PB_Anshu_Preprocess.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed and cropped image saved as: E:\\TCV_project\\Teacult\\new_data\\TV_11_1\\IMG20241120145942.jpg\n",
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     ]
    }
   ],
   "source": [
    "#this code crops the black boundary and makes it properly aligned\n",
    "import cv2\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "# Set the path to the folder containing images\n",
    "folder_path = r\"E:\\TCV_project\\Teacult\\new_data\\TV_11\"\n",
    "\n",
    "# Set the path for saving processed images\n",
    "output_folder = r\"E:\\TCV_project\\Teacult\\new_data\\TV_11_1\"\n",
    "\n",
    "# Create the output folder if it does not exist\n",
    "os.makedirs(output_folder, exist_ok=True)\n",
    "\n",
    "# Get a list of all image files in the folder\n",
    "image_files = [f for f in os.listdir(folder_path) if f.endswith(('.png', '.jpg', '.jpeg'))]\n",
    "\n",
    "for image_file in image_files:\n",
    "    image_path = os.path.join(folder_path, image_file)\n",
    "    \n",
    "    # Read the current image\n",
    "    image = cv2.imread(image_path)\n",
    "\n",
    "    # Convert to grayscale\n",
    "    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "    # Apply GaussianBlur to reduce noise and improve thresholding\n",
    "    blurred = cv2.GaussianBlur(gray, (5, 5), 0)\n",
    "\n",
    "    # Use adaptive thresholding to create a binary mask\n",
    "    binary_mask = cv2.adaptiveThreshold(blurred, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, \n",
    "                                        cv2.THRESH_BINARY_INV, 11, 2)\n",
    "\n",
    "    # Find contours of the A4 sheet (largest contour)\n",
    "    contours, _ = cv2.findContours(binary_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n",
    "\n",
    "    # Filter contours to find the one that most likely corresponds to the A4 sheet\n",
    "    a4_contour = max(contours, key=cv2.contourArea)\n",
    "\n",
    "    # Approximate the contour to get a polygon (4 corners)\n",
    "    epsilon = 0.02 * cv2.arcLength(a4_contour, True)\n",
    "    approx = cv2.approxPolyDP(a4_contour, epsilon, True)\n",
    "\n",
    "    if len(approx) == 4:  # Check if we have found a quadrilateral\n",
    "        # Order the points\n",
    "        pts = approx.reshape(4, 2)\n",
    "        rect = np.zeros((4, 2), dtype=\"float32\")\n",
    "\n",
    "        # Top-left point has the smallest sum, bottom-right has the largest sum\n",
    "        s = pts.sum(axis=1)\n",
    "        rect[0] = pts[np.argmin(s)]  # Top-left\n",
    "        rect[2] = pts[np.argmax(s)]  # Bottom-right\n",
    "\n",
    "        # Bottom-left will have the smallest difference, top-right has the largest difference\n",
    "        diff = np.diff(pts, axis=1)\n",
    "        rect[1] = pts[np.argmin(diff)]  # Top-right\n",
    "        rect[3] = pts[np.argmax(diff)]  # Bottom-left\n",
    "\n",
    "        # Compute the width and height of the new image\n",
    "        widthA = np.sqrt(((rect[2][0] - rect[3][0]) ** 2) + ((rect[2][1] - rect[3][1]) ** 2))\n",
    "        widthB = np.sqrt(((rect[1][0] - rect[0][0]) ** 2) + ((rect[1][1] - rect[0][1]) ** 2))\n",
    "        maxWidth = max(int(widthA), int(widthB))\n",
    "\n",
    "        heightA = np.sqrt(((rect[1][0] - rect[2][0]) ** 2) + ((rect[1][1] - rect[2][1]) ** 2))\n",
    "        heightB = np.sqrt(((rect[0][0] - rect[3][0]) ** 2) + ((rect[0][1] - rect[3][1]) ** 2))\n",
    "        maxHeight = max(int(heightA), int(heightB))\n",
