\n",
        "  count = 0\n",
        "  fig = plt.figure(figsize=(14, 5))\n",
        "  for n in range(num_imgs):\n",
        "      for num, image_title in enumerate([\"(a) Image\", \"(b) UNet\", \"(c) PSPNet\", \"(d) DeepLabV3+\", \"(e) DeepLabV3+ Pseudo\", \"(f) Ground Truth\"]):\n",
        "          fig.add_subplot(3, 6, count+1)\n",
        "          if image_title == \"(f) Ground Truth\":\n",
        "              plt.imshow(cv2.resize(image.imread(im_paths[count])[:, :, 0], (4000, 3000)))\n",
        "          elif image_title == \"(a) Image\":\n",
        "            plt.imshow(cv2.resize(image.imread(im_paths[count]), (4000, 3000)))\n",
        "          else:\n",
        "              plt.imshow(image.imread(im_paths[count]))\n",
        "          plt.axis(\"off\")\n",
        "          if count < 6:\n",
        "              plt.title(image_title)\n",
        "          count += 1\n",
        "  plt.savefig(f'/content/drive/MyDrive/512_Images/plt_inference/{i}.png')\n",
        "  plt.clf()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6323d3463575421faa6e79216fbb8c41",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=133.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:24: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1008x360 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1008x360 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1008x360 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1008x360 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1008x360 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1008x360 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
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        {
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        {
          "output_type": "display_data",
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        {
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        {
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        },
        {
          "output_type": "display_data",
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              "<Figure size 1008x360 with 0 Axes>"
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          "metadata": {
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        },
        {
          "output_type": "display_data",
          "data": {
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              "<Figure size 1008x360 with 0 Axes>"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1008x360 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1008x360 with 0 Axes>"
            ]
          },
          "metadata": {
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          }
        },
        {
          "output_type": "display_data",
          "data": {
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              "<Figure size 1008x360 with 0 Axes>"
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          "metadata": {
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0EvEkpV4N0Ba",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "124228e2-87e5-469e-d92f-d23c91d0b817"
      },
      "source": [
        "imgs[0:3]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['/content/512_Images/Train/Labeled/Flooded/image/10165.jpg',\n",
              " '/content/512_Images/Train/Labeled/Flooded/image/10166.jpg',\n",
              " '/content/512_Images/Train/Labeled/Flooded/image/6279.jpg']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mmhlpk5oxj-D"
      },
      "source": [
        "with open(\"img_paths.txt\", \"w\") as f:\n",
        "  for im in imgs:\n",
        "    f.write(im.replace(\"/content/512_Images/\", \"\") + \"\\n\")\n",
        "f.close()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BAv9Fdo4yNHf",
        "outputId": "409275e4-dcf0-4ef0-8614-7d3bd23b498e"
      },
      "source": [
        "!zip -r plt_inference.zip /content/drive/MyDrive/512_Images/plt_inference/"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "  adding: content/drive/MyDrive/512_Images/plt_inference/ (stored 0%)\n",
            "  adding: content/drive/MyDrive/512_Images/plt_inference/0.png (deflated 2%)\n",
            "  adding: content/drive/MyDrive/512_Images/plt_inference/1.png (deflated 1%)\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6Z3lgf1Y1v0d"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}