Pipeline.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K2mFNlSgNi79",
        "outputId": "f3426ced-53c4-4d30-e8f1-2cc7afd6dc61"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.0/7.0 MB\u001b[0m \u001b[31m41.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m29.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m224.5/224.5 kB\u001b[0m \u001b[31m12.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install transformers --quiet"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "secgN_A1QNXk"
      },
      "outputs": [],
      "source": [
        "!git clone https://github.com/avisinghal6/VIDEO_CAPTIONING_PIPELINE.git --quiet"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5VNtPm77QaAA",
        "outputId": "6c0a971c-ed85-4ce8-8320-ba7c38aeeca0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/VIDEO_CAPTIONING_PIPELINE\n"
          ]
        }
      ],
      "source": [
        "\n",
        "\n",
        "%cd VIDEO_CAPTIONING_PIPELINE"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HX0OJw_QUq4b",
        "outputId": "f97a9e56-5a11-4a24-ca2e-34106c1b401a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/2.9 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m2.9/2.9 MB\u001b[0m \u001b[31m116.9 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.9/2.9 MB\u001b[0m \u001b[31m72.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m193.6/193.6 kB\u001b[0m \u001b[31m21.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m357.2/357.2 kB\u001b[0m \u001b[31m37.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.1/2.1 MB\u001b[0m \u001b[31m41.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m129.7/129.7 kB\u001b[0m \u001b[31m15.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install --upgrade yt-dlp --quiet"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_0ChC_LTwanR",
        "outputId": "d7a7da72-8ad6-4718-8662-0d4de1f9eb34"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/VIDEO_CAPTIONING_PIPELINE\n"
          ]
        }
      ],
      "source": [
        "!pwd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aoOEJsCCuBzJ"
      },
      "outputs": [],
      "source": [
        "!pip3 install --upgrade GoogleBard --quiet"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cg9FiDa-5bbu",
        "outputId": "cf17f734-05e3-4224-cfc5-f86fa3c5339a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m81.4/81.4 kB\u001b[0m \u001b[31m7.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m212.2/212.2 kB\u001b[0m \u001b[31m22.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m132.9/132.9 kB\u001b[0m \u001b[31m15.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m110.5/110.5 kB\u001b[0m \u001b[31m13.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m468.7/468.7 kB\u001b[0m \u001b[31m37.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.0/1.0 MB\u001b[0m \u001b[31m71.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m114.2/114.2 kB\u001b[0m \u001b[31m13.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m158.8/158.8 kB\u001b[0m \u001b[31m17.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m269.4/269.4 kB\u001b[0m \u001b[31m26.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install evaluate --quiet"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "smSK3pwD6INM",
        "outputId": "faad15c2-bbb3-4127-cd12-6137622282e8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/61.1 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m61.1/61.1 kB\u001b[0m \u001b[31m7.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install bert_score --quiet"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dsSeS0bNq8Zk"
      },
      "outputs": [],
      "source": [
        "\n",
        "!pip install -q youtube-transcript-api --quiet"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 254,
          "referenced_widgets": [
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            "2114a5c7206a45948145d207a9a5ad95"
          ]
        },
        "id": "cUrktl6lcddj",
