siamese_model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras import Model\n",
    "from keras.layers import *\n",
    "from keras.utils import plot_model\n",
    "import keras.backend as K\n",
    "import os\n",
    "import numpy as np\n",
    "import cv2\n",
    "import pandas as pd\n",
    "from tqdm import tqdm\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "from keras.saving import save_model, load_model\n",
    "from keras import regularizers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "BATCH_SIZE = 10\n",
    "EPOCHS = 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_network():\n",
    "    x = Input(shape=(256, 256, 1), batch_size=BATCH_SIZE)\n",
    "    # conv1 = Conv2D(activation=\"relu\", kernel_size=(10, 10), filters=64)(x)\n",
    "    # maxpool1 = MaxPooling2D()(conv1)\n",
    "    # conv2 = Conv2D(activation=\"relu\", kernel_size=(7, 7), filters=128)(maxpool1)\n",
    "    # maxpool2 = MaxPooling2D()(conv2)\n",
    "    # conv3 = Conv2D(activation=\"relu\", kernel_size=(4, 4), filters=128)(x)\n",
    "    # maxpool3 = MaxPooling2D()(conv3)\n",
    "    # conv4 = Conv2D(activation=\"relu\", kernel_size=(4, 4), filters=256)(maxpool3)\n",
    "    # flattened = Flatten()(conv4)\n",
    "    # dense1 = Dense(4096)(flattened)\n",
    "\n",
    "    conv3 = Conv2D(activation=\"relu\", kernel_size=(11, 11), filters=16, kernel_regularizer=regularizers.l2(0.003))(x)\n",
    "    maxpool3 = MaxPooling2D()(conv3)\n",
    "    conv4 = Conv2D(activation=\"relu\", kernel_size=(7, 7), filters=32)(maxpool3)\n",
    "    maxpool4 = MaxPooling2D()(conv4)\n",
    "    conv4 = Conv2D(activation=\"relu\", kernel_size=(4, 4), filters=64)(maxpool4)\n",
    "    flattened = Flatten()(conv4)\n",
    "    dense1 = Dense(1024, activation=\"relu\")(flattened)\n",
    "\n",
    "    return (x, dense1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "upper_layer = model_network()\n",
    "lower_layer = model_network()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "lambda_layer = Lambda(lambda x: (K.square(x[0] - x[1])))([upper_layer[1], lower_layer[1]])\n",
    "dense_final = Dense(1, activation=\"sigmoid\")(lambda_layer)\n",
    "\n",
    "model = Model(inputs=[upper_layer[0], lower_layer[0]], outputs=[dense_final])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_model(model, show_shapes=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer=\"adam\", loss=\"binary_crossentropy\", metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_grayscaled_roi(folder_name, to_save_to):\n",
    "    i = 0\n",
    "    images = os.listdir(folder_name)\n",
    "    face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')\n",
    "\n",
    "    for i, img in enumerate(tqdm(images)):\n",
    "        if(img.startswith(\".\") == False):\n",
    "            try:\n",
    "                frame = cv2.imread(f\"{folder_name}/{img}\")\n",
    "                gray = cv2.cvtColor(src = frame, code = cv2.COLOR_BGR2GRAY)\n",
    "                faces = face_cascade.detectMultiScale(image = gray, scaleFactor=1.3, minNeighbors=5)\n",
    "                (x,y,w,h) = faces[0]\n",
    "                grayscaled_roi = gray[y:y+h, x:x+w]                \n",
    "                gray = cv2.resize(grayscaled_roi, (256, 256))\n",
    "\n",
    "                cv2.imwrite(f\"{to_save_to}/{i}.png\", img = gray)\n",
    "                i += 1\n",
    "            except Exception as e:\n",
    "                print(\",\", end = ',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 18%|█▊        | 26/142 [00:00<00:01, 109.52it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ",,,,,,,,,,,,,,,,,,"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 74%|███████▍  | 105/142 [00:00<00:00, 216.27it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ",,,,,,,,,,,,,,"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 142/142 [00:00<00:00, 189.03it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ",,"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 11%|█▏        | 9/80 [00:00<00:02, 27.23it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ",,"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 61%|██████▏   | 49/80 [00:01<00:00, 32.20it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ",,"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 80/80 [00:02<00:00, 29.38it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ",,,,"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 38%|███▊      | 30/79 [00:00<00:01, 30.95it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ",,"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 58%|█████▊    | 46/79 [00:01<00:01, 30.42it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ",,,,"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 89%|████████▊ | 70/79 [00:02<00:00, 31.10it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ",,"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 79/79 [00:02<00:00, 30.70it/s]\n"
     ]
    }
   ],
   "source": [
    "get_grayscaled_roi(\"negative_real\", \"negative_grayed\")\n",
    "get_grayscaled_roi(\"positive_real\", \"positive_grayed\")\n",
    "get_grayscaled_roi(\"anchor_real\", \"anchor_grayed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length of positive data 75\n",
      "Length of negative data 124\n",
      "Length of anchor data 74\n"
     ]
    }
   ],
   "source": [
    "print(\"Length of positive data\", len(os.listdir(\"positive_grayed\")))\n",
    "print(\"Length of negative data\", len(os.listdir(\"negative_grayed\")))\n",
    "print(\"Length of anchor data\", len(os.listdir(\"anchor_grayed\")))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_to_img_data(dir):\n",
    "    data = []\n",
    "    for i in os.listdir(dir):\n",
    "        image = np.array(cv2.imread(os.path.join(dir, i), flags = cv2.IMREAD_GRAYSCALE))\n",
    "        data.append(image/255)\n",
    "    \n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "positive_data = convert_to_img_data(\"positive_grayed\")\n",
    "negative_data = convert_to_img_data(\"negative_grayed\")\n",
    "anchor_data = convert_to_img_data(\"anchor_grayed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "valid = list(zip(positive_data, anchor_data[0:len(positive_data)], [1]*len(positive_data)))\n",
    "invalid = list(zip(negative_data, anchor_data[0:len(negative_data)], [0]*len(negative_data)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length of matching data 74\n",
      "Length of invalid data 74\n"
     ]
    }
   ],
   "source": [
    "print(\"Length of matching data\", len(valid))\n",
    "print(\"Length of invalid data\", len(invalid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = int(0.8*(len(valid)))\n",
    "train_data = []\n",
    "cv_data = []\n",
    "\n",
    "train_data.extend(valid[:a])\n",
    "train_data.extend(invalid[:a])\n",
    "cv_data.extend(valid[a:])\n",
    "cv_data.extend(invalid[a:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "118\n",
      "30\n"
     ]
    }
   ],
   "source": [
    "print(len(train_data))\n",
    "print(len(cv_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "first_input = []\n",
    "second_input = []\n",
    "third_label = []\n",
    "\n",
    "first_input_cv = []\n",
    "second_input_cv = []\n",
    "third_label_cv = []\n",
    "\n",
    "for i in train_data:\n",
    "    first_input.append(i[0])\n",
    "    second_input.append(i[1])\n",
    "    third_label.append(i[2])\n",
    "\n",
    "for i in cv_data:\n",
    "    first_input_cv.append(i[0])\n",
    "    second_input_cv.append(i[1])\n",
    "    third_label_cv.append(i[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/6\n",
      "12/12 [==============================] - 70s 6s/step - loss: 6.3189 - accuracy: 0.5678 - val_loss: 1.2240 - val_accuracy: 0.5000\n",
      "Epoch 2/6\n",
      "12/12 [==============================] - 74s 6s/step - loss: 0.6825 - accuracy: 0.6441 - val_loss: 1.0446 - val_accuracy: 0.8000\n",
      "Epoch 3/6\n",
      "12/12 [==============================] - 58s 5s/step - loss: 0.8916 - accuracy: 0.7458 - val_loss: 1.0043 - val_accuracy: 0.5333\n",
      "Epoch 4/6\n",
      "12/12 [==============================] - 77s 6s/step - loss: 0.5261 - accuracy: 0.8305 - val_loss: 0.4093 - val_accuracy: 0.8333\n",
      "Epoch 5/6\n",
      "12/12 [==============================] - 80s 7s/step - loss: 0.3470 - accuracy: 0.8983 - val_loss: 0.3937 - val_accuracy: 0.8333\n",
      "Epoch 6/6\n",
      "12/12 [==============================] - 76s 6s/step - loss: 0.2179 - accuracy: 0.9237 - val_loss: 0.2895 - val_accuracy: 0.8667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/keras/src/engine/training.py:3000: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
      "  saving_api.save_model(\n"
     ]
    }
   ],
   "source": [
    "model.fit([np.array(first_input), np.array(second_input)], np.array(third_label), epochs=EPOCHS, verbose=1, batch_size=BATCH_SIZE, validation_data=([np.array(first_input_cv), np.array(second_input_cv)], np.array(third_label_cv)))\n",
    "model.save(\"model_final.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'plt' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mplt\u001b[49m\u001b[38;5;241m.\u001b[39mplot(model\u001b[38;5;241m.\u001b[39mhistory\u001b[38;5;241m.\u001b[39mhistory[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mloss\u001b[39m\u001b[38;5;124m'\u001b[39m], color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m'\u001b[39m, label \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTrain Loss\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m      2\u001b[0m plt\u001b[38;5;241m.\u001b[39mplot(model\u001b[38;5;241m.\u001b[39mhistory\u001b[38;5;241m.\u001b[39mhistory[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mval_loss\u001b[39m\u001b[38;5;124m'\u001b[39m], color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m'\u001b[39m, label \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mValidation loss\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m      4\u001b[0m plt\u001b[38;5;241m.\u001b[39mtitle(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLoss Analysis\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'plt' is not defined"
     ]
    }
   ],
   "source": [
    "plt.plot(model.history.history['loss'], color='b', label = \"Train Loss\")\n",
    "plt.plot(model.history.history['val_loss'], color='r', label = \"Validation loss\")\n",
    "\n",
    "plt.title(\"Loss Analysis\")\n",
    "plt.xlabel(\"epochs\")\n",
    "plt.ylabel(\"Loss\")\n",
    "\n",
    "plt.legend(\"x\")\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')\n",
    "img1 = cv2.imread(\"positive_grayed/1.png\", flags = cv2.IMREAD_GRAYSCALE)\n",
    "mat = cv2.imread(\"s1.png\", flags = cv2.IMREAD_GRAYSCALE)\n",
    "faces = face_cascade.detectMultiScale(image = mat, scaleFactor=1.3, minNeighbors=5)\n",
    "\n",
    "if (len(faces) > 0):\n",
    "    (x, y, w, h) = faces[0]\n",
    "\n",
    "    mat = mat[y:y+h, x:x+w]\n",
    "    mat = cv2.resize(src = mat, dsize=(256, 256))\n",
    "\n",
    "    img1 = cv2.resize(src = img1, dsize=(256, 256))\n",
    "    img1 = img1 / 255\n",
    "    img1 = img1[None, :]\n",
    "\n",
    "    mat = mat / 255\n",
    "    mat = mat[None, :]\n",
    "    \n",
    "    value = model.predict([mat, img1])[0][0]\n",
    "    if(value > 0.5):\n",
    "        print(\"Yes\")\n",
    "    else:\n",
    "        print(\"No\")\n",
    "else:\n",
    "    print(\"No Face Detected\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model = load_model(\"model_final.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_live(image_person):    \n",
    "    face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')\n",
    "    video = cv2.VideoCapture(0)\n",
    "\n",
    "    mat = cv2.imread(image_person, flags = cv2.IMREAD_GRAYSCALE)\n",
    "    faces = face_cascade.detectMultiScale(image = mat, scaleFactor=1.3, minNeighbors=5)\n",
    "    (x, y, w, h) = faces[0]\n",
    "\n",
    "    mat = mat[y:y+h, x:x+w]\n",
    "    mat = cv2.resize(mat, (256, 256))\n",
    "\n",
    "    mat = mat / 255\n",
    "    mat = mat[None, :, :, None]\n",
    "\n",
    "    while video.isOpened():\n",
    "        ret, frame = video.read()\n",
    "\n",
    "        if ret:\n",
    "            try:\n",
    "                gray = cv2.cvtColor(src = frame, code = cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "                faces = face_cascade.detectMultiScale(image = gray, scaleFactor=1.3, minNeighbors=5) \n",
    "\n",
    "                if(len(faces) > 0):\n",
    "                    (x, y, w, h) = faces[0]\n",
    "                    gray = gray[y:y+h, x:x+w]\n",
    "\n",
    "                    graying = cv2.resize(gray, (256, 256))\n",
    "                    graying = graying / 255\n",
    "\n",
    "                    graying = graying[None, :, :, None]\n",
    "                    \n",
    "                    value = model.predict([graying, mat])\n",
    "\n",
    "                    if(value > 0.5):\n",
    "                        cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)\n",
    "                    else:\n",
    "                        cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 0, 255), 2)\n",
    "                    cv2.imshow(winname = 'Face Detection', mat = np.array(frame))\n",
    "\n",
    "                    print(value)\n",
    "\n",
    "                    if(cv2.waitKey(1) == ord('a')):\n",
    "                        cv2.destroyAllWindows()\n",
    "                else:\n",
    "                    cv2.imshow(winname = 'Face Detection', mat = np.array(frame))\n",
    "                    cv2.putText(np.array(frame), 'Checking Faces...', (50, 50), 1,  \n",
    "                    1, (255, 0, 0) , 3, cv2.LINE_AA)\n",
    "                    if(cv2.waitKey(1) == ord('a')):\n",
    "                        cv2.destroyAllWindows()\n",
    "\n",
    "            except Exception as e:\n",
    "                cv2.imshow(winname = 'Face Detection', mat = np.array(frame))\n",
    "                print(e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 2s 2s/step\n",