    "\n",
    "        # Set destination points for the perspective transformation\n",
    "        dst = np.array([[0, 0], [maxWidth - 1, 0], [maxWidth - 1, maxHeight - 1], [0, maxHeight - 1]], dtype=\"float32\")\n",
    "\n",
    "        # Compute the perspective transformation matrix\n",
    "        M = cv2.getPerspectiveTransform(rect, dst)\n",
    "\n",
    "        # Perform the perspective warp\n",
    "        warped = cv2.warpPerspective(image, M, (maxWidth, maxHeight))\n",
    "\n",
    "        # Save the processed image\n",
    "        output_path = os.path.join(output_folder, image_file)\n",
    "        cv2.imwrite(output_path, warped)\n",
    "\n",
    "        print(f\"Processed and cropped image saved as: {output_path}\")\n",
    "    else:\n",
    "        print(f\"Could not find a quadrilateral in image: {image_file}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Leaf-only image saved as: E:\\TCV_project\\Teacult\\new_data\\TV_13_processed\\IMG20241121105719.jpg\n",
      "Leaf-only image saved as: E:\\TCV_project\\Teacult\\new_data\\TV_13_processed\\IMG20241121105749.jpg\n",
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      "Leaf-only image saved as: E:\\TCV_project\\Teacult\\new_data\\TV_13_processed\\IMG20241121110821.jpg\n",
      "Leaf-only image saved as: E:\\TCV_project\\Teacult\\new_data\\TV_13_processed\\IMG20241121110832.jpg\n",
      "Leaf-only image saved as: E:\\TCV_project\\Teacult\\new_data\\TV_13_processed\\IMG20241121110840.jpg\n",
      "Leaf-only image saved as: E:\\TCV_project\\Teacult\\new_data\\TV_13_processed\\IMG20241121110848.jpg\n",
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      "Leaf-only image saved as: E:\\TCV_project\\Teacult\\new_data\\TV_13_processed\\IMG20241121110906.jpg\n",
      "Leaf-only image saved as: E:\\TCV_project\\Teacult\\new_data\\TV_13_processed\\IMG20241121110914.jpg\n",
      "Leaf-only image saved as: E:\\TCV_project\\Teacult\\new_data\\TV_13_processed\\IMG20241121110922.jpg\n",
      "Leaf-only image saved as: E:\\TCV_project\\Teacult\\new_data\\TV_13_processed\\IMG20241121110933.jpg\n",
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      "Leaf-only image saved as: E:\\TCV_project\\Teacult\\new_data\\TV_13_processed\\IMG20241121110956.jpg\n",
      "Leaf-only image saved as: E:\\TCV_project\\Teacult\\new_data\\TV_13_processed\\IMG20241121111008.jpg\n",
      "Leaf-only image saved as: E:\\TCV_project\\Teacult\\new_data\\TV_13_processed\\IMG20241121111018.jpg\n",
      "Leaf-only image saved as: E:\\TCV_project\\Teacult\\new_data\\TV_13_processed\\IMG20241121111025.jpg\n",
      "Leaf-only image saved as: E:\\TCV_project\\Teacult\\new_data\\TV_13_processed\\IMG20241121111042.jpg\n",
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      "Leaf-only image saved as: E:\\TCV_project\\Teacult\\new_data\\TV_13_processed\\IMG20241121111056.jpg\n",
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      "Leaf-only image saved as: E:\\TCV_project\\Teacult\\new_data\\TV_13_processed\\IMG20241121111114.jpg\n",
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      "Leaf-only image saved as: E:\\TCV_project\\Teacult\\new_data\\TV_13_processed\\IMG20241121111139.jpg\n",
      "Leaf-only image saved as: E:\\TCV_project\\Teacult\\new_data\\TV_13_processed\\IMG20241121111151.jpg\n",
      "Leaf-only image saved as: E:\\TCV_project\\Teacult\\new_data\\TV_13_processed\\IMG20241121111159.jpg\n",
      "Leaf-only image saved as: E:\\TCV_project\\Teacult\\new_data\\TV_13_processed\\IMG20241121111211.jpg\n",