        "outputId": "cab26e24-53e8-47e9-90a6-f78071d89003"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)rocessor_config.json:   0%|          | 0.00/275 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "34ac8550db814f148ca4b4fbc380d011"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Could not find image processor class in the image processor config or the model config. Loading based on pattern matching with the model's feature extractor configuration.\n",
            "The `max_size` parameter is deprecated and will be removed in v4.26. Please specify in `size['longest_edge'] instead`.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/4.13k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f6b9030e8d2e4b35b2dad5b5f76e6ecb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/26.0M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6c4ca7f7ead14b24b9c82fa60cfc1c27"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/7.95k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f0892f1a4e91437fb961325e8fef1b84"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torchvision.models as models\n",
        "import torchvision.transforms as transforms\n",
        "from torch.autograd import Variable\n",
        "from PIL import Image\n",
        "from frames import *\n",
        "from model import *\n",
        "from captions import *\n",
        "from yolo import *\n",
        "from bard import *\n",
        "from meteor_metric import *\n",
        "from transcipts import *\n",
        "import cv2\n",
        "import os"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bWE76d-d4ykH"
      },
      "outputs": [],
      "source": [
        "#CODE FOR EXP3\n",
        "total_vids=set()\n",
        "with open('valid_transcript_id.txt') as f:\n",
        "  lines = f.readlines()\n",
        "  for line in lines:\n",
        "    total_vids.add(line[:-1])\n",
        "\n",
        "val_vids=set()\n",
        "with open(f'{os.getcwd()}/Results/val/val_3.txt') as f:\n",
        "  lines = f.readlines()\n",
        "  for line in lines:\n",
        "    val_vids.add(line[:-1])\n",
        "\n",
        "\n",
        "val_vids=val_vids.intersection(total_vids)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CPioKYeH8HGg"
      },
      "outputs": [],
      "source": [
        "import json\n",
        "with open(f'{os.getcwd()}/Results/val/val_frames_3.json') as json_file:\n",
        "    all_frames = json.load(json_file)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "final_frames={}\n",
        "with open('trial.txt','w') as f:\n",
        "  f.write('vXlmXrKC0FE'+'\\n')\n",
        "  final_frames['vXlmXrKC0FE']=all_frames['vXlmXrKC0FE']\n"
      ],
      "metadata": {
        "id": "t2nyy2k0g-oO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "r_Hjw6d26E1T"
      },
      "outputs": [],
      "source": [
        "final_frames={}\n",
        "with open('trial.txt','w') as f:\n",
        "  for k in val_vids:\n",
        "    f.write(k+'\\n')\n",
        "    try:\n",
        "      final_frames[k]=all_frames[k]\n",
        "    except:\n",
        "      print(k)\n",
        "    break\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QQrPxPoqU4gR",
        "outputId": "430022ac-2194-4497-b0d1-d304ceb9536b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[generic] Extracting URL: www.youtube.com/watch?v=vXlmXrKC0FE\n",
            "\u001b[0;33mWARNING:\u001b[0m [generic] The url doesn't specify the protocol, trying with http\n",
            "[youtube] Extracting URL: http://www.youtube.com/watch?v=vXlmXrKC0FE\n",
            "[youtube] vXlmXrKC0FE: Downloading webpage\n",
            "[youtube] vXlmXrKC0FE: Downloading android player API JSON\n",
            "[info] vXlmXrKC0FE: Downloading 1 format(s): 248+251\n",
            "[dashsegments] Total fragments: 6\n",
            "[download] Destination: /content/VIDEO_CAPTIONING_PIPELINE/vXlmXrKC0FE.f248.webm\n",
            "\u001b[K[download] 100% of   50.11MiB in \u001b[1;37m00:00:02\u001b[0m at \u001b[0;32m22.65MiB/s\u001b[0m\n",
            "[dashsegments] Total fragments: 1\n",
            "[download] Destination: /content/VIDEO_CAPTIONING_PIPELINE/vXlmXrKC0FE.f251.webm\n",
            "\u001b[K[download] 100% of    3.79MiB in \u001b[1;37m00:00:00\u001b[0m at \u001b[0;32m12.79MiB/s\u001b[0m\n",