      "[[0.60362107]]\n",
      "1/1 [==============================] - 0s 477ms/step\n",
      "[[0.99466157]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "1   HIToolbox                           0x000000018ddd05c8 _ZN15MenuBarInstance22EnsureAutoShowObserverEv + 120\n",
      "2   HIToolbox                           0x000000018ddd0188 _ZN15MenuBarInstance14EnableAutoShowEv + 60\n",
      "3   HIToolbox                           0x000000018dd73310 SetMenuBarObscured + 372\n",
      "4   HIToolbox                           0x000000018dd72ee8 _ZN13HIApplication15HandleActivatedEP14OpaqueEventRefhP15OpaqueWindowPtrh + 172\n",
      "5   HIToolbox                           0x000000018dd6cfcc _ZN13HIApplication13EventObserverEjP14OpaqueEventRefPv + 296\n",
      "6   HIToolbox                           0x000000018dd33cd0 _NotifyEventLoopObservers + 176\n",
      "7   HIToolbox                           0x000000018dd6c96c AcquireEventFromQueue + 432\n",
      "8   HIToolbox                           0x000000018dd5bc84 ReceiveNextEventCommon + 320\n",
      "9   HIToolbox                           0x000000018dd5bb2c _BlockUntilNextEventMatchingListInModeWithFilter + 72\n",
      "10  AppKit                              0x000000018790184c _DPSNextEvent + 632\n",
      "11  AppKit                              0x00000001879009dc -[NSApplication(NSEvent) _nextEventMatchingEventMask:untilDate:inMode:dequeue:] + 728\n",
      "12  cv2.abi3.so                         0x0000000162c4a120 cvWaitKey + 476\n",
      "13  cv2.abi3.so                         0x0000000162c441b4 _ZN2cv9waitKeyExEi + 168\n",
      "14  cv2.abi3.so                         0x0000000162c442ac _ZN2cv7waitKeyEi + 48\n",
      "15  cv2.abi3.so                         0x0000000162191bc8 _ZL19pyopencv_cv_waitKeyP7_objectS0_S0_ + 148\n",
      "16  Python                              0x00000001057a9a40 cfunction_call + 60\n",
      "17  Python                              0x0000000105745700 _PyObject_MakeTpCall + 128\n",
      "18  Python                              0x0000000105868c88 _PyEval_EvalFrameDefault + 53048\n",
      "19  Python                              0x000000010585aac4 PyEval_EvalCode + 276\n",
      "20  Python                              0x0000000105855320 builtin_exec + 428\n",
      "21  Python                              0x000000010586a934 _PyEval_EvalFrameDefault + 60388\n",
      "22  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "23  Python                              0x000000010585f8a8 _PyEval_EvalFrameDefault + 15192\n",
      "24  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "25  Python                              0x000000010585f8a8 _PyEval_EvalFrameDefault + 15192\n",
      "26  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "27  Python                              0x0000000105764864 gen_send + 36\n",
      "28  Python                              0x00000001057571bc method_vectorcall_O + 100\n",
      "29  Python                              0x0000000105868ba0 _PyEval_EvalFrameDefault + 52816\n",
      "30  Python                              0x000000010586e6ec _PyEval_Vector + 156\n",
      "31  Python                              0x0000000105749a44 method_vectorcall + 272\n",
      "32  Python                              0x00000001057464ac _PyVectorcall_Call + 116\n",
      "33  Python                              0x000000010586b7d8 _PyEval_EvalFrameDefault + 64136\n",
      "34  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "35  Python                              0x000000010585f8a8 _PyEval_EvalFrameDefault + 15192\n",
      "36  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "37  Python                              0x000000010585f8a8 _PyEval_EvalFrameDefault + 15192\n",
      "38  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "39  Python                              0x000000010585f8a8 _PyEval_EvalFrameDefault + 15192\n",
      "40  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "41  Python                              0x000000010585f8a8 _PyEval_EvalFrameDefault + 15192\n",
      "42  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "43  _asyncio.cpython-311-darwin.so      0x00000001051944e0 task_step_impl + 440\n",
      "44  _asyncio.cpython-311-darwin.so      0x00000001051942b4 task_step + 52\n",
      "45  _asyncio.cpython-311-darwin.so      0x0000000105194d54 task_wakeup + 148\n",
      "46  Python                              0x00000001057aa710 cfunction_vectorcall_O + 92\n",
      "47  Python                              0x000000010589434c _PyObject_VectorcallTstate.4587 + 96\n",
      "48  Python                              0x0000000105894234 context_run + 216\n",
      "49  Python                              0x00000001057aa450 cfunction_vectorcall_FASTCALL_KEYWORDS + 80\n",
      "50  Python                              0x000000010586b7d8 _PyEval_EvalFrameDefault + 64136\n",
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      "52  Python                              0x0000000105855320 builtin_exec + 428\n",
      "53  Python                              0x00000001057aa450 cfunction_vectorcall_FASTCALL_KEYWORDS + 80\n",
      "54  Python                              0x0000000105868ba0 _PyEval_EvalFrameDefault + 52816\n",
      "55  Python                              0x000000010586e6ec _PyEval_Vector + 156\n",
      "56  Python                              0x00000001058fb5e0 pymain_run_module + 256\n",
      "57  Python                              0x00000001058fb080 Py_RunMain + 1784\n",
      "58  Python                              0x00000001058fc230 pymain_main + 52\n",
      "59  Python                              0x00000001058fca68 Py_BytesMain + 40\n",
      "60  dyld                                0x0000000184273e50 start + 2544\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 1s 536ms/step\n",
      "[[0.9947859]]\n",
      "1/1 [==============================] - 0s 201ms/step\n",
      "[[0.99482214]]\n",
      "1/1 [==============================] - 0s 78ms/step\n",
      "[[0.9917974]]\n",
      "1/1 [==============================] - 0s 79ms/step\n",
      "[[0.9861661]]\n",
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      "[[0.9910431]]\n",
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      "[[0.99136364]]\n",
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      "[[0.99005157]]\n"
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     "output_type": "stream",
     "text": [
      "1   HIToolbox                           0x000000018dd5b90c _ZN15MenuBarInstance22RemoveAutoShowObserverEv + 44\n",
      "2   HIToolbox                           0x000000018dd3d8c8 _ZN15MenuBarInstance21UpdateAggregateUIModeE21MenuBarAnimationStylehhh + 1196\n",
      "3   HIToolbox                           0x000000018dd3d30c _ZN15MenuBarInstance16ForEachMenuBarDoEU13block_pointerFvPS_E + 72\n",
      "4   HIToolbox                           0x000000018dd3d2b4 _ZL25UpdateAllAggregateUIModes21MenuBarAnimationStyleh + 120\n",
      "5   HIToolbox                           0x000000018dd3d228 SetSystemUIMode + 140\n",
      "6   AppKit                              0x0000000187bbfd40 -[NSApplication _setPresentationOptions:instance:flags:] + 972\n",
      "7   AppKit                              0x0000000187a55954 -[NSApplication _updateFullScreenPresentationOptionsForInstance:] + 428\n",
      "8   CoreFoundation                      0x0000000184672560 __CFNOTIFICATIONCENTER_IS_CALLING_OUT_TO_AN_OBSERVER__ + 148\n",
      "9   CoreFoundation                      0x0000000184710044 ___CFXRegistrationPost_block_invoke + 88\n",
      "10  CoreFoundation                      0x000000018470ff8c _CFXRegistrationPost + 440\n",
      "11  CoreFoundation                      0x0000000184643b64 _CFXNotificationPost + 708\n",
      "12  Foundation                          0x000000018553338c -[NSNotificationCenter postNotificationName:object:userInfo:] + 88\n",
      "13  AppKit                              0x0000000187bc02b4 spacesNotificationHandler + 96\n",
      "14  SkyLight                            0x00000001892ad214 _ZN12_GLOBAL__N_123notify_datagram_handlerEj15CGSDatagramTypePvmS1_ + 896\n",
      "15  SkyLight                            0x00000001892abe10 CGSSnarfAndDispatchDatagrams + 808\n",
      "16  SkyLight                            0x00000001895d3a9c SLSGetNextEventRecordInternal + 344\n",
      "17  SkyLight                            0x00000001893d4fb0 SLEventCreateNextEvent + 16\n",
      "18  HIToolbox                           0x000000018dd68b58 _ZL38PullEventsFromWindowServerOnConnectionjhP17__CFMachPortBoost + 60\n",
      "19  HIToolbox                           0x000000018dd68ae0 _ZL14MessageHandlerP12__CFMachPortPvlS1_ + 60\n",
      "20  CoreFoundation                      0x00000001846ad410 __CFMachPortPerform + 260\n",
      "21  CoreFoundation                      0x000000018467df98 __CFRUNLOOP_IS_CALLING_OUT_TO_A_SOURCE1_PERFORM_FUNCTION__ + 60\n",
      "22  CoreFoundation                      0x000000018467deb8 __CFRunLoopDoSource1 + 520\n",
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      "24  CoreFoundation                      0x000000018467b878 CFRunLoopRunSpecific + 612\n",
      "25  HIToolbox                           0x000000018dd5bfa0 RunCurrentEventLoopInMode + 292\n",
      "26  HIToolbox                           0x000000018dd5bc30 ReceiveNextEventCommon + 236\n",
      "27  HIToolbox                           0x000000018dd5bb2c _BlockUntilNextEventMatchingListInModeWithFilter + 72\n",
      "28  AppKit                              0x000000018790184c _DPSNextEvent + 632\n",
      "29  AppKit                              0x00000001879009dc -[NSApplication(NSEvent) _nextEventMatchingEventMask:untilDate:inMode:dequeue:] + 728\n",
      "30  cv2.abi3.so                         0x0000000162c4a120 cvWaitKey + 476\n",
      "31  cv2.abi3.so                         0x0000000162c441b4 _ZN2cv9waitKeyExEi + 168\n",
      "32  cv2.abi3.so                         0x0000000162c442ac _ZN2cv7waitKeyEi + 48\n",
      "33  cv2.abi3.so                         0x0000000162191bc8 _ZL19pyopencv_cv_waitKeyP7_objectS0_S0_ + 148\n",
      "34  Python                              0x00000001057a9a40 cfunction_call + 60\n",
      "35  Python                              0x0000000105745700 _PyObject_MakeTpCall + 128\n",
      "36  Python                              0x0000000105868c88 _PyEval_EvalFrameDefault + 53048\n",
      "37  Python                              0x000000010585aac4 PyEval_EvalCode + 276\n",
      "38  Python                              0x0000000105855320 builtin_exec + 428\n",
      "39  Python                              0x000000010586a934 _PyEval_EvalFrameDefault + 60388\n",
      "40  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "41  Python                              0x000000010585f8a8 _PyEval_EvalFrameDefault + 15192\n",
      "42  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "43  Python                              0x000000010585f8a8 _PyEval_EvalFrameDefault + 15192\n",
      "44  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "45  Python                              0x0000000105764864 gen_send + 36\n",
      "46  Python                              0x00000001057571bc method_vectorcall_O + 100\n",
      "47  Python                              0x0000000105868ba0 _PyEval_EvalFrameDefault + 52816\n",
      "48  Python                              0x000000010586e6ec _PyEval_Vector + 156\n",
      "49  Python                              0x0000000105749a44 method_vectorcall + 272\n",
      "50  Python                              0x00000001057464ac _PyVectorcall_Call + 116\n",
      "51  Python                              0x000000010586b7d8 _PyEval_EvalFrameDefault + 64136\n",
      "52  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "53  Python                              0x000000010585f8a8 _PyEval_EvalFrameDefault + 15192\n",
      "54  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "55  Python                              0x000000010585f8a8 _PyEval_EvalFrameDefault + 15192\n",
      "56  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "57  Python                              0x000000010585f8a8 _PyEval_EvalFrameDefault + 15192\n",
      "58  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "59  Python                              0x000000010585f8a8 _PyEval_EvalFrameDefault + 15192\n",
      "60  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "61  _asyncio.cpython-311-darwin.so      0x00000001051944e0 task_step_impl + 440\n",
      "62  _asyncio.cpython-311-darwin.so      0x00000001051942b4 task_step + 52\n",
      "63  _asyncio.cpython-311-darwin.so      0x0000000105194d54 task_wakeup + 148\n",
      "64  Python                              0x00000001057aa710 cfunction_vectorcall_O + 92\n",
      "65  Python                              0x000000010589434c _PyObject_VectorcallTstate.4587 + 96\n",
      "66  Python                              0x0000000105894234 context_run + 216\n",
      "67  Python                              0x00000001057aa450 cfunction_vectorcall_FASTCALL_KEYWORDS + 80\n",
      "68  Python                              0x000000010586b7d8 _PyEval_EvalFrameDefault + 64136\n",
      "69  Python                              0x000000010585aac4 PyEval_EvalCode + 276\n",
      "70  Python                              0x0000000105855320 builtin_exec + 428\n",
      "71  Python                              0x00000001057aa450 cfunction_vectorcall_FASTCALL_KEYWORDS + 80\n",
      "72  Python                              0x0000000105868ba0 _PyEval_EvalFrameDefault + 52816\n",
      "73  Python                              0x000000010586e6ec _PyEval_Vector + 156\n",
      "74  Python                              0x00000001058fb5e0 pymain_run_module + 256\n",
      "75  Python                              0x00000001058fb080 Py_RunMain + 1784\n",
      "76  Python                              0x00000001058fc230 pymain_main + 52\n",
      "77  Python                              0x00000001058fca68 Py_BytesMain + 40\n",
