semantic_segmentation_yesil_ekran.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "semantic_segmentation_yesil_ekran",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_uwzi1JZ-yrm",
        "colab_type": "text"
      },
      "source": [
        "Referanslar:\n",
        "\n",
        "\n",
        "*   https://colab.research.google.com/github/pytorch/pytorch.github.io/blob/master/assets/hub/pytorch_vision_deeplabv3_resnet101.ipynb\n",
        "*   https://towardsdatascience.com/3-ways-to-load-csv-files-into-colab-7c14fcbdcb92\n",
        "* https://www.learnopencv.com/read-write-and-display-a-video-using-opencv-cpp-python/\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "avJiaudV-vNY",
        "colab_type": "code",
        "outputId": "2acc19b0-4e64-4207-9b31-d59562abb45f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "import torch\n",
        "model = torch.hub.load('pytorch/vision', 'fcn_resnet101', pretrained=True)\n",
        "model.eval()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading: \"https://github.com/pytorch/vision/archive/master.zip\" to /root/.cache/torch/hub/master.zip\n",
            "Downloading: \"https://download.pytorch.org/models/resnet101-5d3b4d8f.pth\" to /root/.cache/torch/checkpoints/resnet101-5d3b4d8f.pth\n",
            "100%|██████████| 178728960/178728960 [00:03<00:00, 53469257.74it/s]\n",
            "Downloading: \"https://download.pytorch.org/models/fcn_resnet101_coco-7ecb50ca.pth\" to /root/.cache/torch/checkpoints/fcn_resnet101_coco-7ecb50ca.pth\n",
            "100%|██████████| 217800805/217800805 [00:07<00:00, 29447355.67it/s]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "FCN(\n",
              "  (backbone): IntermediateLayerGetter(\n",
              "    (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
              "    (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (relu): ReLU(inplace)\n",
              "    (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
              "    (layer1): Sequential(\n",
              "      (0): Bottleneck(\n",
              "        (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (relu): ReLU(inplace)\n",
              "        (downsample): Sequential(\n",
              "          (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        )\n",
              "      )\n",
              "      (1): Bottleneck(\n",
              "        (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (relu): ReLU(inplace)\n",
              "      )\n",
              "      (2): Bottleneck(\n",
              "        (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (relu): ReLU(inplace)\n",
              "      )\n",
              "    )\n",
              "    (layer2): Sequential(\n",
              "      (0): Bottleneck(\n",
              "        (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (relu): ReLU(inplace)\n",
              "        (downsample): Sequential(\n",
              "          (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
              "          (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        )\n",
              "      )\n",
              "      (1): Bottleneck(\n",
              "        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (relu): ReLU(inplace)\n",
              "      )\n",
              "      (2): Bottleneck(\n",
              "        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (relu): ReLU(inplace)\n",
              "      )\n",
              "      (3): Bottleneck(\n",
              "        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (relu): ReLU(inplace)\n",
              "      )\n",
              "    )\n",
              "    (layer3): Sequential(\n",
              "      (0): Bottleneck(\n",
              "        (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (relu): ReLU(inplace)\n",
              "        (downsample): Sequential(\n",
              "          (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        )\n",
              "      )\n",
              "      (1): Bottleneck(\n",
              "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)\n",
              "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (relu): ReLU(inplace)\n",
              "      )\n",
              "      (2): Bottleneck(\n",
              "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)\n",
              "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (relu): ReLU(inplace)\n",
              "      )\n",
              "      (3): Bottleneck(\n",
              "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)\n",
              "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (relu): ReLU(inplace)\n",
              "      )\n",
              "      (4): Bottleneck(\n",
              "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)\n",
              "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (relu): ReLU(inplace)\n",
              "      )\n",
              "      (5): Bottleneck(\n",
              "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)\n",
              "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (relu): ReLU(inplace)\n",
              "      )\n",
              "      (6): Bottleneck(\n",
              "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)\n",
              "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (relu): ReLU(inplace)\n",
              "      )\n",
              "      (7): Bottleneck(\n",
              "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)\n",
              "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (relu): ReLU(inplace)\n",
              "      )\n",
              "      (8): Bottleneck(\n",
              "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)\n",
              "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (relu): ReLU(inplace)\n",
