damage_detect.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "b1a5b049-cedf-4eab-ba70-45bb69cdadf5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras.applications import ResNet50\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.layers import Dense, GlobalAveragePooling2D\n",
    "from tensorflow.keras.optimizers import Adam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "2fc0577d-4bde-45f9-bf83-5bba3b435537",
   "metadata": {},
   "outputs": [],
   "source": [
    "base_model = ResNet50(weights='imagenet', include_top=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "2c0668b2-18b1-4ac7-b4e7-d12b2e5d1fc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = base_model.output\n",
    "x = GlobalAveragePooling2D()(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "60355a58-61ec-4ee4-9cb9-13b7b5aa9a62",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = Dense(1024, activation='relu')(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "5a5b2d5b-e62c-455c-a147-7b63edaa7482",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = Dense(1, activation='sigmoid')(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "52e20cda-1566-4ae6-86a2-39fbf293f13d",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Model(inputs=base_model.input, outputs=predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "d3482bd9-c0dd-480e-a499-9c47c1735486",
   "metadata": {},
   "outputs": [],
   "source": [
    "for layer in base_model.layers[:-4]:\n",
    "    layer.trainable = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "ba029461-5cc5-4c6a-bead-5481a3f2f32e",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer=Adam(learning_rate=0.0001), loss='binary_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "391315c7-f117-405d-8a29-45b350144221",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_datagen = ImageDataGenerator(\n",
    "    rescale=1./255,\n",
    "    shear_range=0.2,\n",
    "    zoom_range=0.2,\n",
    "    horizontal_flip=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "b4ee771e-ff1b-4886-a5eb-480c217e4b9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_datagen = ImageDataGenerator(rescale=1./255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "96a63a9a-6c2f-41df-b557-cdfa443a5810",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense\n",
    "\n",
    "model = Sequential([\n",
    "    Dense(64, activation='relu', input_shape=(6,)),\n",
    "    Dense(10, activation='softmax')\n",
    "])\n",
    "\n",
    "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "05f69cdc-dfef-4858-8599-6f503b5564a7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 0 images belonging to 0 classes.\n"
     ]
    }
   ],
   "source": [
    "train_generator = train_datagen.flow_from_directory(\n",
    "    '/Users/hassanazhar/Python/damage_detect/label_images',  # This is the target directory\n",
    "    target_size=(224, 224),  # All images will be resized to 224x224\n",
    "    batch_size=32,\n",
    "    class_mode='binary'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "2ae697e2-5fb7-43d3-a571-fca8739dda89",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 0 images belonging to 0 classes.\n"
     ]
    }
   ],
   "source": [
    "validation_generator = test_datagen.flow_from_directory(\n",
    "    '/Users/hassanazhar/Python/damage_detect/label_images',\n",
    "    target_size=(224, 224),\n",
    "    batch_size=32,\n",
    "    class_mode='binary'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "abc76ce3-6f01-4502-9366-ba67eac4a3c8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "af5de84b-8fdd-4c9c-9e27-8354feeac29a",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Must provide at least one structure",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[62], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m model\u001b[38;5;241m.\u001b[39mfit(\n\u001b[1;32m      2\u001b[0m     train_generator,\n\u001b[1;32m      3\u001b[0m     steps_per_epoch\u001b[38;5;241m=\u001b[39mtrain_generator\u001b[38;5;241m.\u001b[39msamples \u001b[38;5;241m/\u001b[39m\u001b[38;5;241m/\u001b[39m train_generator\u001b[38;5;241m.\u001b[39mbatch_size,\n\u001b[1;32m      4\u001b[0m     validation_data\u001b[38;5;241m=\u001b[39mvalidation_generator,\n\u001b[1;32m      5\u001b[0m     validation_steps\u001b[38;5;241m=\u001b[39mvalidation_generator\u001b[38;5;241m.\u001b[39msamples \u001b[38;5;241m/\u001b[39m\u001b[38;5;241m/\u001b[39m validation_generator\u001b[38;5;241m.