      "Leaf-only image saved as: E:\\TCV_project\\Teacult\\new_data\\TV_13_processed\\IMG20241121111222.jpg\n",
      "Leaf-only image saved as: E:\\TCV_project\\Teacult\\new_data\\TV_13_processed\\IMG20241121111229.jpg\n",
      "Leaf-only image saved as: E:\\TCV_project\\Teacult\\new_data\\TV_13_processed\\IMG20241121111236.jpg\n",
      "Leaf-only image saved as: E:\\TCV_project\\Teacult\\new_data\\TV_13_processed\\IMG20241121111248.jpg\n",
      "Leaf-only image saved as: E:\\TCV_project\\Teacult\\new_data\\TV_13_processed\\IMG20241121111256.jpg\n",
      "Leaf-only image saved as: E:\\TCV_project\\Teacult\\new_data\\TV_13_processed\\IMG20241121111305.jpg\n",
      "Leaf-only image saved as: E:\\TCV_project\\Teacult\\new_data\\TV_13_processed\\IMG20241121111312.jpg\n",
      "Leaf-only image saved as: E:\\TCV_project\\Teacult\\new_data\\TV_13_processed\\IMG20241121111330.jpg\n",
      "Leaf-only image saved as: E:\\TCV_project\\Teacult\\new_data\\TV_13_processed\\IMG20241121111334.jpg\n",
      "Leaf-only image saved as: E:\\TCV_project\\Teacult\\new_data\\TV_13_processed\\IMG20241121111342.jpg\n",
      "Leaf-only image saved as: E:\\TCV_project\\Teacult\\new_data\\TV_13_processed\\IMG20241121111351.jpg\n",
      "Leaf-only image saved as: E:\\TCV_project\\Teacult\\new_data\\TV_13_processed\\IMG20241121111401.jpg\n",
      "Leaf-only image saved as: E:\\TCV_project\\Teacult\\new_data\\TV_13_processed\\IMG20241121111413.jpg\n",
      "Leaf-only image saved as: E:\\TCV_project\\Teacult\\new_data\\TV_13_processed\\IMG20241121111421.jpg\n",
      "Leaf-only image saved as: E:\\TCV_project\\Teacult\\new_data\\TV_13_processed\\IMG20241121111433.jpg\n",
      "Leaf-only image saved as: E:\\TCV_project\\Teacult\\new_data\\TV_13_processed\\IMG20241121111443.jpg\n",
      "Leaf-only image saved as: E:\\TCV_project\\Teacult\\new_data\\TV_13_processed\\IMG20241121111452.jpg\n",
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      "Leaf-only image saved as: E:\\TCV_project\\Teacult\\new_data\\TV_13_processed\\IMG20241121111506.jpg\n",
      "Leaf-only image saved as: E:\\TCV_project\\Teacult\\new_data\\TV_13_processed\\IMG20241121111521.jpg\n",
      "Leaf-only image saved as: E:\\TCV_project\\Teacult\\new_data\\TV_13_processed\\IMG20241121111600.jpg\n",
      "Leaf-only image saved as: E:\\TCV_project\\Teacult\\new_data\\TV_13_processed\\IMG20241121111630.jpg\n",
      "Leaf-only image saved as: E:\\TCV_project\\Teacult\\new_data\\TV_13_processed\\IMG20241121111640.jpg\n",
      "Leaf-only image saved as: E:\\TCV_project\\Teacult\\new_data\\TV_13_processed\\IMG20241121111647.jpg\n",
      "Leaf-only image saved as: E:\\TCV_project\\Teacult\\new_data\\TV_13_processed\\IMG20241121111654.jpg\n",
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      "Leaf-only image saved as: E:\\TCV_project\\Teacult\\new_data\\TV_13_processed\\IMG20241121111707.jpg\n",
      "Leaf-only image saved as: E:\\TCV_project\\Teacult\\new_data\\TV_13_processed\\IMG20241121111714.jpg\n",
      "Leaf-only image saved as: E:\\TCV_project\\Teacult\\new_data\\TV_13_processed\\IMG20241121111722.jpg\n",
      "Leaf-only image saved as: E:\\TCV_project\\Teacult\\new_data\\TV_13_processed\\IMG20241121111732.jpg\n",
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      "Leaf-only image saved as: E:\\TCV_project\\Teacult\\new_data\\TV_13_processed\\IMG20241121111751.jpg\n",
      "Leaf-only image saved as: E:\\TCV_project\\Teacult\\new_data\\TV_13_processed\\IMG20241121111800.jpg\n",