      "78  dyld                                0x0000000184273e50 start + 2544\n"
     ]
    },
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      "[[0.9823115]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "1   HIToolbox                           0x000000018ddd05c8 _ZN15MenuBarInstance22EnsureAutoShowObserverEv + 120\n",
      "2   HIToolbox                           0x000000018dd9a478 _ZL17BroadcastInternaljPvh + 184\n",
      "3   SkyLight                            0x00000001892ad214 _ZN12_GLOBAL__N_123notify_datagram_handlerEj15CGSDatagramTypePvmS1_ + 896\n",
      "4   SkyLight                            0x00000001892abe10 CGSSnarfAndDispatchDatagrams + 808\n",
      "5   SkyLight                            0x00000001895d3a9c SLSGetNextEventRecordInternal + 344\n",
      "6   SkyLight                            0x00000001893d4fb0 SLEventCreateNextEvent + 16\n",
      "7   HIToolbox                           0x000000018dd68b58 _ZL38PullEventsFromWindowServerOnConnectionjhP17__CFMachPortBoost + 60\n",
      "8   HIToolbox                           0x000000018dd68ae0 _ZL14MessageHandlerP12__CFMachPortPvlS1_ + 60\n",
      "9   CoreFoundation                      0x00000001846ad410 __CFMachPortPerform + 260\n",
      "10  CoreFoundation                      0x000000018467df98 __CFRUNLOOP_IS_CALLING_OUT_TO_A_SOURCE1_PERFORM_FUNCTION__ + 60\n",
      "11  CoreFoundation                      0x000000018467deb8 __CFRunLoopDoSource1 + 520\n",
      "12  CoreFoundation                      0x000000018467c8a4 __CFRunLoopRun + 2264\n",
      "13  CoreFoundation                      0x000000018467b878 CFRunLoopRunSpecific + 612\n",
      "14  HIToolbox                           0x000000018dd5bfa0 RunCurrentEventLoopInMode + 292\n",
      "15  HIToolbox                           0x000000018dd5bc30 ReceiveNextEventCommon + 236\n",
      "16  HIToolbox                           0x000000018dd5bb2c _BlockUntilNextEventMatchingListInModeWithFilter + 72\n",
      "17  AppKit                              0x000000018790184c _DPSNextEvent + 632\n",
      "18  AppKit                              0x00000001879009dc -[NSApplication(NSEvent) _nextEventMatchingEventMask:untilDate:inMode:dequeue:] + 728\n",
      "19  cv2.abi3.so                         0x0000000162c4a120 cvWaitKey + 476\n",
      "20  cv2.abi3.so                         0x0000000162c441b4 _ZN2cv9waitKeyExEi + 168\n",
      "21  cv2.abi3.so                         0x0000000162c442ac _ZN2cv7waitKeyEi + 48\n",
      "22  cv2.abi3.so                         0x0000000162191bc8 _ZL19pyopencv_cv_waitKeyP7_objectS0_S0_ + 148\n",
      "23  Python                              0x00000001057a9a40 cfunction_call + 60\n",
      "24  Python                              0x0000000105745700 _PyObject_MakeTpCall + 128\n",
      "25  Python                              0x0000000105868c88 _PyEval_EvalFrameDefault + 53048\n",
      "26  Python                              0x000000010585aac4 PyEval_EvalCode + 276\n",
      "27  Python                              0x0000000105855320 builtin_exec + 428\n",
      "28  Python                              0x000000010586a934 _PyEval_EvalFrameDefault + 60388\n",
      "29  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "30  Python                              0x000000010585f8a8 _PyEval_EvalFrameDefault + 15192\n",
      "31  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "32  Python                              0x000000010585f8a8 _PyEval_EvalFrameDefault + 15192\n",
      "33  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "34  Python                              0x0000000105764864 gen_send + 36\n",
      "35  Python                              0x00000001057571bc method_vectorcall_O + 100\n",
      "36  Python                              0x0000000105868ba0 _PyEval_EvalFrameDefault + 52816\n",
      "37  Python                              0x000000010586e6ec _PyEval_Vector + 156\n",
      "38  Python                              0x0000000105749a44 method_vectorcall + 272\n",
      "39  Python                              0x00000001057464ac _PyVectorcall_Call + 116\n",
      "40  Python                              0x000000010586b7d8 _PyEval_EvalFrameDefault + 64136\n",
      "41  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "42  Python                              0x000000010585f8a8 _PyEval_EvalFrameDefault + 15192\n",
      "43  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "44  Python                              0x000000010585f8a8 _PyEval_EvalFrameDefault + 15192\n",
      "45  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "46  Python                              0x000000010585f8a8 _PyEval_EvalFrameDefault + 15192\n",
      "47  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "48  Python                              0x000000010585f8a8 _PyEval_EvalFrameDefault + 15192\n",
      "49  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "50  _asyncio.cpython-311-darwin.so      0x00000001051944e0 task_step_impl + 440\n",
      "51  _asyncio.cpython-311-darwin.so      0x00000001051942b4 task_step + 52\n",
      "52  _asyncio.cpython-311-darwin.so      0x0000000105194d54 task_wakeup + 148\n",
      "53  Python                              0x00000001057aa710 cfunction_vectorcall_O + 92\n",
      "54  Python                              0x000000010589434c _PyObject_VectorcallTstate.4587 + 96\n",
      "55  Python                              0x0000000105894234 context_run + 216\n",
      "56  Python                              0x00000001057aa450 cfunction_vectorcall_FASTCALL_KEYWORDS + 80\n",
      "57  Python                              0x000000010586b7d8 _PyEval_EvalFrameDefault + 64136\n",
      "58  Python                              0x000000010585aac4 PyEval_EvalCode + 276\n",
      "59  Python                              0x0000000105855320 builtin_exec + 428\n",
      "60  Python                              0x00000001057aa450 cfunction_vectorcall_FASTCALL_KEYWORDS + 80\n",
      "61  Python                              0x0000000105868ba0 _PyEval_EvalFrameDefault + 52816\n",
      "62  Python                              0x000000010586e6ec _PyEval_Vector + 156\n",
      "63  Python                              0x00000001058fb5e0 pymain_run_module + 256\n",
      "64  Python                              0x00000001058fb080 Py_RunMain + 1784\n",
      "65  Python                              0x00000001058fc230 pymain_main + 52\n",
      "66  Python                              0x00000001058fca68 Py_BytesMain + 40\n",
      "67  dyld                                0x0000000184273e50 start + 2544\n"
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      "[[0.9779892]]\n",
      "1/1 [==============================] - 0s 73ms/step\n",
      "[[0.97763073]]\n",
      "1/1 [==============================] - 0s 73ms/step\n",
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      "[[0.98903984]]\n",
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      "[[0.9924879]]\n",
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      "[[0.99189365]]\n",
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      "[[0.9898528]]\n",
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      "[[0.99110514]]\n",
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      "[[0.9904144]]\n",
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      "[[0.98395205]]\n",
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      "[[0.9901609]]\n",
      "1/1 [==============================] - 0s 74ms/step\n",
      "[[0.9736083]]\n",
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      "[[0.97923404]]\n",
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      "[[0.9828104]]\n",
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      "[[0.96578485]]\n",
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      "[[0.9680397]]\n",
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      "[[0.97004944]]\n",
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      "[[0.94903356]]\n",
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      "[[0.9782357]]\n",
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      "[[0.9709634]]\n",
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      "1/1 [==============================] - 0s 84ms/step\n",
      "[[0.9846048]]\n"
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     "text": [
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      "2   HIToolbox                           0x000000018dd3d8c8 _ZN15MenuBarInstance21UpdateAggregateUIModeE21MenuBarAnimationStylehhh + 1196\n",
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      "4   HIToolbox                           0x000000018dd3d2b4 _ZL25UpdateAllAggregateUIModes21MenuBarAnimationStyleh + 120\n",
      "5   HIToolbox                           0x000000018dd3d228 SetSystemUIMode + 140\n",
      "6   AppKit                              0x0000000187bbfd40 -[NSApplication _setPresentationOptions:instance:flags:] + 972\n",
      "7   AppKit                              0x0000000187a55954 -[NSApplication _updateFullScreenPresentationOptionsForInstance:] + 428\n",
      "8   CoreFoundation                      0x0000000184672560 __CFNOTIFICATIONCENTER_IS_CALLING_OUT_TO_AN_OBSERVER__ + 148\n",
      "9   CoreFoundation                      0x0000000184710044 ___CFXRegistrationPost_block_invoke + 88\n",
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      "11  CoreFoundation                      0x0000000184643b64 _CFXNotificationPost + 708\n",
      "12  Foundation                          0x000000018553338c -[NSNotificationCenter postNotificationName:object:userInfo:] + 88\n",
      "13  AppKit                              0x0000000187bc02b4 spacesNotificationHandler + 96\n",
      "14  SkyLight                            0x00000001892ad214 _ZN12_GLOBAL__N_123notify_datagram_handlerEj15CGSDatagramTypePvmS1_ + 896\n",
      "15  SkyLight                            0x00000001892abe10 CGSSnarfAndDispatchDatagrams + 808\n",
      "16  SkyLight                            0x00000001895d3a9c SLSGetNextEventRecordInternal + 344\n",
      "17  SkyLight                            0x00000001893d4fb0 SLEventCreateNextEvent + 16\n",
      "18  HIToolbox                           0x000000018dd68b58 _ZL38PullEventsFromWindowServerOnConnectionjhP17__CFMachPortBoost + 60\n",
      "19  HIToolbox                           0x000000018dd68ae0 _ZL14MessageHandlerP12__CFMachPortPvlS1_ + 60\n",
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      "29  AppKit                              0x00000001879009dc -[NSApplication(NSEvent) _nextEventMatchingEventMask:untilDate:inMode:dequeue:] + 728\n",
      "30  cv2.abi3.so                         0x0000000162c4a120 cvWaitKey + 476\n",
      "31  cv2.abi3.so                         0x0000000162c441b4 _ZN2cv9waitKeyExEi + 168\n",
      "32  cv2.abi3.so                         0x0000000162c442ac _ZN2cv7waitKeyEi + 48\n",
      "33  cv2.abi3.so                         0x0000000162191bc8 _ZL19pyopencv_cv_waitKeyP7_objectS0_S0_ + 148\n",
      "34  Python                              0x00000001057a9a40 cfunction_call + 60\n",
      "35  Python                              0x0000000105745700 _PyObject_MakeTpCall + 128\n",
      "36  Python                              0x0000000105868c88 _PyEval_EvalFrameDefault + 53048\n",
      "37  Python                              0x000000010585aac4 PyEval_EvalCode + 276\n",
      "38  Python                              0x0000000105855320 builtin_exec + 428\n",
      "39  Python                              0x000000010586a934 _PyEval_EvalFrameDefault + 60388\n",
      "40  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "41  Python                              0x000000010585f8a8 _PyEval_EvalFrameDefault + 15192\n",
      "42  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "43  Python                              0x000000010585f8a8 _PyEval_EvalFrameDefault + 15192\n",
      "44  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "45  Python                              0x0000000105764864 gen_send + 36\n",
      "46  Python                              0x00000001057571bc method_vectorcall_O + 100\n",
      "47  Python                              0x0000000105868ba0 _PyEval_EvalFrameDefault + 52816\n",
      "48  Python                              0x000000010586e6ec _PyEval_Vector + 156\n",
      "49  Python                              0x0000000105749a44 method_vectorcall + 272\n",
      "50  Python                              0x00000001057464ac _PyVectorcall_Call + 116\n",
      "51  Python                              0x000000010586b7d8 _PyEval_EvalFrameDefault + 64136\n",
      "52  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "53  Python                              0x000000010585f8a8 _PyEval_EvalFrameDefault + 15192\n",
      "54  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "55  Python                              0x000000010585f8a8 _PyEval_EvalFrameDefault + 15192\n",
      "56  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "57  Python                              0x000000010585f8a8 _PyEval_EvalFrameDefault + 15192\n",
      "58  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "59  Python                              0x000000010585f8a8 _PyEval_EvalFrameDefault + 15192\n",
      "60  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "61  _asyncio.cpython-311-darwin.so      0x00000001051944e0 task_step_impl + 440\n",
      "62  _asyncio.cpython-311-darwin.so      0x00000001051942b4 task_step + 52\n",
      "63  _asyncio.cpython-311-darwin.so      0x0000000105194d54 task_wakeup + 148\n",
      "64  Python                              0x00000001057aa710 cfunction_vectorcall_O + 92\n",
      "65  Python                              0x000000010589434c _PyObject_VectorcallTstate.4587 + 96\n",
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      "68  Python                              0x000000010586b7d8 _PyEval_EvalFrameDefault + 64136\n",
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      "70  Python                              0x0000000105855320 builtin_exec + 428\n",