            "[Merger] Merging formats into \"/content/VIDEO_CAPTIONING_PIPELINE/vXlmXrKC0FE.webm\"\n",
            "Deleting original file /content/VIDEO_CAPTIONING_PIPELINE/vXlmXrKC0FE.f251.webm (pass -k to keep)\n",
            "Deleting original file /content/VIDEO_CAPTIONING_PIPELINE/vXlmXrKC0FE.f248.webm (pass -k to keep)\n",
            "[INFO] Downloaded video vXlmXrKC0FE\n",
            "find: ‘../raw_videos’: No such file or directory\n"
          ]
        }
      ],
      "source": [
        "!python download_youcookii_videos.py "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RAduu9yIJgnI"
      },
      "outputs": [],
      "source": [
        "#GENERATING FRAMES\n",
        "videos_list={}\n",
        "videos_time_stamps=get_saving_frames_durations(5,f\"{os.getcwd()}/Results/val/val_3.json\")\n",
        "for time_stamp in videos_time_stamps:\n",
        "  for key,value in time_stamp.items():\n",
        "    generate_frames(f'{key}.webm',value,videos_list)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aYpyyQfbFXvy"
      },
      "outputs": [],
      "source": [
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XLdWtbQsLjRz",
        "outputId": "4d69c30c-6ce4-4b15-833a-612a6c7c43e5"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.\n",
            "  warnings.warn(msg)\n",
            "100%|██████████| 1221/1221 [00:23<00:00, 52.20it/s]\n",
            "100%|██████████| 2697/2697 [00:38<00:00, 70.61it/s]\n",
            "100%|██████████| 1766/1766 [02:58<00:00,  9.88it/s]\n",
            "100%|██████████| 3253/3253 [01:04<00:00, 50.08it/s]\n",
            "100%|██████████| 3942/3942 [06:27<00:00, 10.17it/s]\n",
            "100%|██████████| 1061/1061 [00:49<00:00, 21.32it/s]\n",
            "100%|██████████| 648/648 [01:04<00:00, 10.03it/s]\n",
            "100%|██████████| 2029/2029 [01:32<00:00, 21.88it/s]\n",
            "100%|██████████| 2656/2656 [02:06<00:00, 21.03it/s]\n"
          ]
        }
      ],
      "source": [
        "#GENERATING FEATURES\n",
        "img2vec = Img2VecResnet18(device) \n",
        "videos_features_vectors={}\n",
        "for time_stamp in videos_time_stamps:\n",
        "  allVectors={}\n",
        "  for key,value in time_stamp.items():\n",
        "    for image in tqdm(os.listdir(f'{key}-opencv')):\n",
        "      I = Image.open(os.path.join(f'{key}-opencv', image))\n",
        "      vec = img2vec.getVec(I)\n",
        "      allVectors[image] = vec\n",
        "      I.close() \n",
        "    videos_features_vectors[key]=allVectors"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ANUTv_W7I26K"
      },
      "outputs": [],
      "source": [
        "#COMPUTING SIMILARITY BETWEEN FRAMES\n",
        "from collections import defaultdict\n",
        "result=get_similar_frames(videos_features_vectors,videos_list)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cqhhOL4DJB1B"
      },
      "outputs": [],
      "source": [
        "#GETTING FINAL FRAMES FOR EACH VIDEO\n",
        "final_frames=get_final_frames(result)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-3GsR1Ucg9Hs"
      },
      "outputs": [],
      "source": [
        "# load json module\n",
        "import json\n",
        "\n",
        "# create json object from dictionary\n",
        "json = json.dumps(final_frames)\n",
        "\n",
        "# open file for writing, \"w\" \n",
        "f = open(\"val_frames_1.json\",\"w\")\n",
        "\n",
        "# write json object to file\n",
        "f.write(json)\n",
        "\n",
        "# close file\n",
        "f.close()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NpZyuZtB9gGL"
      },
      "outputs": [],
      "source": [
        "import json\n",
        "with open('val_frames_1.json') as json_file:\n",
        "    final_frames = json.load(json_file)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "96exk7HrC9M7"
      },
      "outputs": [],
      "source": [
        "captions=augmented_captions(final_frames)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ylqji1yr-U9J"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XtipJdiIJf2o",