      "Leaf-only image saved as: E:\\TCV_project\\Teacult\\new_data\\TV_13_processed\\IMG20241121111806.jpg\n",
      "Leaf-only image saved as: E:\\TCV_project\\Teacult\\new_data\\TV_13_processed\\IMG20241121111817.jpg\n",
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      "Leaf-only image saved as: E:\\TCV_project\\Teacult\\new_data\\TV_13_processed\\IMG20241121111834.jpg\n",
      "Leaf-only image saved as: E:\\TCV_project\\Teacult\\new_data\\TV_13_processed\\IMG20241121111846.jpg\n",
      "Leaf-only image saved as: E:\\TCV_project\\Teacult\\new_data\\TV_13_processed\\IMG20241121111853.jpg\n",
      "Leaf-only image saved as: E:\\TCV_project\\Teacult\\new_data\\TV_13_processed\\IMG20241121111859.jpg\n",
      "Leaf-only image saved as: E:\\TCV_project\\Teacult\\new_data\\TV_13_processed\\IMG20241121111907.jpg\n",
      "Leaf-only image saved as: E:\\TCV_project\\Teacult\\new_data\\TV_13_processed\\IMG20241121111915.jpg\n",
      "Leaf-only image saved as: E:\\TCV_project\\Teacult\\new_data\\TV_13_processed\\IMG20241121111925.jpg\n",
      "Leaf-only image saved as: E:\\TCV_project\\Teacult\\new_data\\TV_13_processed\\IMG20241121111938.jpg\n",
      "Leaf-only image saved as: E:\\TCV_project\\Teacult\\new_data\\TV_13_processed\\IMG20241121111945.jpg\n",
      "Leaf-only image saved as: E:\\TCV_project\\Teacult\\new_data\\TV_13_processed\\IMG20241121111955.jpg\n",
      "Leaf-only image saved as: E:\\TCV_project\\Teacult\\new_data\\TV_13_processed\\IMG20241121112002.jpg\n",
      "Leaf-only image saved as: E:\\TCV_project\\Teacult\\new_data\\TV_13_processed\\IMG20241121112009.jpg\n",
      "Leaf-only image saved as: E:\\TCV_project\\Teacult\\new_data\\TV_13_processed\\IMG20241121112017.jpg\n",
      "Leaf-only image saved as: E:\\TCV_project\\Teacult\\new_data\\TV_13_processed\\IMG20241121112027.jpg\n",
      "Leaf-only image saved as: E:\\TCV_project\\Teacult\\new_data\\TV_13_processed\\IMG20241121112034.jpg\n"
     ]
    }
   ],
   "source": [
    "#this code removes shadow for leaf without removing stem of the leaf(modified version)\n",
    "import cv2\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "# Set the path to the folder containing processed images\n",
    "input_folder_path = r\"E:\\TCV_project\\Teacult\\new_data\\TV_13_A4\"\n",
    "# Set the path for saving leaf images\n",
    "output_folder_path = r\"E:\\TCV_project\\Teacult\\new_data\\TV_13_processed\"\n",
    "\n",
    "# Create the output folder if it does not exist\n",
    "os.makedirs(output_folder_path, exist_ok=True)\n",
    "\n",
    "# Get a list of all image files in the folder\n",
    "image_files = [f for f in os.listdir(input_folder_path) if f.endswith(('.png', '.jpg', '.jpeg'))]\n",
    "\n",
    "# Define a wider range of green colors in HSV\n",
    "lower_green = np.array([20, 37, 37])  # Slightly lower for darker greens\n",
    "upper_green = np.array([95, 255, 255])  # Expanded upper range for lighter greens\n",
    "\n",
    "for image_file in image_files:\n",
    "    image_path = os.path.join(input_folder_path, image_file)\n",
    "\n",
    "    # Read the current image\n",
    "    image = cv2.imread(image_path)\n",
    "\n",
    "    # Convert the image to HSV color space\n",
    "    hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)\n",
    "\n",
    "    # Create a mask for the green colors\n",
    "    mask = cv2.inRange(hsv, lower_green, upper_green)\n",
    "\n",
    "    # Apply morphological operations to clean up the mask\n",
    "    kernel_size = 15  # Slightly increased kernel size to handle leaf complexity\n",