      "71  Python                              0x00000001057aa450 cfunction_vectorcall_FASTCALL_KEYWORDS + 80\n",
      "72  Python                              0x0000000105868ba0 _PyEval_EvalFrameDefault + 52816\n",
      "73  Python                              0x000000010586e6ec _PyEval_Vector + 156\n",
      "74  Python                              0x00000001058fb5e0 pymain_run_module + 256\n",
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      "78  dyld                                0x0000000184273e50 start + 2544\n"
     ]
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      "[[0.97939837]]\n",
      "1/1 [==============================] - 0s 80ms/step\n",
      "[[0.984629]]\n",
      "1/1 [==============================] - 0s 77ms/step\n",
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      "1/1 [==============================] - 0s 76ms/step\n",
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      "1/1 [==============================] - 0s 76ms/step\n",
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      "1/1 [==============================] - 0s 81ms/step\n",
      "[[0.9712397]]\n",
      "1/1 [==============================] - 0s 477ms/step\n",
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      "1/1 [==============================] - 0s 167ms/step\n",
      "[[0.9802162]]\n",
      "1/1 [==============================] - 0s 107ms/step\n",
      "[[0.98059106]]\n",
      "1/1 [==============================] - 0s 92ms/step\n",
      "[[0.9789021]]\n",
      "1/1 [==============================] - 0s 91ms/step\n",
      "[[0.9731424]]\n",
      "1/1 [==============================] - 0s 90ms/step\n",
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      "1/1 [==============================] - 0s 84ms/step\n",
      "[[0.97945076]]\n"
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     "output_type": "stream",
     "text": [
      "1   HIToolbox                           0x000000018ddd05c8 _ZN15MenuBarInstance22EnsureAutoShowObserverEv + 120\n",
      "2   HIToolbox                           0x000000018dd9a478 _ZL17BroadcastInternaljPvh + 184\n",
      "3   SkyLight                            0x00000001892ad214 _ZN12_GLOBAL__N_123notify_datagram_handlerEj15CGSDatagramTypePvmS1_ + 896\n",
      "4   SkyLight                            0x00000001892abe10 CGSSnarfAndDispatchDatagrams + 808\n",
      "5   SkyLight                            0x00000001895d3a9c SLSGetNextEventRecordInternal + 344\n",
      "6   SkyLight                            0x00000001893d4fb0 SLEventCreateNextEvent + 16\n",
      "7   HIToolbox                           0x000000018dd68b58 _ZL38PullEventsFromWindowServerOnConnectionjhP17__CFMachPortBoost + 60\n",
      "8   HIToolbox                           0x000000018dd68ae0 _ZL14MessageHandlerP12__CFMachPortPvlS1_ + 60\n",
      "9   CoreFoundation                      0x00000001846ad410 __CFMachPortPerform + 260\n",
      "10  CoreFoundation                      0x000000018467df98 __CFRUNLOOP_IS_CALLING_OUT_TO_A_SOURCE1_PERFORM_FUNCTION__ + 60\n",
      "11  CoreFoundation                      0x000000018467deb8 __CFRunLoopDoSource1 + 520\n",
      "12  CoreFoundation                      0x000000018467c8a4 __CFRunLoopRun + 2264\n",
      "13  CoreFoundation                      0x000000018467b878 CFRunLoopRunSpecific + 612\n",
      "14  HIToolbox                           0x000000018dd5bfa0 RunCurrentEventLoopInMode + 292\n",
      "15  HIToolbox                           0x000000018dd5bc30 ReceiveNextEventCommon + 236\n",
      "16  HIToolbox                           0x000000018dd5bb2c _BlockUntilNextEventMatchingListInModeWithFilter + 72\n",
      "17  AppKit                              0x000000018790184c _DPSNextEvent + 632\n",
      "18  AppKit                              0x00000001879009dc -[NSApplication(NSEvent) _nextEventMatchingEventMask:untilDate:inMode:dequeue:] + 728\n",
      "19  cv2.abi3.so                         0x0000000162c4a120 cvWaitKey + 476\n",
      "20  cv2.abi3.so                         0x0000000162c441b4 _ZN2cv9waitKeyExEi + 168\n",
      "21  cv2.abi3.so                         0x0000000162c442ac _ZN2cv7waitKeyEi + 48\n",
      "22  cv2.abi3.so                         0x0000000162191bc8 _ZL19pyopencv_cv_waitKeyP7_objectS0_S0_ + 148\n",
      "23  Python                              0x00000001057a9a40 cfunction_call + 60\n",
      "24  Python                              0x0000000105745700 _PyObject_MakeTpCall + 128\n",
      "25  Python                              0x0000000105868c88 _PyEval_EvalFrameDefault + 53048\n",
      "26  Python                              0x000000010585aac4 PyEval_EvalCode + 276\n",
      "27  Python                              0x0000000105855320 builtin_exec + 428\n",
      "28  Python                              0x000000010586a934 _PyEval_EvalFrameDefault + 60388\n",
      "29  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "30  Python                              0x000000010585f8a8 _PyEval_EvalFrameDefault + 15192\n",
      "31  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "32  Python                              0x000000010585f8a8 _PyEval_EvalFrameDefault + 15192\n",
      "33  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "34  Python                              0x0000000105764864 gen_send + 36\n",
      "35  Python                              0x00000001057571bc method_vectorcall_O + 100\n",
      "36  Python                              0x0000000105868ba0 _PyEval_EvalFrameDefault + 52816\n",
      "37  Python                              0x000000010586e6ec _PyEval_Vector + 156\n",
      "38  Python                              0x0000000105749a44 method_vectorcall + 272\n",
      "39  Python                              0x00000001057464ac _PyVectorcall_Call + 116\n",
      "40  Python                              0x000000010586b7d8 _PyEval_EvalFrameDefault + 64136\n",
      "41  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "42  Python                              0x000000010585f8a8 _PyEval_EvalFrameDefault + 15192\n",
      "43  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "44  Python                              0x000000010585f8a8 _PyEval_EvalFrameDefault + 15192\n",
      "45  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "46  Python                              0x000000010585f8a8 _PyEval_EvalFrameDefault + 15192\n",
      "47  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "48  Python                              0x000000010585f8a8 _PyEval_EvalFrameDefault + 15192\n",
      "49  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "50  _asyncio.cpython-311-darwin.so      0x00000001051944e0 task_step_impl + 440\n",
      "51  _asyncio.cpython-311-darwin.so      0x00000001051942b4 task_step + 52\n",
      "52  _asyncio.cpython-311-darwin.so      0x0000000105194d54 task_wakeup + 148\n",
      "53  Python                              0x00000001057aa710 cfunction_vectorcall_O + 92\n",
      "54  Python                              0x000000010589434c _PyObject_VectorcallTstate.4587 + 96\n",
      "55  Python                              0x0000000105894234 context_run + 216\n",
      "56  Python                              0x00000001057aa450 cfunction_vectorcall_FASTCALL_KEYWORDS + 80\n",
      "57  Python                              0x000000010586b7d8 _PyEval_EvalFrameDefault + 64136\n",
      "58  Python                              0x000000010585aac4 PyEval_EvalCode + 276\n",
      "59  Python                              0x0000000105855320 builtin_exec + 428\n",
      "60  Python                              0x00000001057aa450 cfunction_vectorcall_FASTCALL_KEYWORDS + 80\n",
      "61  Python                              0x0000000105868ba0 _PyEval_EvalFrameDefault + 52816\n",
      "62  Python                              0x000000010586e6ec _PyEval_Vector + 156\n",
      "63  Python                              0x00000001058fb5e0 pymain_run_module + 256\n",
      "64  Python                              0x00000001058fb080 Py_RunMain + 1784\n",
      "65  Python                              0x00000001058fc230 pymain_main + 52\n",
      "66  Python                              0x00000001058fca68 Py_BytesMain + 40\n",
      "67  dyld                                0x0000000184273e50 start + 2544\n"
     ]
    },
    {
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     "output_type": "stream",
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      "[[0.00045752]]\n",
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      "[[0.90101665]]\n",
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      "[[0.94035375]]\n",
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      "[[0.9516072]]\n",
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      "[[0.96114427]]\n",
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      "[[0.9502833]]\n",
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      "[[0.93023485]]\n",
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      "[[0.94332266]]\n",
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      "[[0.958475]]\n",
      "1/1 [==============================] - 0s 85ms/step\n",
      "[[0.967267]]\n",
      "1/1 [==============================] - 0s 94ms/step\n",
      "[[0.97193956]]\n",
      "1/1 [==============================] - 0s 120ms/step\n",
      "[[0.9707262]]\n",
      "1/1 [==============================] - 0s 107ms/step\n",
      "[[0.9745684]]\n",
      "1/1 [==============================] - 1s 510ms/step\n",
      "[[0.9698199]]\n"
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     "text": [
      "1   HIToolbox                           0x000000018dd5b90c _ZN15MenuBarInstance22RemoveAutoShowObserverEv + 44\n",
      "2   HIToolbox                           0x000000018dd3d8c8 _ZN15MenuBarInstance21UpdateAggregateUIModeE21MenuBarAnimationStylehhh + 1196\n",
      "3   HIToolbox                           0x000000018dd3d30c _ZN15MenuBarInstance16ForEachMenuBarDoEU13block_pointerFvPS_E + 72\n",
      "4   HIToolbox                           0x000000018dd3d2b4 _ZL25UpdateAllAggregateUIModes21MenuBarAnimationStyleh + 120\n",
      "5   HIToolbox                           0x000000018dd3d228 SetSystemUIMode + 140\n",
      "6   AppKit                              0x0000000187bbfd40 -[NSApplication _setPresentationOptions:instance:flags:] + 972\n",
      "7   AppKit                              0x0000000187a55954 -[NSApplication _updateFullScreenPresentationOptionsForInstance:] + 428\n",
      "8   CoreFoundation                      0x0000000184672560 __CFNOTIFICATIONCENTER_IS_CALLING_OUT_TO_AN_OBSERVER__ + 148\n",
      "9   CoreFoundation                      0x0000000184710044 ___CFXRegistrationPost_block_invoke + 88\n",
      "10  CoreFoundation                      0x000000018470ff8c _CFXRegistrationPost + 440\n",
      "11  CoreFoundation                      0x0000000184643b64 _CFXNotificationPost + 708\n",
      "12  Foundation                          0x000000018553338c -[NSNotificationCenter postNotificationName:object:userInfo:] + 88\n",
      "13  AppKit                              0x0000000187bc02b4 spacesNotificationHandler + 96\n",
      "14  SkyLight                            0x00000001892ad214 _ZN12_GLOBAL__N_123notify_datagram_handlerEj15CGSDatagramTypePvmS1_ + 896\n",
      "15  SkyLight                            0x00000001892abe10 CGSSnarfAndDispatchDatagrams + 808\n",
      "16  SkyLight                            0x00000001895d3a9c SLSGetNextEventRecordInternal + 344\n",
      "17  SkyLight                            0x00000001893d4fb0 SLEventCreateNextEvent + 16\n",
      "18  HIToolbox                           0x000000018dd68b58 _ZL38PullEventsFromWindowServerOnConnectionjhP17__CFMachPortBoost + 60\n",
      "19  HIToolbox                           0x000000018dd68ae0 _ZL14MessageHandlerP12__CFMachPortPvlS1_ + 60\n",
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      "26  HIToolbox                           0x000000018dd5bc30 ReceiveNextEventCommon + 236\n",
      "27  HIToolbox                           0x000000018dd5bb2c _BlockUntilNextEventMatchingListInModeWithFilter + 72\n",
      "28  AppKit                              0x000000018790184c _DPSNextEvent + 632\n",
      "29  AppKit                              0x00000001879009dc -[NSApplication(NSEvent) _nextEventMatchingEventMask:untilDate:inMode:dequeue:] + 728\n",
      "30  cv2.abi3.so                         0x0000000162c4a120 cvWaitKey + 476\n",
      "31  cv2.abi3.so                         0x0000000162c441b4 _ZN2cv9waitKeyExEi + 168\n",
      "32  cv2.abi3.so                         0x0000000162c442ac _ZN2cv7waitKeyEi + 48\n",
      "33  cv2.abi3.so                         0x0000000162191bc8 _ZL19pyopencv_cv_waitKeyP7_objectS0_S0_ + 148\n",
      "34  Python                              0x00000001057a9a40 cfunction_call + 60\n",
      "35  Python                              0x0000000105745700 _PyObject_MakeTpCall + 128\n",
      "36  Python                              0x0000000105868c88 _PyEval_EvalFrameDefault + 53048\n",
      "37  Python                              0x000000010585aac4 PyEval_EvalCode + 276\n",
      "38  Python                              0x0000000105855320 builtin_exec + 428\n",
      "39  Python                              0x000000010586a934 _PyEval_EvalFrameDefault + 60388\n",
      "40  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "41  Python                              0x000000010585f8a8 _PyEval_EvalFrameDefault + 15192\n",
      "42  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "43  Python                              0x000000010585f8a8 _PyEval_EvalFrameDefault + 15192\n",
      "44  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "45  Python                              0x0000000105764864 gen_send + 36\n",
      "46  Python                              0x00000001057571bc method_vectorcall_O + 100\n",
      "47  Python                              0x0000000105868ba0 _PyEval_EvalFrameDefault + 52816\n",
      "48  Python                              0x000000010586e6ec _PyEval_Vector + 156\n",
