Llama2_llamaindex.ipynb

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            "Collecting pypdf\n",
            "  Downloading pypdf-4.1.0-py3-none-any.whl (286 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m286.1/286.1 kB\u001b[0m \u001b[31m5.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: pypdf\n",
            "Successfully installed pypdf-4.1.0\n"
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m290.1/290.1 kB\u001b[0m \u001b[31m8.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m810.5/810.5 kB\u001b[0m \u001b[31m14.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m102.2/102.2 MB\u001b[0m \u001b[31m8.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m269.1/269.1 kB\u001b[0m \u001b[31m26.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install sentence_transformers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zaWqXXTfukmJ",
        "outputId": "ddd24161-0055-4e29-d2ea-ae3f712888c0"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting sentence_transformers\n",
            "  Downloading sentence_transformers-2.6.0-py3-none-any.whl (163 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m163.1/163.1 kB\u001b[0m \u001b[31m4.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: transformers<5.0.0,>=4.32.0 in /usr/local/lib/python3.10/dist-packages (from sentence_transformers) (4.38.2)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from sentence_transformers) (4.66.2)\n",
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            "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.11.0->sentence_transformers) (12.1.105)\n",
            "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.11.0->sentence_transformers) (12.1.105)\n",
            "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.11.0->sentence_transformers) (12.1.105)\n",
            "Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /usr/local/lib/python3.10/dist-packages (from torch>=1.11.0->sentence_transformers) (8.9.2.26)\n",
            "Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /usr/local/lib/python3.10/dist-packages (from torch>=1.11.0->sentence_transformers) (12.1.3.1)\n",
            "Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /usr/local/lib/python3.10/dist-packages (from torch>=1.11.0->sentence_transformers) (11.0.2.54)\n",
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            "Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /usr/local/lib/python3.10/dist-packages (from torch>=1.11.0->sentence_transformers) (2.19.3)\n",
            "Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.11.0->sentence_transformers) (12.1.105)\n",
            "Requirement already satisfied: triton==2.2.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.11.0->sentence_transformers) (2.2.0)\n",
            "Requirement already satisfied: nvidia-nvjitlink-cu12 in /usr/local/lib/python3.10/dist-packages (from nvidia-cusolver-cu12==11.4.5.107->torch>=1.11.0->sentence_transformers) (12.4.99)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers<5.0.0,>=4.32.0->sentence_transformers) (2023.12.25)\n",
            "Requirement already satisfied: tokenizers<0.19,>=0.14 in /usr/local/lib/python3.10/dist-packages (from transformers<5.0.0,>=4.32.0->sentence_transformers) (0.15.2)\n",
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            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.15.1->sentence_transformers) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.15.1->sentence_transformers) (2024.2.2)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.11.0->sentence_transformers) (1.3.0)\n",
            "Installing collected packages: sentence_transformers\n",
            "Successfully installed sentence_transformers-2.6.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install llama_index"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8A5G4GzhvUvO",
        "outputId": "e26e8bf2-0f70-4070-9a05-a2b39c92111e"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting llama_index\n",
            "  Downloading llama_index-0.10.23-py3-none-any.whl (6.9 kB)\n",
            "Collecting llama-index-agent-openai<0.2.0,>=0.1.4 (from llama_index)\n",
            "  Downloading llama_index_agent_openai-0.1.7-py3-none-any.whl (12 kB)\n",
            "Collecting llama-index-cli<0.2.0,>=0.1.2 (from llama_index)\n",
            "  Downloading llama_index_cli-0.1.11-py3-none-any.whl (26 kB)\n",
            "Collecting llama-index-core<0.11.0,>=0.10.23 (from llama_index)\n",
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            "\u001b[?25hCollecting llama-index-embeddings-openai<0.2.0,>=0.1.5 (from llama_index)\n",
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            "\u001b[?25hCollecting llama-index-llms-openai<0.2.0,>=0.1.5 (from llama_index)\n",
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            "Collecting llama-index-program-openai<0.2.0,>=0.1.3 (from llama_index)\n",
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            "Collecting llama-index-question-gen-openai<0.2.0,>=0.1.2 (from llama_index)\n",
            "  Downloading llama_index_question_gen_openai-0.1.3-py3-none-any.whl (2.9 kB)\n",
            "Collecting llama-index-readers-file<0.2.0,>=0.1.4 (from llama_index)\n",
            "  Downloading llama_index_readers_file-0.1.12-py3-none-any.whl (36 kB)\n",
            "Collecting llama-index-readers-llama-parse<0.2.0,>=0.1.2 (from llama_index)\n",
            "  Downloading llama_index_readers_llama_parse-0.1.4-py3-none-any.whl (2.5 kB)\n",
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            "  Downloading Deprecated-1.2.14-py2.py3-none-any.whl (9.6 kB)\n",
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            "Installing collected packages: striprtf, dirtyjson, PyMuPDFb, h11, deprecated, tiktoken, pymupdf, httpcore, bs4, httpx, openai, llamaindex-py-client, llama-index-legacy, llama-index-core, llama-parse, llama-index-readers-file, llama-index-llms-openai, llama-index-indices-managed-llama-cloud, llama-index-embeddings-openai, llama-index-readers-llama-parse, llama-index-multi-modal-llms-openai, llama-index-cli, llama-index-agent-openai, llama-index-program-openai, llama-index-question-gen-openai, llama_index\n",
            "Successfully installed PyMuPDFb-1.24.0 bs4-0.0.2 deprecated-1.2.14 dirtyjson-1.0.8 h11-0.14.0 httpcore-1.0.4 httpx-0.27.0 llama-index-agent-openai-0.1.7 llama-index-cli-0.1.11 llama-index-core-0.10.23.post1 llama-index-embeddings-openai-0.1.7 llama-index-indices-managed-llama-cloud-0.1.5 llama-index-legacy-0.9.48 llama-index-llms-openai-0.1.12 llama-index-multi-modal-llms-openai-0.1.4 llama-index-program-openai-0.1.4 llama-index-question-gen-openai-0.1.3 llama-index-readers-file-0.1.12 llama-index-readers-llama-parse-0.1.4 llama-parse-0.4.0 llama_index-0.10.23 llamaindex-py-client-0.1.13 openai-1.14.2 pymupdf-1.24.0 striprtf-0.0.26 tiktoken-0.6.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from llama_index.core import VectorStoreIndex,SimpleDirectoryReader,ServiceContext"
      ],
      "metadata": {
        "id": "kSHsanyjvljx"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pip install llama-index-llms-huggingface"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qfRzAAmayEkv",
        "outputId": "28c995db-75da-4d6d-bffe-c780fad1837a"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting llama-index-llms-huggingface\n",
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            "Requirement already satisfied: annotated-types>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from pydantic>=1.10->llamaindex-py-client<0.2.0,>=0.1.13->llama-index-core<0.11.0,>=0.10.1->llama-index-llms-huggingface) (0.6.0)\n",
            "Requirement already satisfied: pydantic-core==2.16.3 in /usr/local/lib/python3.10/dist-packages (from pydantic>=1.10->llamaindex-py-client<0.2.0,>=0.1.13->llama-index-core<0.11.0,>=0.10.1->llama-index-llms-huggingface) (2.16.3)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->llama-index-core<0.11.0,>=0.10.1->llama-index-llms-huggingface) (1.16.0)\n",
            "Installing collected packages: llama-index-llms-huggingface\n",
            "Successfully installed llama-index-llms-huggingface-0.1.4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from llama_index.llms.huggingface import HuggingFaceLLM"
      ],
      "metadata": {
        "id": "ChrbTABkwNvy"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from llama_index.core.prompts.prompts import SimpleInputPrompt"
      ],
      "metadata": {
        "id": "JAQF-0SkwnjM"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "afQhmVHb01p9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n"
      ],
      "metadata": {
        "id": "rr8ItIrG22Qa"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "documents = SimpleDirectoryReader(\"/content/data\").load_data()"
      ],
      "metadata": {
        "id": "7uwVlHkHy6At"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "documents\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sELbesRF1C8m",
        "outputId": "2ac2c553-453f-4acb-fa99-f56334c054c8"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Document(id_='1ad94abf-9eef-457a-b702-c3111d1fcb93', embedding=None, metadata={'page_label': '1', 'file_name': 'dimage (1).pdf', 'file_path': '/content/data/dimage (1).pdf', 'file_type': 'application/pdf', 'file_size': 5513104, 'creation_date': '2024-03-25', 'last_modified_date': '2024-03-25'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={}, text=' \\n \\n 9 Unit 1                         Introduction to Digital Image  \\nUNIT1 \\nINTRODUCTION TO DIGITAL \\nIMAGE  \\nStructure        Page No. \\n \\n1.1 Introduction          9 \\nObjectives \\n1.2 Introduction to an image?      10 \\n1.3 Image Acquisition       12 \\n1.4 Digitization of Images       15 \\n1.5 Representation of Digital Image     20 \\n1.6 Types of Image       21 \\n1.7 Image Characteristics       24 \\n1.8 Image Resolution        26 \\n1.9 Relations between the Pixels      31 \\n1.10 Areas of Applications of Digital Images    34 \\n1.11 Summary        36 \\n1.12 Solutions/Answers       36 \\n \\n1.1 INTRODUCTION \\n \\nThere is a famous saying that a picture is equivalent to thousands of \\nwords, and this turns out to be true when we come to the field of image \\nprocessing and pattern recognition. It is the interpretation of image \\nwhich deteremines various types of outcomes viz. for an arts person it \\nmay cary a different meaning but for a science person the meaning of \\nthat image may be entirely different. Based on the utility of the images, \\ndifferent formats are deviced to store the different type of images, which \\nare stored in different file formats and they contain a variety of \\ninformation in the form of colour, contrast, intensity, brightness etc., but \\nresolution of these images holds a great importance in the field of image \\nprocessing, because it affects the quality of image when it is scaled up. \\nThis unit relates to the understanding of essentials of image processing \\nand tofind major applications of image processing in our everyday life.  \\nBroadly speaking, image processing is an area that deals with \\nmanipulation of visual information called images, and one of the major \\nobjectives of image processing is to improve the quality of pictorial \\ninformation for better human interpretation and to facilitate the \\nautomatic machine interpretation of images.  \\n', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'),\n",
              " Document(id_='090ae7e5-fb28-45f6-821d-7db5c3d6bb69', embedding=None, metadata={'page_label': '2', 'file_name': 'dimage (1).pdf', 'file_path': '/content/data/dimage (1).pdf', 'file_type': 'application/pdf', 'file_size': 5513104, 'creation_date': '2024-03-25', 'last_modified_date': '2024-03-25'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={}, text=' \\n 10Block 1              Digital Image Processing-I  \\nWe shall begin our discussion by defining an image in Sec. 1.2 and will \\ncontinue image acquisition and image digitization in Sec. 1.3 and Sec. \\n1.4 respectively. In the subsequent sections, we shall define the basic \\nterms and concepts, which will be used throughout the course such as \\nrepresentation of image, resolution of image, characteristics of image \\nand types of images. We shall end the unit by discussing various areas \\nin which digital image processing is used. \\n Now, we shall list the objectives of this unit. After going through the unit, \\nplease read this list again and make sure that you have achieved the \\nobjectives. \\n \\nObjectives \\n \\nAfter going through this unit, you should be able to \\n \\n\\uf0b7 define and represent an image; \\n\\uf0b7 define the various terms used in digital image processing; \\n\\uf0b7 apply sampling and quantization for image digitization; \\n\\uf0b7 list various classifications of images along with their descriptions; \\n\\uf0b7 relate the areas where digital image processing can be applied. \\n \\nNow, let us find the answer to the question “what an image is?” in the \\nfollowing section . \\n \\n1.2 INTRODUCTION TO AN IMAGE? \\n \\nWhen we studied optics as a subject in the field of physics, we learned \\nthat when two or more rays of light meet at a point an image is formed, \\nand we studied various phenomenons like reflection, refraction, \\ndiffraction, dispersion, scattering etc., this means we were in the \\nprocess of image generation and analysis, even much before the \\ndevelopment of computers. But, with the advent of the computers in our \\nday to day life this field of image processing has transformed in to an \\nentirely new field i.e. digital image processing, where sampling and \\nquantization techniques are applied to convert the image into its \\ndiscrete form, this process of conversion is collectively known as image \\ndigitization, after digitization the image is readily available in a form \\nsuitable for further processing by digital computers. We shall discuss \\nabout the concept of sampling and quantization in the subsequent \\nsections, here we will discuss about the concept of image only.  \\n \\nMany times the term ‘picture’ is used, but this term relates to the analog \\nor raw image data and the term ‘image’ is used to refer to digital data \\nthat is suitable for the processing of images using digital computers. \\nInfact Images are imitations of a real world objects.Image may be \\nconsidered as a projection of the real world ( to be more precise 2D \\nprojection of 3D world). From a photographers point of view it is a \\nphotograph (i.e. projection of real world), and from the point of view of a \\ncomputer engineer an image may be a two-dimensional (2D) signal. \\nTherefore, an image is a two-dimensional function )y,x(fwhere for \\n', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'),\n",