        "outputId": "2b5435f6-31b3-46b8-fc12-09834f8b0d65"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'vXlmXrKC0FE': \"[Music].hello everyone please welcome and join.second step will be the sauce.half cup all-purpose flour quarter cup.second step will be the sauce one.tablespoon.olive oil one tablespoon sesame oil.one teaspoon each black pepper veggie.[Music].hello everyone please welcome and join.two pound thai chicken it's cut into.small pieces how do i make a delicious.olive oil one tablespoon sesame oil.one teaspoon each black pepper veggie.second step will be the sauce one.tablespoon.\"}"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "captions"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "final_frames"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eI8qXSh0VKO-",
        "outputId": "a6abb7ca-23cf-483a-98f6-50088157afe8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'vXlmXrKC0FE': {'1': 'frame0-00-08.77.jpg',\n",
              "  '2': 'frame0-00-09.77.jpg',\n",
              "  '3': 'frame0-01-08.60.jpg',\n",
              "  '4': 'frame0-01-35.13.jpg',\n",
              "  '5': 'frame0-01-55.37.jpg',\n",
              "  '6': 'frame0-02-44.93.jpg',\n",
              "  '7': 'frame0-03-00.67.jpg',\n",
              "  '8': 'frame0-03-21.00.jpg',\n",
              "  '9': 'frame0-03-44.53.jpg',\n",
              "  '10': 'frame0-03-46.53.jpg',\n",
              "  '11': 'frame0-03-54.53.jpg'}}"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# image=cv2.imread('UHhuaRTF1UY'+\"-opencv/\"+ 'frame0-05-44.33.jpg')\n",
        "# image=Image.open('UHhuaRTF1UY'+\"-opencv/\"+ 'frame0-05-44.33.jpg')\n",
        "# imshow(np.asarray(image))\n",
        "# image.show()"
      ],
      "metadata": {
        "id": "Br3mp9R6c4Ao"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HOTpFwXlJB3e"
      },
      "outputs": [],
      "source": [
        "#USE FINAL FRAMES FOR CAPTIONING"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3zyOe8x6P9ht"
      },
      "outputs": [],
      "source": [
        "#USING YOLO FOR OBJECT DETECTION AND ADDING THE BOUNDING BOXES ALONG WITH LABELS\n",
        "for k,v in final_frames.items():\n",
        "  for k1,v1 in v.items():\n",
        "    yolo_box(k,v1)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297,
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            "da15f12ae1104c4891c091f3283a15ac",
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            "c291d588d6774a5e9a086ba38a5f6102"
          ]
        },
        "id": "3GNt8vnETHpo",
        "outputId": "0fd11966-a664-495f-c61c-57ca0c194517"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)rocessor_config.json:   0%|          | 0.00/287 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d71c32f64d794396b0476706f5d17af5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/438 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "08675a7445b14bedb5f80de4a566b1b5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ab667f5d30e14af1b3b3a405975c1e5f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/711k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f8211c14e8be44fe814153c9f98dd213"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/125 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "219697fecebb477f83955ccb48033788"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/4.56k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d97c5d5d358f4471bf9b64fd85ef14ad"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/990M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a8e59cad82e1409684a8217418db10a2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/transformers/generation/utils.py:1313: UserWarning: Using `max_length`'s default (20) to control the generation length. This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we recommend using `max_new_tokens` to control the maximum length of the generation.\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "#GET CAPTIONS\n",
        "config={\n",
        "    \"processor\": \"Salesforce/blip-image-captioning-base\",\n",
        "\n",
        "    \"model\":\"Salesforce/blip-image-captioning-base\"\n",
        "}\n",
