image-data-download.ipynb

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   "source": [
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as imd\n",
    "from tensorflow.keras.preprocessing.image import load_img,img_to_array\n",
    "from tensorflow.keras.applications.resnet50 import ResNet50,preprocess_input\n",
    "from tensorflow.keras.layers import Flatten,GlobalMaxPool2D\n",
    "from numpy.linalg import norm\n",
    "import os\n",
    "from tqdm import tqdm\n",
    "path = '/kaggle/input/fashion-product-images-dataset/fashion-dataset/images'"
   ]
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     "text": [
      "100%|██████████| 44441/44441 [22:18<00:00, 33.21it/s]\n"
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   "source": [
    "preprocess_image_data_list = []\n",
    "for image in tqdm(os.listdir(path)):\n",
    "    img_path = os.path.join(path, image)\n",
    "    img = load_img(img_path, target_size=(224, 224))\n",
    "    img_array = img_to_array(img)\n",
    "    preprocess_image_data_list.append(img_array)"
   ]
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    "tags": []
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   "outputs": [],
   "source": [
    "import pickle\n",
    "pickle.dump(preprocess_image_data_list,open('image_data.pkl','wb'))"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "import gzip\n",
    "name = '/kaggle/working/image_data.pkl'\n",
    "output = 'compressed_image_data.pkl.gz'\n",
    "with open(name, 'rb') as f_in:\n",
    "    with gzip.open(output, 'wb') as f_out:\n",
    "        f_out.write(f_in.read())"
   ]
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