Normal Image to Hazy Image Conversion .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "00f950d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import torch\n",
    "import torch.nn.functional as F\n",
    "from torchvision import transforms\n",
    "from PIL import Image\n",
    "\n",
    "# Define the paths for the original images folder and the output images folder\n",
    "original_images_folder = r\"C:\\Users\\tanvi\\OneDrive\\Desktop\\New folder\\Papers\\Image Dehazing\\Datasets\\HD DATASET\\Sample1\"\n",
    "output_folder = r\"C:\\Users\\tanvi\\OneDrive\\Desktop\\New folder\\Papers\\Image Dehazing\\Datasets\\HD DATASET\\Sample2\"\n",
    "\n",
    "# Create the output folder if it doesn't exist\n",
    "if not os.path.exists(output_folder):\n",
    "    os.makedirs(output_folder)\n",
    "\n",
    "# Define a function to add foggy effect to an image\n",
    "def add_fog(image_path, fog_intensity):\n",
    "    # Load the image\n",
    "    img = Image.open(image_path)\n",
    "\n",
    "    # Define the foggy effect parameters\n",
    "    fog_intensity = fog_intensity  # Adjust this value to control the intensity of the fog\n",
    "\n",
    "    # Apply the foggy effect to the image\n",
    "    img_tensor = transforms.ToTensor()(img)\n",
    "    fog_tensor = torch.rand_like(img_tensor) * fog_intensity\n",
    "    fog_img_tensor = img_tensor + fog_tensor\n",
    "    fog_img_tensor = torch.clamp(fog_img_tensor, 0, 1)\n",
    "\n",
    "    # Convert the tensor back to an image\n",
    "    fog_img = transforms.ToPILImage()(fog_img_tensor)\n",
    "\n",
    "    return fog_img\n",
    "\n",
    "# Define a function to add smoky effect to an image\n",
    "def add_smoke(image_path, smoke_intensity):\n",
    "    # Load the image\n",
    "    img = Image.open(image_path)\n",
    "\n",
    "    # Define the smoky effect parameters\n",
    "    smoke_intensity = smoke_intensity  # Adjust this value to control the intensity of the smoke\n",
    "\n",
    "    # Apply the smoky effect to the image\n",
    "    img_tensor = transforms.ToTensor()(img)\n",
    "    smoke_tensor = torch.rand_like(img_tensor) * smoke_intensity\n",
    "    smoke_img_tensor = img_tensor + smoke_tensor\n",
    "    smoke_img_tensor = torch.clamp(smoke_img_tensor, 0, 1)\n",
    "\n",
    "    # Convert the tensor back to an image\n",
    "    smoke_img = transforms.ToPILImage()(smoke_img_tensor)\n",
    "\n",
    "    return smoke_img\n",
    "\n",
    "# Define a function to add hazy effect to an image\n",
    "def add_haze(image_path, hazy_intensity):\n",
    "    # Load the image\n",
    "    img = Image.open(image_path, hazy_intensity)\n",
    "\n",
    "    # Define the hazy effect parameters\n",
    "    hazy_intensity = hazy_intensity  # Adjust this value to control the intensity of the haze\n",
    "\n",
    "    # Apply the hazy effect to the image\n",
    "    img_tensor = transforms.ToTensor()(img)\n",
    "    haze_tensor = torch.rand_like(img_tensor) * hazy_intensity\n",
    "    hazy_img_tensor = img_tensor + haze_tensor\n",
    "    hazy_img_tensor = torch.clamp(hazy_img_tensor, 0, 1)\n",
    "\n",
    "    # Convert the tensor back to an image\n",
    "    hazy_img = transforms.ToPILImage()(hazy_img_tensor)\n",
    "\n",
    "    return hazy_img\n",
    "\n",
    "\n",
    "# Define a function to add hazy light effect to an image\n",
    "def add_hazy_light(image_path, hazy_light_intensity):\n",
    "    # Load the image\n",
    "    img = Image.open(image_path)\n",
    "\n",
    "    # Define the hazy light effect parameters\n",
    "    hazy_light_intensity = 0.5  # Adjust this value to control the intensity of the hazy light\n",
    "\n",
    "    # Apply the hazy light effect to the image\n",
    "    img_tensor = transforms.ToTensor()(img)\n",
    "    hazy_light_tensor = torch.rand_like(img_tensor) * hazy_light_intensity\n",
    "    hazy_light_img_tensor = img_tensor + hazy_light_tensor\n",
    "    hazy_light_img_tensor = torch.clamp(hazy_light_img_tensor, 0, 1)\n",
    "\n",
    "    # Convert the tensor back to an image\n",
    "    hazy_light_img = transforms.ToPILImage()(hazy_light_img_tensor)\n",
    "\n",
    "    return hazy_light_img\n",
    "\n",
    "\n",
    "# Define a function to increase image contrast\n",
    "def increase_contrast(image_path, contrast_factor):\n",
    "    # Load the image\n",
    "    img = Image.open(image_path)\n",
    "\n",
    "    # Define the contrast enhancement factor\n",
    "    contrast_factor = contrast_factor  # Adjust this value to control the contrast enhancement\n",
    "\n",
    "    # Apply the contrast enhancement to the image\n",
    "    enhancer = transforms.ColorJitter(contrast=contrast_factor)\n",
    "    contrast_img = enhancer(img)\n",
    "\n",