\u001b[39mbatch_size,\n\u001b[1;32m      6\u001b[0m     epochs\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m10\u001b[39m\n\u001b[1;32m      7\u001b[0m )\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/keras/src/utils/traceback_utils.py:122\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    119\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n\u001b[1;32m    120\u001b[0m     \u001b[38;5;66;03m# To get the full stack trace, call:\u001b[39;00m\n\u001b[1;32m    121\u001b[0m     \u001b[38;5;66;03m# `keras.config.disable_traceback_filtering()`\u001b[39;00m\n\u001b[0;32m--> 122\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mwith_traceback(filtered_tb) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    123\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m    124\u001b[0m     \u001b[38;5;28;01mdel\u001b[39;00m filtered_tb\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/keras/src/utils/tree.py:236\u001b[0m, in \u001b[0;36mmap_structure\u001b[0;34m(func, *structures)\u001b[0m\n\u001b[1;32m    234\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m`func` must be callable. Received: func=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    235\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m structures:\n\u001b[0;32m--> 236\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMust provide at least one structure\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    237\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m other \u001b[38;5;129;01min\u001b[39;00m structures[\u001b[38;5;241m1\u001b[39m:]:\n\u001b[1;32m    238\u001b[0m     assert_same_structure(structures[\u001b[38;5;241m0\u001b[39m], other, check_types\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n",
      "\u001b[0;31mValueError\u001b[0m: Must provide at least one structure"
     ]
    }
   ],
   "source": [
    "model.fit(\n",
    "    train_generator,\n",
    "    steps_per_epoch=train_generator.samples // train_generator.batch_size,\n",
    "    validation_data=validation_generator,\n",
    "    validation_steps=validation_generator.samples // validation_generator.batch_size,\n",
    "    epochs=10\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "db37f2a3-9a64-499d-b11b-61c7d7c1aecc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import tensorflow_hub as hub\n",
    "import numpy as np\n",
    "import cv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "6c843827-7524-4c9d-a988-7a84a2770a23",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Importing a function (__inference___call___32344) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:absl:Importing a function (__inference_EfficientDet-D0_layer_call_and_return_conditional_losses_97451) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:absl:Importing a function (__inference_bifpn_layer_call_and_return_conditional_losses_77595) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:absl:Importing a function (__inference_EfficientDet-D0_layer_call_and_return_conditional_losses_103456) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:absl:Importing a function (__inference_EfficientDet-D0_layer_call_and_return_conditional_losses_93843) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:absl:Importing a function (__inference_EfficientDet-D0_layer_call_and_return_conditional_losses_107064) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n",
      "WARNING:absl:Importing a function (__inference_bifpn_layer_call_and_return_conditional_losses_75975) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n"
     ]
    }
   ],
   "source": [
    "model_url = \"https://tfhub.dev/tensorflow/efficientdet/d0/1\"  # Example URL\n",
    "model = hub.load(model_url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "55a12d82-3016-4b3c-89e2-9e79f9f88d87",
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_image(image_path):\n",
    "    img = cv2.imread(image_path)\n",
    "    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
    "    img = cv2.resize(img, (512, 512))\n",
    "    img = img / 255.0  # Normalize the image\n",
    "    img = np.expand_dims(img, axis=0)\n",
    "    return img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "220b297b-9c4f-41d4-9d8b-a0295bb3d935",
   "metadata": {},
   "outputs": [],
   "source": [
    "image_path = 'Damage_Images/image01.jpg'\n",
    "image = preprocess_image(image_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "1c2d487f-72f5-45e8-9732-26596b590cc8",
   "metadata": {},
   "outputs": [],
   "source": [
    "outputs = model(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "4d13725c-988d-4db2-abf9-2d19c7be27f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "boxes = outputs['detection_boxes'].numpy()\n",
    "scores = outputs['detection_scores'].numpy()\n",
    "classes = outputs['detection_classes'].numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "2cbb7768-1ac2-430b-a348-7501d4d9a5f8",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[58], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(scores)):\n\u001b[0;32m----> 2\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m scores[i] \u001b[38;5;241m>\u001b[39m scores:  \u001b[38;5;66;03m# Threshold for displaying\u001b[39;00m\n\u001b[1;32m      3\u001b[0m         box \u001b[38;5;241m=\u001b[39m boxes[i]\n\u001b[1;32m      4\u001b[0m         class_id \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mint\u001b[39m(classes[i])\n",
      "\u001b[0;31mValueError\u001b[0m: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()"