        "processor, model = get_model(config,device)\n",
        "captions = get_captions(final_frames, processor, model,device)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from bard import *"
      ],
      "metadata": {
        "id": "S9AmTZdxZOBP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#with yolo\n",
        "captions"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LIsRhmKfW_Pi",
        "outputId": "aaf5298f-2635-43ac-bb12-0b7716f9d879"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'vXlmXrKC0FE': 'general general general general general general general general general general general general general general general general general general general. a black background with a white and red flower. a table with several different sauces and sauces. a person is putting spices into a bowl. a pan with a liquid inside of it. a blue and white plate with food on it. a pot filled with soup and a spoon. chicken curry recipe in a pan. chicken curry recipe in a pan. a white plate topped with a dish of food. a white plate topped with chicken and vegetables. '}"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#without yolo\n",
        "captions"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gxbk0CEwWn4Y",
        "outputId": "e101e3df-ba1b-4be8-8512-5cae56a2614f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'vXlmXrKC0FE': 'general general general general general general general general general general general general general general general general general general general. a black background with a white and red flower. a table with several different sauces and sauces. a person is putting spices into a bowl. a pan with a liquid inside of it. a blue and white plate with food in it. a pot filled with soup and a spoon. a pan filled with chicken and sauce. chicken curry recipe in a pan. a white plate topped with a dish of food. a white plate topped with chicken and vegetables. '}"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gEynwK_rPtAV"
      },
      "outputs": [],
      "source": [
        "#GENERATING CAPTIONS FROM LLM\n",
        "bard_captions = get_captions_from_bard(captions)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#WITHOUT YOLO\n",
        "bard_captions"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6RHNDBjNc-Ee",
        "outputId": "dc971207-b641-43b2-830f-58e7622617bd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'vXlmXrKC0FE': 'sure, here is the recipe for chicken curry, rephrased to only include instructions:\\n\\n**ingredients:**\\n\\n* 1 tablespoon olive oil\\n* 1 onion, chopped\\n* 2 cloves garlic, minced\\n* 1 inch ginger, grated\\n* 1 tablespoon curry powder\\n* 1 teaspoon ground cumin\\n* 1/2 teaspoon turmeric\\n* 1/2 teaspoon cayenne pepper\\n* 1/2 teaspoon salt\\n* 1/4 teaspoon black pepper\\n* 1 pound boneless, skinless chicken breasts, cut into 1-inch pieces\\n* 1 (14.5 ounce) can diced tomatoes, undrained\\n* 1 (15 ounce) can coconut milk\\n* 1/2 cup chicken broth\\n* 1/4 cup chopped fresh cilantro\\n\\n**instructions:**\\n\\n1. heat the olive oil in a large skillet over medium heat.\\n2. add the onion and cook until softened, about 5 minutes.\\n3. add the garlic and ginger and cook for 1 minute more.\\n4. stir in the curry powder, cumin, turmeric, cayenne pepper, salt, and black pepper. cook for 1 minute more.\\n5. add the chicken and cook until browned on all sides.\\n6. stir in the diced tomatoes, coconut milk, and chicken broth. bring to a boil, then reduce heat to low and simmer for 20 minutes, or until the chicken is cooked through.\\n7. stir in the cilantro and serve over rice.\\n\\nenjoy!'}"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#WITH YOLO\n",
        "bard_captions"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TpWFH8F7feOP",