    "    return contrast_img\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d8416461",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Iterate through the original images folder and add foggy, smoky, and hazy effects to each image\n",
    "\n",
    "for filename in os.listdir(original_images_folder):\n",
    "    if filename.startswith(\"GT_\") and (filename.endswith(\".jpg\") or filename.endswith(\".png\")):\n",
    "        input_image_path = os.path.join(original_images_folder, filename)\n",
    "\n",
    "        # Adding foggy effect\n",
    "        foggy_output_image = add_fog(input_image_path, 0.4)\n",
    "        foggy_output_filename = \"Foggy_\" + filename\n",
    "        foggy_output_path = os.path.join(output_folder, foggy_output_filename)\n",
    "        foggy_output_image.save(foggy_output_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8f52fecd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Iterate through the original images folder and add foggy, smoky, and hazy effects to each image\n",
    "for filename in os.listdir(original_images_folder):\n",
    "    if filename.startswith(\"GT_\") and (filename.endswith(\".jpg\") or filename.endswith(\".png\")):\n",
    "        input_image_path = os.path.join(original_images_folder, filename)\n",
    "\n",
    "        # Adding smoky effect\n",
    "        smoky_output_image = add_smoke(input_image_path, 0.7)\n",
    "        smoky_output_filename = \"Smoky_\" + filename\n",
    "        smoky_output_path = os.path.join(output_folder, smoky_output_filename)\n",
    "        smoky_output_image.save(smoky_output_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "541d50a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Iterate through the original images folder and add foggy, smoky, and hazy effects to each image\n",
    "for filename in os.listdir(original_images_folder):\n",
    "    if filename.startswith(\"GT_\") and (filename.endswith(\".jpg\") or filename.endswith(\".png\")):\n",
    "        input_image_path = os.path.join(original_images_folder, filename)\n",
    "        \n",
    "        # Adding hazy effect\n",
    "        hazy_output_image = add_haze(input_image_path, 0.2)\n",
    "        hazy_output_filename = \"Hazy_\" + filename\n",
    "        hazy_output_path = os.path.join(output_folder, hazy_output_filename)\n",
    "        hazy_output_image.save(hazy_output_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0e21ab93",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Iterate through the original images folder and add foggy, smoky, and hazy effects to each image\n",
    "for filename in os.listdir(original_images_folder):\n",
    "    if filename.startswith(\"GT_\") and (filename.endswith(\".jpg\") or filename.endswith(\".png\")):\n",
    "        input_image_path = os.path.join(original_images_folder, filename)\n",
    "        \n",
    "        # Adding hazy light effect\n",
    "        hazy_light_output_image = add_hazy_light(input_image_path, 0.5)\n",
    "        hazy_light_output_filename = \"HazyLight_\" + filename\n",
    "        hazy_light_output_path = os.path.join(output_folder, hazy_light_output_filename)\n",
    "        hazy_light_output_image.save(hazy_light_output_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d99129cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Iterate through the original images folder and add foggy, smoky, and hazy effects to each image\n",
    "for filename in os.listdir(original_images_folder):\n",
    "    if filename.startswith(\"GT_\") and (filename.endswith(\".jpg\") or filename.endswith(\".png\")):\n",
    "        input_image_path = os.path.join(original_images_folder, filename)\n",
    "        \n",
    "        # Increasing image contrast\n",
    "        contrast_output_image = increase_contrast(input_image_path, 2)\n",
    "        contrast_output_filename = \"Contrast_\" + filename\n",
    "        contrast_output_path = os.path.join(output_folder, contrast_output_filename)\n",
    "        contrast_output_image.save(contrast_output_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a8430298",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6687a999",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Blur Image : https://www.youtube.com/watch?v=qC9GJJeNtbY\n",
    "# Blur Image : https://www.youtube.com/watch?v=cEnXW8S4KW8\n",
    "# Blur Image : https://www.youtube.com/watch?v=-7pCyqV9ocw\n",
    "# Sharping the Image : https://www.youtube.com/watch?v=SEj1imXHjqM\n",
    "\n",
    "# Fog Image in PSD : https://www.youtube.com/shorts/ov0OiIbijyk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c7847f37",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce79e622",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1af8956a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Images with light layer added and saved successfully!\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import torch\n",