              " Document(id_='6df9c071-6b95-4256-8309-0891f1c52ffd', embedding=None, metadata={'page_label': '3', 'file_name': 'dimage (1).pdf', 'file_path': '/content/data/dimage (1).pdf', 'file_type': 'application/pdf', 'file_size': 5513104, 'creation_date': '2024-03-25', 'last_modified_date': '2024-03-25'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={}, text=' \\n \\n 11 Unit 1                         Introduction to Digital Image  \\neach position )y,x(in the projection plane, the values of the function \\n)y,x(frepresent the amplitude or light intensity of the image.  \\n \\nImages are of two types, analog and digital. Analog image can be \\nmathematically represented as a continuous range of values \\nrepresenting position and intensity. A digital image is composed of \\npicture elements called pixels.  \\n  \\nYou may note here that through out the course we shall use the word \\nimage which refers to digital image. \\n \\nAs we mentioned earlier an image is a projection of the environment \\nunder consideration, if the environment is 3D then its projection will be \\nin 2D. If we are having an n-Dimensional space then its projection will \\nbe of n-1 dimensions.  Magnetic resonance images and computerized \\ntomography (CT) images, which are 3D images, are mathematically \\nrepresented by 3D function ),z,y,x(f wherez,y,xare spatial coordinates \\nwhich may represent the amplitude or intensity or any other parameter \\nof the image. \\n \\nA digital image is defined as a 2D discrete signal that varies over the \\nspatial coordinates x and ,yand can be written mathematically as \\n).y,x(f It is also an nn\\uf0b4 array of elements, and each element \\nrepresents the sampled intensity. \\n \\nIn general, the image )y,x(fis divided into X rows and Ycolumns. The \\ncoordinate ranges are }1m,...,1,0X{ \\uf02d \\uf03d and }.1n,...,2,1,0Y{ \\uf02d \\uf03d  The \\nimage can be written as a mathematical function )y,x(fas given below:  \\n \\n \\n\\uf0fa\\uf0fa\\uf0fa\\uf0fa\\uf0fa\\uf0fa\\n\\uf0fb\\uf0f9\\n\\uf0ea\\uf0ea\\uf0ea\\uf0ea\\uf0ea\\uf0ea\\n\\uf0eb\\uf0e9\\n\\uf02d\\uf02d \\uf02d \\uf02d \\uf02d\\uf02d\\uf02d\\n\\uf03d\\n)1n,1m(f)2,1m(f)1,1m(f)0,1m(f...............................................................................................................................................................................)1y,1(f.........).........2,1(f)1,1(f)0,1(f)1y,0(f........).........2,0(f)1,0(f)0,0(f\\n)y,x(f  \\n \\nA sample digital image is shown in Fig. 1(a) and its equivalent matrix is \\nshown in Fig.1(b). \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n (a)   (b) \\n \\nFig. 1: (a)Sample Digital Image and (b) Matrix of the Image  \\n 0 0 0 0 0 0 \\n0 0 0 0 0 0 \\n0 1 1 1 1 0 \\n0 0 1 1 0 0 \\n0 0 1 1 0 0 \\n  1 1   \\n  1 1   \\n  1 1   \\n      \\n \\n', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'),\n",
              " Document(id_='1d6d1b4f-5cbd-481d-af13-0aa291b41ec1', embedding=None, metadata={'page_label': '4', 'file_name': 'dimage (1).pdf', 'file_path': '/content/data/dimage (1).pdf', 'file_type': 'application/pdf', 'file_size': 5513104, 'creation_date': '2024-03-25', 'last_modified_date': '2024-03-25'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={}, text=' \\n 12Block 1              Digital Image Processing-I  \\nIn Fig. 1(b), the elements are shown as either 0 or 1, which represents \\nthe value of the function ),y,x(f each element is known as pixels i.e. \\n‘picture element’. They represent the discrete data of any digital image, \\nand serve as the actual building block of digital images. The value of the \\nfunction )y,x(fat every point indexed by a row and a column is a \\nnumber and has no units, and it is known as the grey value  of intensity \\nof the image at that point. \\n \\nYou may again refer to Fig. 1(b) , the number of rows in a digital image \\nis called vertical resolution. The number of columns is called \\nhorizontal resolution . We shall discuss about these concepts of image \\nresolution, later in this unit. The number of rows and columns describes \\nthe dimensions  of the image. The image size is often expressed in \\nterms of the rectangular pixel dimensions of the array. Images can be of \\nvarious sizes. Some examples of image size are 256 x 256, 512 x 512, \\netc. For a digital camera, the image size is defined as the total number \\nof pixels (specified in megapixels). For example an image with \\nresolution 2048x2048 will have 4x106 pixels, that is, 4 Megapixels. \\nMillions of pixels combine together to give a digital image, and their \\nmeaning varies with context i.e. a pixel can be considered a single \\nsensor, photosite (physical element of the sensor array of a digital \\ncamera), element of a matrix, or display element on a monitor. \\n \\nGenerally, the value of the pixel is the intensity value of the image at \\nthat point, which is  quantized value of the light that is captured by the \\nsensor at the point. Typically the quantization is done in 256 levels. \\nThus the pixels will have values going from 0 to 255, and every pixel will \\nneed 8bits to store this information.   But many times, the value of the \\npixel is not always the intensity value. For example, in the case of an X-\\nray image, the value of the pixel indicates the attenuation of the X-ray at \\nthe point. Similarly, the average Magnetic Resonance (MR) signal \\nintensity denotes the pixel value in an MRI.  \\n \\nYou may try the following exercises. \\n \\n E1)  What do you mean by the term image file format? Mention some \\nof the frequently used image file formats. \\n \\nE2)  Differentiate analog image and digital image. Also, give example in \\nsupport of your answer. \\n \\n \\nNow, in the following section, we shall discuss image acquisition. \\n \\n1.3 IMAGE ACQUISITION \\n \\nAccording to the fundamental concept of optics, when two or more rays \\nof light meet at a point due to reflection or refraction or any other \\nphenomenon, an image is formed. The information of happened \\nphenomenon is recorded or acquired by the respective sensors as an \\nimage of that event. The process of capturing real world images and \\nstoring them into a computer is called image acquisition . \\n', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'),\n",
              " Document(id_='b4cf01c7-3d34-4216-a3c9-2584d587521e', embedding=None, metadata={'page_label': '5', 'file_name': 'dimage (1).pdf', 'file_path': '/content/data/dimage (1).pdf', 'file_type': 'application/pdf', 'file_size': 5513104, 'creation_date': '2024-03-25', 'last_modified_date': '2024-03-25'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={}, text=' \\nUnit 1      \\nThe question arises here, that how do we get the image in digital form. \\nThe answer is that t he sensors are actually the electronic devices which \\ntaps the various types of signals, like therm\\nelectromagnetic or any other type. Generally, the data is gathered in the \\nanalog form, which is digitized through a digitizer, and this digital signal \\nis finally utilized by the digital computer for its processing\\nthree types of image processing :–  \\n \\n\\uf0b7 Optical Image Processing\\n\\uf0b7 Analog Image Processing\\n\\uf0b7 Digital Image Processing\\n    \\nWe define these types briefly: \\n \\n\\uf0b7 Optical Image Processing: An optical image of 3D object is is\\nfact its 2D projection, which is the continuous \\na 2D surface. This 2D projection holds\\nlike 3D objects that are in focus .\\nradiation source, and other optical processes. This optical image is \\navailable in the optical form until it is converted into analog form, \\nleading to the area of analog image processing.\\n \\n\\uf0b7 Analog Image Processing: This \\nsignals using analog circuits. A nalog signals are \\ncontinuous signals, often referred\\nsignals are transformed in to digital image through the process of \\nsampling and quantization, performed by using a digitizer and the \\nprocess is known as d igitization.\\nimage processing.  \\n \\n \\nFig. 2: Analog Image Processing\\n \\nYou may note that t he imaging systems that use film for recording \\nimages are also known as analog imaging systems, in medical \\nimaging, still films are used, because \\nquality than digital systems.   \\n \\n\\uf0b7 Digital Image Processing: It relates to the field of using digital \\ncircuits, systems, and software algorithms to carry out the image \\nprocessing operations, which include quality enhancement of an \\nimage, counting of objects, image analysis, and many \\n \\nNow, we know that when an interaction happens between object and \\nrays of light (or signals) some optical or thermal or relevant \\nphenomenon happens. T he phenomenon is recorded or acquired  by \\nthe respective sensors as an image of that event\\nthe combination of millions of pixels, each of which is carrying the \\ninformation for function )y,x(for (f\\nray attenuation value, or Magnetic Resonance Intensity or any other \\nparameter. This is gathered by using respective sensors or other \\n                    Introduction to Digital Image\\nThe question arises here, that how do we get the image in digital form. \\nhe sensors are actually the electronic devices which \\ntaps the various types of signals, like therm al or optical or \\ntype. Generally, the data is gathered in the \\nanalog form, which is digitized through a digitizer, and this digital signal \\nis finally utilized by the digital computer for its processing . This leads to \\n  \\nOptical Image Processing  \\nAnalog Image Processing  \\nDigital Image Processing      \\nAn optical image of 3D object is is  in \\nwhich is the continuous distribution of light on \\na 2D surface. This 2D projection holds  various types of information \\n. It also includes the study of the \\nradiation source, and other optical processes. This optical image is \\nform until it is converted into analog form, \\nleading to the area of analog image processing.  \\nThis relates to the processing of analog \\nnalog signals are the time varying \\nreferred to as pictures. These analog \\ntransformed in to digital image through the process of \\nsampling and quantization, performed by using a digitizer and the \\nigitization.  Fig. 2 shows the steps of analog \\n \\nFig. 2: Analog Image Processing  \\nhe imaging systems that use film for recording \\nimages are also known as analog imaging systems, in medical \\nimaging, still films are used, because these films provide better \\nrelates to the field of using digital \\ncircuits, systems, and software algorithms to carry out the image \\nprocessing operations, which include quality enhancement of an \\nimage, counting of objects, image analysis, and many more. \\nNow, we know that when an interaction happens between object and \\nrays of light (or signals) some optical or thermal or relevant \\nhe phenomenon is recorded or acquired  by \\nthe respective sensors as an image of that event . This image relates to \\nthe combination of millions of pixels, each of which is carrying the \\n),z,y,x(which may be intensity or X-\\nray attenuation value, or Magnetic Resonance Intensity or any other \\nis gathered by using respective sensors or other \\n13 Introduction to Digital Image  \\n', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'),\n",
              " Document(id_='a507b5a0-4dae-4d14-a001-9814c4ea9bf8', embedding=None, metadata={'page_label': '6', 'file_name': 'dimage (1).pdf', 'file_path': '/content/data/dimage (1).pdf', 'file_type': 'application/pdf', 'file_size': 5513104, 'creation_date': '2024-03-25', 'last_modified_date': '2024-03-25'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={}, text=' \\n14Block 1     \\ndevices, this data gathering relates to the field of image acquisition, \\nwhich is actually the first phase of Digital Imaging. This acquired \\ninformation is stored in to the memory of the digital co\\nnecessary processing, the final outcome of processed image is \\ntransmitted to the concerned as an information.\\nrelevant tasks performed by any digital imaging system are \\nacquisition\\n \\nThus, a typ\\ninvolves components for the image acquisition, storage, processing, \\nand transmission. Types of imaging systems and acquisition systems \\nare shown in Fig.\\n \\n \\n \\nLet us now discuss these types briefly.\\n \\nDirect or Active \\ntemporary storage on film or chemical processes is required\\nsystems the acquired images are in\\nthat in such systems \\nhuman eye\\n \\nThe direct or active imaging systems are divided in to two sub \\ncategories i.e.\\nand the digital camera are example of parallel active imaging systems\\nwhere as scanning micro\\nmicroscopes \\nadvantage of \\nimage. \\n \\nIndirect Imaging Systems:\\nthe Indirect imaging systems involves\\nbefore producing an image for observation. In ind\\nthe image is stored in a film, which is rendered observable by a \\nchemical process. Therefore, there is a delay in the production of \\nimages from films.  \\n \\n          Digital Image\\ndevices, this data gathering relates to the field of image acquisition, \\nwhich is actually the first phase of Digital Imaging. This acquired \\ninformation is stored in to the memory of the digital co mputer for \\nnecessary processing, the final outcome of processed image is \\ntransmitted to the concerned as an information.  This means that the \\nrelevant tasks performed by any digital imaging system are \\nacquisition , storage, manipulation , and transmission\\nThus, a typ ical digital imaging system or digital imaging workstation \\ninvolves components for the image acquisition, storage, processing, \\nand transmission. Types of imaging systems and acquisition systems \\nare shown in Fig.  3.  \\nFig. 3: Types of Imaging and Data Acquisition Systems\\nLet us now discuss these types briefly.  \\nDirect or Active Imaging Systems : These are the systems where no \\ntemporary storage on film or chemical processes is required\\nsystems the acquired images are in  a recognizable form\\nthat in such systems the final image is a digital image . For example, \\nhuman eye , digital camera, scanning confocal microscopes\\nThe direct or active imaging systems are divided in to two sub \\ncategories i.e.  serial an d parallel acquisition systems. The human eye \\nand the digital camera are example of parallel active imaging systems\\nscanning micro densitometer and scanning confocal \\nmicroscopes are examples of s erial acquisition imaging systems. The \\nadvantage of direct imaging systems is that the final image is a digital \\nIndirect Imaging Systems:  In comparison to Direct imaging systems, \\nthe Indirect imaging systems involves  data processing or reconstruction \\nbefore producing an image for observation. In ind irect imaging systems, \\nthe image is stored in a film, which is rendered observable by a \\nchemical process. Therefore, there is a delay in the production of \\nimages from films.   \\nDigital Image  Processing -I \\ndevices, this data gathering relates to the field of image acquisition, \\nwhich is actually the first phase of Digital Imaging. This acquired \\nmputer for \\nnecessary processing, the final outcome of processed image is \\nThis means that the \\nrelevant tasks performed by any digital imaging system are data \\ntransmission .  \\ncal digital imaging system or digital imaging workstation \\ninvolves components for the image acquisition, storage, processing, \\nand transmission. Types of imaging systems and acquisition systems \\n \\nTypes of Imaging and Data Acquisition Systems  \\nsystems where no \\ntemporary storage on film or chemical processes is required . In such \\na recognizable form . We can say \\n. For example, \\nscanning confocal microscopes , etc.  \\nThe direct or active imaging systems are divided in to two sub \\nd parallel acquisition systems. The human eye \\nand the digital camera are example of parallel active imaging systems , \\nensitometer and scanning confocal \\nerial acquisition imaging systems. The \\ndirect imaging systems is that the final image is a digital \\ncomparison to Direct imaging systems, \\ndata processing or reconstruction \\nirect imaging systems, \\nthe image is stored in a film, which is rendered observable by a \\nchemical process. Therefore, there is a delay in the production of \\n', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'),\n",
