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Pre-Processing Techniques in DL
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‎Preprocessing Techniques of DL/Preprocessing Techniques.ipynb

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "1db3b3cd",
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"metadata": {},
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"outputs": [],
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"source": [
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"import cv2\n",
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"\n",
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"# Load an image\n",
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"image = cv2.imread('C:/Users/hp/R.jpeg')\n",
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"\n",
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"# Display the image\n",
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"cv2.imshow('Image', image)\n",
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"cv2.waitKey(0)\n",
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"cv2.destroyAllWindows()\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "2c109797",
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"metadata": {},
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"outputs": [],
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"source": [
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"import cv2\n",
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"image = cv2.imread('C:/Users/hp/R.jpeg')\n",
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"cv2.imshow('Image', image)\n",
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"#cv2.waitKey(0)\n",
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"#cv2.destroyAllWindows()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "5626577e",
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"metadata": {},
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"outputs": [
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{
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"ename": "error",
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"evalue": "OpenCV(4.9.0) D:\\a\\opencv-python\\opencv-python\\opencv\\modules\\imgproc\\src\\resize.cpp:4152: error: (-215:Assertion failed) !ssize.empty() in function 'cv::resize'\n",
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"output_type": "error",
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"traceback": [
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"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[1;31merror\u001b[0m Traceback (most recent call last)",
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"Cell \u001b[1;32mIn[3], line 9\u001b[0m\n\u001b[0;32m 7\u001b[0m width \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m300\u001b[39m\n\u001b[0;32m 8\u001b[0m height \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m200\u001b[39m\n\u001b[1;32m----> 9\u001b[0m resized_image \u001b[38;5;241m=\u001b[39m cv2\u001b[38;5;241m.\u001b[39mresize(image, (width, height))\n\u001b[0;32m 11\u001b[0m \u001b[38;5;66;03m# Display the resized image\u001b[39;00m\n\u001b[0;32m 12\u001b[0m cv2\u001b[38;5;241m.\u001b[39mimshow(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mResized Image\u001b[39m\u001b[38;5;124m'\u001b[39m, resized_image)\n",
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"\u001b[1;31merror\u001b[0m: OpenCV(4.9.0) D:\\a\\opencv-python\\opencv-python\\opencv\\modules\\imgproc\\src\\resize.cpp:4152: error: (-215:Assertion failed) !ssize.empty() in function 'cv::resize'\n"
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]
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}
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],
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"source": [
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"import cv2\n",
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"\n",
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"# Load an image\n",
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"image = cv2.imread('R.jpg')\n",
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"\n",
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"# Resize the image to a specific width and height\n",
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"width = 300\n",
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"height = 200\n",
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"resized_image = cv2.resize(image, (width, height))\n",
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"\n",
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"# Display the resized image\n",
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"cv2.imshow('Resized Image', resized_image)\n",
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"cv2.waitKey(0)\n",
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"cv2.destroyAllWindows()\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "0f3f0beb",
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"metadata": {},
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"outputs": [],
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"source": [
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"import cv2\n",
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"\n",
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"# Load the image\n",
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"image = cv2.imread('R.jpg')\n",
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"\n",
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"# Define the coordinates of the region to be cropped (x, y, width, height)\n",
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"x, y, width, height = 100, 100, 300, 200\n",
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"\n",
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"# Crop the image\n",
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"cropped_image = image[y:y+height, x:x+width]\n",
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"\n",
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"# Display the cropped image\n",
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"cv2.imshow('Cropped Image', cropped_image)\n",
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"cv2.waitKey(0)\n",
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"cv2.destroyAllWindows()\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "e0dcfc5c",
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"metadata": {},
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"outputs": [],
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"source": [
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"import cv2 \n",
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"import glob \n",
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"import os\n",
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"\n",
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"inputFolder = r'C:\\Users\\hp\\Desktop\\dl practice'\n",
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"outputFolder = r'C:\\Users\\hp\\Desktop\\dl practice\\resized'\n",
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"\n",
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"# Create the output directory if it doesn't exist\n",
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"if not os.path.exists(outputFolder):\n",
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" os.mkdir(outputFolder)\n",
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"\n",
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"for img_path in glob.glob(os.path.join(inputFolder, '*.JPEG')):\n",
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" # Read the image\n",
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" image = cv2.imread(img_path)\n",
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" # Resize the image\n",
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" imResized = cv2.resize(image, (224, 224))\n",
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" # Extract the filename\n",
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" filename = os.path.basename(img_path)\n",
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" # Write the resized image to the output folder\n",
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" cv2.imwrite(os.path.join(outputFolder, filename), imResized)\n",
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"\n",
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"print(\"Resizing complete.\")\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "b87145af",
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"metadata": {},
