Graph_Analysis.ipynb

{
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  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!apt-get install tesseract-ocr"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cvuf9-EEiKiC",
        "outputId": "85a8fd68-3e74-45f5-936f-21e3a5212b7d"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree... Done\n",
            "Reading state information... Done\n",
            "tesseract-ocr is already the newest version (4.1.1-2.1build1).\n",
            "0 upgraded, 0 newly installed, 0 to remove and 49 not upgraded.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Phmj5EvQiKfb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pytesseract opencv-python-headless"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V2ffgz0fiKcF",
        "outputId": "a39eaf20-bcbd-478d-9c4e-994518fc7db0"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: pytesseract in /usr/local/lib/python3.10/dist-packages (0.3.13)\n",
            "Requirement already satisfied: opencv-python-headless in /usr/local/lib/python3.10/dist-packages (4.10.0.84)\n",
            "Requirement already satisfied: packaging>=21.3 in /usr/local/lib/python3.10/dist-packages (from pytesseract) (24.2)\n",
            "Requirement already satisfied: Pillow>=8.0.0 in /usr/local/lib/python3.10/dist-packages (from pytesseract) (11.0.0)\n",
            "Requirement already satisfied: numpy>=1.21.2 in /usr/local/lib/python3.10/dist-packages (from opencv-python-headless) (1.26.4)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ksQMZcg5iKZD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "1-oOR5L3iTGB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import cv2  # Import OpenCV for image processing"
      ],
      "metadata": {
        "id": "ein9bdaRjW9e"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pytesseract  # Import pytesseract for OCR (Optical Character Recognition)"
      ],
      "metadata": {
        "id": "LyRQlJIajXAr"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab.patches import cv2_imshow  # This is for displaying images in Colab"
      ],
      "metadata": {
        "id": "rJblyfGxjXCy"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "8-3FWX9XkXav"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "3YSjONTqkXew"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the image"
      ],
      "metadata": {
        "id": "yC_Bb522kXht"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "image_path = '/content/Chart.png'  # Specify the path to the image"
      ],
      "metadata": {
        "id": "kiIF9AK3kXkl"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "img = cv2.imread(image_path)  # Read the image from the path"
      ],
      "metadata": {
        "id": "M7Nx1s7DjXGD"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "7mrh883jjXIU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "hmwboYomjXLf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert the image to grayscale for better text recognition"
      ],
      "metadata": {
        "id": "hSA4zl1HjXOx"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)  # Convert image to grayscale to improve OCR performance\n"
      ],
      "metadata": {
        "id": "C8M-CKuejXRL"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "lyXpMF0qjOcv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Perform OCR to extract text"
      ],
      "metadata": {
        "id": "b50aOm3YjC6b"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "extracted_text = pytesseract.image_to_string(gray)  # Use pytesseract to extract text from the grayscale image\n"
      ],
      "metadata": {
        "id": "_u0jN-mjjAGd"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "hC7Y9HJVlBX9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "fm_10ppelBbm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Print the raw extracted text"
      ],
      "metadata": {
        "id": "3zYGjAEalBgG"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Raw Extracted Text:\")  # Label for raw extracted text output\n",
        "print(extracted_text)  # Print the raw text extracted from the image"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0PLIIdWNlBiq",
        "outputId": "ed28044a-8c20-4e25-f48a-a98f18bf6619"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Raw Extracted Text:\n",
            " \n",
            "\n",
            " \n",
            "\n",
            "Performance - Price Comparison aN\n",
            "1,600\n",
            "1,400\n",
            "1,200\n",
            "4,000\n",
            "\n",
            " \n",
            "\n",
            "score\n",
            "\n",
            "Price\n",
            "\n",
            " \n",
            "\n",
            " \n",
            "\n",
            "Model1  Model2- Model3.-- Model4.— Model’5\n",
            "\n",
            " \n",
            "\n",
            " \n",
            "\f\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "gvtN3_WBlH1x"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "eh5D8ma6lH5B"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Organizing the extracted text into a more structured format"
      ],
      "metadata": {
        "id": "23SqKq-LlH8v"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "title = extracted_text.split('\\n')[0]  # First line as the title"
      ],
      "metadata": {
        "id": "utFZJDYolIAT"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_lines = extracted_text.split('\\n')[1:]  # Remaining lines as raw data"
      ],
      "metadata": {
        "id": "WOvBuy-xlIDD"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "25HxWLSGlPd1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "maszQMLzlPgx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Display organized details from the graph"
      ],
      "metadata": {
        "id": "bBxNg0DElPj7"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Title: {title}\")  # Display the extracted title\n",
        "print(\"Data Points and Labels:\")  # Label for data points and labels section\n",
        "for line in data_lines:  # Loop through each line of extracted data\n",
        "    line = line.strip()  # Remove leading and trailing whitespace from each line\n",
        "    if line:  # Only print non-empty lines\n",
        "        print(f\"  - {line}\")  # Print each non-empty line as a data point or label\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A3_fhHHXlPoL",
        "outputId": "0fae91bb-7b59-431c-a15b-a0bdb2f56d44"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Title:  \n",
            "Data Points and Labels:\n",
            "  - Performance - Price Comparison aN\n",
            "  - 1,600\n",
            "  - 1,400\n",
            "  - 1,200\n",
            "  - 4,000\n",
            "  - score\n",
            "  - Price\n",
            "  - Model1  Model2- Model3.-- Model4.— Model’5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "v7Y6tB4vlVo1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "AdDdjjS2lVrs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Additional analysis of graph elements like axis labels and gridlines\n",
        "# Assuming OCR has returned labels and values for axes, you can search for those in the text\n"
      ],
      "metadata": {
        "id": "mU_g5akhlVvm"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# You may need to adjust based on the structure of the OCR text"
      ],