              "      )\n",
              "      (9): Bottleneck(\n",
              "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)\n",
              "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (relu): ReLU(inplace)\n",
              "      )\n",
              "      (10): Bottleneck(\n",
              "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)\n",
              "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (relu): ReLU(inplace)\n",
              "      )\n",
              "      (11): Bottleneck(\n",
              "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)\n",
              "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (relu): ReLU(inplace)\n",
              "      )\n",
              "      (12): Bottleneck(\n",
              "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)\n",
              "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (relu): ReLU(inplace)\n",
              "      )\n",
              "      (13): Bottleneck(\n",
              "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)\n",
              "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (relu): ReLU(inplace)\n",
              "      )\n",
              "      (14): Bottleneck(\n",
              "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)\n",
              "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (relu): ReLU(inplace)\n",
              "      )\n",
              "      (15): Bottleneck(\n",
              "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)\n",
              "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (relu): ReLU(inplace)\n",
              "      )\n",
              "      (16): Bottleneck(\n",
              "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)\n",
              "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (relu): ReLU(inplace)\n",
              "      )\n",
              "      (17): Bottleneck(\n",
              "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)\n",
              "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (relu): ReLU(inplace)\n",
              "      )\n",
              "      (18): Bottleneck(\n",
              "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)\n",
              "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (relu): ReLU(inplace)\n",
              "      )\n",
              "      (19): Bottleneck(\n",
              "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)\n",
              "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (relu): ReLU(inplace)\n",
              "      )\n",
              "      (20): Bottleneck(\n",
              "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)\n",
              "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (relu): ReLU(inplace)\n",
              "      )\n",
              "      (21): Bottleneck(\n",
              "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)\n",
              "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (relu): ReLU(inplace)\n",
              "      )\n",
              "      (22): Bottleneck(\n",
              "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)\n",
              "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (relu): ReLU(inplace)\n",
              "      )\n",
              "    )\n",
              "    (layer4): Sequential(\n",
              "      (0): Bottleneck(\n",
              "        (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)\n",
              "        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (relu): ReLU(inplace)\n",
              "        (downsample): Sequential(\n",
              "          (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        )\n",
              "      )\n",
              "      (1): Bottleneck(\n",
              "        (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(4, 4), dilation=(4, 4), bias=False)\n",
              "        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (relu): ReLU(inplace)\n",
              "      )\n",
              "      (2): Bottleneck(\n",
              "        (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(4, 4), dilation=(4, 4), bias=False)\n",
              "        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (relu): ReLU(inplace)\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (classifier): FCNHead(\n",
              "    (0): Conv2d(2048, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "    (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (2): ReLU()\n",
              "    (3): Dropout(p=0.1)\n",
              "    (4): Conv2d(512, 21, kernel_size=(1, 1), stride=(1, 1))\n",
              "  )\n",
              "  (aux_classifier): FCNHead(\n",
              "    (0): Conv2d(1024, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "    (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (2): ReLU()\n",
              "    (3): Dropout(p=0.1)\n",
              "    (4): Conv2d(256, 21, kernel_size=(1, 1), stride=(1, 1))\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ysLH9kHw_5qO",
        "colab_type": "code",
        "outputId": "896a537d-a316-4039-e6cd-2ba84dbb8427",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 75
        }
      },
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-46b3ad20-276a-439c-b01a-a7b80b7c81e4\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-46b3ad20-276a-439c-b01a-a7b80b7c81e4\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving output.mp4 to output.mp4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "imWdzmdY_O65",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import cv2\n",
        "from torchvision import transforms\n",
        "\n",
        "preprocess = transforms.Compose([\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n",