      "49  Python                              0x0000000105749a44 method_vectorcall + 272\n",
      "50  Python                              0x00000001057464ac _PyVectorcall_Call + 116\n",
      "51  Python                              0x000000010586b7d8 _PyEval_EvalFrameDefault + 64136\n",
      "52  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "53  Python                              0x000000010585f8a8 _PyEval_EvalFrameDefault + 15192\n",
      "54  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "55  Python                              0x000000010585f8a8 _PyEval_EvalFrameDefault + 15192\n",
      "56  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "57  Python                              0x000000010585f8a8 _PyEval_EvalFrameDefault + 15192\n",
      "58  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "59  Python                              0x000000010585f8a8 _PyEval_EvalFrameDefault + 15192\n",
      "60  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "61  _asyncio.cpython-311-darwin.so      0x00000001051944e0 task_step_impl + 440\n",
      "62  _asyncio.cpython-311-darwin.so      0x00000001051942b4 task_step + 52\n",
      "63  _asyncio.cpython-311-darwin.so      0x0000000105194d54 task_wakeup + 148\n",
      "64  Python                              0x00000001057aa710 cfunction_vectorcall_O + 92\n",
      "65  Python                              0x000000010589434c _PyObject_VectorcallTstate.4587 + 96\n",
      "66  Python                              0x0000000105894234 context_run + 216\n",
      "67  Python                              0x00000001057aa450 cfunction_vectorcall_FASTCALL_KEYWORDS + 80\n",
      "68  Python                              0x000000010586b7d8 _PyEval_EvalFrameDefault + 64136\n",
      "69  Python                              0x000000010585aac4 PyEval_EvalCode + 276\n",
      "70  Python                              0x0000000105855320 builtin_exec + 428\n",
      "71  Python                              0x00000001057aa450 cfunction_vectorcall_FASTCALL_KEYWORDS + 80\n",
      "72  Python                              0x0000000105868ba0 _PyEval_EvalFrameDefault + 52816\n",
      "73  Python                              0x000000010586e6ec _PyEval_Vector + 156\n",
      "74  Python                              0x00000001058fb5e0 pymain_run_module + 256\n",
      "75  Python                              0x00000001058fb080 Py_RunMain + 1784\n",
      "76  Python                              0x00000001058fc230 pymain_main + 52\n",
      "77  Python                              0x00000001058fca68 Py_BytesMain + 40\n",
      "78  dyld                                0x0000000184273e50 start + 2544\n"
     ]
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      "1/1 [==============================] - 0s 323ms/step\n",
      "[[0.9729038]]\n",
      "1/1 [==============================] - 0s 79ms/step\n",
      "[[0.9509314]]\n",
      "1/1 [==============================] - 0s 79ms/step\n",
      "[[0.97892636]]\n",
      "1/1 [==============================] - 0s 77ms/step\n",
      "[[0.97048515]]\n",
      "1/1 [==============================] - 0s 77ms/step\n",
      "[[0.98822534]]\n",
      "1/1 [==============================] - 0s 78ms/step\n",
      "[[0.98181105]]\n",
      "1/1 [==============================] - 0s 77ms/step\n",
      "[[0.97875035]]\n",
      "1/1 [==============================] - 0s 78ms/step\n",
      "[[0.9865982]]\n",
      "1/1 [==============================] - 0s 78ms/step\n",
      "[[0.98218364]]\n",
      "1/1 [==============================] - 0s 76ms/step\n",
      "[[0.98509777]]\n",
      "1/1 [==============================] - 0s 81ms/step\n",
      "[[0.9857933]]\n",
      "1/1 [==============================] - 0s 78ms/step\n",
      "[[0.9896235]]\n",
      "1/1 [==============================] - 0s 76ms/step\n",
      "[[0.9899909]]\n",
      "1/1 [==============================] - 0s 83ms/step\n",
      "[[0.9888248]]\n",
      "1/1 [==============================] - 0s 94ms/step\n",
      "[[0.9927544]]\n",
      "1/1 [==============================] - 0s 79ms/step\n",
      "[[0.9736308]]\n",
      "1/1 [==============================] - 0s 79ms/step\n",
      "[[0.9869797]]\n",
      "1/1 [==============================] - 0s 80ms/step\n",
      "[[0.99167675]]\n",
      "1/1 [==============================] - 0s 124ms/step\n",
      "[[0.99106044]]\n"
     ]
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "1   HIToolbox                           0x000000018ddd05c8 _ZN15MenuBarInstance22EnsureAutoShowObserverEv + 120\n",
      "2   HIToolbox                           0x000000018dd9a478 _ZL17BroadcastInternaljPvh + 184\n",
      "3   SkyLight                            0x00000001892ad214 _ZN12_GLOBAL__N_123notify_datagram_handlerEj15CGSDatagramTypePvmS1_ + 896\n",
      "4   SkyLight                            0x00000001892abe10 CGSSnarfAndDispatchDatagrams + 808\n",
      "5   SkyLight                            0x00000001895d3a9c SLSGetNextEventRecordInternal + 344\n",
      "6   SkyLight                            0x00000001893d4fb0 SLEventCreateNextEvent + 16\n",
      "7   HIToolbox                           0x000000018dd68b58 _ZL38PullEventsFromWindowServerOnConnectionjhP17__CFMachPortBoost + 60\n",
      "8   HIToolbox                           0x000000018dd68ae0 _ZL14MessageHandlerP12__CFMachPortPvlS1_ + 60\n",
      "9   CoreFoundation                      0x00000001846ad410 __CFMachPortPerform + 260\n",
      "10  CoreFoundation                      0x000000018467df98 __CFRUNLOOP_IS_CALLING_OUT_TO_A_SOURCE1_PERFORM_FUNCTION__ + 60\n",
      "11  CoreFoundation                      0x000000018467deb8 __CFRunLoopDoSource1 + 520\n",
      "12  CoreFoundation                      0x000000018467c8a4 __CFRunLoopRun + 2264\n",
      "13  CoreFoundation                      0x000000018467b878 CFRunLoopRunSpecific + 612\n",
      "14  HIToolbox                           0x000000018dd5bfa0 RunCurrentEventLoopInMode + 292\n",
      "15  HIToolbox                           0x000000018dd5bc30 ReceiveNextEventCommon + 236\n",
      "16  HIToolbox                           0x000000018dd5bb2c _BlockUntilNextEventMatchingListInModeWithFilter + 72\n",
      "17  AppKit                              0x000000018790184c _DPSNextEvent + 632\n",
      "18  AppKit                              0x00000001879009dc -[NSApplication(NSEvent) _nextEventMatchingEventMask:untilDate:inMode:dequeue:] + 728\n",
      "19  cv2.abi3.so                         0x0000000162c4a120 cvWaitKey + 476\n",
      "20  cv2.abi3.so                         0x0000000162c441b4 _ZN2cv9waitKeyExEi + 168\n",
      "21  cv2.abi3.so                         0x0000000162c442ac _ZN2cv7waitKeyEi + 48\n",
      "22  cv2.abi3.so                         0x0000000162191bc8 _ZL19pyopencv_cv_waitKeyP7_objectS0_S0_ + 148\n",
      "23  Python                              0x00000001057a9a40 cfunction_call + 60\n",
      "24  Python                              0x0000000105745700 _PyObject_MakeTpCall + 128\n",
      "25  Python                              0x0000000105868c88 _PyEval_EvalFrameDefault + 53048\n",
      "26  Python                              0x000000010585aac4 PyEval_EvalCode + 276\n",
      "27  Python                              0x0000000105855320 builtin_exec + 428\n",
      "28  Python                              0x000000010586a934 _PyEval_EvalFrameDefault + 60388\n",
      "29  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "30  Python                              0x000000010585f8a8 _PyEval_EvalFrameDefault + 15192\n",
      "31  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "32  Python                              0x000000010585f8a8 _PyEval_EvalFrameDefault + 15192\n",
      "33  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "34  Python                              0x0000000105764864 gen_send + 36\n",
      "35  Python                              0x00000001057571bc method_vectorcall_O + 100\n",
      "36  Python                              0x0000000105868ba0 _PyEval_EvalFrameDefault + 52816\n",
      "37  Python                              0x000000010586e6ec _PyEval_Vector + 156\n",
      "38  Python                              0x0000000105749a44 method_vectorcall + 272\n",
      "39  Python                              0x00000001057464ac _PyVectorcall_Call + 116\n",
      "40  Python                              0x000000010586b7d8 _PyEval_EvalFrameDefault + 64136\n",
      "41  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "42  Python                              0x000000010585f8a8 _PyEval_EvalFrameDefault + 15192\n",
      "43  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "44  Python                              0x000000010585f8a8 _PyEval_EvalFrameDefault + 15192\n",
      "45  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "46  Python                              0x000000010585f8a8 _PyEval_EvalFrameDefault + 15192\n",
      "47  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "48  Python                              0x000000010585f8a8 _PyEval_EvalFrameDefault + 15192\n",
      "49  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "50  _asyncio.cpython-311-darwin.so      0x00000001051944e0 task_step_impl + 440\n",
      "51  _asyncio.cpython-311-darwin.so      0x00000001051942b4 task_step + 52\n",
      "52  _asyncio.cpython-311-darwin.so      0x0000000105194d54 task_wakeup + 148\n",
      "53  Python                              0x00000001057aa710 cfunction_vectorcall_O + 92\n",
      "54  Python                              0x000000010589434c _PyObject_VectorcallTstate.4587 + 96\n",
      "55  Python                              0x0000000105894234 context_run + 216\n",
      "56  Python                              0x00000001057aa450 cfunction_vectorcall_FASTCALL_KEYWORDS + 80\n",
      "57  Python                              0x000000010586b7d8 _PyEval_EvalFrameDefault + 64136\n",
      "58  Python                              0x000000010585aac4 PyEval_EvalCode + 276\n",
      "59  Python                              0x0000000105855320 builtin_exec + 428\n",
      "60  Python                              0x00000001057aa450 cfunction_vectorcall_FASTCALL_KEYWORDS + 80\n",
      "61  Python                              0x0000000105868ba0 _PyEval_EvalFrameDefault + 52816\n",
      "62  Python                              0x000000010586e6ec _PyEval_Vector + 156\n",
      "63  Python                              0x00000001058fb5e0 pymain_run_module + 256\n",
      "64  Python                              0x00000001058fb080 Py_RunMain + 1784\n",
      "65  Python                              0x00000001058fc230 pymain_main + 52\n",
      "66  Python                              0x00000001058fca68 Py_BytesMain + 40\n",
      "67  dyld                                0x0000000184273e50 start + 2544\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 78ms/step\n",
      "[[0.99137646]]\n",
      "1/1 [==============================] - 0s 76ms/step\n",
      "[[0.9930178]]\n",
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      "[[0.9964907]]\n",
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      "1/1 [==============================] - 0s 76ms/step\n",
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      "1/1 [==============================] - 0s 74ms/step\n",
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      "1/1 [==============================] - 0s 77ms/step\n",
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      "1/1 [==============================] - 0s 77ms/step\n",
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      "1/1 [==============================] - 0s 77ms/step\n",
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      "1/1 [==============================] - 0s 80ms/step\n",
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      "[[0.9856766]]\n",
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      "[[0.99197745]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "1   HIToolbox                           0x000000018dd5b90c _ZN15MenuBarInstance22RemoveAutoShowObserverEv + 44\n",
      "2   HIToolbox                           0x000000018dd3d8c8 _ZN15MenuBarInstance21UpdateAggregateUIModeE21MenuBarAnimationStylehhh + 1196\n",
      "3   HIToolbox                           0x000000018dd3d30c _ZN15MenuBarInstance16ForEachMenuBarDoEU13block_pointerFvPS_E + 72\n",
      "4   HIToolbox                           0x000000018dd3d2b4 _ZL25UpdateAllAggregateUIModes21MenuBarAnimationStyleh + 120\n",
      "5   HIToolbox                           0x000000018dd3d228 SetSystemUIMode + 140\n",
      "6   AppKit                              0x0000000187bbfd40 -[NSApplication _setPresentationOptions:instance:flags:] + 972\n",
      "7   AppKit                              0x0000000187a55954 -[NSApplication _updateFullScreenPresentationOptionsForInstance:] + 428\n",
      "8   CoreFoundation                      0x0000000184672560 __CFNOTIFICATIONCENTER_IS_CALLING_OUT_TO_AN_OBSERVER__ + 148\n",
      "9   CoreFoundation                      0x0000000184710044 ___CFXRegistrationPost_block_invoke + 88\n",
      "10  CoreFoundation                      0x000000018470ff8c _CFXRegistrationPost + 440\n",
      "11  CoreFoundation                      0x0000000184643b64 _CFXNotificationPost + 708\n",
      "12  Foundation                          0x000000018553338c -[NSNotificationCenter postNotificationName:object:userInfo:] + 88\n",
      "13  AppKit                              0x0000000187bc02b4 spacesNotificationHandler + 96\n",
      "14  SkyLight                            0x00000001892ad214 _ZN12_GLOBAL__N_123notify_datagram_handlerEj15CGSDatagramTypePvmS1_ + 896\n",
      "15  SkyLight                            0x00000001892abe10 CGSSnarfAndDispatchDatagrams + 808\n",
      "16  SkyLight                            0x00000001895d3a9c SLSGetNextEventRecordInternal + 344\n",
      "17  SkyLight                            0x00000001893d4fb0 SLEventCreateNextEvent + 16\n",
      "18  HIToolbox                           0x000000018dd68b58 _ZL38PullEventsFromWindowServerOnConnectionjhP17__CFMachPortBoost + 60\n",
