Caption Impact Assessment/Object Detection and Caption Impact Assessment/objectDetectionWithoutText.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Object Detection in Meme Images with Text Filtered out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1: Importing the required libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From c:\\Users\\soumi\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\src\\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead.\n",
      "\n",
      "WARNING:tensorflow:From c:\\Users\\soumi\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\src\\backend.py:873: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\soumi\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import cv2\n",
    "import numpy as np\n",
    "import keras_ocr\n",
    "from PIL import Image\n",
    "from transformers import DetrImageProcessor, DetrForObjectDetection\n",
    "import torch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2: Loading the ResNet-50 model and Initializing Keras OCR Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking for C:\\Users\\soumi\\.keras-ocr\\craft_mlt_25k.h5\n",
      "WARNING:tensorflow:From c:\\Users\\soumi\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\src\\layers\\normalization\\batch_normalization.py:979: The name tf.nn.fused_batch_norm is deprecated. Please use tf.compat.v1.nn.fused_batch_norm instead.\n",
      "\n",
      "WARNING:tensorflow:From c:\\Users\\soumi\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\tensorflow\\python\\util\\dispatch.py:1260: resize_bilinear (from tensorflow.python.ops.image_ops_impl) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use `tf.image.resize(...method=ResizeMethod.BILINEAR...)` instead.\n",
      "WARNING:tensorflow:From c:\\Users\\soumi\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\src\\optimizers\\__init__.py:309: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
      "\n",
      "Looking for C:\\Users\\soumi\\.keras-ocr\\crnn_kurapan.h5\n"
     ]
    }
   ],
   "source": [
    "processor = DetrImageProcessor.from_pretrained(\"facebook/detr-resnet-50\", revision=\"no_timm\")\n",
    "model = DetrForObjectDetection.from_pretrained(\"facebook/detr-resnet-50\", revision=\"no_timm\")\n",
    "\n",
    "pipeline = keras_ocr.pipeline.Pipeline()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3: Function to preprocess the image to better filter out the text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_image(image):\n",
    "    \"\"\"Preprocess the image to enhance text detection.\"\"\"\n",
    "    image = cv2.bilateralFilter(image, 5, 55, 60)\n",
    "    image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
    "    _, image = cv2.threshold(image, 240, 255, cv2.THRESH_BINARY)\n",
    "    return image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4: Function to calculate the midpoint of two points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def midpoint(x1, y1, x2, y2):\n",
    "    \"\"\"Calculate the midpoint between two points.\"\"\"\n",
    "    return int((x1 + x2) / 2), int((y1 + y2) / 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5: Function to Remove the Text and Fill it (inpainting)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def inpaint_text(img_path, pipeline):\n",
    "    \"\"\"Inpaint the text in the image based on OCR detection.\"\"\"\n",
    "    img = cv2.imread(img_path)\n",
    "    preprocessed_img = preprocess_image(img)\n",
    "    # Convert the preprocessed image back to three channels before passing it to OCR\n",
    "    preprocessed_img_color = cv2.cvtColor(preprocessed_img, cv2.COLOR_GRAY2BGR)\n",
    "    prediction_groups = pipeline.recognize([preprocessed_img_color])\n",
    "    mask = np.zeros(img.shape[:2], dtype=\"uint8\")\n",
    "    \n",
    "    for box in prediction_groups[0]:\n",
    "        pts = np.array([box[1]], dtype=np.int32)\n",
    "        cv2.fillPoly(mask, pts, (255, 255, 255))\n",
    "    \n",
    "    inpainted_img = cv2.inpaint(img, mask, 7, cv2.INPAINT_NS)\n",
    "    return inpainted_img"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6: Function to perform the object detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def perform_object_detection(image, processor, model):\n",
    "    \"\"\"Perform object detection on the given image.\"\"\"\n",
    "    image_pil = Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))\n",
    "    inputs = processor(images=image_pil, return_tensors=\"pt\", input_data_format=\"channels_last\")\n",
    "    outputs = model(**inputs)\n",
    "    target_sizes = torch.tensor([image_pil.size[::-1]])\n",
    "    results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.9)[0]\n",
    "    return results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7: Function to save the image with the text removed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_image(image, output_folder, original_filename):\n",
    "    \"\"\"Save the inpainted image to the specified output folder.\"\"\"\n",
    "    if not os.path.exists(output_folder):\n",
    "        os.makedirs(output_folder)\n",
    "    output_path = os.path.join(output_folder, f\"inpainted_{original_filename}\")\n",
    "    cv2.imwrite(output_path, image)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8: Wrapper Function to Combine all the above functions and perform the required task"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_images(img_folder, output_file_path, output_img_folder):\n",
    "    \"\"\"Process images in the folder for object detection after inpainting text and save them.\"\"\"\n",
    "    with open(output_file_path, \"w\") as output_file:\n",
    "        for filename in os.listdir(img_folder):\n",
    "            if filename.endswith(\".png\") or filename.endswith(\".jpg\") or filename.endswith(\".jpeg\"):\n",
    "                img_path = os.path.join(img_folder, filename)\n",
    "                inpainted_img = inpaint_text(img_path, pipeline)\n",
    "                results = perform_object_detection(inpainted_img, processor, model)\n",
    "\n",
    "                save_image(inpainted_img, output_img_folder, filename)\n",
    "\n",
    "                output_file.write(f\"Image - {img_path} :\\n\")\n",
    "                for score, label, box in zip(results[\"scores\"], results[\"labels\"], results[\"boxes\"]):\n",
    "                    box = [round(i, 2) for i in box.tolist()]\n",
    "                    output_file.write(f\"Detected {model.config.id2label[label.item()]} with confidence {round(score.item(), 3)} at location {box}\\n\")\n",
    "                output_file.write(\"\\n\")\n",
    "\n",
    "    print(f\"Detection results saved to {output_file_path}\")\n",
    "    print(f\"Inpainted images saved to {output_img_folder}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 9: Setting up the paths and calling the wrapper function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "1/1 [==============================] - 1s 1s/step\n",
      "1/1 [==============================] - 0s 294ms/step\n",
      "Detection results saved to objectDetectionWithoutText.txt\n",
      "Inpainted images saved to imagesWithTextRemoved\n"
     ]
    }
   ],
   "source": [
    "# Directory containing images\n",
    "img_folder = \"C:\\\\Users\\\\soumi\\\\Desktop\\\\precog\\\\img\\\\train_data\\\\hateful_memes\"\n",
    "\n",
    "# Output file path for detection results\n",
    "output_file_path = \"objectDetectionWithoutText.txt\"\n",
    "\n",
    "# Output folder for saving inpainted images\n",
    "output_img_folder = \"imagesWithTextRemoved\"\n",
    "\n",
    "process_images(img_folder, output_file_path, output_img_folder)"
   ]
  }
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