              " Document(id_='28dde8cf-0dd8-410d-869c-00bb83c20615', embedding=None, metadata={'page_label': '7', 'file_name': 'dimage (1).pdf', 'file_path': '/content/data/dimage (1).pdf', 'file_type': 'application/pdf', 'file_size': 5513104, 'creation_date': '2024-03-25', 'last_modified_date': '2024-03-25'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={}, text=' \\n \\n 15 Unit 1                         Introduction to Digital Image  \\nYou may now try the following exercises. \\n \\n \\nE3)  Distinguish among optical image processing, analog image \\nprocessing and digital image processing using examples. \\n \\n \\nE4)  Differentiate between direct and indirect imaging system. \\n \\n \\nAfter data or image acquisition, image digitization is to be performed. \\nWe shall be using the image digitization process in image digitization.  \\n \\n1.4 DIGITIZATION OF IMAGES  \\n \\nIn this section, we will try to find out the answers to three basic \\nquestions. The first question is why do we need digitization? Then we \\nwill try to find the answer to what is meant by digitization and thirdly, we \\nwill go to how to digitize an image. \\n \\nWe have learned from the previous sections that images are actually \\nthe imitations of real-world objects, which is generally a two-dimensional \\n(2D) signal ),y,x(f where the values of the function )y,x(frepresent the \\namplitude or intensity at different points (pixels) of an image. In the \\nprocessing of the analog signals or information by using digital \\ncomputers, we need to convert this analog image into a discrete form \\nusing the process of digitization. This process of Digitization involves \\nsubprocess of sampling and quantization, performed through a Digitizer. \\nThe process of sampling and quantization transforms the acquired data \\nin to the form which is suitable for further processing by digital \\ncomputers.  \\n \\nThus, image digitization refers to the process of sampling and \\nquantization of the analog signals, which is required to convert the \\nanalog signal in to digital form. The digital image processing is \\nperformed using Digitizers, and this digitization process consists of two \\nsteps. One is called sampling  and the other is called quantization . \\nSampling refers to considering the image only at a finite number of \\npoints, each image sample is called a pixel and Quantization refers to \\nthe representation of the grey level value at the sampling point using \\nfinite number of bits. For example 256 level quantization results in \\nimage storage of 8 bits per pixel.  \\n \\nLet us discuss the two concepts in detail. \\n \\nSampling \\n \\nConsider any image. As you know that an image can be viewed as a 2 \\ndimensional function given in the form of ).y,x(f Now, the image has \\ncertain length and certain height. Suppose the height of the image is H \\nand the length of the image is L. The units of length and height would \\nbe the same. We can identify the coordinates X any Yat any space on \\nthe image. As you have seen the matrix representation of function \\n', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'),\n",
              " Document(id_='76a3393b-ed9c-4432-8d65-f629cd3c5107', embedding=None, metadata={'page_label': '8', 'file_name': 'dimage (1).pdf', 'file_path': '/content/data/dimage (1).pdf', 'file_type': 'application/pdf', 'file_size': 5513104, 'creation_date': '2024-03-25', 'last_modified_date': '2024-03-25'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={}, text=\" \\n 16Block 1              Digital Image Processing-I  \\n)y,x(f in Sec. 1.2, x-axis is taken vertically and y-axis is taken \\nhorizontally, so X coordinate will vary from 0 to H and Y coordinate \\nwill vary from 0 to L. At any point )Y,X(we identify two features and \\nwrite XY as product of them. Suppose these features are )Y,X(rand \\n)Y,X(irepresent the reflectance of the surface point and intensity of \\nlight respectively. If we consider that the maximum and minimum values \\nof intensity aremaxI and min,Ithen in case of continuous image )y,x(f \\ncannot attain the values either according to XYor )y,x(i).Y,X(r . \\n \\nFrom the theory of real numbers you know that given any 2 points, there \\nare infinite numbers of points. So again, when I come to this image as \\nX varies from0 to ,Hthere can be infinite possible values of X between \\n0 and .HSimilarly, there can be infinite values of Ybetween 0 and .L \\nSo effectively, that means that if we want to represent this image in a \\ncomputer, then this image has to be represented by infinite number of \\npoints and secondly when we consider the intensity value at a particular \\npoint, it varies between certain minimum I min and certain maximum \\nImax values, which is infinite in number.   \\n \\nIt is clear from here that the number of points in any case would be \\ninfinite which would not be possible for computer to work on. Therefore, \\na way to overcome from this problem is to consider some discrete set of \\npoints, and this process of taking discrete data for any image is known \\nas sampling. \\n \\nFrom this discussion, we can say that since the computers can not \\nhandle the continuous data, so the continuous signal needs to be \\nconverted into digital signal and the process of this conversion is \\nreferred to as sampling.  \\n \\nWe can visualize the process as overlaying an uniform grid on the \\nimage and sampling the image function at the center of each grid \\nsquare. As we make the grid finer, we get better resolution of the image, \\nbut as we make the gridcoarser, we observe more “ pixelization ''. At \\neach pixel (or at each grid square) we usually represent the gray level \\nvalue using an integer ranging from 0 for black to 255 for fully white. \\n \\nTo sample the signal, the signal should be frozen, as sampling cannot \\nbe applied to moving signals. If the signal is frozen for 𝑇 seconds, 𝑇 is \\ncalled the sampling period. The sampling period  is measured in \\nseconds, milliseconds, or microseconds. For example, if the sampling \\nrate is 1000Hz, it means that the signal is sampled every millisecond. \\n  \\nThe sampling rate  or sampling frequency  is the reciprocal of the \\nsampling period and is measured in samples per second or Hertz. The \\nsampling process is the multiplication of a continuous signal )t(fby a \\nrailing function ).t(r Railing function can be a pulse of unit amplitude at \\nexactly the sampling time instant. We can also say that If the signal is \\nfrozen for 𝑇 seconds ( 𝑇is the sampling period) thensampled function\\n)n(fis mathematically represented as the product of continuous signal \\n)t(f by a railing function ),t(r \\n \\n)nT(f)t(r)t(f)n(f\\uf03d\\uf0b4\\uf03d  \\n\", start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'),\n",
              " Document(id_='438bcd95-b2e5-484b-ae29-c98f4ae55f37', embedding=None, metadata={'page_label': '9', 'file_name': 'dimage (1).pdf', 'file_path': '/content/data/dimage (1).pdf', 'file_type': 'application/pdf', 'file_size': 5513104, 'creation_date': '2024-03-25', 'last_modified_date': '2024-03-25'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={}, text=' \\n \\n 17 Unit 1                         Introduction to Digital Image  \\nAll these components are graphically shown in Fig. 4.  \\n \\n     \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\nFig. 4: Sampling process (a) Original signal )t(f(b) Sampled image\\n)t(r*)t(f)n(f\\uf03d (c)  Reconstructed image (d) Train of the impulse function r(t) \\n \\nSince, sampling is a reversible process, the reconstruction of original \\nsignal from the sampled signal is possible in both frequency and time \\ndomain. In frequency domain, the information of the base band \\ncomponent is required, and this base-band component over the band \\nsf\\uf0b1(wheresfis the sampling frequency) can be extracted from the \\ninfinite spectrum of the infinite spectrum of the sampled signal, by using \\nthe low pass filters.(This statement is derived from the Shannon’s \\nSampling Theorem).In image processing, the sampling frequency \\ndecides the distance between the samples. This distance determines \\nthe linear pixel size. Alternatively, the equivalent operation in time \\ndomain is the interpolation operation where an ideallow-pass filter is \\nused in convolution with the interpolation function (e.g., sine function). \\nThe function ),t(fthe railing function ),t(rand the sampled and \\nreconstructed functions are all shown in Fig. 5.  \\n \\nThis idea of one-dimensional sampling can be extended to 2D images \\nalso. The 2D railing function is known as comb function . It is arranged \\nas a rectangular grid of unit impulses separated byx\\uf044and.y\\uf044 The 2D \\nsampling process can again be viewed as the multiplication of the \\nrailing function with the continuous function to give discrete samples. \\nThe values of x\\uf044and y\\uf044play an important role in image processing. \\nNormally they are kept the same in both the horizontal and the vertical \\ndirections, so that the pixels will be a square pixels. This is called pixel \\naspect ratio . In addition, the size of the pixel is important for image \\nquality. If the size is very large, there will be a lesser number of pixels. \\nHence, the details become less, which makes the image distracting and  1 \\n0.5 \\n0 \\n-0.5 \\n5 \\n(a) 10 \\n1 \\n0.5 \\n0 \\n-0.5 \\n5 \\n(c) 10  \\n-1 \\n0 1 \\n0.5 \\n0 \\n-0.5 \\n5 \\n(b) 10  \\n∆x (d) \\n', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'),\n",
              " Document(id_='763e7cc5-c68a-4e12-ab48-1339ccc9d053', embedding=None, metadata={'page_label': '10', 'file_name': 'dimage (1).pdf', 'file_path': '/content/data/dimage (1).pdf', 'file_type': 'application/pdf', 'file_size': 5513104, 'creation_date': '2024-03-25', 'last_modified_date': '2024-03-25'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={}, text=' \\n 18Block 1              Digital Image Processing-I  \\nmeaningless. This is called pixelization error  where the grey level \\ndiscontinuities at the edges of the pixel become poor. Then question \\narises that, What should be the ideal size of the pixel? Should it be big \\nor small? The answer is give by the Shannon-Nyquist theorem.  \\n \\nAs per Shannon-Nyquist theorem, the sampling frequency should be \\ngreater than or equal to ,f2max\\uf0b4wheremaxfis the highest frequency \\npresent in the image. Otherwise, the original signal cannot be \\nreconstructed. In other words, the number of samples required is \\ndictated by Shannon-Nyquist theorem, this sampling theorem can be \\nstated in terms of distance das.f21d\\nmax\\uf0a3Alternatively, it should be less \\nthan or equal to the smallest detail that is present in the image.  \\n \\nAnother frequency that is helpful in image processing is the Nyquist \\nfrequency. The Nyquist frequency )f(Nis\\uf0b42/1(sampling frequency). \\nTherefore, the Nyquist frequency )f(Nshould be greater than or equal to \\n.fmax If the sampled image has frequencies higher than the Nyquist \\nfrequency, it results in a condition called aliasing where the high \\nfrequencies masquerade as low frequency components. This results in \\nan image where interpretation becomes difficult. This problem is called \\naliasing.  \\n \\nNow, let us solve the following examples to understand this concept \\nbetter. \\n \\nExample 1: What should be the physical size of a 2D image of a \\ndocument  with dimensions is 24002400\\uf0b4, when scanned at 300dpi. \\nHere dpi stands for dots per inch. \\n \\nSolution: The physical size of image is \\n \\nsolutionReheightinpixelsofNumber\\nsolutionRewidthinpixelsofNumber\\uf0b4 \\uf03d  \\n \\n 3002400\\n3002400\\uf0b4\\uf03d  \\n \\n inches8inches8\\uf0b4 \\uf03d  \\n*** \\nExample 2: If the physical size of a medical image is 8×8 inches and \\nthe sampling resolution is 5 cycles/mm, then how many pixels per cycle \\nare required to have a better quality image? Will an image of size \\n256×256 be enough? \\n \\nSolution: It is given that the sampling resolution is 5 cycles/mm. \\nTherefore, for better quality, 2 pixels per cycle are required. This is \\nbecause sampling theorem states that the sampling frequency should \\nbe greater than twice the maximum signal frequency. This means that \\n10 pixels per mm are required. So, the pixel size is 0.1 mm. \\n \\nYou may note here that this double rates in both directions are called \\nNyquist rates. \\nThe given image size (since cm54.2inch1\\uf03d ) is \\n', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'),\n",
              " Document(id_='5779584e-6cf7-459c-b4aa-465756c750dd', embedding=None, metadata={'page_label': '11', 'file_name': 'dimage (1).pdf', 'file_path': '/content/data/dimage (1).pdf', 'file_type': 'application/pdf', 'file_size': 5513104, 'creation_date': '2024-03-25', 'last_modified_date': '2024-03-25'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={}, text=' \\n \\n 19 Unit 1                         Introduction to Digital Image  \\n22\\ncm32.2032.20cm54.2854.28\\n\\uf0b4\\uf03d\\uf0b4\\uf0b4\\uf0b4\\uf03d \\n \\nTherefore, the minimum number of required pixels 20322032\\uf0b4\\uf03d pixels. \\nSo, an image of size 256256\\uf0b4is not enough.  \\n*** \\nNow try the following exercises. \\n  \\n  \\nE5)  The dimension of an image is 5 x 8 inches and the frequency is \\n500 dots per inches in each direction. Find the number of samples \\nrequired to preserve the information in the image. \\n \\n \\nNow let us discuss the other step of image digitization, which is image \\nquantization. \\n \\nQuantization \\n \\nImage quantization is the process of converting the sampled analog \\nvalue of the function )y,x(finto a discrete-valued integer. An analog \\nsignal has an infinitely larger number of distinct values. So, it is \\nnecessary to convert the continuous values into a smaller set through \\nthe process of quantization. The sampled analog image i.e. a natural \\nimage has continuously varying shades and colours and is known as a \\ncontinuous tone image. This continuous tone image is required to be \\nconverted into a discrete image. The image quantizer maps a \\ncontinuous value xinto a discrete variable ,x\\uf0a2where discrete points of \\ngrey-tone or brightness are used. \\n  \\nThe process of quantization involves the partitioning of input values into \\nequally spaced intervals. The end points of the interval are called \\ndecision boundaries. Let the decision boundaries be given by\\n},b,...,b,b{Bm 10\\uf03d and let the input values be in the rangemaxX\\uf02dto \\n.Xmax\\uf02b The length of the interval between successive decision \\nboundaries i.e. the step size )(\\uf044is given by .mX2max The midpoint \\nbetween successive decision boundaries is called output or \\nreconstruction level and is given by }.y,..,y,y{)R(m21\\uf03d Then \\nquantization is in the range ,2to2from\\uf0fa\\uf0fb\\uf0f9\\n\\uf0ea\\uf0eb\\uf0e9 \\uf044\\uf044\\uf02d and the number of bits \\nnecessary to encode the output levels is given by ].m[logR2\\uf03d  \\n \\nTry the following exercises. \\n \\n \\nE6)  What is the role of Sampling and Quantization in the process of \\nDigitization? \\n \\nE7)  What do you mean by pixel aspect ratio and pixelization error? \\n', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'),\n",