    "import torch.nn.functional as F\n",
    "from torchvision import transforms\n",
    "from PIL import Image, ImageEnhance\n",
    "\n",
    "# Define the paths for the original images folder and the output images folder\n",
    "original_images_folder = r\"C:\\Users\\tanvi\\OneDrive\\Desktop\\New folder\\Papers\\Image Dehazing\\Datasets\\HD DATASET\\Sample1\"\n",
    "output_folder = r\"C:\\Users\\tanvi\\OneDrive\\Desktop\\New folder\\Papers\\Image Dehazing\\Datasets\\HD DATASET\\Sample2\"\n",
    "\n",
    "# Create the output folder if it doesn't exist\n",
    "if not os.path.exists(output_folder):\n",
    "    os.makedirs(output_folder)\n",
    "\n",
    "# Define a function to add a light layer to an image\n",
    "def add_light_layer(image_path):\n",
    "    # Load the image\n",
    "    img = Image.open(image_path)\n",
    "\n",
    "    # Define the light layer parameters\n",
    "    light_intensity = 0.5  # Adjust this value to control the intensity of the light layer\n",
    "\n",
    "    # Create a white image with the same size as the original image\n",
    "    light_img = Image.new(\"RGB\", img.size, (255, 255, 255))\n",
    "\n",
    "    # Blend the light layer with the original image\n",
    "    blended_img = Image.blend(img, light_img, light_intensity)\n",
    "\n",
    "    return blended_img\n",
    "\n",
    "# Iterate through the original images folder and add a light layer to each image\n",
    "for filename in os.listdir(original_images_folder):\n",
    "    if filename.endswith(\".jpg\") or filename.endswith(\".png\"):\n",
    "        input_image_path = os.path.join(original_images_folder, filename)\n",
    "\n",
    "        # Adding the light layer to the image\n",
    "        light_layer_output_image = add_light_layer(input_image_path)\n",
    "\n",
    "        # Save the image with the light layer to the output folder\n",
    "        output_path = os.path.join(output_folder, f\"LightLayer_{filename}\")\n",
    "        light_layer_output_image.save(output_path)\n",
    "\n",
    "print(\"Images with light layer added and saved successfully!\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4eeb67d6",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[19], line 61\u001b[0m\n\u001b[0;32m     58\u001b[0m input_image_path \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(original_images_folder, filename)\n\u001b[0;32m     60\u001b[0m \u001b[38;5;66;03m# Adding the light layer with natural noise to the image\u001b[39;00m\n\u001b[1;32m---> 61\u001b[0m light_layer_output_image \u001b[38;5;241m=\u001b[39m \u001b[43madd_light_layer\u001b[49m\u001b[43m(\u001b[49m\u001b[43minput_image_path\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     63\u001b[0m \u001b[38;5;66;03m# Save the image with the light layer to the output folder\u001b[39;00m\n\u001b[0;32m     64\u001b[0m output_path \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(output_folder, \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLightLayer_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfilename\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
      "Cell \u001b[1;32mIn[19], line 48\u001b[0m, in \u001b[0;36madd_light_layer\u001b[1;34m(image_path)\u001b[0m\n\u001b[0;32m     46\u001b[0m         noise_g \u001b[38;5;241m=\u001b[39m random\u001b[38;5;241m.\u001b[39muniform(\u001b[38;5;241m-\u001b[39mnoise_intensity \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m255\u001b[39m, noise_intensity \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m255\u001b[39m)\n\u001b[0;32m     47\u001b[0m         noise_b \u001b[38;5;241m=\u001b[39m random\u001b[38;5;241m.\u001b[39muniform(\u001b[38;5;241m-\u001b[39mnoise_intensity \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m255\u001b[39m, noise_intensity \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m255\u001b[39m)\n\u001b[1;32m---> 48\u001b[0m         \u001b[43mnoisy_img\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mputpixel\u001b[49m\u001b[43m(\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mint\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mr\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mnoise_r\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mint\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mg\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mnoise_g\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mint\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mb\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mnoise_b\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     50\u001b[0m \u001b[38;5;66;03m# Blend the noisy image with the original image\u001b[39;00m\n\u001b[0;32m     51\u001b[0m final_img \u001b[38;5;241m=\u001b[39m Image\u001b[38;5;241m.\u001b[39mblend(img, noisy_img, noise_intensity)\n",