     ]
    }
   ],
   "source": [
    "for i in range(len(scores)):\n",
    "    if scores[i] > scores:  # Threshold for displaying\n",
    "        box = boxes[i]\n",
    "        class_id = int(classes[i])\n",
    "        print(f\"Detected class: {class_id} with score: {scores[i]}\")\n",
    "        print(f\"Bounding box: {box}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "0ede1166-c1c2-4d8b-ac1c-ded20052485e",
   "metadata": {},
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "cannot import name 'isPath' from 'PIL._util' (/Users/hassanazhar/anaconda3/lib/python3.11/site-packages/PIL/_util.py)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[59], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01multralytics\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m YOLO\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mcv2\u001b[39;00m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/ultralytics/__init__.py:5\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Ultralytics YOLO 🚀, AGPL-3.0 license\u001b[39;00m\n\u001b[1;32m      3\u001b[0m __version__ \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m8.0.148\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m----> 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01multralytics\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mhub\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m start\n\u001b[1;32m      6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01multralytics\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodels\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m RTDETR, SAM, YOLO\n\u001b[1;32m      7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01multralytics\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodels\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfastsam\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m FastSAM\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/ultralytics/hub/__init__.py:5\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Ultralytics YOLO 🚀, AGPL-3.0 license\u001b[39;00m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mrequests\u001b[39;00m\n\u001b[0;32m----> 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01multralytics\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdata\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m HUBDatasetStats\n\u001b[1;32m      6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01multralytics\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mhub\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mauth\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Auth\n\u001b[1;32m      7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01multralytics\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mhub\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m HUB_API_ROOT, HUB_WEB_ROOT, PREFIX\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/ultralytics/data/__init__.py:3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Ultralytics YOLO 🚀, AGPL-3.0 license\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbase\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m BaseDataset\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbuild\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m build_dataloader, build_yolo_dataset, load_inference_source\n\u001b[1;32m      5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdataset\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ClassificationDataset, SemanticDataset, YOLODataset\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/ultralytics/data/base.py:18\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdata\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Dataset\n\u001b[1;32m     16\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtqdm\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m tqdm\n\u001b[0;32m---> 18\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01multralytics\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m DEFAULT_CFG, LOCAL_RANK, LOGGER, NUM_THREADS, TQDM_BAR_FORMAT\n\u001b[1;32m     20\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m HELP_URL, IMG_FORMATS\n\u001b[1;32m     23\u001b[0m \u001b[38;5;28;01mclass\u001b[39;00m \u001b[38;5;21;01mBaseDataset\u001b[39;00m(Dataset):\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/ultralytics/utils/__init__.py:19\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtyping\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Union\n\u001b[1;32m     18\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mcv2\u001b[39;00m\n\u001b[0;32m---> 19\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n\u001b[1;32m     20\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m     21\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/matplotlib/__init__.py:161\u001b[0m\n\u001b[1;32m    157\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpackaging\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mversion\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m parse \u001b[38;5;28;01mas\u001b[39;00m parse_version\n\u001b[1;32m    159\u001b[0m \u001b[38;5;66;03m# cbook must import matplotlib only within function\u001b[39;00m\n\u001b[1;32m    160\u001b[0m \u001b[38;5;66;03m# definitions, so it is safe to import from it here.