              " Document(id_='a78297cf-b26b-4a7f-8a9b-51462223ef24', embedding=None, metadata={'page_label': '12', 'file_name': 'dimage (1).pdf', 'file_path': '/content/data/dimage (1).pdf', 'file_type': 'application/pdf', 'file_size': 5513104, 'creation_date': '2024-03-25', 'last_modified_date': '2024-03-25'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={}, text=' \\n 20Block 1              Digital Image Processing-I  \\nE8) How Shannon-Nyquist theorem relates to the determination of the \\nideal size of the pixel? \\n \\n \\nIn the following section, we shall discuss the ways how an image is \\nrepresented. \\n \\n1.5 REPRESENTATION OF DIGITAL IMAGE \\n \\nIn conventional image processing, a matrix is used to represent \\nintensity values. Many times we need to convert this matrix to a vector \\nand then use the normal equations or regularization based methods to \\nrepresent intensity values. The normal matrix representation of image is \\ncalled the Spatial Domain. There are other representation of images \\nobtained by transforming the spatial domain image like Fast Fourier \\nTransform (FFT), Discrete Cosine Transform (DCT), Discrete Wavelet \\nTransform (DWT) etc. Each of these transformations  represents the \\nimage in the form of a mathematical formula. Further, some Partial \\nDifferential Equations (PDE) based methods use kernels to process \\nimages using 2D convolution. Sometimes we use the notion of images \\nas point clouds and then construct a graph based on some distance \\nmeasure. Then we study the Laplacians (Diffusion maps) or the \\nHamiltonian operators of these graphs. The Eigen values of these \\noperators lead to the notion of heat kernel signatures and wave kernel \\nsignatures. We have these alternate views of an image based on the \\napplication, we will study some of these image representation \\nmechanism in this section. \\n \\nGoing back to the basics, a digital image represents a mapping on a \\nvisual scene recorded by an optical sensor into an array of picture \\nelement intensities. Here we are discussing some of the models of \\nimage representation. \\n \\n\\uf0b7 Pixel model:   In this model,  the RGB optical sensors maps to a \\nvector of discrete intensities which are integers.  Let\\nZZZBGR:p \\uf0b4\\uf0b4\\uf0ae\\uf0b4\\uf0b4 defined by ),zB,zG,zR()b,g,r(p\\uf03d where b,g,r\\nare real valued colour intensities and zB,zG,zR  are integer-valued \\ncolour intensities in the range from 0 to 255. In this case, a pixel is \\nrepresented by a 31\\uf0b4column vector used to \"paint\" a tiny square in \\nan image display. \\n \\n\\uf0b7 Vector model:  In this model, the RGB optical sensor values are \\nmapped to vectors and the displayed image conforms to a form of \\ntriangulated image space instead of a collection of pixels.  In this \\nmodel, a vector image is a collection of vertices and edges.  The end \\nresult of vector images is the formation of a basis for applications \\nsuch as Adobe Photoshop and various video games. \\n \\nYou can try the following exercise. \\n \\n \\nE9)  Compare Pixel Model and Vector Model for the representation of \\nDigital Image. \\n \\n', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'),\n",
              " Document(id_='eb78e43e-f161-48a6-8ad4-0c154f2e7f13', embedding=None, metadata={'page_label': '13', 'file_name': 'dimage (1).pdf', 'file_path': '/content/data/dimage (1).pdf', 'file_type': 'application/pdf', 'file_size': 5513104, 'creation_date': '2024-03-25', 'last_modified_date': '2024-03-25'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={}, text=' \\n \\n 21 Unit 1                         Introduction to Digital Image  \\nIn the following section we will discuss about the various types of \\nimages, and their respective characteristics. \\n \\n1.6 TYPES OF IMAGES \\n \\nClassification of images can be performed on the basis of various \\ncriteria like attributes, colour, dimension and data types. Based on \\nAttributes criteria the images are classified as raster and vector, \\nwhereas  on the basis of colour we can classify an image as binary, \\ngreyscale, true colour and pseudo colour. Further on the basis of \\ndimensions, we have 2D images and 3D images, and on the basis of \\ndata types the images are classified as signed integer type, unsigned \\ninteger type, float type, logical and double type. So there is no single \\naccepted way of classifying images. The image classification is \\ndescribed in detail now:   \\n \\n\\uf0b7 Classification of Images on the basis of Attributes: Based on \\nattributes of any image, it is classified as raster and vector graphic \\nimages. \\n \\no Vector graphics  uses graphic primitives (point/line/circle/ellipse \\netc)to describe an image. Hence, the notion of resolution is \\npractically not present in graphic.  \\n \\no Raster images  are pixel-based, and hence their quality is \\ndependent on the number of pixels. So, operations such as \\nenlarging or blowing-up of a raster image often result in quality \\nreduction.  \\n \\n\\uf0b7 Classification of images on the basis of colour: On the basis of \\ncolour, the images can be classified into the following categories: \\n \\no Monochrome images are the images where the colour \\ncomponent is absent, they are further classified as Grey scale \\nand binary images.  \\n \\no Grey scale images  : The term grey scale refers to the range of \\nshades between white and black or vice versa, such images \\nhave many shades of grey, and eight bits (28 = 256) are enough \\nto represent the grey scale because the human visual system \\ncan not distinguish more than 32 different grey levels, and the \\nadditional bits are necessary to cover noise margins. Most \\nmedical images such as X-rays, CT images, MRIs, and \\nultrasound images are grey scale images. \\n \\no Binary images  :The binary images are just special case of grey \\nscale images, where the process of Thresholding is applied, \\nthey are actually Bi-level images where the pixels assumes the \\nvalues of 0 or 1, i.e. only one bit is sufficient to represent the \\npixel value. The thresholding process involves the comparison of \\nthe threshold value i.e. the pixel value is compared with the \\nthreshold value, if the pixel value of the grey scale image is \\ngreater than the threshold value, the pixel value in the binary \\n', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'),\n",
              " Document(id_='ea84e717-8591-4ff7-9f61-2f65c15c3e53', embedding=None, metadata={'page_label': '14', 'file_name': 'dimage (1).pdf', 'file_path': '/content/data/dimage (1).pdf', 'file_type': 'application/pdf', 'file_size': 5513104, 'creation_date': '2024-03-25', 'last_modified_date': '2024-03-25'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={}, text=' \\n22Block 1     \\nimage is considered as 1, Otherwise, the pixel value is 0. So far \\nas the utility of Bi\\nin representing basic shapes and line drawings. They are also \\nused as masks. In addition, image processing operations \\nproduce binary images at intermediate stages. \\n \\no True \\npixel has a \\nred, green, and blue. Each \\na grey scale image using eight bits. Mostly, true \\nuse 24 bits to represent all the \\ncan be considered as three\\ncolour\\ncolour\\nconverter (DAC) to convert the \\nof the monitor, refer to \\n \\n \\n \\n \\n \\n \\n \\nTrue \\nthemselves. Thus, they are quite similar to the actual object and \\nhence called true \\ndo not use any lookup table but store the pixel information with \\nfull precision. \\no Pseudocolo\\ncolo\\ninterpretation of data. Like true \\nimages are also used widely in images processing. We know \\nthat \\nremote sensing applic\\nspectral images are generally used. These images, which are \\ncaptured by satellites, contain many bands. A typical remote \\nsensing image may have 3 to 11 bands in an image.This \\ninformation is beyond the human perceptual rang\\nis mostly not visible to the human observer. So \\nartificially added to these bands, so as to distinguish the \\nbands and to increase operational convenience. These are \\ncalled artificial \\nimages \\nthe \\n  \\nWe shall discuss few examples for the better understanding of the topic.\\n \\nExample3 : \\npixels of a binary image?\\n \\nSolution: For a binary image, one bit is sufficient for representing the \\npixel value. So\\n          Digital Image\\nimage is considered as 1, Otherwise, the pixel value is 0. So far \\nas the utility of Bi nary images is concerned, they are often used \\nin representing basic shapes and line drawings. They are also \\nused as masks. In addition, image processing operations \\nproduce binary images at intermediate stages. \\nTrue colour (or full colour)  images are the images where the \\npixel has a colour that is obtained by mixing the primary \\nred, green, and blue. Each colour component is represented like \\na grey scale image using eight bits. Mostly, true \\nuse 24 bits to represent all the colour. Henc e true \\ncan be considered as three -band images. The number of \\ncolours that is possible is 2563 (i.e. 256*256*256= 1,67,77,216 \\ncolours). A display controller then uses a digital\\nconverter (DAC) to convert the colour value to the pixel \\nof the monitor, refer to Fig.5 . \\n \\nFig. 5: True ColourImage \\nTrue colour images carries the full range of available \\nthemselves. Thus, they are quite similar to the actual object and \\nhence called true colour images. Further, the true \\ndo not use any lookup table but store the pixel information with \\nfull precision.  \\n \\nPseudocolo ur images are infact false colour\\ncolour component is manipulated artificially, based on the \\ninterpretation of data. Like true colour images, pseudocolo\\nimages are also used widely in images processing. We know \\nthat true colour images are called three- band images. But, in \\nremote sensing applic ation, multi- band images or multi\\nspectral images are generally used. These images, which are \\ncaptured by satellites, contain many bands. A typical remote \\nsensing image may have 3 to 11 bands in an image.This \\ninformation is beyond the human perceptual rang\\nis mostly not visible to the human observer. So \\nartificially added to these bands, so as to distinguish the \\nbands and to increase operational convenience. These are \\ncalled artificial colour or pseudocolour images. Pseudo\\nimages are popular in the medical domain also. For example, \\nthe doppler colour image is a pseudocolour  \\n \\nWe shall discuss few examples for the better understanding of the topic.\\n: Determine the storage space required to store 1024 x1024 \\na binary image?  \\nFor a binary image, one bit is sufficient for representing the \\npixel value. So , the number of bits required will be 1024 x 1024 x 1\\nDigital Image  Processing -I \\nimage is considered as 1, Otherwise, the pixel value is 0. So far \\nnary images is concerned, they are often used \\nin representing basic shapes and line drawings. They are also \\nused as masks. In addition, image processing operations \\nproduce binary images at intermediate stages.  \\nimages where the \\nthat is obtained by mixing the primary colours \\ncomponent is represented like \\na grey scale image using eight bits. Mostly, true colour images \\ne true colour image \\nband images. The number of \\n(i.e. 256*256*256= 1,67,77,216 \\ns). A display controller then uses a digital -to-analog \\nvalue to the pixel intensity \\nimages carries the full range of available colours in \\nthemselves. Thus, they are quite similar to the actual object and \\nimages. Further, the true colour images \\ndo not use any lookup table but store the pixel information with \\ncolour images, their \\ncomponent is manipulated artificially, based on the \\nimages, pseudocolo ur \\nimages are also used widely in images processing. We know \\nband images. But, in \\nband images or multi -\\nspectral images are generally used. These images, which are \\ncaptured by satellites, contain many bands. A typical remote \\nsensing image may have 3 to 11 bands in an image.This \\ninformation is beyond the human perceptual rang e. Hence it \\nis mostly not visible to the human observer. So colour is \\nartificially added to these bands, so as to distinguish the \\nbands and to increase operational convenience. These are \\nimages. Pseudo colour \\nare popular in the medical domain also. For example, \\n image.  \\nWe shall discuss few examples for the better understanding of the topic.  \\nDetermine the storage space required to store 1024 x1024 \\nFor a binary image, one bit is sufficient for representing the \\nthe number of bits required will be 1024 x 1024 x 1 \\uf03d\\n', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'),\n",