      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python310\\site-packages\\PIL\\Image.py:1969\u001b[0m, in \u001b[0;36mImage.putpixel\u001b[1;34m(self, xy, value)\u001b[0m\n\u001b[0;32m   1967\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mreadonly:\n\u001b[0;32m   1968\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_copy()\n\u001b[1;32m-> 1969\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1971\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpyaccess:\n\u001b[0;32m   1972\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpyaccess\u001b[38;5;241m.\u001b[39mputpixel(xy, value)\n",
      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python310\\site-packages\\PIL\\Image.py:872\u001b[0m, in \u001b[0;36mImage.load\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    870\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpyaccess:\n\u001b[0;32m    871\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpyaccess\n\u001b[1;32m--> 872\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mim\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpixel_access\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreadonly\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "import os\n",
    "import torch\n",
    "import torch.nn.functional as F\n",
    "from torchvision import transforms\n",
    "from PIL import Image, ImageEnhance\n",
    "import random\n",
    "\n",
    "# Define the paths for the original images folder and the output images folder\n",
    "original_images_folder = r\"C:\\Users\\tanvi\\OneDrive\\Desktop\\New folder\\Papers\\Image Dehazing\\Datasets\\HD DATASET\\Sample1\"\n",
    "output_folder = r\"C:\\Users\\tanvi\\OneDrive\\Desktop\\New folder\\Papers\\Image Dehazing\\Datasets\\HD DATASET\\Sample2\"\n",
    "\n",
    "# Create the output folder if it doesn't exist\n",
    "if not os.path.exists(output_folder):\n",
    "    os.makedirs(output_folder)\n",
    "\n",
    "# Define a function to add a light layer with natural noise to a random region of the image\n",
    "def add_light_layer(image_path):\n",
    "    # Load the image\n",
    "    img = Image.open(image_path)\n",
    "\n",
    "    # Define the light layer parameters\n",
    "    light_intensity = 0.2  # Adjust this value to control the intensity of the light layer\n",
    "    noise_intensity = 0.5  # Adjust this value to control the intensity of the noise\n",
    "\n",
    "    # Randomly select the region for the light layer\n",
    "    width, height = img.size\n",
    "    x1 = random.randint(0, width // 2)\n",
    "    y1 = random.randint(0, height // 2)\n",
    "    x2 = x1 + (width // 2)\n",
    "    y2 = y1 + (height // 2)\n",
    "\n",
    "    # Create a white image with the same size as the original image\n",
    "    light_img = Image.new(\"RGB\", img.size, (255, 255, 255))\n",
    "\n",
    "    # Blend the light layer with the original image in the selected region\n",
    "    blended_img = Image.blend(img, light_img, light_intensity)\n",
    "\n",
    "    # Create a copy of the blended image\n",
    "    noisy_img = blended_img.copy()\n",
    "\n",
    "    # Add natural noise to the selected region of the blended image\n",
    "    for y in range(y1, y2):\n",
    "        for x in range(x1, x2):\n",
    "            r, g, b = blended_img.getpixel((x, y))\n",
    "            noise_r = random.uniform(-noise_intensity * 255, noise_intensity * 255)\n",
    "            noise_g = random.uniform(-noise_intensity * 255, noise_intensity * 255)\n",
    "            noise_b = random.uniform(-noise_intensity * 255, noise_intensity * 255)\n",
    "            noisy_img.putpixel((x, y), (int(r + noise_r), int(g + noise_g), int(b + noise_b)))\n",
    "\n",
    "    # Blend the noisy image with the original image\n",
    "    final_img = Image.blend(img, noisy_img, noise_intensity)\n",
    "\n",
    "    return final_img\n",
    "\n",
    "# Iterate through the original images folder and add a light layer with natural noise to random regions of each image\n",
    "for filename in os.listdir(original_images_folder):\n",
    "    if filename.endswith(\".jpg\") or filename.endswith(\".png\"):\n",
    "        input_image_path = os.path.join(original_images_folder, filename)\n",
    "\n",
    "        # Adding the light layer with natural noise to the image\n",
    "        light_layer_output_image = add_light_layer(input_image_path)\n",
    "\n",
    "        # Save the image with the light layer to the output folder\n",
    "        output_path = os.path.join(output_folder, f\"LightLayer_{filename}\")\n",
    "        light_layer_output_image.save(output_path)\n",
    "\n",
    "print(\"Images with random light layer added and saved successfully!\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bad52949",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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