      "19  HIToolbox                           0x000000018dd68ae0 _ZL14MessageHandlerP12__CFMachPortPvlS1_ + 60\n",
      "20  CoreFoundation                      0x00000001846ad410 __CFMachPortPerform + 260\n",
      "21  CoreFoundation                      0x000000018467df98 __CFRUNLOOP_IS_CALLING_OUT_TO_A_SOURCE1_PERFORM_FUNCTION__ + 60\n",
      "22  CoreFoundation                      0x000000018467deb8 __CFRunLoopDoSource1 + 520\n",
      "23  CoreFoundation                      0x000000018467c8a4 __CFRunLoopRun + 2264\n",
      "24  CoreFoundation                      0x000000018467b878 CFRunLoopRunSpecific + 612\n",
      "25  HIToolbox                           0x000000018dd5bfa0 RunCurrentEventLoopInMode + 292\n",
      "26  HIToolbox                           0x000000018dd5bc30 ReceiveNextEventCommon + 236\n",
      "27  HIToolbox                           0x000000018dd5bb2c _BlockUntilNextEventMatchingListInModeWithFilter + 72\n",
      "28  AppKit                              0x000000018790184c _DPSNextEvent + 632\n",
      "29  AppKit                              0x00000001879009dc -[NSApplication(NSEvent) _nextEventMatchingEventMask:untilDate:inMode:dequeue:] + 728\n",
      "30  cv2.abi3.so                         0x0000000162c4a120 cvWaitKey + 476\n",
      "31  cv2.abi3.so                         0x0000000162c441b4 _ZN2cv9waitKeyExEi + 168\n",
      "32  cv2.abi3.so                         0x0000000162c442ac _ZN2cv7waitKeyEi + 48\n",
      "33  cv2.abi3.so                         0x0000000162191bc8 _ZL19pyopencv_cv_waitKeyP7_objectS0_S0_ + 148\n",
      "34  Python                              0x00000001057a9a40 cfunction_call + 60\n",
      "35  Python                              0x0000000105745700 _PyObject_MakeTpCall + 128\n",
      "36  Python                              0x0000000105868c88 _PyEval_EvalFrameDefault + 53048\n",
      "37  Python                              0x000000010585aac4 PyEval_EvalCode + 276\n",
      "38  Python                              0x0000000105855320 builtin_exec + 428\n",
      "39  Python                              0x000000010586a934 _PyEval_EvalFrameDefault + 60388\n",
      "40  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "41  Python                              0x000000010585f8a8 _PyEval_EvalFrameDefault + 15192\n",
      "42  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "43  Python                              0x000000010585f8a8 _PyEval_EvalFrameDefault + 15192\n",
      "44  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "45  Python                              0x0000000105764864 gen_send + 36\n",
      "46  Python                              0x00000001057571bc method_vectorcall_O + 100\n",
      "47  Python                              0x0000000105868ba0 _PyEval_EvalFrameDefault + 52816\n",
      "48  Python                              0x000000010586e6ec _PyEval_Vector + 156\n",
      "49  Python                              0x0000000105749a44 method_vectorcall + 272\n",
      "50  Python                              0x00000001057464ac _PyVectorcall_Call + 116\n",
      "51  Python                              0x000000010586b7d8 _PyEval_EvalFrameDefault + 64136\n",
      "52  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "53  Python                              0x000000010585f8a8 _PyEval_EvalFrameDefault + 15192\n",
      "54  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "55  Python                              0x000000010585f8a8 _PyEval_EvalFrameDefault + 15192\n",
      "56  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "57  Python                              0x000000010585f8a8 _PyEval_EvalFrameDefault + 15192\n",
      "58  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "59  Python                              0x000000010585f8a8 _PyEval_EvalFrameDefault + 15192\n",
      "60  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "61  _asyncio.cpython-311-darwin.so      0x00000001051944e0 task_step_impl + 440\n",
      "62  _asyncio.cpython-311-darwin.so      0x00000001051942b4 task_step + 52\n",
      "63  _asyncio.cpython-311-darwin.so      0x0000000105194d54 task_wakeup + 148\n",
      "64  Python                              0x00000001057aa710 cfunction_vectorcall_O + 92\n",
      "65  Python                              0x000000010589434c _PyObject_VectorcallTstate.4587 + 96\n",
      "66  Python                              0x0000000105894234 context_run + 216\n",
      "67  Python                              0x00000001057aa450 cfunction_vectorcall_FASTCALL_KEYWORDS + 80\n",
      "68  Python                              0x000000010586b7d8 _PyEval_EvalFrameDefault + 64136\n",
      "69  Python                              0x000000010585aac4 PyEval_EvalCode + 276\n",
      "70  Python                              0x0000000105855320 builtin_exec + 428\n",
      "71  Python                              0x00000001057aa450 cfunction_vectorcall_FASTCALL_KEYWORDS + 80\n",
      "72  Python                              0x0000000105868ba0 _PyEval_EvalFrameDefault + 52816\n",
      "73  Python                              0x000000010586e6ec _PyEval_Vector + 156\n",
      "74  Python                              0x00000001058fb5e0 pymain_run_module + 256\n",
      "75  Python                              0x00000001058fb080 Py_RunMain + 1784\n",
      "76  Python                              0x00000001058fc230 pymain_main + 52\n",
      "77  Python                              0x00000001058fca68 Py_BytesMain + 40\n",
      "78  dyld                                0x0000000184273e50 start + 2544\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "1/1 [==============================] - 0s 78ms/step\n",
      "[[0.98285294]]\n",
      "1/1 [==============================] - 0s 80ms/step\n",
      "[[0.9787787]]\n",
      "1/1 [==============================] - 0s 78ms/step\n",
      "[[0.989094]]\n",
      "1/1 [==============================] - 0s 78ms/step\n",
      "[[0.9839936]]\n",
      "1/1 [==============================] - 0s 80ms/step\n",
      "[[0.9843352]]\n",
      "1/1 [==============================] - 0s 77ms/step\n",
      "[[0.9869139]]\n",
      "1/1 [==============================] - 0s 76ms/step\n",
      "[[0.9836469]]\n",
      "1/1 [==============================] - 0s 76ms/step\n",
      "[[0.98883575]]\n",
      "1/1 [==============================] - 0s 86ms/step\n",
      "[[0.9892382]]\n",
      "1/1 [==============================] - 0s 78ms/step\n",
      "[[0.9838746]]\n",
      "1/1 [==============================] - 0s 148ms/step\n",
      "[[0.9853117]]\n",
      "1/1 [==============================] - 0s 80ms/step\n",
      "[[0.98392016]]\n",
      "1/1 [==============================] - 0s 89ms/step\n",
      "[[0.9829288]]\n",
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      "[[0.98511034]]\n",
      "1/1 [==============================] - 0s 79ms/step\n",
      "[[0.9830705]]\n",
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      "[[0.9851009]]\n",
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      "[[0.9865095]]\n",
      "1/1 [==============================] - 0s 79ms/step\n",
      "[[0.98774064]]\n",
      "1/1 [==============================] - 0s 80ms/step\n",
      "[[0.99002534]]\n",
      "1/1 [==============================] - 0s 77ms/step\n",
      "[[0.9859455]]\n",
      "1/1 [==============================] - 0s 78ms/step\n",
      "[[0.9850358]]\n",
      "1/1 [==============================] - 0s 77ms/step\n",
      "[[0.9879032]]\n",
      "1/1 [==============================] - 0s 77ms/step\n",
      "[[0.98823]]\n",
      "1/1 [==============================] - 0s 80ms/step\n",
      "[[0.98543954]]\n",
      "1/1 [==============================] - 0s 78ms/step\n",
      "[[0.9792103]]\n",
      "1/1 [==============================] - 0s 76ms/step\n",
      "[[0.9885407]]\n",
      "1/1 [==============================] - 0s 77ms/step\n",
      "[[0.98578686]]\n",
      "1/1 [==============================] - 0s 80ms/step\n",
      "[[0.98488945]]\n",
      "1/1 [==============================] - 0s 92ms/step\n",
      "[[0.9796203]]\n",
      "1/1 [==============================] - 0s 77ms/step\n",
      "[[0.98027295]]\n",
      "1/1 [==============================] - 0s 77ms/step\n",
      "[[0.9872969]]\n",
      "1/1 [==============================] - 0s 87ms/step\n",
      "[[0.982801]]\n",
      "1/1 [==============================] - 0s 88ms/step\n",
      "[[0.98691523]]\n",
      "1/1 [==============================] - 0s 83ms/step\n",
      "[[0.98520994]]\n",
      "1/1 [==============================] - 0s 83ms/step\n",
      "[[0.9876842]]\n",
      "1/1 [==============================] - 0s 90ms/step\n",
      "[[0.97982407]]\n",
      "1/1 [==============================] - 0s 121ms/step\n",
      "[[0.9865693]]\n"
     ]
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "1   HIToolbox                           0x000000018ddd05c8 _ZN15MenuBarInstance22EnsureAutoShowObserverEv + 120\n",
      "2   HIToolbox                           0x000000018dd9a478 _ZL17BroadcastInternaljPvh + 184\n",
      "3   SkyLight                            0x00000001892ad214 _ZN12_GLOBAL__N_123notify_datagram_handlerEj15CGSDatagramTypePvmS1_ + 896\n",
      "4   SkyLight                            0x00000001892abe10 CGSSnarfAndDispatchDatagrams + 808\n",
      "5   SkyLight                            0x00000001895d3a9c SLSGetNextEventRecordInternal + 344\n",
      "6   SkyLight                            0x00000001893d4fb0 SLEventCreateNextEvent + 16\n",
      "7   HIToolbox                           0x000000018dd68b58 _ZL38PullEventsFromWindowServerOnConnectionjhP17__CFMachPortBoost + 60\n",
      "8   HIToolbox                           0x000000018dd68ae0 _ZL14MessageHandlerP12__CFMachPortPvlS1_ + 60\n",
      "9   CoreFoundation                      0x00000001846ad410 __CFMachPortPerform + 260\n",
      "10  CoreFoundation                      0x000000018467df98 __CFRUNLOOP_IS_CALLING_OUT_TO_A_SOURCE1_PERFORM_FUNCTION__ + 60\n",
      "11  CoreFoundation                      0x000000018467deb8 __CFRunLoopDoSource1 + 520\n",
      "12  CoreFoundation                      0x000000018467c8a4 __CFRunLoopRun + 2264\n",
      "13  CoreFoundation                      0x000000018467b878 CFRunLoopRunSpecific + 612\n",
      "14  HIToolbox                           0x000000018dd5bfa0 RunCurrentEventLoopInMode + 292\n",
      "15  HIToolbox                           0x000000018dd5bc30 ReceiveNextEventCommon + 236\n",
      "16  HIToolbox                           0x000000018dd5bb2c _BlockUntilNextEventMatchingListInModeWithFilter + 72\n",
      "17  AppKit                              0x000000018790184c _DPSNextEvent + 632\n",
      "18  AppKit                              0x00000001879009dc -[NSApplication(NSEvent) _nextEventMatchingEventMask:untilDate:inMode:dequeue:] + 728\n",
      "19  cv2.abi3.so                         0x0000000162c4a120 cvWaitKey + 476\n",
      "20  cv2.abi3.so                         0x0000000162c441b4 _ZN2cv9waitKeyExEi + 168\n",
      "21  cv2.abi3.so                         0x0000000162c442ac _ZN2cv7waitKeyEi + 48\n",
      "22  cv2.abi3.so                         0x0000000162191bc8 _ZL19pyopencv_cv_waitKeyP7_objectS0_S0_ + 148\n",
      "23  Python                              0x00000001057a9a40 cfunction_call + 60\n",
      "24  Python                              0x0000000105745700 _PyObject_MakeTpCall + 128\n",
      "25  Python                              0x0000000105868c88 _PyEval_EvalFrameDefault + 53048\n",
      "26  Python                              0x000000010585aac4 PyEval_EvalCode + 276\n",
      "27  Python                              0x0000000105855320 builtin_exec + 428\n",
      "28  Python                              0x000000010586a934 _PyEval_EvalFrameDefault + 60388\n",
      "29  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "30  Python                              0x000000010585f8a8 _PyEval_EvalFrameDefault + 15192\n",
      "31  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "32  Python                              0x000000010585f8a8 _PyEval_EvalFrameDefault + 15192\n",
      "33  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "34  Python                              0x0000000105764864 gen_send + 36\n",
      "35  Python                              0x00000001057571bc method_vectorcall_O + 100\n",
      "36  Python                              0x0000000105868ba0 _PyEval_EvalFrameDefault + 52816\n",
      "37  Python                              0x000000010586e6ec _PyEval_Vector + 156\n",
      "38  Python                              0x0000000105749a44 method_vectorcall + 272\n",
      "39  Python                              0x00000001057464ac _PyVectorcall_Call + 116\n",
      "40  Python                              0x000000010586b7d8 _PyEval_EvalFrameDefault + 64136\n",
      "41  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "42  Python                              0x000000010585f8a8 _PyEval_EvalFrameDefault + 15192\n",
      "43  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "44  Python                              0x000000010585f8a8 _PyEval_EvalFrameDefault + 15192\n",
      "45  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "46  Python                              0x000000010585f8a8 _PyEval_EvalFrameDefault + 15192\n",
      "47  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "48  Python                              0x000000010585f8a8 _PyEval_EvalFrameDefault + 15192\n",
      "49  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "50  _asyncio.cpython-311-darwin.so      0x00000001051944e0 task_step_impl + 440\n",
      "51  _asyncio.cpython-311-darwin.so      0x00000001051942b4 task_step + 52\n",
      "52  _asyncio.cpython-311-darwin.so      0x0000000105194d54 task_wakeup + 148\n",
      "53  Python                              0x00000001057aa710 cfunction_vectorcall_O + 92\n",
      "54  Python                              0x000000010589434c _PyObject_VectorcallTstate.4587 + 96\n",