              " Document(id_='e2031ad1-7cdd-42db-9694-97bbf9fe41ef', embedding=None, metadata={'page_label': '15', 'file_name': 'dimage (1).pdf', 'file_path': '/content/data/dimage (1).pdf', 'file_type': 'application/pdf', 'file_size': 5513104, 'creation_date': '2024-03-25', 'last_modified_date': '2024-03-25'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={}, text=' \\n \\n 23 Unit 1                         Introduction to Digital Image  \\n10,48,576 bits \\uf03d [(10,48,576)/8]bytes \\uf03d1,31,072 bytes \\uf03d 128 \\nKB.Since, 1 KB is 1024 bytes, the storage requirement is 128 KB.  \\n*** \\nExample 4: What is the storage requirement for a 1024 x 1024, 24-bit \\ncolour image? \\n \\nSolution: Since colour images are three-band images (red, green, and \\nblue components), the storage requirement is 1024*1024*3 bytes = \\n31,45,728 bytes. Since 1 KB is 1024 bytes, the storage requirement is \\n3072 KB or 3 Mega pixels.  \\n*** \\nWe shall resume the discussion on image classification. \\n \\n\\uf0b7 Classification of Images on the basis of Dimensions : Images \\ncan be classified on the basis of dimensions also. Normally, digital \\nimages are a 2D rectangular array of pixels. If another dimension, of \\ndepth or any other characteristic, is considered, which may be \\nnecessary to use, then a higher-order stack of images like 3D \\nimages are produced. A good example of a 3D image is a volume \\nimage, where pixels are called voxels. By ‘3D image’, it is meant that \\nthe dimension of the target in the imaging system (may be a scene \\nor an object.) is three dimensional ,y,x(depth). In medical imaging, \\nsome of the frequently encountered 3D images are CT images, \\nMRIs, and microscopy images. These are stored basically as  2D \\nimage slices taken across the body or the skull. Range images \\n(often used in remote sensing application) are also 3D images as \\nthey also incorporate the depth information. \\n \\n\\uf0b7 Classification of Images on the basis of Data Types:  Images \\nmay be classified based on their data type. Sometimes, image \\nprocessing operations produce images with negative number, \\ndecimal fraction, and complex number. For example, Fourier \\ntransforms produce image involving complex numbers. To handle \\nnegative numbers, signed and unsigned integer types are used. In \\nthese data types, the first bit is used to encode whether the number \\nis positive or negative. For example, the signed data type encodes \\nthe numbers from – 128 to 127 where one bit is used to encode the \\nsign. In general, an 1n\\uf02dbit signed integer can represent integers \\nfrom 1n2\\uf02d\\uf02dto ,121n\\uf02d\\uf02da total of .2nUnsigned integers represent all \\nintegers from 0 to 12n\\uf02dwith n bits. \\n \\nFloating-point involves storing the data in scientific notation. For \\nexample, 1,230 can be represented as 0.123*104 , where 0.123 is \\ncalled the significant and the power is called exponent. There are \\nmany floating-point conventions.  \\n \\nThe quality of such data representation is characterized by \\nparameters such as data accuracy and precision. Data accuracy is \\nthe property of how well the pixel values of an image are able to \\nrepresent the physical properties of the object that is being imaged. \\nData accuracy is an important parameter, as the failure to capture \\nthe actual physical properties of the image leads to the loss of vital \\n', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'),\n",
              " Document(id_='e1da9c2a-6e41-4eeb-b455-00fbf773806a', embedding=None, metadata={'page_label': '16', 'file_name': 'dimage (1).pdf', 'file_path': '/content/data/dimage (1).pdf', 'file_type': 'application/pdf', 'file_size': 5513104, 'creation_date': '2024-03-25', 'last_modified_date': '2024-03-25'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={}, text=' \\n 24Block 1              Digital Image Processing-I  \\ninformation that can affect the quality of the application. While \\naccuracy refers to the correctness of measurement, precision refers \\nto the repeatability of the measurement. In other words, repeated \\nmeasurements of the physical properties of the object should give \\nthe same result. Most software use the data type ‘double’ to \\nmaintain precision as well as accuracy.  \\n \\nNow try the following exercises. \\n \\n E10)  Make a list of all the types of classifications of images based on \\nvarious parameters.  \\n \\nE11)  Compare true Colour Images and Monochromatic Images. \\n \\n \\nIn this section, we learned various types of images. Now it is time to \\nlearn about the fundamental characteristics of images because to \\nimprove the quality of an image, it is necessary to assess its quality, \\nand this assessment requires understanding of characteristics of \\nimages, which is discussed in the following section. \\n \\n1.7 IMAGE CHARACTERISTICS \\n \\nTo improve the quality of an image, it is necessary to assess its quality, \\nand this assessment requires understanding of characteristics of \\nimages. some of the essential characteristics of image are Intensity, \\nContrast, Brightness, Noise and Resolution, we will discuss them one \\nby one. \\n \\n\\uf0b7 Intensity:  The term Intensity refers to the amount of light or the \\nnumerical value of a pixel, it is the measure of energy of a wave, \\nwhich is directly proportional to square of amplitude of the signal. \\nFrom the point of view of image processing it is a numerical value \\nwhich represents a pixel, for example  for an 8-bit gray scale image \\nthe value of a pixel can be in the range of [0,255], where (say) \\nnumericvalue “0” represents black colour and the numeric value \\n“255” represents white colour. Therefore, the higher the value of a \\npixel(i.e. intensity) whiter the  pixel will appear, i.e. in a grey scale \\nimages, intensity is depicted by the grey level value at each pixel \\ni.e., two pixels with grey level value 127 and 220, then it can be \\ninterpreted that pixel with value 127 is darker than pixel with value \\n220 .  \\n \\n\\uf0b7 Contrast: The term Contrast of an image relates to recording of \\nthe differences in the magnitude of the intensity at the surface of an \\nobject. There are many ways in which contrast can be \\nmeasured,i.e.it can be described as a product of the sensor signal \\ncontrast (this depends on the energy source and the physical \\nproperties of the object) and detector contrast (this depends on the \\nway signal is detected, captured, and stored). But, a common \\n', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'),\n",
              " Document(id_='98c1ea9b-74a9-4dc4-9220-1e0091a7592e', embedding=None, metadata={'page_label': '17', 'file_name': 'dimage (1).pdf', 'file_path': '/content/data/dimage (1).pdf', 'file_type': 'application/pdf', 'file_size': 5513104, 'creation_date': '2024-03-25', 'last_modified_date': '2024-03-25'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={}, text=' \\n \\n 25 Unit 1                         Introduction to Digital Image  \\nmeasurer of contrast (C) involves intensity of foreground )I(objectand \\nbackground )I(backgroundobjects, i.e.  \\n \\nbackground objectbackground object\\nIIIIC\\uf02b\\uf02d\\uf03d  \\n   \\nWhere, objectIis the average pixel intensity of the object pixels and backgroundIis \\nthe average pixel intensity of the background. Another useful contrast \\nmeasure using the same parameters objectI and backgroundIis \\n \\n  \\nbackgroundobject\\n10IIlogC\\uf03d  \\n \\nWe know, Contrast of an image relates to recording of the differences in \\nthe magnitude of the intensity at the surface of an object. So, Contrast \\nis the difference between maximum and minimum pixel intensities in an \\nimage. Consider two images A& B having pixel intensities between 30 \\nto 200 and 70 to 130, respectively. Then A has more contrast than B. \\nAgain contrast is also relative. Contrast can be simply explained as the \\ndifference between maximum and minimum pixel intensity in an image. \\nFor example, consider an image, whose maximum and minimum value \\nof intensity is 100 i.e. same then contrast will be zero because, Contrast \\n= maximum pixel intensity – minimum pixel intensity .0100100\\uf03d\\uf02d\\uf03d  \\n \\n\\uf0b7 Brightness: It refers to the average pixel intensity of the image, \\nSimilar to contrast, the image should be reasonably bright to show \\nall the information to the viewer. However, excessive brightness may \\naffect the quality of the image. An image may have a higher \\nbrightness but if the intensity is not optimal, the image again will not \\nexhibit good quality. Brightness resolution  is defined as the number \\nof identified distinct colours and it is also called as colour resolution . \\n \\nBrightness is a relative term which can be understood visually you \\ncan say the higher the intensity the brighter is the pixel. Since \\nbrightness is a relative term, so brightness can be defined as the \\namount of energy output by a source of light relative to the source \\nwe are comparing it to. In some cases we can easily say that the \\nimage is bright, and in some cases, it’s not easy to perceive. Now, \\nQuestion is How to make an image brighter? Brightness can be \\nsimply increased or decreased by simple addition or subtraction, to \\nthe pixel values of the image matrix. Consider a black image of 5 \\nrows and 5 columns . All the entries of the image matrix are going to \\nbe zero since it is a completely black image. To make it brighter we \\nshall perform some operation onthis mage. What we will do is, that \\nwe will simply add a value of 50 (or any value from 1 to 255) to each \\nof the matrix value of image. After adding the image would become \\nsomewhat brighter then its previous state of brightness. \\n \\n\\uf0b7 Noise: Noise is an unwanted disturbance that causes fluctuations \\nin the pixel value. It is a random or stochastic process and hence its \\ntrue value cannot be predicted accurately. However, noise obeys all \\nthe statistic properties. So a pixel is characterized as a random \\n', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'),\n",
              " Document(id_='a32e7751-6d02-414d-b279-1b6cbe2709ba', embedding=None, metadata={'page_label': '18', 'file_name': 'dimage (1).pdf', 'file_path': '/content/data/dimage (1).pdf', 'file_type': 'application/pdf', 'file_size': 5513104, 'creation_date': '2024-03-25', 'last_modified_date': '2024-03-25'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={}, text=' \\n 26Block 1              Digital Image Processing-I  \\nvariable for statistical analysis. It is the next important \\ncharacteristic, related to the quality of image, Image applications \\nare frequently affected by the noise present in the image.  \\n \\n\\uf0b7 Resolution: Resolution refers to the number of pixels in an image. \\nResolution is sometimes identified by the width and height of the \\nimage as well as the total number of pixels in the image. For \\nexample, an image that is 2048 pixels wide and 1536 pixels high \\n(2048 x 1536) contains (multiply) 3,145,728 pixels (or 3.1 \\nMegapixels). You could call it a 2048 x 1536 or a 3.1 Megapixel \\nimage. \\n \\nYou may now try the following exercises. \\n \\n E12)  What do you understand by the term “Brightness of Image”? How \\nit is different from the Contrast of any Image? \\n \\n \\nSo far, we have been using the word resolution and discussed it at \\nseveral places. In the following section, we shall highlight resolution in \\ndetail. \\n \\n1.8 IMAGE RESOLUTION \\n \\nHow does image resolution play out on my computer monitor? The \\ncomputer screen you are looking at right now is set at a particular \\nresolution as well. The larger the screen, the larger you likely have your \\nscreen resolution set. If you have a 17\" monitor, it is likely you have it \\nset at 800 x 600 pixels. If you have a 19\" screen it is likely set at 1024 x \\n768. You can change the settings but these are optimum for those \\nscreen sizes. \\n \\nNow, if your monitor is set to 800 x 600 and you open up an image that \\nis 640 x 480, it will only fill up a part of your screen. If you open up an \\nimage that is 2048 x 1536 (3.1 megapixels) then you will find yourself \\nmoving the slider bar around to see all the different parts of the image. It \\njust won\\'t fit. Add to that the fact that the computer monitor has a finite \\nnumber of pixels per inch available (like 72) so if you are going to \\ndisplay your image on a monitor only, you would have to reduce the \\nquality down to 72 to save file space. If you are going to put your image \\non a webpage or email it to a friend then you will want to first make it a \\nuseful size. Not too big, not too small. May be 200 or 300 pixels high \\nwould be a nice size. This means that Image resolution is highly related \\nto the size of image and its pixel density. \\n \\nWe learned from the last section that the term Image Resolution relates \\nto pixel density i.e. the number of pixels in an image, which relates to \\nclarity of image. Parameters used to determine the Resolution of any \\nimage involves the dimensions of image i.e. its width and height and the \\ntotal number of pixels in the image, as well. Say, an image has following \\ndimensions i.e. 2048 pixels wide and 1536 pixels high thus total number \\nof pixels in the image is (2048 x 1536) i.e. 3,145,728 pixels (or 3.1 \\nMegapixels), i.e the image has resolution of 3.1 megapixels. As the \\n', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'),\n",
              " Document(id_='f1dd7898-e844-4b33-b5ca-f477374d8850', embedding=None, metadata={'page_label': '19', 'file_name': 'dimage (1).pdf', 'file_path': '/content/data/dimage (1).pdf', 'file_type': 'application/pdf', 'file_size': 5513104, 'creation_date': '2024-03-25', 'last_modified_date': '2024-03-25'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={}, text=' \\n \\n 27 Unit 1                         Introduction to Digital Image  \\nmegapixels in the pickup device in your camera increase so does the \\npossible maximum size image you can produce. This means that a 5 \\nmegapixel camera is capable of capturing a larger image than a 3 \\nmegapixel camera. \\n \\nIn general the Resolution is classified as Spatial Resolution and \\nIntensity. In simple terms, Spatial Resolution relates to the \\nconcentration of pixels in the creation of digital images and Intensity \\nresolution of an image relates to blurredness or sharpness of an image, \\ndepending on the intensity of resolution.  \\n \\n\\uf0b7 Intensity resolution  deals with the Intensity of resolution i.e. the \\nnumber of pixels per square inch, which determines the clarity or \\nsharpness of an image. Whereas, Spatial resolution refers to the \\nnumber of pixels used in making an image.  \\n \\n\\uf0b7 Spatial resolution , in general is defined as the number of pixels per \\ninch of an image, the term deals with the number of pixels, pixel \\ndensity, and quantization levels. Thus, it depends on the sampling \\nand quantization processes. If the spatial resolution is not \\nsatisfactory, the quality of the image would not be satisfactory. In \\nGeneral, if the image has more pixels, the quality of the image is \\nsupposed to be higher. but, this is not correct, because an image \\nmay have more pixels, but it may not be providing sufficient \\ninformation regarding other factors such as optical resolution, the \\ndistance of an object from the camera ,the field of view etc. which \\nplays an important role in defining image quality. \\n \\nImages with a higher number of pixels per square inch are sharp and \\nhence said to have a higher Spatial resolution i.e. Spatial resolution is \\nthe number of pixels used for the construction of a digital image. Images \\nthat have a higher spatial resolution are made of a greater number of \\npixels and they are much clear as compared to the images with lower \\nspatial resolution. In radiology, spatial resolution is used to differentiate \\nbetween objects located close to each other. What do we understand by \\nwhat spatial resolution? If we have to compare two images, to see \\nwhich one is more clear or which has more spatial resolution, we need \\nto bring the two images to same size. \\n \\nSince, the spatial resolution is a measure to the clarity of image, so for \\ndifferent devices, different measure has been made to measure it. For \\nexample Dots per inch(Dots per inch or DPI is usually used in printers), \\nLines per inch(Lines per inch or LPI is usually used in laser printers), \\nPixels per inch (Pixel per inch or PPI is measure for different devices \\nsuch as tablets , Mobile phones etc.).The higher is the PPI, the higher is \\nthe quality. In order to understand that how PPI is calculated, lets \\ncalculate the PPI of a mobile phone. \\n \\nFirst of all we will use Pythagoras theorem to calculate the diagonal \\nresolution in pixels. It can be given as22baC\\uf02b\\uf03d , where a and b are \\nthe height and width resolutions in pixel and C is the diagonal \\nresolution in pixels. Now we will calculate PPI \\nPPI = C / diagonal size in inches \\n \\n', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'),\n",