      "metadata": {
        "id": "CVMbgWE-lVzF"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_axis_label = \"X-Axis\"  # Placeholder for X-axis label"
      ],
      "metadata": {
        "id": "SyF0gqvIlV10"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_axis_label = \"Y-Axis\"  # Placeholder for Y-axis label"
      ],
      "metadata": {
        "id": "XzYGxpdFlrGi"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_points = []  # Initialize an empty list to collect data points"
      ],
      "metadata": {
        "id": "COz1cXi7lrKJ"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_names = []  # Initialize an empty list to collect model names"
      ],
      "metadata": {
        "id": "Kcirc4_olrOf"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "-pS2dQeRl0x3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "qv3aCJIOl02n"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# For now, manually input data points based on what you can see in the OCR output\n",
        "# This should be updated with actual values from the OCR result or identified visually"
      ],
      "metadata": {
        "id": "Tb5p5Elql06a"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_points = [1600, 1400, 1200, 4000]  # Example data points from the graph"
      ],
      "metadata": {
        "id": "vx_QceLNlrRl"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_names = [\"Model1\", \"Model2\", \"Model3\", \"Model4\", \"Model5\"]  # Example model names from the graph\n"
      ],
      "metadata": {
        "id": "Ay7spFCslrU3"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ikcU0SN1l81p"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "-cYhuTXkl84c"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Now extract trends or patterns from the data points"
      ],
      "metadata": {
        "id": "tKikp61nl87i"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def identify_trends(data_points):  # Function to identify the trend in the data points\n",
        "    if data_points == sorted(data_points):  # Check if data points are sorted in increasing order\n",
        "        return \"Increasing\"  # If sorted in increasing order, return \"Increasing\"\n",
        "    elif data_points == sorted(data_points, reverse=True):  # Check if data points are sorted in decreasing order\n",
        "        return \"Decreasing\"  # If sorted in decreasing order, return \"Decreasing\"\n",
        "    else:  # If data points are not sorted in any clear order\n",
        "        return \"No Clear Trend\"  # Return \"No Clear Trend\" if no clear trend is identified\n"
      ],
      "metadata": {
        "id": "QlIDiPNBl8-x"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "U3zkssVRl9CQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trend = identify_trends(data_points)  # Call the function to identify the trend based on data points\n"
      ],
      "metadata": {
        "id": "7e4DuEhqi7sf"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "-dIb2aQ9mD3F"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "RX8--GgJmD6N"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Print detailed graph information"
      ],
      "metadata": {
        "id": "EsMQgatNmD-C"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nDetailed Graph Information:\")  # Label for detailed graph information section\n",
        "print(f\"Title: {title}\")  # Print the graph title\n",
        "print(f\"X-Axis Label: {x_axis_label}\")  # Print the X-axis label\n",
        "print(f\"Y-Axis Label: {y_axis_label}\")  # Print the Y-axis label\n",
        "print(f\"Models: {', '.join(model_names)}\")  # Print the model names in a comma-separated format\n",
        "print(f\"Data Points: {data_points}\")  # Print the data points list\n",
        "print(f\"Trend Analysis: {trend}\")  # Print the identified trend"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aSyUqQZCmEBJ",
        "outputId": "4b26fb9b-c18e-4d34-eaf1-7a2f9418555f"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Detailed Graph Information:\n",
            "Title:  \n",
            "X-Axis Label: X-Axis\n",
            "Y-Axis Label: Y-Axis\n",
            "Models: Model1, Model2, Model3, Model4, Model5\n",
            "Data Points: [1600, 1400, 1200, 4000]\n",
            "Trend Analysis: No Clear Trend\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "pFbAgkPRmKKI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "-JPsdIazmLB3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "WJVj7v6lmLLA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Optional: Edge detection and gridline detection using Canny (for further image analysis)\n"
      ],
      "metadata": {
        "id": "iLjp6fsvmLZc"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "edges = cv2.Canny(gray, 100, 200)  # Perform Canny edge detection to find edges in the image\n"
      ],
      "metadata": {
        "id": "1SuVlMhImWBY"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "VZZje0VhmWEy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Use cv2_imshow to display the edges in the image"
      ],
      "metadata": {
        "id": "VwcOCU2DmadA"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cv2_imshow(edges)  # Display the edges in the image (visual aid for edge detection)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333
        },
        "id": "2t58XedpmWKK",
        "outputId": "e2263c30-12b0-45ff-9eea-7bcacda609db"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=L size=508x316>"
            ],
            "image/png": 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\n",
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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "OrvrM1I4me-A"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "J9LWwWp5mfBQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Alternatively, use contour detection to find the gridlines and data points"
      ],
      "metadata": {
        "id": "AOQZsegemfNu"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "contours, _ = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)  # Find contours in the edges\n"
      ],
      "metadata": {
        "id": "3ju7ZXoamWOV"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for contour in contours:  # Loop through each detected contour\n",
        "    cv2.drawContours(img, [contour], -1, (0, 255, 0), 2)  # Draw each contour on the image in green\n"
      ],
      "metadata": {
        "id": "I3RYCe7ZmWSK"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "KHMlWb_bml1w"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "KkOU6eSHml65"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Use cv2_imshow to display the contours in the image"
      ],
      "metadata": {
        "id": "Z_FX4mgemmHo"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cv2_imshow(img)  # Display the contours overlaid on the image (visual aid for contour detection)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333
        },
        "id": "wYdbWyejmojI",
        "outputId": "9c8b6215-df77-4c5c-fd81-5670e4268846"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=508x316>"
            ],
            "image/png": 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\n",
            "image/jpeg": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "YvM1RxbTmoms"
      },
      "execution_count": 66,
      "outputs": []
    }
  ]
}