      "55  Python                              0x0000000105894234 context_run + 216\n",
      "56  Python                              0x00000001057aa450 cfunction_vectorcall_FASTCALL_KEYWORDS + 80\n",
      "57  Python                              0x000000010586b7d8 _PyEval_EvalFrameDefault + 64136\n",
      "58  Python                              0x000000010585aac4 PyEval_EvalCode + 276\n",
      "59  Python                              0x0000000105855320 builtin_exec + 428\n",
      "60  Python                              0x00000001057aa450 cfunction_vectorcall_FASTCALL_KEYWORDS + 80\n",
      "61  Python                              0x0000000105868ba0 _PyEval_EvalFrameDefault + 52816\n",
      "62  Python                              0x000000010586e6ec _PyEval_Vector + 156\n",
      "63  Python                              0x00000001058fb5e0 pymain_run_module + 256\n",
      "64  Python                              0x00000001058fb080 Py_RunMain + 1784\n",
      "65  Python                              0x00000001058fc230 pymain_main + 52\n",
      "66  Python                              0x00000001058fca68 Py_BytesMain + 40\n",
      "67  dyld                                0x0000000184273e50 start + 2544\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 80ms/step\n",
      "[[0.9845667]]\n",
      "1/1 [==============================] - 0s 125ms/step\n",
      "[[0.9857349]]\n",
      "1/1 [==============================] - 0s 117ms/step\n",
      "[[0.9889706]]\n",
      "1/1 [==============================] - 0s 92ms/step\n",
      "[[0.9856072]]\n",
      "1/1 [==============================] - 0s 82ms/step\n",
      "[[0.98360586]]\n",
      "1/1 [==============================] - 0s 78ms/step\n",
      "[[0.9834224]]\n",
      "1/1 [==============================] - 0s 81ms/step\n",
      "[[0.98783493]]\n",
      "1/1 [==============================] - 0s 78ms/step\n",
      "[[0.98735136]]\n",
      "1/1 [==============================] - 0s 75ms/step\n",
      "[[0.9887385]]\n",
      "1/1 [==============================] - 0s 76ms/step\n",
      "[[0.98519415]]\n",
      "1/1 [==============================] - 0s 75ms/step\n",
      "[[0.9897666]]\n",
      "1/1 [==============================] - 0s 74ms/step\n",
      "[[0.9882527]]\n",
      "1/1 [==============================] - 0s 75ms/step\n",
      "[[0.9870012]]\n",
      "1/1 [==============================] - 0s 480ms/step\n",
      "[[0.9834061]]\n",
      "1/1 [==============================] - 1s 541ms/step\n",
      "[[0.98361135]]\n",
      "1/1 [==============================] - 0s 110ms/step\n",
      "[[0.9914905]]\n"
     ]
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "1   HIToolbox                           0x000000018dd5b90c _ZN15MenuBarInstance22RemoveAutoShowObserverEv + 44\n",
      "2   HIToolbox                           0x000000018dd3d8c8 _ZN15MenuBarInstance21UpdateAggregateUIModeE21MenuBarAnimationStylehhh + 1196\n",
      "3   HIToolbox                           0x000000018dd3d30c _ZN15MenuBarInstance16ForEachMenuBarDoEU13block_pointerFvPS_E + 72\n",
      "4   HIToolbox                           0x000000018dd3d2b4 _ZL25UpdateAllAggregateUIModes21MenuBarAnimationStyleh + 120\n",
      "5   HIToolbox                           0x000000018dd3d228 SetSystemUIMode + 140\n",
      "6   AppKit                              0x0000000187bbfd40 -[NSApplication _setPresentationOptions:instance:flags:] + 972\n",
      "7   AppKit                              0x0000000187a55954 -[NSApplication _updateFullScreenPresentationOptionsForInstance:] + 428\n",
      "8   CoreFoundation                      0x0000000184672560 __CFNOTIFICATIONCENTER_IS_CALLING_OUT_TO_AN_OBSERVER__ + 148\n",
      "9   CoreFoundation                      0x0000000184710044 ___CFXRegistrationPost_block_invoke + 88\n",
      "10  CoreFoundation                      0x000000018470ff8c _CFXRegistrationPost + 440\n",
      "11  CoreFoundation                      0x0000000184643b64 _CFXNotificationPost + 708\n",
      "12  Foundation                          0x000000018553338c -[NSNotificationCenter postNotificationName:object:userInfo:] + 88\n",
      "13  AppKit                              0x0000000187bc02b4 spacesNotificationHandler + 96\n",
      "14  SkyLight                            0x00000001892ad214 _ZN12_GLOBAL__N_123notify_datagram_handlerEj15CGSDatagramTypePvmS1_ + 896\n",
      "15  SkyLight                            0x00000001892abe10 CGSSnarfAndDispatchDatagrams + 808\n",
      "16  SkyLight                            0x00000001895d3a9c SLSGetNextEventRecordInternal + 344\n",
      "17  SkyLight                            0x00000001893d4fb0 SLEventCreateNextEvent + 16\n",
      "18  HIToolbox                           0x000000018dd68b58 _ZL38PullEventsFromWindowServerOnConnectionjhP17__CFMachPortBoost + 60\n",
      "19  HIToolbox                           0x000000018dd68ae0 _ZL14MessageHandlerP12__CFMachPortPvlS1_ + 60\n",
      "20  CoreFoundation                      0x00000001846ad410 __CFMachPortPerform + 260\n",
      "21  CoreFoundation                      0x000000018467df98 __CFRUNLOOP_IS_CALLING_OUT_TO_A_SOURCE1_PERFORM_FUNCTION__ + 60\n",
      "22  CoreFoundation                      0x000000018467deb8 __CFRunLoopDoSource1 + 520\n",
      "23  CoreFoundation                      0x000000018467c8a4 __CFRunLoopRun + 2264\n",
      "24  CoreFoundation                      0x000000018467b878 CFRunLoopRunSpecific + 612\n",
      "25  HIToolbox                           0x000000018dd5bfa0 RunCurrentEventLoopInMode + 292\n",
      "26  HIToolbox                           0x000000018dd5bc30 ReceiveNextEventCommon + 236\n",
      "27  HIToolbox                           0x000000018dd5bb2c _BlockUntilNextEventMatchingListInModeWithFilter + 72\n",
      "28  AppKit                              0x000000018790184c _DPSNextEvent + 632\n",
      "29  AppKit                              0x00000001879009dc -[NSApplication(NSEvent) _nextEventMatchingEventMask:untilDate:inMode:dequeue:] + 728\n",
      "30  cv2.abi3.so                         0x0000000162c4a120 cvWaitKey + 476\n",
      "31  cv2.abi3.so                         0x0000000162c441b4 _ZN2cv9waitKeyExEi + 168\n",
      "32  cv2.abi3.so                         0x0000000162c442ac _ZN2cv7waitKeyEi + 48\n",
      "33  cv2.abi3.so                         0x0000000162191bc8 _ZL19pyopencv_cv_waitKeyP7_objectS0_S0_ + 148\n",
      "34  Python                              0x00000001057a9a40 cfunction_call + 60\n",
      "35  Python                              0x0000000105745700 _PyObject_MakeTpCall + 128\n",
      "36  Python                              0x0000000105868c88 _PyEval_EvalFrameDefault + 53048\n",
      "37  Python                              0x000000010585aac4 PyEval_EvalCode + 276\n",
      "38  Python                              0x0000000105855320 builtin_exec + 428\n",
      "39  Python                              0x000000010586a934 _PyEval_EvalFrameDefault + 60388\n",
      "40  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "41  Python                              0x000000010585f8a8 _PyEval_EvalFrameDefault + 15192\n",
      "42  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "43  Python                              0x000000010585f8a8 _PyEval_EvalFrameDefault + 15192\n",
      "44  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "45  Python                              0x0000000105764864 gen_send + 36\n",
      "46  Python                              0x00000001057571bc method_vectorcall_O + 100\n",
      "47  Python                              0x0000000105868ba0 _PyEval_EvalFrameDefault + 52816\n",
      "48  Python                              0x000000010586e6ec _PyEval_Vector + 156\n",
      "49  Python                              0x0000000105749a44 method_vectorcall + 272\n",
      "50  Python                              0x00000001057464ac _PyVectorcall_Call + 116\n",
      "51  Python                              0x000000010586b7d8 _PyEval_EvalFrameDefault + 64136\n",
      "52  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "53  Python                              0x000000010585f8a8 _PyEval_EvalFrameDefault + 15192\n",
      "54  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "55  Python                              0x000000010585f8a8 _PyEval_EvalFrameDefault + 15192\n",
      "56  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "57  Python                              0x000000010585f8a8 _PyEval_EvalFrameDefault + 15192\n",
      "58  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "59  Python                              0x000000010585f8a8 _PyEval_EvalFrameDefault + 15192\n",
      "60  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "61  _asyncio.cpython-311-darwin.so      0x00000001051944e0 task_step_impl + 440\n",
      "62  _asyncio.cpython-311-darwin.so      0x00000001051942b4 task_step + 52\n",
      "63  _asyncio.cpython-311-darwin.so      0x0000000105194d54 task_wakeup + 148\n",
      "64  Python                              0x00000001057aa710 cfunction_vectorcall_O + 92\n",
      "65  Python                              0x000000010589434c _PyObject_VectorcallTstate.4587 + 96\n",
      "66  Python                              0x0000000105894234 context_run + 216\n",
      "67  Python                              0x00000001057aa450 cfunction_vectorcall_FASTCALL_KEYWORDS + 80\n",
      "68  Python                              0x000000010586b7d8 _PyEval_EvalFrameDefault + 64136\n",
      "69  Python                              0x000000010585aac4 PyEval_EvalCode + 276\n",
      "70  Python                              0x0000000105855320 builtin_exec + 428\n",
      "71  Python                              0x00000001057aa450 cfunction_vectorcall_FASTCALL_KEYWORDS + 80\n",
      "72  Python                              0x0000000105868ba0 _PyEval_EvalFrameDefault + 52816\n",
      "73  Python                              0x000000010586e6ec _PyEval_Vector + 156\n",
      "74  Python                              0x00000001058fb5e0 pymain_run_module + 256\n",
      "75  Python                              0x00000001058fb080 Py_RunMain + 1784\n",
      "76  Python                              0x00000001058fc230 pymain_main + 52\n",
      "77  Python                              0x00000001058fca68 Py_BytesMain + 40\n",
      "78  dyld                                0x0000000184273e50 start + 2544\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 110ms/step\n",
      "[[0.9738563]]\n",
      "1/1 [==============================] - 0s 90ms/step\n",
      "[[0.9846243]]\n",
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      "[[0.9871014]]\n",
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      "[[0.9826725]]\n",
      "1/1 [==============================] - 0s 87ms/step\n",
      "[[0.9815762]]\n",
      "1/1 [==============================] - 0s 79ms/step\n",
      "[[0.98472273]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "1   HIToolbox                           0x000000018ddd05c8 _ZN15MenuBarInstance22EnsureAutoShowObserverEv + 120\n",
      "2   HIToolbox                           0x000000018dd7327c SetMenuBarObscured + 224\n",
      "3   HIToolbox                           0x000000018dd72ee8 _ZN13HIApplication15HandleActivatedEP14OpaqueEventRefhP15OpaqueWindowPtrh + 172\n",
      "4   HIToolbox                           0x000000018dd6cfcc _ZN13HIApplication13EventObserverEjP14OpaqueEventRefPv + 296\n",
      "5   HIToolbox                           0x000000018dd33cd0 _NotifyEventLoopObservers + 176\n",
      "6   HIToolbox                           0x000000018dd6c96c AcquireEventFromQueue + 432\n",
      "7   HIToolbox                           0x000000018dd5bc84 ReceiveNextEventCommon + 320\n",
      "8   HIToolbox                           0x000000018dd5bb2c _BlockUntilNextEventMatchingListInModeWithFilter + 72\n",
      "9   AppKit                              0x000000018790184c _DPSNextEvent + 632\n",
      "10  AppKit                              0x00000001879009dc -[NSApplication(NSEvent) _nextEventMatchingEventMask:untilDate:inMode:dequeue:] + 728\n",
      "11  cv2.abi3.so                         0x0000000162c4a120 cvWaitKey + 476\n",
      "12  cv2.abi3.so                         0x0000000162c441b4 _ZN2cv9waitKeyExEi + 168\n",
      "13  cv2.abi3.so                         0x0000000162c442ac _ZN2cv7waitKeyEi + 48\n",
      "14  cv2.abi3.so                         0x0000000162191bc8 _ZL19pyopencv_cv_waitKeyP7_objectS0_S0_ + 148\n",
      "15  Python                              0x00000001057a9a40 cfunction_call + 60\n",
      "16  Python                              0x0000000105745700 _PyObject_MakeTpCall + 128\n",
      "17  Python                              0x0000000105868c88 _PyEval_EvalFrameDefault + 53048\n",
      "18  Python                              0x000000010585aac4 PyEval_EvalCode + 276\n",
      "19  Python                              0x0000000105855320 builtin_exec + 428\n",
      "20  Python                              0x000000010586a934 _PyEval_EvalFrameDefault + 60388\n",
      "21  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "22  Python                              0x000000010585f8a8 _PyEval_EvalFrameDefault + 15192\n",
      "23  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "24  Python                              0x000000010585f8a8 _PyEval_EvalFrameDefault + 15192\n",
      "25  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "26  Python                              0x0000000105764864 gen_send + 36\n",
      "27  Python                              0x00000001057571bc method_vectorcall_O + 100\n",
      "28  Python                              0x0000000105868ba0 _PyEval_EvalFrameDefault + 52816\n",
      "29  Python                              0x000000010586e6ec _PyEval_Vector + 156\n",
      "30  Python                              0x0000000105749a44 method_vectorcall + 272\n",
      "31  Python                              0x00000001057464ac _PyVectorcall_Call + 116\n",
      "32  Python                              0x000000010586b7d8 _PyEval_EvalFrameDefault + 64136\n",