\u001b[39;00m\n\u001b[0;32m--> 161\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m _api, _version, cbook, _docstring, rcsetup\n\u001b[1;32m    162\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcbook\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m sanitize_sequence\n\u001b[1;32m    163\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_api\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m MatplotlibDeprecationWarning\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/matplotlib/rcsetup.py:27\u001b[0m\n\u001b[1;32m     25\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m _api, cbook\n\u001b[1;32m     26\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcbook\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ls_mapper\n\u001b[0;32m---> 27\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcolors\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Colormap, is_color_like\n\u001b[1;32m     28\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_fontconfig_pattern\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m parse_fontconfig_pattern\n\u001b[1;32m     29\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_enums\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m JoinStyle, CapStyle\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/matplotlib/colors.py:53\u001b[0m\n\u001b[1;32m     50\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mre\u001b[39;00m\n\u001b[1;32m     52\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mPIL\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Image\n\u001b[0;32m---> 53\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mPIL\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mPngImagePlugin\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m PngInfo\n\u001b[1;32m     55\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mmpl\u001b[39;00m\n\u001b[1;32m     56\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/PIL/PngImagePlugin.py:42\u001b[0m\n\u001b[1;32m     39\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mzlib\u001b[39;00m\n\u001b[1;32m     40\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01menum\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m IntEnum\n\u001b[0;32m---> 42\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Image, ImageChops, ImageFile, ImagePalette, ImageSequence\n\u001b[1;32m     43\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_binary\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m i16be \u001b[38;5;28;01mas\u001b[39;00m i16\n\u001b[1;32m     44\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_binary\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m i32be \u001b[38;5;28;01mas\u001b[39;00m i32\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/PIL/ImageFile.py:36\u001b[0m\n\u001b[1;32m     33\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01msys\u001b[39;00m\n\u001b[1;32m     35\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Image\n\u001b[0;32m---> 36\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_util\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m isPath\n\u001b[1;32m     38\u001b[0m MAXBLOCK \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m65536\u001b[39m\n\u001b[1;32m     40\u001b[0m SAFEBLOCK \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1024\u001b[39m \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m1024\u001b[39m\n",
      "\u001b[0;31mImportError\u001b[0m: cannot import name 'isPath' from 'PIL._util' (/Users/hassanazhar/anaconda3/lib/python3.11/site-packages/PIL/_util.py)"
     ]
    }
   ],
   "source": [
    "from ultralytics import YOLO\n",
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load a pre-trained YOLOv8 model\n",
    "model = YOLO('yolov8n.pt')  # You can also try 'yolov8m.pt', 'yolov8l.pt', etc.\n",
    "\n",
    "# Load and preprocess the image\n",
    "image_path = 'Damage_images/image01.jpg'\n",
    "image = cv2.imread(image_path)\n",
    "image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
    "\n",
    "# Perform inference\n",
    "results = model(image_rgb)\n",
    "\n",
    "# Print results\n",
    "results.print()\n",
    "\n",
    "# Extract bounding boxes and labels\n",
    "boxes = results.xyxy[0].numpy()  # Bounding boxes\n",
    "scores = results.xyxy[0][:, 4].numpy()  # Confidence scores\n",
    "labels = results.xyxy[0][:, 5].numpy()  # Class labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4151cb29-5043-48a5-b94b-aa7c81f7ef82",
   "metadata": {},
   "outputs": [
    {
     "ename": "OSError",
