tempered-image-detection-transfer-learning.ipynb

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    "tags": []
   },
   "source": [
    "# Preprocess Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
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   "source": [
    "# converting image to ela image\n",
    "from glob import glob \n",
    "from tqdm import tqdm\n",
    "from PIL import Image, ImageChops, ImageEnhance\n",
    "import os \n",
    "\n",
    "def convert_to_ela_image(image_path, quality=90):\n",
    "    \"\"\"Converts an image to an ELA image.\n",
    "    :param image_path: Path to the image\n",
    "    :param quality: Quality of the image to be saved\n",
    "    :return: ELA image\n",
    "    \"\"\"\n",
    "    # Save the image at the given quality\n",
    "    temp_file = 'temp.jpg'\n",
    "    im = Image.open(image_path)\n",
    "    im.save(temp_file, 'JPEG', quality=quality)\n",
    "\n",
    "    # Open the saved image and the original image\n",
    "    saved = Image.open(temp_file)\n",
    "    orignal = Image.open(image_path)\n",
    "\n",
    "    # Find the absolute difference between the images\n",
    "    diff = ImageChops.difference(orignal, saved)\n",
    "\n",
    "    # Normalize the difference by multiplying with a scale factor and convert to grayscale\n",
    "    extrema = diff.getextrema()\n",
    "    max_diff = max([ex[1] for ex in extrema])\n",
    "    scale = 255.0 / max_diff\n",
    "    diff = ImageEnhance.Brightness(diff).enhance(scale)\n",
    "\n",
    "    # Remove the temporary file\n",
    "    os.remove(temp_file)\n",
    "\n",
    "    return diff"
   ]
  },
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total images:  9418\n"
     ]
    }
   ],
   "source": [
    "# all iamges\n",
    "images = glob('../input/casia-dataset/CASIA2/*/*.jpg')\n",
    "print('Total images: ', len(images))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "327c7220",
   "metadata": {
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     "end_time": "2022-11-21T11:57:18.135575",
     "exception": false,
     "start_time": "2022-11-21T11:57:18.132853",
     "status": "completed"
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    "tags": []
   },
   "source": [
    "# Converting to ELA images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "eaf52edc",
   "metadata": {
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   "outputs": [
    {
     "name": "stderr",
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     "text": [
      "100%|██████████| 9418/9418 [04:26<00:00, 35.36it/s]\n"
     ]
    }
   ],
   "source": [
    "# converting images to ela images\n",
    "for image in tqdm(images):\n",
    "    # converting image to ela image\n",
    "    ela_image = convert_to_ela_image(image)\n",
    "    # creating a new directory for ela images\n",
    "    if not os.path.exists('/CASIA2_ELA'):\n",
    "        os.mkdir('/CASIA2_ELA')\n",
    "    ela_image.save('/CASIA2_ELA/' + image.split('/')[-1])"
   ]
  },
  {
   "cell_type": "markdown",
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    "tags": []
   },
   "source": [
    "# Importing Fastai "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cb4a1a07",
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     "status": "completed"
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    "tags": []
   },
   "outputs": [],
   "source": [
    "from fastai.vision.all import * "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ba32c919",
   "metadata": {
    "execution": {
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    {
     "data": {
      "text/plain": [
       "(#9418) [Path('Tp_D_NRN_M_B_art00045_pla00088_10745.jpg'),Path('Au_nat_30257.jpg'),Path('Au_ind_00027.jpg'),Path('Au_pla_30366.jpg'),Path('Au_sec_10105.jpg'),Path('Tp_D_NRN_S_N_nat10159_ani00058_12051.jpg'),Path('Tp_D_NRN_M_N_sec00064_cha00070_11408.jpg'),Path('Au_art_30675.jpg'),Path('Au_sec_30351.jpg'),Path('Au_arc_30220.jpg')...]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
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   ],
   "source": [
    "path = Path('/CASIA2_ELA')\n",
    "Path.BASE_PATH = path\n",
    "path.ls()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14e54cbc",
   "metadata": {
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     "exception": false,
     "start_time": "2022-11-21T12:01:48.992898",
     "status": "completed"
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    "tags": []
   },
   "source": [
    "# DataBloack and DataLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b6ff8c59",
   "metadata": {
    "execution": {
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   "source": [
    "# function to get labels\n",
    "def get_label(x): return x.name.split('_')[0]\n",
    "\n",
    "# creating a datablock\n",
    "dblock = DataBlock(blocks=(ImageBlock, CategoryBlock),\n",
    "                        get_items=get_image_files,\n",
    "                        splitter=RandomSplitter(valid_pct=0.2, seed=42),\n",
    "                        get_y=get_label,\n",
    "                        item_tfms=Resize(224))\n",
    "                    \n",
    "# creating a dataloader\n",
    "dls = dblock.dataloaders(path)"
   ]
  },
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   "id": "5fe0e038",
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     "start_time": "2022-11-21T12:01:53.318880",
     "status": "completed"
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    "tags": []
   },
   "source": [
    "# Resnet18"
   ]
  },
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     "text": [
      "Downloading: \"https://download.pytorch.org/models/resnet18-f37072fd.pth\" to /root/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth\n"
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.559847</td>\n",
       "      <td>0.280136</td>\n",
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       "      <td>00:54</td>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>0</td>\n",
       "      <td>0.279894</td>\n",
       "      <td>0.166795</td>\n",
       "      <td>0.935210</td>\n",
       "      <td>00:48</td>\n",
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       "      <td>1</td>\n",
       "      <td>0.178742</td>\n",
       "      <td>0.130508</td>\n",
       "      <td>0.945300</td>\n",
       "      <td>00:49</td>\n",
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       "      <td>2</td>\n",
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       "      <td>0.100365</td>\n",
       "      <td>0.963356</td>\n",
       "      <td>00:48</td>\n",
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       "      <td>3</td>\n",
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       "      <td>0.097242</td>\n",
       "      <td>0.967605</td>\n",
       "      <td>00:49</td>\n",
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   ],
   "source": [
    "# creating a vision learner\n",
    "learn = vision_learner(dls, resnet18, metrics=accuracy)\n",
    "\n",
    "# training the model\n",
    "learn.fine_tune(4)"
   ]
  },
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "interp = ClassificationInterpretation.from_learner(learn)\n",
    "interp.plot_confusion_matrix()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "aeebbc32",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-21T12:06:25.636780Z",
     "iopub.status.busy": "2022-11-21T12:06:25.636460Z",
     "iopub.status.idle": "2022-11-21T12:06:25.728348Z",
     "shell.execute_reply": "2022-11-21T12:06:25.727363Z"
    },
    "papermill": {
     "duration": 0.198634,
     "end_time": "2022-11-21T12:06:25.730856",
     "exception": false,
     "start_time": "2022-11-21T12:06:25.532222",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "torch.save(learn.model.state_dict(), './resnet.pth')"
   ]
  }
 ],
 "metadata": {
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   "display_name": "Python 3",
   "language": "python",
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  },
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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  "papermill": {
   "default_parameters": {},
   "duration": 558.717352,
   "end_time": "2022-11-21T12:06:28.277535",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2022-11-21T11:57:09.560183",
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  },
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