      "33  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "34  Python                              0x000000010585f8a8 _PyEval_EvalFrameDefault + 15192\n",
      "35  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "36  Python                              0x000000010585f8a8 _PyEval_EvalFrameDefault + 15192\n",
      "37  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "38  Python                              0x000000010585f8a8 _PyEval_EvalFrameDefault + 15192\n",
      "39  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "40  Python                              0x000000010585f8a8 _PyEval_EvalFrameDefault + 15192\n",
      "41  Python                              0x0000000105764054 gen_send_ex2 + 204\n",
      "42  _asyncio.cpython-311-darwin.so      0x00000001051944e0 task_step_impl + 440\n",
      "43  _asyncio.cpython-311-darwin.so      0x00000001051942b4 task_step + 52\n",
      "44  _asyncio.cpython-311-darwin.so      0x0000000105194d54 task_wakeup + 148\n",
      "45  Python                              0x00000001057aa710 cfunction_vectorcall_O + 92\n",
      "46  Python                              0x000000010589434c _PyObject_VectorcallTstate.4587 + 96\n",
      "47  Python                              0x0000000105894234 context_run + 216\n",
      "48  Python                              0x00000001057aa450 cfunction_vectorcall_FASTCALL_KEYWORDS + 80\n",
      "49  Python                              0x000000010586b7d8 _PyEval_EvalFrameDefault + 64136\n",
      "50  Python                              0x000000010585aac4 PyEval_EvalCode + 276\n",
      "51  Python                              0x0000000105855320 builtin_exec + 428\n",
      "52  Python                              0x00000001057aa450 cfunction_vectorcall_FASTCALL_KEYWORDS + 80\n",
      "53  Python                              0x0000000105868ba0 _PyEval_EvalFrameDefault + 52816\n",
      "54  Python                              0x000000010586e6ec _PyEval_Vector + 156\n",
      "55  Python                              0x00000001058fb5e0 pymain_run_module + 256\n",
      "56  Python                              0x00000001058fb080 Py_RunMain + 1784\n",
      "57  Python                              0x00000001058fc230 pymain_main + 52\n",
      "58  Python                              0x00000001058fca68 Py_BytesMain + 40\n",
      "59  dyld                                0x0000000184273e50 start + 2544\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "[[0.99006265]]\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[6], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mpredict_live\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mpositive_real/00279dfe-b4a1-11ee-ba8d-1eebf6296962.png\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "Cell \u001b[0;32mIn[5], line 33\u001b[0m, in \u001b[0;36mpredict_live\u001b[0;34m(image_person)\u001b[0m\n\u001b[1;32m     29\u001b[0m graying \u001b[38;5;241m=\u001b[39m graying \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m255\u001b[39m\n\u001b[1;32m     31\u001b[0m graying \u001b[38;5;241m=\u001b[39m graying[\u001b[38;5;28;01mNone\u001b[39;00m, :, :, \u001b[38;5;28;01mNone\u001b[39;00m]\n\u001b[0;32m---> 33\u001b[0m value \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[43mgraying\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmat\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     35\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m(value \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0.5\u001b[39m):\n\u001b[1;32m     36\u001b[0m     cv2\u001b[38;5;241m.\u001b[39mrectangle(frame, (x, y), (x\u001b[38;5;241m+\u001b[39mw, y\u001b[38;5;241m+\u001b[39mh), (\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m255\u001b[39m, \u001b[38;5;241m0\u001b[39m), \u001b[38;5;241m2\u001b[39m)\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/keras/src/utils/traceback_utils.py:65\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     63\u001b[0m filtered_tb \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m     64\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m---> 65\u001b[0m     \u001b[39mreturn\u001b[39;00m fn(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m     66\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m     67\u001b[0m     filtered_tb \u001b[39m=\u001b[39m _process_traceback_frames(e\u001b[39m.\u001b[39m__traceback__)\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/keras/src/engine/training.py:2554\u001b[0m, in \u001b[0;36mModel.predict\u001b[0;34m(self, x, batch_size, verbose, steps, callbacks, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   2552\u001b[0m \u001b[39mfor\u001b[39;00m step \u001b[39min\u001b[39;00m data_handler\u001b[39m.\u001b[39msteps():\n\u001b[1;32m   2553\u001b[0m     callbacks\u001b[39m.\u001b[39mon_predict_batch_begin(step)\n\u001b[0;32m-> 2554\u001b[0m     tmp_batch_outputs \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mpredict_function(iterator)\n\u001b[1;32m   2555\u001b[0m     \u001b[39mif\u001b[39;00m data_handler\u001b[39m.\u001b[39mshould_sync:\n\u001b[1;32m   2556\u001b[0m         context\u001b[39m.\u001b[39masync_wait()\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/tensorflow/python/util/traceback_utils.py:150\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    148\u001b[0m filtered_tb \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m    149\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 150\u001b[0m   \u001b[39mreturn\u001b[39;00m fn(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m    151\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m    152\u001b[0m   filtered_tb \u001b[39m=\u001b[39m _process_traceback_frames(e\u001b[39m.\u001b[39m__traceback__)\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py:825\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    822\u001b[0m compiler \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mxla\u001b[39m\u001b[39m\"\u001b[39m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_jit_compile \u001b[39melse\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39mnonXla\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    824\u001b[0m \u001b[39mwith\u001b[39;00m OptionalXlaContext(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_jit_compile):\n\u001b[0;32m--> 825\u001b[0m   result \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_call(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwds)\n\u001b[1;32m    827\u001b[0m new_tracing_count \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mexperimental_get_tracing_count()\n\u001b[1;32m    828\u001b[0m without_tracing \u001b[39m=\u001b[39m (tracing_count \u001b[39m==\u001b[39m new_tracing_count)\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py:864\u001b[0m, in \u001b[0;36mFunction._call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    861\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_lock\u001b[39m.\u001b[39mrelease()\n\u001b[1;32m    862\u001b[0m \u001b[39m# In this case we have not created variables on the first call. So we can\u001b[39;00m\n\u001b[1;32m    863\u001b[0m \u001b[39m# run the first trace but we should fail if variables are created.\u001b[39;00m\n\u001b[0;32m--> 864\u001b[0m results \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_variable_creation_fn(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwds)\n\u001b[1;32m    865\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_created_variables \u001b[39mand\u001b[39;00m \u001b[39mnot\u001b[39;00m ALLOW_DYNAMIC_VARIABLE_CREATION:\n\u001b[1;32m    866\u001b[0m   \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mCreating variables on a non-first call to a function\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    867\u001b[0m                    \u001b[39m\"\u001b[39m\u001b[39m decorated with tf.function.\u001b[39m\u001b[39m\"\u001b[39m)\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/tensorflow/python/eager/polymorphic_function/tracing_compiler.py:148\u001b[0m, in \u001b[0;36mTracingCompiler.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    145\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_lock:\n\u001b[1;32m    146\u001b[0m   (concrete_function,\n\u001b[1;32m    147\u001b[0m    filtered_flat_args) \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_maybe_define_function(args, kwargs)\n\u001b[0;32m--> 148\u001b[0m \u001b[39mreturn\u001b[39;00m concrete_function\u001b[39m.\u001b[39;49m_call_flat(\n\u001b[1;32m    149\u001b[0m     filtered_flat_args, captured_inputs\u001b[39m=\u001b[39;49mconcrete_function\u001b[39m.\u001b[39;49mcaptured_inputs)\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/tensorflow/python/eager/polymorphic_function/monomorphic_function.py:1349\u001b[0m, in \u001b[0;36mConcreteFunction._call_flat\u001b[0;34m(self, args, captured_inputs)\u001b[0m\n\u001b[1;32m   1345\u001b[0m possible_gradient_type \u001b[39m=\u001b[39m gradients_util\u001b[39m.\u001b[39mPossibleTapeGradientTypes(args)\n\u001b[1;32m   1346\u001b[0m \u001b[39mif\u001b[39;00m (possible_gradient_type \u001b[39m==\u001b[39m gradients_util\u001b[39m.\u001b[39mPOSSIBLE_GRADIENT_TYPES_NONE\n\u001b[1;32m   1347\u001b[0m     \u001b[39mand\u001b[39;00m executing_eagerly):\n\u001b[1;32m   1348\u001b[0m   \u001b[39m# No tape is watching; skip to running the function.\u001b[39;00m\n\u001b[0;32m-> 1349\u001b[0m   \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_build_call_outputs(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_inference_function(\u001b[39m*\u001b[39;49margs))\n\u001b[1;32m   1350\u001b[0m forward_backward \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_select_forward_and_backward_functions(\n\u001b[1;32m   1351\u001b[0m     args,\n\u001b[1;32m   1352\u001b[0m     possible_gradient_type,\n\u001b[1;32m   1353\u001b[0m     executing_eagerly)\n\u001b[1;32m   1354\u001b[0m forward_function, args_with_tangents \u001b[39m=\u001b[39m forward_backward\u001b[39m.\u001b[39mforward()\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/tensorflow/python/eager/polymorphic_function/atomic_function.py:196\u001b[0m, in \u001b[0;36mAtomicFunction.__call__\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m    194\u001b[0m \u001b[39mwith\u001b[39;00m record\u001b[39m.\u001b[39mstop_recording():\n\u001b[1;32m    195\u001b[0m   \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_bound_context\u001b[39m.\u001b[39mexecuting_eagerly():\n\u001b[0;32m--> 196\u001b[0m     outputs \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_bound_context\u001b[39m.\u001b[39;49mcall_function(\n\u001b[1;32m    197\u001b[0m         \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mname,\n\u001b[1;32m    198\u001b[0m         \u001b[39mlist\u001b[39;49m(args),\n\u001b[1;32m    199\u001b[0m         \u001b[39mlen\u001b[39;49m(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mfunction_type\u001b[39m.\u001b[39;49mflat_outputs),\n\u001b[1;32m    200\u001b[0m     )\n\u001b[1;32m    201\u001b[0m   \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    202\u001b[0m     outputs \u001b[39m=\u001b[39m make_call_op_in_graph(\u001b[39mself\u001b[39m, \u001b[39mlist\u001b[39m(args))\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/tensorflow/python/eager/context.py:1457\u001b[0m, in \u001b[0;36mContext.call_function\u001b[0;34m(self, name, tensor_inputs, num_outputs)\u001b[0m\n\u001b[1;32m   1455\u001b[0m cancellation_context \u001b[39m=\u001b[39m cancellation\u001b[39m.\u001b[39mcontext()\n\u001b[1;32m   1456\u001b[0m \u001b[39mif\u001b[39;00m cancellation_context \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m-> 1457\u001b[0m   outputs \u001b[39m=\u001b[39m execute\u001b[39m.\u001b[39;49mexecute(\n\u001b[1;32m   1458\u001b[0m       name\u001b[39m.\u001b[39;49mdecode(\u001b[39m\"\u001b[39;49m\u001b[39mutf-8\u001b[39;49m\u001b[39m\"\u001b[39;49m),\n\u001b[1;32m   1459\u001b[0m       num_outputs\u001b[39m=\u001b[39;49mnum_outputs,\n\u001b[1;32m   1460\u001b[0m       inputs\u001b[39m=\u001b[39;49mtensor_inputs,\n\u001b[1;32m   1461\u001b[0m       attrs\u001b[39m=\u001b[39;49mattrs,\n\u001b[1;32m   1462\u001b[0m       ctx\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m,\n\u001b[1;32m   1463\u001b[0m   )\n\u001b[1;32m   1464\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m   1465\u001b[0m   outputs \u001b[39m=\u001b[39m execute\u001b[39m.\u001b[39mexecute_with_cancellation(\n\u001b[1;32m   1466\u001b[0m       name\u001b[39m.\u001b[39mdecode(\u001b[39m\"\u001b[39m\u001b[39mutf-8\u001b[39m\u001b[39m\"\u001b[39m),\n\u001b[1;32m   1467\u001b[0m       num_outputs\u001b[39m=\u001b[39mnum_outputs,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1471\u001b[0m       cancellation_manager\u001b[39m=\u001b[39mcancellation_context,\n\u001b[1;32m   1472\u001b[0m   )\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/tensorflow/python/eager/execute.py:53\u001b[0m, in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     51\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m     52\u001b[0m   ctx\u001b[39m.\u001b[39mensure_initialized()\n\u001b[0;32m---> 53\u001b[0m   tensors \u001b[39m=\u001b[39m pywrap_tfe\u001b[39m.\u001b[39;49mTFE_Py_Execute(ctx\u001b[39m.\u001b[39;49m_handle, device_name, op_name,\n\u001b[1;32m     54\u001b[0m                                       inputs, attrs, num_outputs)\n\u001b[1;32m     55\u001b[0m \u001b[39mexcept\u001b[39;00m core\u001b[39m.\u001b[39m_NotOkStatusException \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m     56\u001b[0m   \u001b[39mif\u001b[39;00m name \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    },
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mThe Kernel crashed while executing code in the the current cell or a previous cell. Please review the code in the cell(s) to identify a possible cause of the failure. Click <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. View Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "predict_live(\"positive_real/00279dfe-b4a1-11ee-ba8d-1eebf6296962.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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