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"outputs": [],
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"source": [
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"import cv2\n",
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"import glob\n",
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"import os\n",
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"\n",
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"inputFolder = 'C:/Users/hp/Desktop/dl practice' # Corrected the path separator\n",
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"os.makedirs(inputFolder, exist_ok=True) # Corrected function name and made sure the directory exists\n",
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"\n",
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"for img_path in glob.glob(os.path.join(inputFolder, '*.JPEG')): # Corrected the path and file extension\n",
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" image = cv2.imread(img_path) # Corrected function name\n",
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" if image is not None: # Check if image is loaded successfully\n",
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" imResized = cv2.resize(image, (224, 224))\n",
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" new_img_path = os.path.join(inputFolder, f\"resized_{os.path.basename(img_path)}\") # Construct new file path\n",
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" cv2.imwrite(new_img_path, imResized) # Write the resized image\n",
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" print(f\"Resized and saved: {new_img_path}\")\n",
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" else:\n",
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" print(f\"Unable to read image: {img_path}\")\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "7cf7c0d7",
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"metadata": {},
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"outputs": [],
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"source": [
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"import cv2\n",
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"import glob\n",
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"import os\n",
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"\n",
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"inputFolder = 'C:/Users/hp/Desktop/dl practice' # Corrected the path separator\n",
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"os.makedirs(inputFolder, exist_ok=True) # Corrected function name and made sure the directory exists\n",
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"\n",
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"# Define the crop dimensions\n",
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"x_start, y_start, width, height = 50, 50, 150, 150 # Example values, adjust as needed\n",
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"\n",
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"for img_path in glob.glob(os.path.join(inputFolder, '*.JPEG')): # Corrected the path and file extension\n",
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" image = cv2.imread(img_path) # Corrected function name\n",
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" if image is not None: # Check if image is loaded successfully\n",
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" cropped_img = image[y_start:y_start+height, x_start:x_start+width]\n",
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" new_img_path = os.path.join(inputFolder, f\"cropped_{os.path.basename(img_path)}\") # Construct new file path\n",
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" cv2.imwrite(new_img_path, cropped_img) # Write the cropped image\n",
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" print(f\"Cropped and saved: {new_img_path}\")\n",
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" else:\n",
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" print(f\"Unable to read image: {img_path}\")\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "f299c243",
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"metadata": {},
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"outputs": [],
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"source": [
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"import cv2 \n",
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"import glob \n",
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"import os\n",
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"\n",
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"inputFolder = r'C:\\Users\\hp\\Desktop\\dl practice'\n",
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"outputFolder = r'C:\\Users\\hp\\Desktop\\dl practice\\resized'\n",
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"\n",
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"# Create the output directory if it doesn't exist\n",
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"if not os.path.exists(outputFolder):\n",
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" os.mkdir(outputFolder)\n",
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"\n",
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"for img_path in glob.glob(os.path.join(inputFolder, '*.JPEG')):\n",
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" # Read the image\n",
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" image = cv2.imread(img_path)\n",
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" \n",
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" # Apply Gaussian Blur for noise removal\n",
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" blurred_image = cv2.GaussianBlur(image, (5, 5), 0) # You can adjust the kernel size (5, 5) if needed\n",
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" \n",
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" # Resize the image\n",
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" imResized = cv2.resize(blurred_image, (224, 224))\n",
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" \n",
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" # Extract the filename\n",
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" filename = os.path.basename(img_path)\n",
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" \n",
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" # Write the resized and denoised image to the output folder\n",
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" cv2.imwrite(os.path.join(outputFolder, filename), imResized)\n",
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"\n",
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"print(\"Resizing and noise removal complete.\")\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "e15aece0",
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"metadata": {},
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"outputs": [],
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"source": [
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"import cv2 \n",
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"import glob \n",
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"import os\n",
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"\n",
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"inputFolder = r'C:\\Users\\hp\\Desktop\\dl practice'\n",
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"outputFolder = r'C:\\Users\\hp\\Desktop\\dl practice\\resized'\n",
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"\n",
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"# Create the output directory if it doesn't exist\n",
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"if not os.path.exists(outputFolder):\n",
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" os.mkdir(outputFolder)\n",
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"\n",
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"for img_path in glob.glob(os.path.join(inputFolder, '*.JPEG')):\n",
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" # Read the image\n",
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" image = cv2.imread(img_path)\n",
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" \n",
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" # Apply Gaussian Blur for noise removal\n",
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" blurred_image = cv2.GaussianBlur(image, (5, 5), 0)\n",
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" \n",
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" # Convert the image to grayscale\n",
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" gray_image = cv2.cvtColor(blurred_image, cv2.COLOR_BGR2GRAY)\n",