        "outputId": "2b014384-c463-4164-e861-c5c6e3831354"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'vXlmXrKC0FE': 'i cannot rephrase the text you provided, because it is not a recipe. it is a list of numbers and strings.\\r\\n\\r\\nhere is an example of a recipe:\\r\\n\\r\\n**ingredients:**\\r\\n\\r\\n* 1 cup all-purpose flour\\r\\n* 1 teaspoon baking powder\\r\\n* 1/2 teaspoon baking soda\\r\\n* 1/2 teaspoon salt\\r\\n* 1 cup sugar\\r\\n* 1/2 cup unsalted butter, softened\\r\\n* 1 large egg\\r\\n* 1 teaspoon vanilla extract\\r\\n* 1 cup milk\\r\\n\\r\\n**instructions:**\\r\\n\\r\\n1. preheat oven to 350 degrees f (175 degrees c). grease and flour a 9x13 inch baking pan.\\r\\n2. in a medium bowl, whisk together the flour, baking powder, baking soda, and salt.\\r\\n3. in a large bowl, cream together the sugar and butter until light and fluffy. beat in the egg and vanilla extract.\\r\\n4. gradually add the dry ingredients to the wet ingredients, alternating with the milk. beat until just combined.\\r\\n5. pour batter into prepared pan and bake for 30-35 minutes, or until a toothpick inserted into the center comes out clean.\\r\\n6. let cool completely before frosting.'}"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bkFyanr6w-F3"
      },
      "outputs": [],
      "source": [
        "import json\n",
        "f = open('yc2_val.json')\n",
        "data = json.load(f)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "z8ylEQA-CC1-"
      },
      "outputs": [],
      "source": [
        "#EVALUATING THE RECIPE GENERATED\n",
        "output = get_meteor_score(data, captions)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xHaHu0_URNEW"
      },
      "outputs": [],
      "source": [
        "#EVALUATING THE RECIPE GENERATED\n",
        "output = get_meteor_score(data, bard_captions)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "owmZc6KTKG8x",
        "outputId": "1aaf4afe-8e45-4f65-d930-b0fcd07624a7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'WlHWRPyA7_g': 0.006273525721455458}"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ],
      "source": [
        "output"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QAl3UfMgCGR_"
      },
      "outputs": [],
      "source": [
        "#EVALUATING THE RECIPE GENERATED\n",
        "output = get_bert_score(data, captions)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "caz7-lpQ6oZ4",
        "outputId": "750929c9-2f28-4533-d283-c6eac4b3deb7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "sdkskdjsdsdksj jfksjfksjfksjf skfjksjfkj \n"
          ]
        }
      ],
      "source": [
        "#EVALUATING THE RECIPE GENERATED\n",
        "output = get_bert_score(data, bard_captions)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LA31p-Jl2Bcx",
        "outputId": "0028b11b-1c64-427f-b80a-f8633f2f204a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'WlHWRPyA7_g': {'precision': [0.715690016746521],\n",
              "  'recall': [0.7637552618980408],\n",
              "  'f1': [0.7389419078826904],\n",
              "  'hashcode': 'roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.28.1)'}}"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ],
      "source": [
        "output"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UtpgwBmD55p3"
      },
      "outputs": [],
      "source": [
        "import locale\n",
        "def getpreferredencoding(do_setlocale = True):\n",
        "    return \"UTF-8\"\n",
        "locale.getpreferredencoding = getpreferredencoding"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A-hImv9esmhj",
        "outputId": "b0c8020a-1d18-416d-f6bf-9f89886559c1"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Package wordnet is already up-to-date!\n"
          ]
        }
      ],
      "source": [
        "from nltk.translate import meteor\n",
        "from nltk import word_tokenize\n",
        "from nltk.translate.bleu_score import corpus_bleu\n",
        "import json\n",
        "import nltk\n",
        "from evaluate import load\n",
        "bertscore = load(\"bertscore\")\n",
        "nltk.download('punkt')\n",
        "nltk.download('wordnet')\n",
        "\n",
        "def get_meteor_score(data, bard_captions):\n",
        "  output = {}\n",
        "  for key in bard_captions:\n",
        "    input_captions = \"\"\n",
        "    k=f'v_{key[2:]}'\n",
        "    for i in data[k]['sentences']:\n",
        "      input_captions = input_captions + i+'. '\n",
        "    output[key] = meteor([word_tokenize(input_captions)],word_tokenize(bard_captions[key]))\n",