    "    kernel = np.ones((kernel_size, kernel_size), np.uint8)\n",
    "    mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel)  # Close small holes\n",
    "    mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel)  # Remove noise\n",
    "    mask = cv2.erode(mask, kernel, iterations=2)  # Erode less aggressively to avoid cutting the leaf\n",
    "    mask = cv2.dilate(mask, kernel, iterations=3)  # Increase dilation to sharpen edges\n",
    "\n",
    "    # Find contours from the mask\n",
    "    contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n",
    "\n",
    "    # Create an output image initialized to white\n",
    "    output_image = np.ones_like(image) * 255\n",
    "\n",
    "    if contours:\n",
    "        # Get the largest contour (the leaf)\n",
    "        largest_contour = max(contours, key=cv2.contourArea)\n",
    "\n",
    "        # Approximate the contour to reduce the number of points\n",
    "        epsilon = 0.0001 * cv2.arcLength(largest_contour, True)  # Keep the epsilon small for better fit\n",
    "        approx_contour = cv2.convexHull(largest_contour)  # Using convexHull instead of approxPolyDP\n",
    "\n",
    "        # Draw the contour on the output image\n",
    "        cv2.drawContours(output_image, [approx_contour], -1, (0, 0, 0), thickness=cv2.FILLED)\n",
    "\n",
    "        # Create a mask to keep the leaf interior unaffected\n",
    "        mask = np.zeros_like(image)\n",
    "        cv2.fillPoly(mask, [approx_contour], (255, 255, 255))\n",
    "\n",
    "        # Combine the output image with the original image using the mask\n",
    "        output_image = cv2.bitwise_or(output_image, cv2.bitwise_and(image, mask))\n",
    "\n",
    "        # Further morphological operations to remove small white patches inside the leaf\n",
    "        output_mask = cv2.cvtColor(output_image, cv2.COLOR_BGR2GRAY)\n",
    "        output_mask[output_mask < 255] = 0  # Convert to binary mask (0s and 255s)\n",
    "\n",
    "        # Find connected components in the mask\n",
    "        num_labels, labels_im = cv2.connectedComponents(output_mask)\n",
    "\n",
    "        # Keep only the largest component (the leaf)\n",
    "        min_area_threshold = 1000  # Adjust as needed\n",
    "        largest_area = 0\n",
    "        largest_label = 0\n",
    "        \n",
    "        for label in range(1, num_labels):  # Skip the background label (0)\n",
    "            area = np.sum(labels_im == label)\n",
    "            if area > largest_area and area >= min_area_threshold:\n",
    "                largest_area = area\n",
    "                largest_label = label\n",
    "\n",
    "        # Create a mask for the largest connected component\n",
    "        final_mask = np.zeros_like(output_mask)\n",
    "        final_mask[labels_im == largest_label] = 255\n",
    "\n",
    "        # Use the final mask to get the final output image\n",
    "        output_image = cv2.bitwise_or(output_image, cv2.cvtColor(final_mask, cv2.COLOR_GRAY2BGR))\n",
    "\n",
    "        # Save the final image with the white background\n",
    "        output_path = os.path.join(output_folder_path, image_file)\n",
    "        cv2.imwrite(output_path, output_image)\n",
    "\n",
    "        print(f\"Leaf-only image saved as: {output_path}\")\n",
    "    else:\n",
    "        print(f\"No green contours found in image: {image_file}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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