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leafAfflictions_MODELS/correct_model.ipynb

+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "source": [
+        "import cv2\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "def is_likely_leaf(img_rgb, mask):\n",
+        "    total_pixels = mask.sum() / 255  # Count of white pixels in the mask\n",
+        "\n",
+        "    # If the object is too small, it's likely not a leaf\n",
+        "    if total_pixels < 5000:  # this threshold might need adjustment based on your image resolutions\n",
+        "        return False\n",
+        "\n",
+        "    # Convert image to HSV\n",
+        "    hsv = cv2.cvtColor(img_rgb, cv2.COLOR_RGB2HSV)\n",
+        "\n",
+        "    # Define a mask for green pixels\n",
+        "    green_mask = cv2.inRange(hsv, (28, 0, 26), (95, 255, 255))\n",
+        "\n",
+        "    # Count green pixels within the object (leaf)\n",
+        "    green_pixels = cv2.bitwise_and(green_mask, green_mask, mask=mask).sum() / 255\n",
+        "\n",
+        "    green_ratio = green_pixels / total_pixels\n",
+        "\n",
+        "    # If more than 30% of the leaf is green, it's likely a leaf\n",
+        "    if green_ratio > 0.3:  # adjust this threshold as needed\n",
+        "        return True\n",
+        "    return False\n",
+        "\n",
+        "input_path = 'acc_check4.png'\n",
+        "\n",
+        "# Open the image\n",
+        "img = cv2.imread(input_path)\n",
+        "\n",
+        "# Convert to RGB for matplotlib\n",
+        "img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
+        "\n",
+        "# Convert the image to grayscale\n",
+        "gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
+        "\n",
+        "# Blur the grayscale image\n",
+        "blurred_gray = cv2.GaussianBlur(gray, (5, 5), 0)\n",
+        "\n",
+        "# Adaptive thresholding to binarize the image\n",
+        "binary = cv2.adaptiveThreshold(blurred_gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 11, 2)\n",
+        "\n",
+        "# Morphological operations to refine the binary mask\n",
+        "kernel = np.ones((5, 5), np.uint8)\n",
+        "binary = cv2.morphologyEx(binary, cv2.MORPH_CLOSE, kernel)\n",
+        "\n",
+        "# Find the contours of the image\n",
+        "contours, _ = cv2.findContours(binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n",
+        "\n",
+        "# Create a mask of the leaf\n",
+        "mask = np.zeros_like(gray)\n",
+        "if contours:\n",
+        "    leaf_contour = max(contours, key=cv2.contourArea)\n",
+        "    cv2.drawContours(mask, [leaf_contour], -1, (255), thickness=cv2.FILLED)\n",
+        "\n",
+        "# Extract the leaf with a gray background\n",
+        "gray_background = np.ones_like(img_rgb) * 127  # A (127,127,127) RGB image\n",
+        "leaf_on_gray = cv2.bitwise_and(gray_background, gray_background, mask=cv2.bitwise_not(mask)) + cv2.bitwise_and(img_rgb, img_rgb, mask=mask)\n",
+        "\n",
+        "# Check if it's likely a leaf\n",
+        "if is_likely_leaf(leaf_on_gray, mask):\n",
+        "    # Convert the leaf image to HSV color space\n",
+        "    img_HSV = cv2.cvtColor(leaf_on_gray, cv2.COLOR_RGB2HSV)\n",
+        "\n",
+        "    # Detecting healthy areas using the HSV range for healthy green color\n",
+        "    healthy_mask = cv2.inRange(img_HSV, (28,0,26), (95,255,255))\n",
+        "    inverse_healthy_mask = cv2.bitwise_not(healthy_mask)\n",
+        "\n",
+        "     # Calculate the total area of the leaf (white pixels in the mask after background removal)\n",
+        "    total_leaf_area = np.sum(mask == 255)\n",
+        "\n",
+        "    # Calculate the afflicted area of the leaf\n",
+        "    afflicted_area = np.sum((inverse_healthy_mask == 255) & (mask == 255))\n",
+        "\n",
+        "    # Calculate the affliction percentage\n",
+        "    affliction_percentage = (afflicted_area / total_leaf_area) * 100\n",
+        "\n",
+        "    print(f\"Affliction Percentage: {affliction_percentage:.2f}%\")\n",
+        "\n",
+        "    # Generate final mask for display (healthy = black, afflictions = white, background = gray)\n",
+        "    final_mask_display = np.where(mask == 255, inverse_healthy_mask, 127)\n",
+        "\n",
+        "    # Display images\n",
+        "    plt.figure(figsize=(20, 5))\n",
+        "\n",
+        "    plt.subplot(1, 4, 1)\n",
+        "    plt.imshow(leaf_on_gray)\n",
+        "    plt.title('Leaf with Gray Background')\n",
+        "\n",
+        "    plt.subplot(1, 4, 2)\n",
+        "    plt.imshow(cv2.bitwise_and(leaf_on_gray, leaf_on_gray, mask=healthy_mask))\n",
+        "    plt.title('Healthy Areas on Leaf')\n",
+        "\n",
+        "    plt.subplot(1, 4, 3)\n",
+        "    plt.imshow(cv2.bitwise_and(leaf_on_gray, leaf_on_gray, mask=inverse_healthy_mask))\n",
+        "    plt.title('Afflictions on Leaf')\n",
+        "\n",
+        "    plt.subplot(1, 4, 4)\n",
+        "    plt.imshow(final_mask_display, cmap='gray')\n",
+        "    plt.title('Mask Image')\n",
+        "\n",
+        "    plt.tight_layout()\n",
+        "    plt.show()\n",
+        "\n",
+        "else:\n",
+        "    print(\"Please upload a clear picture of a leaf.\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 419
+        },
+        "id": "SjPqEEX5kvWv",
+        "outputId": "feee4df4-872c-4dbe-9ccd-c55116a10ef9"
+      },
+      "execution_count": 24,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Affliction Percentage: 26.00%\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 2000x500 with 4 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "pBj7eRi3k8Nf"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file