        "  return output\n",
        "\n",
        "def get_bleu_score(data, bard_captions):\n",
        "  output = {}\n",
        "  for key in bard_captions:\n",
        "    input_captions = \"\"\n",
        "    k=f'v_{key[2:]}'\n",
        "    for i in data[k]['sentences']:\n",
        "      input_captions = input_captions + i+'. '\n",
        "    output[key] = corpus_bleu([word_tokenize(input_captions)],[word_tokenize(bard_captions[key])])\n",
        "    print([word_tokenize(input_captions)])\n",
        "  return output\n",
        "\n",
        "def get_bert_score(data, bard_captions):\n",
        "  output = {}\n",
        "  for key in bard_captions:\n",
        "    input_captions = \"\"\n",
        "    k=f'v_{key[2:]}'\n",
        "    for i in data[k]['sentences']:\n",
        "      input_captions = input_captions + i+'. '\n",
        "    \n",
        "    output[key] =bertscore.compute(predictions=[bard_captions[key]], references=[input_captions], lang=\"en\")\n",
        "    # output[key] = meteor([word_tokenize(input_captions)],word_tokenize(bard_captions[key]))\n",
        "  return output"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kZ3RI1pA3_sL",
        "outputId": "beb0a9c4-434d-4618-c7a9-facf7a62aa4f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[['cut', 'chicken', 'breasts', 'pieces', 'and', 'add', 'marinate', 'with', 'soy', 'sauce', 'cooking', 'sherry', 'and', 'cornstarch', '.', 'add', 'soy', 'sauce', 'cooking', 'sherry', 'white', 'vinegar', 'water', 'and', 'cornstarch', 'to', 'a', 'bowl', 'and', 'mix', 'the', 'ingredients', '.', 'stir', 'fry', 'the', 'chicken', '.', 'remove', 'the', 'chicken', 'from', 'the', 'pan', '.', 'stir', 'fry', 'red', 'bell', 'pepper', 'garlic', 'garlic', 'red', 'chilis', 'and', 'peanuts', '.', 'add', 'the', 'sauce', 'to', 'the', 'pan', '.', 'add', 'the', 'chicken', 'to', 'the', 'pan', 'and', 'stir', '.']]\n",
            "[['crush', 'and', 'chop', 'the', 'garlic', '.', 'add', 'oil', 'garlic', 'and', 'salt', 'to', 'a', 'bowl', '.', 'mix', 'the', 'tomoatoes', 'with', 'the', 'oil', 'mixture', '.', 'chop', 'the', 'basil', '.', 'spread', 'the', 'oil', 'mixture', 'onto', 'the', 'dough', '.', 'place', 'provolone', 'cheese', 'and', 'mozzarella', 'cheese', 'onto', 'the', 'dough', '.', 'add', 'the', 'basil', 'to', 'the', 'pizza', '.', 'place', 'the', 'tomatoes', 'on', 'the', 'pizza', '.', 'sprinkle', 'cheese', 'onto', 'the', 'pizza', '.', 'bake', 'the', 'pizza', 'in', 'an', 'oven', '.']]\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/nltk/translate/bleu_score.py:552: UserWarning: \n",
            "The hypothesis contains 0 counts of 2-gram overlaps.\n",
            "Therefore the BLEU score evaluates to 0, independently of\n",
            "how many N-gram overlaps of lower order it contains.\n",
            "Consider using lower n-gram order or use SmoothingFunction()\n",
            "  warnings.warn(_msg)\n",
            "/usr/local/lib/python3.9/dist-packages/nltk/translate/bleu_score.py:552: UserWarning: \n",
            "The hypothesis contains 0 counts of 3-gram overlaps.\n",
            "Therefore the BLEU score evaluates to 0, independently of\n",
            "how many N-gram overlaps of lower order it contains.\n",
            "Consider using lower n-gram order or use SmoothingFunction()\n",
            "  warnings.warn(_msg)\n",
            "/usr/local/lib/python3.9/dist-packages/nltk/translate/bleu_score.py:552: UserWarning: \n",
            "The hypothesis contains 0 counts of 4-gram overlaps.\n",
            "Therefore the BLEU score evaluates to 0, independently of\n",
            "how many N-gram overlaps of lower order it contains.\n",
            "Consider using lower n-gram order or use SmoothingFunction()\n",
            "  warnings.warn(_msg)\n"
          ]
        }
      ],
      "source": [
        "#EVALUATING THE RECIPE GENERATED\n",
        "output = get_bleu_score(data, bard_captions)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vlHudQ6xJ1xG"
      },
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      "source": []
    }
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