              " Document(id_='d1802d47-1e63-4f6b-8161-55b4fbfed019', embedding=None, metadata={'page_label': '20', 'file_name': 'dimage (1).pdf', 'file_path': '/content/data/dimage (1).pdf', 'file_type': 'application/pdf', 'file_size': 5513104, 'creation_date': '2024-03-25', 'last_modified_date': '2024-03-25'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={}, text=' \\n 28Block 1              Digital Image Processing-I  \\nWe shall see the following example. \\n \\nExample 5:  Calculate PPI or pixel density. For the sample mobile \\nphone with the height and width resolutions in pixel as 1080 x 1920 \\npixels, and the diagonal size in inches of sample mobile phone is 5.0 \\ninches. \\n \\nSolution: So, putting those values in Pythagoras theorem i.e. the \\nequation 22baC\\uf02b\\uf03d gives the result C = 2202.90717, Given, the \\ndiagonal size in inches of sample mobile phone is 5.0 inches, therefore \\nPPI = 2202.90717/5.0 = 440.58 = 441 (approx). \\n \\nThat means that the pixel density of sample mobile phone is 441 PPI. \\n*** \\nPixel resolution , the term resolution refers to the total number of count \\nof pixels in an digital image. For example. If an image has M rows and \\nN columns, then its resolution can be defined as M X N. If we define \\nresolution as the total number of pixels, then pixel resolution can be \\ndefined with set of two numbers. The first number the width of the \\npicture, or the pixels across columns, and the second number is height \\nof the picture, or the pixels across its height (rows).We can say that the \\nhigher is the pixel resolution, the higher is the quality of the image.We \\ncan calculate mega pixels of a camera using pixel resolution. \\n \\nPixel resolution in Megapixels  = {Column pixels (width ) X Row pixels \\n( height )} / 1 Million. \\n \\nThus the size of an image can also be defined by using its pixel \\nresolution. \\n \\nSize of image  = pixel resolution X bpp( bits per pixel) \\n \\nExample 6: Calculate pixel resolution of a camera in mega pixels, \\ncapturing an image of dimension: 2500 X 3192. \\n \\nSolution:  Its pixel resolution = 2500 * 3192 = 7982350 bytes. Dividing \\nit by 1 million = 7.9 = 8 mega pixel (approximately). \\n \\n*** \\nNow, let us discuss some important terms like Aspect ratio, Dots per \\nInch, Lines per Inch , etc. which are closely associated with the concept \\nof image resolution \\n \\n\\uf0b7 Aspect ratio is another important concept with the pixel resolution . \\nAspect ratio is the ratio between width of an image and the height of \\nan image. It is commonly explained as two numbers separated by a \\ncolon (8:9). This ratio differs in different images, and in different \\nscreens. The common aspect ratios are: 1.33:1, 1.37:1, 1.43:1, \\n1.50:1, 1.56:1, 1.66:1, 1.75:1, 1.78:1, 1.85:1, 2.00:1, etc. \\n \\nAdvantage of Aspect ratio is that it maintains a balance between the \\nappearance of an image on the screen, means it maintains a ratio \\n', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'),\n",
              " Document(id_='293c96f3-52c0-49b8-9961-314f31bac864', embedding=None, metadata={'page_label': '21', 'file_name': 'dimage (1).pdf', 'file_path': '/content/data/dimage (1).pdf', 'file_type': 'application/pdf', 'file_size': 5513104, 'creation_date': '2024-03-25', 'last_modified_date': '2024-03-25'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={}, text=' \\n \\n 29 Unit 1                         Introduction to Digital Image  \\nbetween horizontal and vertical pixels. It does not let the image get \\ndistorted when aspect ratio is increased. Aspect ratio tells us many \\nthings. With the aspect ratio, you can calculate the dimensions of the \\nimage along with the size of the image. \\n \\nExample 7: Given an image is an gray scale image with aspect ratio of \\n6:2 and pixel resolution of 480000 pixels, calculate the following:  \\n \\na) Resolve pixel resolution to calculate the dimensions of image \\nb) Calculate the size of the image \\n \\nSolution:  Following are given: \\nAspect ratio (i.e 2:6)r:c\\uf03d \\n \\nPixel resolution (i.e. 480000)cr\\uf03d\\uf0b4  \\n \\nWe know that bits per pixel for a grayscale image is 8bpp.  \\n   \\nHere, we are required to find the number of rows(r) and the number of \\ncolumns(c). \\n \\nUsing the Aspect ratio, we get 2:6r:c\\uf03dthat is 2/r6c\\uf03d  \\n \\nUsing the pixel resolution, we get r/480000c\\uf03d  \\n \\nComparing both relations in c and ,r we get  \\n \\n62*480000(rr480000\\n2r62\\uf03d\\uf0de \\uf03d i.e.400r\\uf03d \\n \\nPutting ,400r\\uf03dwe get ;1200c\\uf03d \\n \\nThus, Rows 400\\uf03dand Columns 1200\\uf03d \\n \\nNow we solve for the size of image.  \\n \\nSize of image in bits \\uf03d rows * cols * bpp  \\n \\nSize of image in bits 38400008*1200*400 \\uf03d \\uf03d bits \\n \\nSize of image in bytes \\uf03d Size of image in bits /8 = 480000 bytes \\n \\nSize of image in kilo bytes kb469\\uf03d(approx). \\n*** \\n\\uf0b7 Dots per inch(DPI): If you have an image that is 640 x 480. How big \\na print can you make? Well, the true answer is you can make as big \\na print as you want but very quickly you will start to see \"blocks\" \\n(pixelization) and the quality will drop off. To maximize the capability \\nof your printer, you should print a picture a size that the printer can \\nhandle. Here we introduce a new term \"dots per inch\" (dpi) or \"pixels \\nper inch\" (ppi).  \\n \\n', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'),\n",
              " Document(id_='1ded6e2a-deb6-4522-bdbe-63945eded9ea', embedding=None, metadata={'page_label': '22', 'file_name': 'dimage (1).pdf', 'file_path': '/content/data/dimage (1).pdf', 'file_type': 'application/pdf', 'file_size': 5513104, 'creation_date': '2024-03-25', 'last_modified_date': '2024-03-25'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={}, text=' \\n 30Block 1              Digital Image Processing-I  \\nThe DPI is often relates to PPI (Pixels Per Inch), whereas there is a \\ndifference between these two. DPI or dots per inch is a measure of \\nspatial resolution of printers. In case of printers, DPI means that \\nhow many dots of ink are printed per inch when an image gets \\nprinted out from the printer. Remember, it is not necessary that \\neach Pixel per inch is printed by one dot per inch. There may be \\nmany dots per inch used for printing one pixel. The reason behind \\nthis that most of the colour printers uses CMYK model. The colours \\nare limited. Printer has to choose from these colours to make the \\ncolour of the pixel whereas within pc, you have hundreds of \\nthousands of colours. The higher is the dpi of the printer, the higher \\nis the quality of the printed document or image on paper. Usually \\nsome of the laser printers have dpi of 300 and some have 600 or \\nmore. \\n \\nExample 8:  Determine the optimum size of the 640 X 480 image that \\ncan be printed at 200 DPI. \\n \\nSolution: We have a 640 x 480 image and you want to print it at 200 \\ndpi. 640 divided by 200 equals 3.2 and 480 divided by 200 equals 2.4 \\nso if you print this picture at 3.2\" x 2.4\" you will get a print with 200 dots \\nper inch. We recommend 200 dpi as a minimum for good quality prints. \\n*** \\nExample 9 :  Let\\'s say we want to print an 8\" X 10\" picture at 300 dpi. \\nWhat resolution must we have?  \\n \\nSolution: 300 times 8 is 2400 and 300 times 10 is 3,000. So, we would \\nneed a 3,000 x 2,400 image i.e., 7.2 megapixels. \\n*** \\n\\uf0b7 Lines per inch: As dpi refers to dots per inch, liner per inch refers \\nto lines of dots per inch. The resolution of halftone screen is \\nmeasured in lines per inch. Table 2 shows some of the lines per \\ninch capacity of the printers. \\nTable 2 \\nPrinter LPI Screen printing 45-65 lpi \\nLaser printer (300 dpi) 65 lpi \\nLaser printer (600 dpi) 85-105 lpi \\nOffset Press (newsprint paper) 85 lpi \\nOffset Press (coated paper) 85-185 lpi \\n \\nTry an exercise.  \\n \\n \\nE13) What do you understand by the term “ Resolution of an Image”? \\nHow Intensity Resolution differs from Spatial Resolution? \\n \\n \\nIn the following section, we shall discuss various applications where we \\nuser digital images.  \\n \\n', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'),\n",
              " Document(id_='42c4b425-9719-48b1-b395-70819805abf5', embedding=None, metadata={'page_label': '23', 'file_name': 'dimage (1).pdf', 'file_path': '/content/data/dimage (1).pdf', 'file_type': 'application/pdf', 'file_size': 5513104, 'creation_date': '2024-03-25', 'last_modified_date': '2024-03-25'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={}, text=' \\n \\n 31 Unit 1                         Introduction to Digital Image  \\n1.9 APPLICATIONS THAT USE DIGITAL IMAGES \\n \\nImage processing has got wide variety of application areas, and it is \\nwidely utilized by engineers, scientists, media , fine arts professionals \\nand many more. Nowadays it is an exciting interdisciplinary field that \\nborrows ideas freely from many fields. Fig. 11  illustrates the \\nrelationships between image processing and other related fields. \\n \\nOptics       Digital Signal Processing \\n \\n \\nStatistics  Image Processing  Graphics \\n \\n \\nVideo       Machine Vision \\n \\nFig. 11: Image processing and other closely related fields. \\n \\nImage processing and Computer Graphics: Computer graphics and \\nimage processing are very closely related areas. Image processing \\ndeals with raster data or bitmaps, whereas computer graphics primarily \\ndeals with vector data. Raster data or bitmaps are stored in a 2D matrix \\nform and often used to depict real images. However, vector images are \\ncomposed of vectors, which represent the mathematical relationships \\nbetween the objects. Vectors are lines or primitive curves that are used \\nto describe an image. Vector graphics are often used to represent \\nabstract, basic line drawings. \\n \\nThe algorithms in computer graphics often take numerical data as input \\nand produce an image as output. However in image processing, the \\ninput is often an image. The goal of image processing is to enhance the \\nquality of the image to assist in its interpretation. Hence, the result of \\nimage processing is often an image or the description of an image. \\nThus, image processing is a logical extension of computer graphics and \\nserves as a complementary field.  \\n \\nImage Processing and Machine Vision : The main goal of machine \\nvision is to interpret the image and to extract its physical, geometric, or \\ntopological properties. Thus, the output of image processing operations \\ncan be subjected to more techniques, to produce additional information \\nfor interpretation. Artificial vision  is a vast field, with two main subfields-\\nmachine vision and computer vision. The domain of machine vision  \\nincludes many aspects such as lighting and camera, as part of the \\nimplementation of industrial projects, since most of the applications \\nassociated with machine vision are automated visual inspection \\nsystems. The applications involving machine vision aim to inspect a \\nlarge number of products and achieve improved quality controls. \\nComputer vision  is more ambitious. It tries to mimic the human visual \\nsystem and is often associated with scene understanding . Most image \\nprocessing algorithms produce results that can serve as the first input \\nfor machine vision algorithms.  \\n \\nImage Processing and Video Processing: Image processing is about \\nstill images. In fact, analog video cameras can be used to capture still \\nimages. A video can be considered as a collection of images indexed by \\ntime. Most image processing algorithms work with video readily. Thus, \\n', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'),\n",
              " Document(id_='8564513f-e465-49b6-b636-241d5d37ab8f', embedding=None, metadata={'page_label': '24', 'file_name': 'dimage (1).pdf', 'file_path': '/content/data/dimage (1).pdf', 'file_type': 'application/pdf', 'file_size': 5513104, 'creation_date': '2024-03-25', 'last_modified_date': '2024-03-25'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={}, text=' \\n 32Block 1              Digital Image Processing-I  \\nvideo processing is an extension of image processing. In addition, \\nimages are strongly related to multimedia, as the field of multimedia \\nbroadly includes the study of audio, video images, graphics, and \\nanimation.  \\n \\nImage Process and Optics: Optical image processing deals with \\nlenses, light, lighting conditions, and associated optical circuits. The \\nstudy of lenses and lighting conditions has an important role in the study \\nof image processing.  \\n \\nImage Processing and Statistics: Image analysis is an area that \\nconcerns the extraction and analysis of object information from the \\nimage. Imaging applications involve both simple statistics such as \\ncounting and Mensuration and complex statistics such as advanced \\nstatistical inference. So statistics play an important role in imaging \\napplications.  \\n \\nYou may try the following exercise. \\n \\n E14) Write any three applications of digital image processing. \\n \\n \\nNow, we summarise what we have studied in the unit. \\n \\n \\n1.10 SUMMARY \\n \\nWe have covered the following points: \\n \\n1. A digital image is defined as a 2D discrete signal that varies over the \\nspatial coordinates xand ,yand can be written mathematically as\\n)y,x(f. It is also an nn\\uf0b4array of elements, and each element \\nrepresents the sampled intensity. \\n2. Image acquisition can be done using various ways and the concepts \\nrequired to understand the acquisition of any image. \\n3. The two steps of image digitization are sampling and quantization. \\n4. Two models for representing an image. \\n5. Classification of the image on the basis of various attributes of \\nimage like colour, dimension and data types, etc.  \\n6. Various terms which will be used again and again in the course. \\n \\n1.11  SOLUTIONS/ANSWERS \\n \\nE1)  Image file format is an algorithm or a method used to store and \\ndisplay an image. Some of the frequently used file formats are \\nJPEG, PNG, BMP, GIF, TIFF, etc. \\n \\nE2)  Some of the points of difference in analogue and digital images \\nare: \\n \\n', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'),\n",