     "evalue": "broken data stream when reading image file",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[24], line 16\u001b[0m\n\u001b[1;32m     13\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m np\u001b[38;5;241m.\u001b[39marray(images)\n\u001b[1;32m     15\u001b[0m image_paths \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDamage_Images/image01.jpg\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m---> 16\u001b[0m images \u001b[38;5;241m=\u001b[39m load_and_preprocess_images(image_paths)\n\u001b[1;32m     18\u001b[0m \u001b[38;5;66;03m# Split data into training and validation sets\u001b[39;00m\n\u001b[1;32m     19\u001b[0m x_train, x_val \u001b[38;5;241m=\u001b[39m train_test_split(images, test_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.2\u001b[39m, random_state\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m42\u001b[39m)\n",
      "Cell \u001b[0;32mIn[24], line 9\u001b[0m, in \u001b[0;36mload_and_preprocess_images\u001b[0;34m(image_paths, img_size)\u001b[0m\n\u001b[1;32m      7\u001b[0m images \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m      8\u001b[0m \u001b[38;5;66;03m# for path in image_paths:\u001b[39;00m\n\u001b[0;32m----> 9\u001b[0m img \u001b[38;5;241m=\u001b[39m tf\u001b[38;5;241m.\u001b[39mkeras\u001b[38;5;241m.\u001b[39mpreprocessing\u001b[38;5;241m.\u001b[39mimage\u001b[38;5;241m.\u001b[39mload_img(image_paths, target_size\u001b[38;5;241m=\u001b[39mimg_size)\n\u001b[1;32m     10\u001b[0m img \u001b[38;5;241m=\u001b[39m tf\u001b[38;5;241m.\u001b[39mkeras\u001b[38;5;241m.\u001b[39mpreprocessing\u001b[38;5;241m.\u001b[39mimage\u001b[38;5;241m.\u001b[39mimg_to_array(img)\n\u001b[1;32m     11\u001b[0m img \u001b[38;5;241m=\u001b[39m img \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m255.0\u001b[39m  \u001b[38;5;66;03m# Normalize to [0, 1]\u001b[39;00m\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/keras/src/utils/image_utils.py:292\u001b[0m, in \u001b[0;36mload_img\u001b[0;34m(path, color_mode, target_size, interpolation, keep_aspect_ratio)\u001b[0m\n\u001b[1;32m    290\u001b[0m             img \u001b[38;5;241m=\u001b[39m img\u001b[38;5;241m.\u001b[39mresize(width_height_tuple, resample, box\u001b[38;5;241m=\u001b[39mcrop_box)\n\u001b[1;32m    291\u001b[0m         \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 292\u001b[0m             img \u001b[38;5;241m=\u001b[39m img\u001b[38;5;241m.\u001b[39mresize(width_height_tuple, resample)\n\u001b[1;32m    293\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m img\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/PIL/Image.py:2043\u001b[0m, in \u001b[0;36mImage.resize\u001b[0;34m(self, size, resample, box, reducing_gap)\u001b[0m\n\u001b[1;32m   2040\u001b[0m     im \u001b[38;5;241m=\u001b[39m im\u001b[38;5;241m.\u001b[39mresize(size, resample, box)\n\u001b[1;32m   2041\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m im\u001b[38;5;241m.\u001b[39mconvert(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmode)\n\u001b[0;32m-> 2043\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mload()\n\u001b[1;32m   2045\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m reducing_gap \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m resample \u001b[38;5;241m!=\u001b[39m Resampling\u001b[38;5;241m.\u001b[39mNEAREST:\n\u001b[1;32m   2046\u001b[0m     factor_x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mint\u001b[39m((box[\u001b[38;5;241m2\u001b[39m] \u001b[38;5;241m-\u001b[39m box[\u001b[38;5;241m0\u001b[39m]) \u001b[38;5;241m/\u001b[39m size[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m/\u001b[39m reducing_gap) \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;241m1\u001b[39m\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/PIL/ImageFile.py:276\u001b[0m, in \u001b[0;36mImageFile.load\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    272\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfp \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    274\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmap \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m LOAD_TRUNCATED_IMAGES \u001b[38;5;129;01mand\u001b[39;00m err_code \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m    275\u001b[0m     \u001b[38;5;66;03m# still raised if decoder fails to return anything\u001b[39;00m\n\u001b[0;32m--> 276\u001b[0m     raise_oserror(err_code)\n\u001b[1;32m    278\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m Image\u001b[38;5;241m.\u001b[39mImage\u001b[38;5;241m.\u001b[39mload(\u001b[38;5;28mself\u001b[39m)\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/PIL/ImageFile.py:71\u001b[0m, in \u001b[0;36mraise_oserror\u001b[0;34m(error)\u001b[0m\n\u001b[1;32m     69\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m message:\n\u001b[1;32m     70\u001b[0m     message \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdecoder error \u001b[39m\u001b[38;5;132;01m{\u001b[39;00merror\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m---> 71\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m(message \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m when reading image file\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "\u001b[0;31mOSError\u001b[0m: broken data stream when reading image file"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, UpSampling2D\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "def load_and_preprocess_images(image_paths, img_size=(256, 256)):\n",