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" \n",
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" # Apply Sobel operator for edge detection\n",
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" sobelx = cv2.Sobel(gray_image, cv2.CV_64F, 1, 0, ksize=5)\n",
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" sobely = cv2.Sobel(gray_image, cv2.CV_64F, 0, 1, ksize=5)\n",
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" edge_image = cv2.sqrt(cv2.addWeighted(cv2.pow(sobelx, 2.0), 1.0, cv2.pow(sobely, 2.0), 1.0, 0))\n",
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" \n",
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" # Resize the image\n",
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" resized_edge_image = cv2.resize(edge_image, (224, 224))\n",
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" \n",
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" # Extract the filename\n",
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" filename = os.path.basename(img_path)\n",
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" \n",
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" # Write the resized and edge-detected image to the output folder\n",
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" cv2.imwrite(os.path.join(outputFolder, filename), resized_edge_image)\n",
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"\n",
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"print(\"Resizing and edge detection complete.\")\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "39807909",
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"metadata": {},
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"outputs": [],
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"source": [
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"import cv2 \n",
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"import glob \n",
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"import os\n",
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"\n",
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"inputFolder = r'C:\\Users\\hp\\Desktop\\dl practice'\n",
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"outputFolder = r'C:\\Users\\hp\\Desktop\\dl practice\\converted'\n",
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"\n",
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"# Create the output directory if it doesn't exist\n",
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"if not os.path.exists(outputFolder):\n",
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" os.mkdir(outputFolder)\n",
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"\n",
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"for img_path in glob.glob(os.path.join(inputFolder, '*.JPEG')):\n",
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" # Read the image\n",
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" image = cv2.imread(img_path)\n",
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" \n",
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" # Apply Gaussian Blur for noise removal\n",
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" blurred_image = cv2.GaussianBlur(image, (5, 5), 0)\n",
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" \n",
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" # Convert the image to grayscale\n",
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" gray_image = cv2.cvtColor(blurred_image, cv2.COLOR_BGR2GRAY)\n",
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" \n",
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" # Apply Sobel operator for edge detection\n",
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" sobelx = cv2.Sobel(gray_image, cv2.CV_64F, 1, 0, ksize=5)\n",
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" sobely = cv2.Sobel(gray_image, cv2.CV_64F, 0, 1, ksize=5)\n",
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" edge_image = cv2.sqrt(cv2.addWeighted(cv2.pow(sobelx, 2.0), 1.0, cv2.pow(sobely, 2.0), 1.0, 0))\n",
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" \n",
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" # Resize the image\n",
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" resized_edge_image = cv2.resize(edge_image, (224, 224))\n",
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" \n",
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" # Convert the image to PNG format\n",
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" output_img_path = os.path.join(outputFolder, os.path.splitext(os.path.basename(img_path))[0] + '.png')\n",
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" cv2.imwrite(output_img_path, resized_edge_image)\n",
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"\n",
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"print(\"Conversion complete.\")\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "9413a9a1",
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"metadata": {},
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"outputs": [],
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"source": [
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"import cv2 \n",
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"import glob \n",
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"import os\n",
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"\n",
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"inputFolder = r'C:\\Users\\hp\\Desktop\\dl practice'\n",
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"outputFolder = r'C:\\Users\\hp\\Desktop\\dl practice\\equalized'\n",
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"\n",
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"# Create the output directory if it doesn't exist\n",
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"if not os.path.exists(outputFolder):\n",
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" os.mkdir(outputFolder)\n",
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"\n",
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"for img_path in glob.glob(os.path.join(inputFolder, '*.JPEG')):\n",
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" # Read the image\n",
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" image = cv2.imread(img_path)\n",
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" \n",
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" # Convert the image to grayscale\n",
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" gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
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" \n",
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" # Apply histogram equalization\n",
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" equalized_image = cv2.equalizeHist(gray_image)\n",
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" \n",
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" # Apply Gaussian Blur for noise removal\n",
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" blurred_image = cv2.GaussianBlur(equalized_image, (5, 5), 0)\n",
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" \n",
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" # Apply Sobel operator for edge detection\n",
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" sobelx = cv2.Sobel(blurred_image, cv2.CV_64F, 1, 0, ksize=5)\n",
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" sobely = cv2.Sobel(blurred_image, cv2.CV_64F, 0, 1, ksize=5)\n",
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" edge_image = cv2.sqrt(cv2.addWeighted(cv2.pow(sobelx, 2.0), 1.0, cv2.pow(sobely, 2.0), 1.0, 0))\n",
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" \n",
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" # Resize the image\n",
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" resized_edge_image = cv2.resize(edge_image, (224, 224))\n",
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" \n",
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" # Convert the image to PNG format\n",
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" output_img_path = os.path.join(outputFolder, os.path.splitext(os.path.basename(img_path))[0] + '.png')\n",
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" cv2.imwrite(output_img_path, resized_edge_image)\n",
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"\n",
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"print(\"Histogram equalization complete.\")\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "25bf561e",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

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