              " Document(id_='a4b92739-2e22-40ef-a672-18dc4286607b', embedding=None, metadata={'page_label': '25', 'file_name': 'dimage (1).pdf', 'file_path': '/content/data/dimage (1).pdf', 'file_type': 'application/pdf', 'file_size': 5513104, 'creation_date': '2024-03-25', 'last_modified_date': '2024-03-25'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={}, text=' \\n \\n 33 Unit 1                         Introduction to Digital Image  \\ni. Digital image’s pixel value must be discrete whereas \\nAnalog image’s pixel value must be continuous. \\n \\nii. The amplitude of digital image is finite whereas the \\namplitude of analog image is infinite. \\n \\niii. It is quite possible to store all the pixels of digital image \\nwhereas It is quite impossible to store all the pixels of \\nanalog image. \\n \\nE3)  Analog Image Processing:  It is applied on analog \\nsignals/images and it processes only 2 D images. Examples are \\ntelevision, images, photographs, painting and medical images, \\netc. \\n \\n Digital Image Processing:  It is applied to digital images (a \\nmatrix of small pixels and elements). For example, colour \\nprocessing, image recognition, video processing, etc. \\n \\nOptical Image Processing:  An optical image of 3D object is is in \\nfact its 2D projection, which is the continuous distribution of light \\non a 2D surface. This 2D projection holds various types of \\ninformation like 3D objects that are in focus. It also includes the \\nstudy of the radiation source, and other optical processes. This \\noptical image is available in the optical form until it is converted \\ninto analog form, leading to the area of analog image processing. \\n \\nE4)  Direct or Active Imaging Systems : These are the systems \\nwhere no temporary storage on film or chemical processes is \\nrequired. In such systems the acquired images are in a \\nrecognizable form. We can say that in such systems the final \\nimage is a digital image. For example, human eye, digital \\ncamera, scanning confocal microscopes, etc. \\n \\nIndirect Imaging Systems: Incomparison to Direct imaging \\nsystems, the Indirect imaging systems involves data processing \\nor reconstruction before producing an image for observation. In \\nindirect imaging systems, the image is stored in a film, which is \\nrendered observable by a chemical process. Therefore, there is a \\ndelay in the production of images from films.  \\n \\nE5)  The bandwidth = 500 dots per inch in both directions \\n Therefore sample size = 1000 dots per inch [using sample \\ntheorem] \\n \\n Total number of samples = 5 x 1000 x 8 x 1000 = 40000000 \\n \\nE6)  An image may be continuous with respect to the x- and y- \\ncoordinates, and also in amplitude. Converting such an image to \\ndigital form requires that the coordinates, as well as the \\namplitude, be digitized.  \\n \\n Sampling is digitizing the coordinate values and quantization is \\ndigitizing the amplitude values.  \\n \\nE7) The dimensions of pixel is represented by x\\uf044and ,y\\uf044and they \\nare kept the same in both the horizontal and vertical directions, \\nso that the pixels will look like square pixels. This is called pixel \\n', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'),\n",
              " Document(id_='6c18f2e5-da26-4cc3-84fd-0383518631f3', embedding=None, metadata={'page_label': '26', 'file_name': 'dimage (1).pdf', 'file_path': '/content/data/dimage (1).pdf', 'file_type': 'application/pdf', 'file_size': 5513104, 'creation_date': '2024-03-25', 'last_modified_date': '2024-03-25'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={}, text=' \\n 34Block 1              Digital Image Processing-I  \\naspect ratio. Sometimes the size of the pixel is very large and \\nnumbers of pixels and very less, which makes the image \\ndistracting and meaning less. This is known as pixelization error.  \\n \\nE8) As per Shannon-Nyquist theorem, the sampling frequency should \\nbe greater than or equal to ,f2max\\uf0b4wheremaxfis the highest \\nfrequency present in the image. Otherwise, the original signal \\ncannot be reconstructed. In other words, the number of samples \\nrequired is dictated by Shannon-Nyquist theorem, this sampling \\ntheorem can be stated in terms of distance das .f21d\\nmax\\uf0a3 \\nAlternatively, it should be less than or equal to the smallest detail \\nthat is present in the image. \\n \\nE9) Pixel model:   In this model,  the RGB optical sensors maps to a \\nvector of discrete intensities which are integers.  Let \\nZZZBGR:p \\uf0b4\\uf0b4\\uf0ae\\uf0b4\\uf0b4  defined by ),zB,zG,zR()b,g,r(p\\uf03d where \\nwhere b,g,rare real valued colour intensities and zB,zG,zR  are \\ninteger-valued colour intensities in the range from 0 to 255. In  \\nthis case, a pixel is represented by a 31\\uf0b4column vector used to \\n\"paint\" a tiny square in an image display. \\nVector model:  In this model, the RGB optical sensor values are \\nmapped to vectors and the displayed image conforms to a form \\nof triangulated image space instead of a collection of pixels.  In \\nthis model, a vector image is a collection of vertices and edges.  \\nThe end result of vector images is the formation of a basis for \\napplications such as Adobe Photoshop and various video games. \\n \\nE10) The classification of an image can be done on the basis of \\n \\ni) Attributes \\nii) Colour \\niii) Dimensions  \\niv) Data type. \\n \\nE11) Monochrome images are the images where the colour \\ncomponent is absent, they are further classified as Grey scale \\nand binary images.  \\n \\nTrue colour (or full colour)  images are the images where the \\npixel has a colour that is obtained by mixing the primary colours \\nred, green, and blue. Each colour component is represented like \\na grey scale image using eight bits. Mostly, true colour images \\nuse 24 bits to represent all the colour. Hence true colour image \\ncan be considered as three-band images. The number of colours \\nthat is possible is 2563 (i.e. 256*256*256= 1,67,77,216 colours).  \\n \\nE12) Brightness: It refers to the average pixel intensity of the image, \\nSimilar to contrast, the image should be reasonably bright to \\nshow all the information to the viewer. However, excessive \\nbrightness may affect the quality of the image. An image may \\nhave a higher brightness but if the intensity is not optimal, the \\nimage again will not exhibit good quality. Brightness resolution  is \\ndefined as the number of identified distinct colours and it is also \\ncalled as colour resolution . \\n \\n', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'),\n",
              " Document(id_='3e4cf1fb-aac8-4eca-9e70-86d1ec817a81', embedding=None, metadata={'page_label': '27', 'file_name': 'dimage (1).pdf', 'file_path': '/content/data/dimage (1).pdf', 'file_type': 'application/pdf', 'file_size': 5513104, 'creation_date': '2024-03-25', 'last_modified_date': '2024-03-25'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={}, text=' \\n \\n 35 Unit 1                         Introduction to Digital Image  \\nWe know, Contrast of an image relates to recording of the \\ndifferences in the magnitude of the intensity at the surface of an \\nobject. So, Contrast is the difference between maximum and \\nminimum pixel intensities in an image. Consider two images A& \\nB having pixel intensities between 30 to 200 and 70 to 130, \\nrespectively. Then Ahas more contrast than .B Again contrast is \\nalso relative. Contrast can be simply explained as the difference \\nbetween maximum and minimum pixel intensity in an image. For \\nexample, consider an image, whose maximum and minimum \\nvalue of intensity is 100 i.e. same then contrast will be zero \\nbecause, Contrast = maximum pixel intensity – minimum pixel \\nintensity .0100100\\uf03d\\uf02d\\uf03d  \\n \\nE13) Pixel resolution , the term resolution refers to the total number of \\ncount of pixels in an digital image. For example. If an image has \\nMrows and Ncolumns, then its resolution can be defined as \\n.NM\\uf0b4If we define resolution as the total number of pixels, then \\npixel resolution can be defined with set of two numbers.  \\n \\nE14)   Refer Section 1.9 or Browse information over relevant websites. \\n', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n')]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "Mgog4UGX0tRb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "system_prompt=\"\"\"\n",
        "You are a Q&A assistance .Your goal is to answer questions as accurately as possible based on the instructions and context provided\n",
        "\"\"\""
      ],
      "metadata": {
        "id": "Isj2SoPB583b"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## Default format supportable by LLama2\n",
        "query_wrapper_prompt=SimpleInputPrompt(\"<|USER|>{query_str}<|ASSISTANT|>\")"
      ],
      "metadata": {
        "id": "42a-qswp6Ut-"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "! huggingface-cli login"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i4ZHbuS97MOu",
        "outputId": "8040da90-ad57-442b-bd0a-aadcf8928277"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "    _|    _|  _|    _|    _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|_|_|_|    _|_|      _|_|_|  _|_|_|_|\n",
            "    _|    _|  _|    _|  _|        _|          _|    _|_|    _|  _|            _|        _|    _|  _|        _|\n",
            "    _|_|_|_|  _|    _|  _|  _|_|  _|  _|_|    _|    _|  _|  _|  _|  _|_|      _|_|_|    _|_|_|_|  _|        _|_|_|\n",
            "    _|    _|  _|    _|  _|    _|  _|    _|    _|    _|    _|_|  _|    _|      _|        _|    _|  _|        _|\n",
            "    _|    _|    _|_|      _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|        _|    _|    _|_|_|  _|_|_|_|\n",
            "\n",
            "    To login, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens .\n",
            "Token: \n",
            "Add token as git credential? (Y/n) y\n",
            "Token is valid (permission: read).\n",
            "\u001b[1m\u001b[31mCannot authenticate through git-credential as no helper is defined on your machine.\n",
            "You might have to re-authenticate when pushing to the Hugging Face Hub.\n",
            "Run the following command in your terminal in case you want to set the 'store' credential helper as default.\n",
            "\n",
            "git config --global credential.helper store\n",
            "\n",
            "Read https://git-scm.com/book/en/v2/Git-Tools-Credential-Storage for more details.\u001b[0m\n",
            "Token has not been saved to git credential helper.\n",
            "Your token has been saved to /root/.cache/huggingface/token\n",
            "Login successful\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch"
      ],
      "metadata": {
        "id": "dTuh6tOZ7VLS"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "llm = HuggingFaceLLM(\n",
        "    context_window=4096,\n",
        "    max_new_tokens=256,\n",
        "    generate_kwargs={\"temperature\":0.0,\"do_sample\":False},\n",
        "    system_prompt=system_prompt,\n",
        "    query_wrapper_prompt=query_wrapper_prompt,\n",
        "    tokenizer_name=\"meta-llama/Llama-2-7b-chat-hf\",\n",
        "    model_name=\"meta-llama/Llama-2-7b-chat-hf\",\n",
        "    device_map=\"auto\",\n",
        "    # uncomment this if using CUDA to reduce memory usage\n",
        "    model_kwargs={\"torch_dtype\":torch.float16,\"load_in_8bit\":True}\n",
        "    # 14:30\n",
        "\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 246,
          "referenced_widgets": [
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            "3b7213b3e13541758f79c80407287e11",
            "28a0a8566e094a328ffbed669885be66",
            "16a222a9e759468ca5b44b65d392e32b",
            "e7561d8804744e9ba783c122b288287e",
            "2b3a4defc0e54f92969bddfb7aa7840b",
            "2d143ea1344b4364bfa4215bcf86dd20",
            "7cf5a05e578247189a026afe788fcc8b",
            "9b3507182fff48c1bae3d59deddba98f"
          ]
        },
        "id": "zSLbqRBe-KMn",
        "outputId": "acb94a82-f8f5-41ff-9782-32e56f6ea785"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The `load_in_4bit` and `load_in_8bit` arguments are deprecated and will be removed in the future versions. Please, pass a `BitsAndBytesConfig` object in `quantization_config` argument instead.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "951d9e9febe84bb48520055f82561149"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "generation_config.json:   0%|          | 0.00/188 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c141dc294a1a445fbda0ed8604528bab"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/1.62k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4744f76cfd174217945c33e994d7cba4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.model:   0%|          | 0.00/500k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a5d4c3dc1b08476697062640d7a792dd"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/1.84M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8f4f115f201344489f2515def2845da2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/414 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "98b653828eaf47e088ca63c4a5217f7b"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install llama-index-embeddings-langchain"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FfxzUG4IhIxp",
        "outputId": "2bcfc269-0bfb-4410-98c3-41968de7049a"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting llama-index-embeddings-langchain\n",
            "  Downloading llama_index_embeddings_langchain-0.1.2-py3-none-any.whl (2.5 kB)\n",
            "Requirement already satisfied: llama-index-core<0.11.0,>=0.10.1 in /usr/local/lib/python3.10/dist-packages (from llama-index-embeddings-langchain) (0.10.23.post1)\n",
            "Requirement already satisfied: PyYAML>=6.0.1 in /usr/local/lib/python3.10/dist-packages (from llama-index-core<0.11.0,>=0.10.1->llama-index-embeddings-langchain) (6.0.1)\n",
            "Requirement already satisfied: SQLAlchemy[asyncio]>=1.4.49 in /usr/local/lib/python3.10/dist-packages (from llama-index-core<0.11.0,>=0.10.1->llama-index-embeddings-langchain) (2.0.28)\n",
            "Requirement already satisfied: aiohttp<4.0.0,>=3.8.6 in /usr/local/lib/python3.10/dist-packages (from llama-index-core<0.11.0,>=0.10.1->llama-index-embeddings-langchain) (3.9.3)\n",
            "Requirement already satisfied: dataclasses-json in /usr/local/lib/python3.10/dist-packages (from llama-index-core<0.11.0,>=0.10.1->llama-index-embeddings-langchain) (0.6.4)\n",
            "Requirement already satisfied: deprecated>=1.2.9.3 in /usr/local/lib/python3.10/dist-packages (from llama-index-core<0.11.0,>=0.10.1->llama-index-embeddings-langchain) (1.2.14)\n",
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            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from llama-index-core<0.11.0,>=0.10.1->llama-index-embeddings-langchain) (2023.6.0)\n",