    "    images = []\n",
    "    # for path in image_paths:\n",
    "    img = tf.keras.preprocessing.image.load_img(image_paths, target_size=img_size)\n",
    "    img = tf.keras.preprocessing.image.img_to_array(img)\n",
    "    img = img / 255.0  # Normalize to [0, 1]\n",
    "    images.append(img)\n",
    "    return np.array(images)\n",
    "\n",
    "image_paths = 'Damage_Images/image01.jpg'\n",
    "images = load_and_preprocess_images(image_paths)\n",
    "\n",
    "# Split data into training and validation sets\n",
    "x_train, x_val = train_test_split(images, test_size=0.2, random_state=42)\n",
    "\n",
    "input_img = Input(shape=(256, 256, 3))  # Adjust shape as needed\n",
    "\n",
    "# Encoder\n",
    "x = Conv2D(32, (3, 3), activation='relu', padding='same')(input_img)\n",
    "x = MaxPooling2D((2, 2), padding='same')(x)\n",
    "x = Conv2D(32, (3, 3), activation='relu', padding='same')(x)\n",
    "encoded = MaxPooling2D((2, 2), padding='same')(x)\n",
    "\n",
    "# Decoder\n",
    "x = Conv2D(32, (3, 3), activation='relu', padding='same')(encoded)\n",
    "x = UpSampling2D((2, 2))(x)\n",
    "x = Conv2D(32, (3, 3), activation='relu', padding='same')(x)\n",
    "x = UpSampling2D((2, 2))(x)\n",
    "decoded = Conv2D(3, (3, 3), activation='sigmoid', padding='same')(x)\n",
    "\n",
    "autoencoder = Model(input_img, decoded)\n",
    "autoencoder.compile(optimizer='adam', loss='binary_crossentropy')\n",
    "\n",
    "# Train the model\n",
    "autoencoder.fit(x_train, x_train,\n",
    "                epochs=50,\n",
    "                batch_size=128,\n",
    "                shuffle=True,\n",
    "                validation_data=(x_val, x_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "24563a28-6eea-4a49-99cf-6662d9697257",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "files = []\n",
    "directory = '/Users/hassanazhar/Python/damage_yolov8/dataset/images/val'\n",
    "for filename in os.listdir(directory):\n",
    "    files.append(filename)\n",
    "\n",
    "def create_label_file(image_path, annotations, output_dir):\n",
    "    # Get image dimensions (replace with actual dimensions)\n",
    "\n",
    "    width, height = 800, 600  # Example dimensions\n",
    "\n",
    "    # Process each annotation and normalize coordinates\n",
    "    label_lines = []\n",
    "    for annotation in annotations:\n",
    "        class_label, x1, y1, x2, y2 = annotation\n",
    "        x_center = (x1 + x2) / 2 / width\n",
    "        y_center = (y1 + y2) / 2 / height\n",
    "        box_width = (x2 - x1) / width\n",
    "        box_height = (y2 - y1) / height\n",
    "        label_lines.append(f\"{class_label} {x_center} {y_center} {box_width} {box_height}\")\n",
    "\n",
    "    # Write to label.txt\n",
    "    base_name = os.path.basename(image_path).split('.')[0]\n",
    "    output_path = os.path.join(output_dir, f\"{base_name}.txt\")\n",
    "    with open(output_path, 'w') as file:\n",
    "        for line in label_lines:\n",
    "            file.write(line + '\\n')\n",
    "\n",
    "def print_file_names(directory):\n",
    "    for filename in os.listdir(directory):\n",
    "        print(filename)\n",
    "\n",
    "# Example usage\n",
    "for f in files:\n",
    "    image_path = '/Users/hassanazhar/Python/damage_yolov8/dataset/images/val/'+f\n",
    "    annotations = [\n",
    "        (0, 0.5, 0.5, 0.2, 0.3),  # (class_label, x1, y1, x2, y2)\n",
    "        # Add more annotations as needed\n",
    "    ]\n",
    "    output_dir = '/Users/hassanazhar/Python/damage_yolov8/dataset/labels/val'\n",
    "    create_label_file(image_path, annotations, output_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b3608e53-6c76-41ed-b29c-ee02ad47650d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "image02.png\n",
      "image 95.png\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "def print_file_names(directory):\n",
    "    for filename in os.listdir(directory):\n",
    "        print(filename)\n",
    "\n",
    "# Example usage\n",
    "directory = '/Users/hassanazhar/Python/damage_detect/label_images'\n",
    "print_file_names(directory)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "896dc1a7-ed5b-4b9d-b219-b449b8ea3480",
   "metadata": {},
   "outputs": [],
   "source": [
    "names = []\n",
    "labels = []\n",
    "with open('label.txt', 'r') as f:\n",
    "    for line in f.readlines():\n",
    "       tokens = line.split(' ')\n",
    "       names.append(tokens[0])\n",
    "       labels.append(int(tokens[1]))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}