        "])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pK9JogpJEgCs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "from PIL import Image\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8GGeZHdj_ztV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "video_okuyucu = cv2.VideoCapture('output.mp4')\n",
        "\n",
        "video_list = []\n",
        "video_list_orig = []\n",
        "\n",
        "\n",
        "counter = 50\n",
        "while(video_okuyucu.isOpened()):\n",
        "  # Capture frame-by-frame\n",
        "  ret, frame = video_okuyucu.read()\n",
        "  \n",
        "  if ret:\n",
        "    \n",
        "    frame = img = cv2.resize(frame,None,fx=0.25,fy=0.25)\n",
        "  \n",
        "    video_list.append(preprocess(frame))\n",
        "    video_list_orig.append(frame)\n",
        "    \n",
        "  else: \n",
        "    break\n",
        "  \n",
        "  if counter == 1:\n",
        "      break\n",
        "  \n",
        "  counter += 1\n",
        "  \n",
        "  \n",
        "video_tensor = torch.ones((len(video_list), video_list[0].shape[0], video_list[0].shape[1], video_list[0].shape[2]))\n",
        "\n",
        "\n",
        "for i, frame in enumerate(video_list):\n",
        "  video_tensor[i] = frame\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3qxJAySEDcCn",
        "colab_type": "code",
        "outputId": "fd8012d5-621a-47c7-df2d-22e72382fa37",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        }
      },
      "source": [
        "if torch.cuda.is_available():\n",
        "    video_tensor = video_tensor.to('cuda')\n",
        "    model.to('cuda')\n",
        "\n",
        "with torch.no_grad():\n",
        "    output = model(video_tensor)['out']\n",
        "output_predictions = output.argmax(1).cpu()\n",
        "\n",
        "\n",
        "output_predictions = (output_predictions==15) # insan pixellerini secelim\n",
        "\n",
        "r = Image.fromarray(output_predictions[0].byte().cpu().numpy()).resize(list(video_list[0].shape[1:3][::-1]))\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "plt.imshow(r)\n",
        "\n",
        "print(output_predictions.shape)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "torch.Size([301, 180, 320])\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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19CvJj5LcnmRrkpuaZSuSXJPknub3YaOucyZJLkiyO8kdU5ZNW396Pt/ss9uSrBtd5dOb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\nAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4MDVTpCft5o4",
        "colab_type": "code",
        "outputId": "c1e8379e-3ab3-4528-a563-80133561e2a8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 217
        }
      },
      "source": [
        "!wget https://upload.wikimedia.org/wikipedia/commons/thumb/8/86/Kordon_%28ekim_2009%29_2_-_panoramio.jpg/256px-Kordon_%28ekim_2009%29_2_-_panoramio.jpg"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2019-09-14 16:20:37--  https://upload.wikimedia.org/wikipedia/commons/thumb/8/86/Kordon_%28ekim_2009%29_2_-_panoramio.jpg/256px-Kordon_%28ekim_2009%29_2_-_panoramio.jpg\n",
            "Resolving upload.wikimedia.org (upload.wikimedia.org)... 91.198.174.208, 2620:0:862:ed1a::2:b\n",
            "Connecting to upload.wikimedia.org (upload.wikimedia.org)|91.198.174.208|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 15472 (15K) [image/jpeg]\n",
            "Saving to: ‘256px-Kordon_(ekim_2009)_2_-_panoramio.jpg’\n",
            "\n",
            "\r          256px-Kor   0%[                    ]       0  --.-KB/s               \r256px-Kordon_(ekim_ 100%[===================>]  15.11K  --.-KB/s    in 0s      \n",
            "\n",
            "2019-09-14 16:20:37 (88.7 MB/s) - ‘256px-Kordon_(ekim_2009)_2_-_panoramio.jpg’ saved [15472/15472]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dUlj32iht8nP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "arka_plan = cv2.imread('256px-Kordon_(ekim_2009)_2_-_panoramio.jpg')\n",
        "\n",
        "arka_plan = cv2.resize(arka_plan, (320,180))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "e5d74fa3-d152-47ca-930d-5dee9130e115",
        "id": "Y8HVgh31HkIW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        }
      },
      "source": [
        "from copy import deepcopy\n",
        "\n",
        "video_tensor = video_tensor.cpu()\n",
        "\n",
        "video_yaratici = cv2.VideoWriter('cikti.avi',cv2.VideoWriter_fourcc('M','J','P','G'), 30, tuple(video_list[0].shape[1:3][::-1]))\n",
        "\n",
        "video_list_orig_cpy = deepcopy(video_list_orig)\n",
        "\n",
        "for i, out in enumerate(output_predictions):\n",
        "  \n",
        "  video_list_orig_cpy[i] *= out.numpy()[...,None] \n",
        "\n",
        "  arka_plan_cpy = arka_plan * (out.numpy() != 1)[...,None] \n",
        "\n",
        "  #video_list_orig_cpy[i] += arka_plan_cpy\n",
        "\n",
        "  #video_list_orig_cpy[i] = cv2.GaussianBlur(video_list_orig_cpy[i],(3,3),0)\n",
        "  \n",
        "  video_yaratici.write(video_list_orig_cpy[i])\n",
        "  \n",
        "video_okuyucu.release()\n",
        "video_yaratici.release()\n",
        "\n",
        "from google.colab.patches import cv2_imshow\n",
        "\n",
        "cv2_imshow((video_list_orig_cpy[45]))\n",
        "\n",
        "\n"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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nTRdGLDO0DACpwsRsmgW87lN/+xRe8v8UlRe64jGsfmSdCLGqUQQkYbbOpd00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\nq83U+AAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=320x180 at 0x7F00C99DF6A0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-RWnstIAdlEl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from google.colab import files\n",
        "files.download('cikti.avi') "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GTh52jVPWkuP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}