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            "Requirement already satisfied: networkx>=3.0 in /usr/local/lib/python3.10/dist-packages (from llama-index-core<0.11.0,>=0.10.1->llama-index-embeddings-langchain) (3.2.1)\n",
            "Requirement already satisfied: nltk<4.0.0,>=3.8.1 in /usr/local/lib/python3.10/dist-packages (from llama-index-core<0.11.0,>=0.10.1->llama-index-embeddings-langchain) (3.8.1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from llama-index-core<0.11.0,>=0.10.1->llama-index-embeddings-langchain) (1.25.2)\n",
            "Requirement already satisfied: openai>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from llama-index-core<0.11.0,>=0.10.1->llama-index-embeddings-langchain) (1.14.2)\n",
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            "Requirement already satisfied: pillow>=9.0.0 in /usr/local/lib/python3.10/dist-packages (from llama-index-core<0.11.0,>=0.10.1->llama-index-embeddings-langchain) (9.4.0)\n",
            "Requirement already satisfied: requests>=2.31.0 in /usr/local/lib/python3.10/dist-packages (from llama-index-core<0.11.0,>=0.10.1->llama-index-embeddings-langchain) (2.31.0)\n",
            "Requirement already satisfied: tenacity<9.0.0,>=8.2.0 in /usr/local/lib/python3.10/dist-packages (from llama-index-core<0.11.0,>=0.10.1->llama-index-embeddings-langchain) (8.2.3)\n",
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            "Requirement already satisfied: annotated-types>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from pydantic>=1.10->llamaindex-py-client<0.2.0,>=0.1.13->llama-index-core<0.11.0,>=0.10.1->llama-index-embeddings-langchain) (0.6.0)\n",
            "Requirement already satisfied: pydantic-core==2.16.3 in /usr/local/lib/python3.10/dist-packages (from pydantic>=1.10->llamaindex-py-client<0.2.0,>=0.1.13->llama-index-core<0.11.0,>=0.10.1->llama-index-embeddings-langchain) (2.16.3)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->llama-index-core<0.11.0,>=0.10.1->llama-index-embeddings-langchain) (1.16.0)\n",
            "Installing collected packages: llama-index-embeddings-langchain\n",
            "Successfully installed llama-index-embeddings-langchain-0.1.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.embeddings.huggingface import HuggingFaceEmbeddings\n",
        "from llama_index.core import ServiceContext\n",
        "from llama_index.embeddings.langchain import LangchainEmbedding\n",
        "\n",
        "embed_model = LangchainEmbedding(\n",
        "    HuggingFaceEmbeddings(model_name=\"sentence-transformers/all-mpnet-base-v2\")\n",
        ")\n"
      ],
      "metadata": {
        "id": "Nrmeh7yd_4X_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369,
          "referenced_widgets": [
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      "source": [
        "service_contex=ServiceContext.from_defaults(\n",
        "    chunk_size = 1024,\n",
        "    llm=llm,\n",
        "    embed_model=embed_model\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0hnDaof-Y4SZ",
        "outputId": "e87e32b3-ca97-43d3-832b-3bf5f3f0d781"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-25-7caa67443407>:1: DeprecationWarning: Call to deprecated class method from_defaults. (ServiceContext is deprecated, please use `llama_index.settings.Settings` instead.) -- Deprecated since version 0.10.0.\n",
            "  service_contex=ServiceContext.from_defaults(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "service_contex"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_LbVNKCZZTu7",
        "outputId": "ef0ab029-8108-457b-c14a-15965b665ee8"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ServiceContext(llm_predictor=LLMPredictor(system_prompt=None, query_wrapper_prompt=None, pydantic_program_mode=<PydanticProgramMode.DEFAULT: 'default'>), prompt_helper=PromptHelper(context_window=4096, num_output=256, chunk_overlap_ratio=0.1, chunk_size_limit=None, separator=' '), embed_model=LangchainEmbedding(model_name='sentence-transformers/all-mpnet-base-v2', embed_batch_size=10, callback_manager=<llama_index.core.callbacks.base.CallbackManager object at 0x7f3180ba57b0>), transformations=[SentenceSplitter(include_metadata=True, include_prev_next_rel=True, callback_manager=<llama_index.core.callbacks.base.CallbackManager object at 0x7f3180ba57b0>, id_func=<function default_id_func at 0x7f325e7bcf70>, chunk_size=1024, chunk_overlap=200, separator=' ', paragraph_separator='\\n\\n\\n', secondary_chunking_regex='[^,.;。？！]+[,.;。？！]?')], llama_logger=<llama_index.core.service_context_elements.llama_logger.LlamaLogger object at 0x7f31601fce20>, callback_manager=<llama_index.core.callbacks.base.CallbackManager object at 0x7f3180ba57b0>)"
            ]
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          "metadata": {},
          "execution_count": 26
        }
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        "index=VectorStoreIndex.from_documents(documents,service_context=service_contex)"
      ],
      "metadata": {
        "id": "1NBB0T57ZWcY"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "query_engine=index.as_query_engine()"
      ],
      "metadata": {
        "id": "ObbdTzNBZ0Lf"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "response=query_engine.query(\"what is digital image?\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "seYie_JmbH-J",
        "outputId": "f495b32e-9e69-4a72-dbbe-567fdd8c152e"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/configuration_utils.py:410: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.0` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/configuration_utils.py:415: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.9` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "response"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yn8DsulMbYgv",
        "outputId": "e4bea8b0-1487-44a1-aaec-3c7824d6256d"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Response(response='A digital image is a two-dimensional (2D) signal represented by a matrix of small pixels and elements. It is a digital representation of an image, where each pixel value is discrete and finite, and the amplitude of the digital image is finite. Digital image processing involves various techniques such as sampling and quantization to convert the image into its discrete form, which is suitable for further processing by digital computers.', source_nodes=[NodeWithScore(node=TextNode(id_='cf57f313-bd54-426e-9682-42de66c31501', embedding=None, metadata={'page_label': '2', 'file_name': 'dimage (1).pdf', 'file_path': '/content/data/dimage (1).pdf', 'file_type': 'application/pdf', 'file_size': 5513104, 'creation_date': '2024-03-25', 'last_modified_date': '2024-03-25'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={<NodeRelationship.SOURCE: '1'>: RelatedNodeInfo(node_id='090ae7e5-fb28-45f6-821d-7db5c3d6bb69', node_type=<ObjectType.DOCUMENT: '4'>, metadata={'page_label': '2', 'file_name': 'dimage (1).pdf', 'file_path': '/content/data/dimage (1).pdf', 'file_type': 'application/pdf', 'file_size': 5513104, 'creation_date': '2024-03-25', 'last_modified_date': '2024-03-25'}, hash='9f7fa18b68d68459a29b21297479ccba8711198b786e852a1836c7f26c10f734'), <NodeRelationship.PREVIOUS: '2'>: RelatedNodeInfo(node_id='3a9147ba-55a0-4ea7-92a3-650412816eda', node_type=<ObjectType.TEXT: '1'>, metadata={'page_label': '1', 'file_name': 'dimage (1).pdf', 'file_path': '/content/data/dimage (1).pdf', 'file_type': 'application/pdf', 'file_size': 5513104, 'creation_date': '2024-03-25', 'last_modified_date': '2024-03-25'}, hash='86db8407710da4c1a4d1a488aa46801dbdbd0389cf1140e71b8c4a432867ef3e'), <NodeRelationship.NEXT: '3'>: RelatedNodeInfo(node_id='674b9780-78a1-4203-961f-5c1dff11c81c', node_type=<ObjectType.TEXT: '1'>, metadata={}, hash='7dfa8b968f545eb0dc10f82e954f5647d8b82c35a485aeee8e5e86953d237a2b')}, text='10Block 1              Digital Image Processing-I  \\nWe shall begin our discussion by defining an image in Sec. 1.2 and will \\ncontinue image acquisition and image digitization in Sec. 1.3 and Sec. \\n1.4 respectively. In the subsequent sections, we shall define the basic \\nterms and concepts, which will be used throughout the course such as \\nrepresentation of image, resolution of image, characteristics of image \\nand types of images. We shall end the unit by discussing various areas \\nin which digital image processing is used. \\n Now, we shall list the objectives of this unit. After going through the unit, \\nplease read this list again and make sure that you have achieved the \\nobjectives. \\n \\nObjectives \\n \\nAfter going through this unit, you should be able to \\n \\n\\uf0b7 define and represent an image; \\n\\uf0b7 define the various terms used in digital image processing; \\n\\uf0b7 apply sampling and quantization for image digitization; \\n\\uf0b7 list various classifications of images along with their descriptions; \\n\\uf0b7 relate the areas where digital image processing can be applied. \\n \\nNow, let us find the answer to the question “what an image is?” in the \\nfollowing section . \\n \\n1.2 INTRODUCTION TO AN IMAGE? \\n \\nWhen we studied optics as a subject in the field of physics, we learned \\nthat when two or more rays of light meet at a point an image is formed, \\nand we studied various phenomenons like reflection, refraction, \\ndiffraction, dispersion, scattering etc., this means we were in the \\nprocess of image generation and analysis, even much before the \\ndevelopment of computers. But, with the advent of the computers in our \\nday to day life this field of image processing has transformed in to an \\nentirely new field i.e. digital image processing, where sampling and \\nquantization techniques are applied to convert the image into its \\ndiscrete form, this process of conversion is collectively known as image \\ndigitization, after digitization the image is readily available in a form \\nsuitable for further processing by digital computers. We shall discuss \\nabout the concept of sampling and quantization in the subsequent \\nsections, here we will discuss about the concept of image only.  \\n \\nMany times the term ‘picture’ is used, but this term relates to the analog \\nor raw image data and the term ‘image’ is used to refer to digital data \\nthat is suitable for the processing of images using digital computers. \\nInfact Images are imitations of a real world objects.Image may be \\nconsidered as a projection of the real world ( to be more precise 2D \\nprojection of 3D world). From a photographers point of view it is a \\nphotograph (i.e. projection of real world), and from the point of view of a \\ncomputer engineer an image may be a two-dimensional (2D) signal. \\nTherefore, an image is a two-dimensional function )y,x(fwhere for', start_char_idx=3, end_char_idx=2807, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), score=0.6672994298730245), NodeWithScore(node=TextNode(id_='8d2fd3c4-2810-4058-b628-ef5185454371', embedding=None, metadata={'page_label': '25', 'file_name': 'dimage (1).pdf', 'file_path': '/content/data/dimage (1).pdf', 'file_type': 'application/pdf', 'file_size': 5513104, 'creation_date': '2024-03-25', 'last_modified_date': '2024-03-25'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={<NodeRelationship.SOURCE: '1'>: RelatedNodeInfo(node_id='a4b92739-2e22-40ef-a672-18dc4286607b', node_type=<ObjectType.DOCUMENT: '4'>, metadata={'page_label': '25', 'file_name': 'dimage (1).pdf', 'file_path': '/content/data/dimage (1).pdf', 'file_type': 'application/pdf', 'file_size': 5513104, 'creation_date': '2024-03-25', 'last_modified_date': '2024-03-25'}, hash='95edfcf334e65234eb10ba4fe3646a90b0b213c071fae3b48bf102c8f948df0c'), <NodeRelationship.PREVIOUS: '2'>: RelatedNodeInfo(node_id='7cde8f80-c0d9-4f7e-99b8-67e5d3b5ca69', node_type=<ObjectType.TEXT: '1'>, metadata={'page_label': '24', 'file_name': 'dimage (1).pdf', 'file_path': '/content/data/dimage (1).pdf', 'file_type': 'application/pdf', 'file_size': 5513104, 'creation_date': '2024-03-25', 'last_modified_date': '2024-03-25'}, hash='11e7354b0e3fa66b0559fe305032378b1ac54df388ffb247e9b39dc5a8e52e36'), <NodeRelationship.NEXT: '3'>: RelatedNodeInfo(node_id='d4f5a831-e22c-4e4b-bd42-31d49b8d1be9', node_type=<ObjectType.TEXT: '1'>, metadata={}, hash='145f39e81b99562dfe5b6ffb9b5d915655345884ff1fdf7922f0dc9725cf778c')}, text='33 Unit 1                         Introduction to Digital Image  \\ni. Digital image’s pixel value must be discrete whereas \\nAnalog image’s pixel value must be continuous. \\n \\nii. The amplitude of digital image is finite whereas the \\namplitude of analog image is infinite. \\n \\niii. It is quite possible to store all the pixels of digital image \\nwhereas It is quite impossible to store all the pixels of \\nanalog image. \\n \\nE3)  Analog Image Processing:  It is applied on analog \\nsignals/images and it processes only 2 D images. Examples are \\ntelevision, images, photographs, painting and medical images, \\netc. \\n \\n Digital Image Processing:  It is applied to digital images (a \\nmatrix of small pixels and elements). For example, colour \\nprocessing, image recognition, video processing, etc. \\n \\nOptical Image Processing:  An optical image of 3D object is is in \\nfact its 2D projection, which is the continuous distribution of light \\non a 2D surface. This 2D projection holds various types of \\ninformation like 3D objects that are in focus. It also includes the \\nstudy of the radiation source, and other optical processes. This \\noptical image is available in the optical form until it is converted \\ninto analog form, leading to the area of analog image processing. \\n \\nE4)  Direct or Active Imaging Systems : These are the systems \\nwhere no temporary storage on film or chemical processes is \\nrequired. In such systems the acquired images are in a \\nrecognizable form. We can say that in such systems the final \\nimage is a digital image. For example, human eye, digital \\ncamera, scanning confocal microscopes, etc. \\n \\nIndirect Imaging Systems: Incomparison to Direct imaging \\nsystems, the Indirect imaging systems involves data processing \\nor reconstruction before producing an image for observation. In \\nindirect imaging systems, the image is stored in a film, which is \\nrendered observable by a chemical process. Therefore, there is a \\ndelay in the production of images from films.  \\n \\nE5)  The bandwidth = 500 dots per inch in both directions \\n Therefore sample size = 1000 dots per inch [using sample \\ntheorem] \\n \\n Total number of samples = 5 x 1000 x 8 x 1000 = 40000000 \\n \\nE6)  An image may be continuous with respect to the x- and y- \\ncoordinates, and also in amplitude. Converting such an image to \\ndigital form requires that the coordinates, as well as the \\namplitude, be digitized.  \\n \\n Sampling is digitizing the coordinate values and quantization is \\ndigitizing the amplitude values.  \\n \\nE7) The dimensions of pixel is represented by x\\uf044and ,y\\uf044and they \\nare kept the same in both the horizontal and vertical directions, \\nso that the pixels will look like square pixels. 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