image_channel_values.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5938d41f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ciprian-Florin Ifrim\\AppData\\Local\\Temp\\ipykernel_16356\\1745932358.py:6: DeprecationWarning: Importing display from IPython.core.display is deprecated since IPython 7.14, please import from IPython display\n",
      "  from IPython.core.display import display, HTML\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>.container { width:100% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "Processing images:  99%|█████████▉| 576160/579552 [03:13<00:01, 3019.30it/s]\u001b[A\n",
      "Processing images:  99%|█████████▉| 576480/579552 [03:14<00:01, 3009.30it/s]\u001b[A\n",
      "Processing images: 100%|█████████▉| 576800/579552 [03:14<00:00, 3019.06it/s]\u001b[A\n",
      "Processing images: 100%|█████████▉| 577120/579552 [03:14<00:00, 3034.55it/s]\u001b[A\n",
      "Processing images: 100%|█████████▉| 577440/579552 [03:14<00:00, 3033.39it/s]\u001b[A\n",
      "Processing images: 100%|█████████▉| 577760/579552 [03:14<00:00, 3040.57it/s]\u001b[A\n",
      "Processing images: 100%|█████████▉| 578080/579552 [03:14<00:00, 3030.38it/s]\u001b[A\n",
      "Processing images: 100%|█████████▉| 578400/579552 [03:14<00:00, 3031.91it/s]\u001b[A\n",
      "Processing images: 100%|█████████▉| 578720/579552 [03:14<00:00, 3000.81it/s]\u001b[A\n",
      "Processing images: 100%|█████████▉| 579040/579552 [03:14<00:00, 3009.09it/s]\u001b[A\n",
      "Processing images: 100%|██████████| 579552/579552 [03:15<00:00, 2971.60it/s]\u001b[A\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of images in mode RGB: 579552\n",
      "Number of images in mode RGBA: 0\n",
      "Number of images in mode L: 0\n",
      "Number of images in mode Unknown: 0\n",
      "Image processing completed.\n"
     ]
    }
   ],
   "source": [
    "from PIL import Image\n",
    "import os\n",
    "from joblib import Parallel, delayed\n",
    "from tqdm import tqdm\n",
    "#-------------------------------------------------------------------------------------JUPYTER NOTEBOOK SETTINGS-------------------------------------------------------------------------------------\n",
    "from IPython.core.display import display, HTML                                    \n",
    "display(HTML(\"<style>.container { width:100% !important; }</style>\"))     \n",
    "\n",
    "# replace with your image file path\n",
    "folder_path = \"pascal_voc_datasets/VOCdevkit/PlotsEnchanced_Original_With-3X-Augmentation/JPEGImages/\" \n",
    "\n",
    "# Counter to keep track of the number of images in each channel mode\n",
    "channel_counters = {'RGB': 0, 'RGBA': 0, 'L': 0, 'Unknown': 0}\n",
    "\n",
    "# List all files in the folder\n",
    "image_files = os.listdir(folder_path)\n",
    "\n",
    "# Function to process each image and update the channel_counters dictionary\n",
    "def process_image(image_file):\n",
    "    image_path = os.path.join(folder_path, image_file)\n",
    "    # Open the image file\n",
    "    img = Image.open(image_path)\n",
    "\n",
    "    # Determine number of channels based on the mode\n",
    "    if img.mode == 'RGB':\n",
    "        num_channels = 3\n",
    "    elif img.mode == 'RGBA':\n",
    "        num_channels = 4\n",
    "    elif img.mode == 'L':\n",
    "        num_channels = 1\n",
    "    else:\n",
    "        print(f\"Unknown mode for {image_file}: {img.mode}\")\n",
    "        num_channels = None\n",
    "\n",
    "    # Update the channel_counters dictionary\n",
    "    channel_counters[img.mode] += 1\n",
    "\n",
    "    # Print the number of channels only if the image is non-RGB\n",
    "    if img.mode != 'RGB':\n",
    "        print(f\"Number of channels for {image_file}: {num_channels}\")\n",
    "\n",
    "# Use joblib Parallel to process images in parallel\n",
    "Parallel(n_jobs=-1, backend=\"threading\")(delayed(process_image)(image_file) for image_file in tqdm(image_files, desc=\"Processing images\"))\n",
    "\n",
    "# Print the total count of images in each channel mode\n",
    "for mode, count in channel_counters.items():\n",
    "    print(f\"Number of images in mode {mode}: {count}\")\n",
    "\n",
    "# Print a message when the processing is completed\n",
    "print(\"Image processing completed.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "63ae9029",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from PIL import Image\n",
    "\n",
    "folder_path = 'path/to/your/folder'\n",
    "channel_counters = {'RGB': 0, 'RGBA': 0, 'L': 0, 'Unknown': 0}\n",
    "image_files = os.listdir(folder_path)\n",
    "\n",
    "def process_image(image_file):\n",
    "    image_path = os.path.join(folder_path, image_file)\n",
    "    img = Image.open(image_path)\n",
    "    mode = img.mode if img.mode in channel_counters else 'Unknown'\n",
    "    channel_counters[mode] += 1\n",
    "\n",
    "    # Convert the image to RGB mode if not already in RGB\n",
    "    if mode != 'RGB':\n",
    "        rgb_img = img.convert('RGB')\n",
    "        rgb_img.save(image_path)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "adadca30",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from PIL import Image\n",
    "\n",
    "folder_path = 'path/to/your/folder'\n",
    "channel_counters = {'RGB': 0, 'RGBA': 0, 'L': 0, 'Unknown': 0}\n",
    "image_files = os.listdir(folder_path)\n",
    "\n",
    "def process_image(image_file):\n",
    "    image_path = os.path.join(folder_path, image_file)\n",
    "    img = Image.open(image_path)\n",
    "    mode = img.mode if img.mode in channel_counters else 'Unknown'\n",
    "    channel_counters[mode] += 1\n",
    "\n",
    "    # Delete the image if not in RGB mode\n",
    "    if mode != 'RGB':\n",
    "        os.remove(image_path)\n"
   ]
  }
 ],
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