Verification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a926385c-4958-4d27-a69c-a949d63353cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-09-07 17:11:56.220993: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2024-09-07 17:11:57.705826: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
     ]
    }
   ],
   "source": [
    "# Import standard dependencies\n",
    "import cv2\n",
    "import os\n",
    "import random\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "# Import tensorflow dependencies - Functional API\n",
    "import tensorflow as tf\n",
    "# Import custom class and functions\n",
    "from utils import L1Dist, cut_frame, preprocess_file, preprocess_np, verify\n",
    "# Import file utils\n",
    "import shutil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a5f2108d-2e4e-4470-b83e-09cc560c6811",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-09-07 17:12:03.134394: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "2024-09-07 17:12:03.477291: W tensorflow/core/common_runtime/gpu/gpu_device.cc:2251] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\n",
      "Skipping registering GPU devices...\n"
     ]
    }
   ],
   "source": [
    "# Reload model \n",
    "siamese_model = os.path.join('data', 'siamesemodelv2.keras')\n",
    "siamese_model = tf.keras.models.load_model(siamese_model, custom_objects={'L1Dist': L1Dist})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ac548d17-c165-4f82-bab0-b2beb4a75c05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"SiameseNetwork\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"SiameseNetwork\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)        </span>┃<span style=\"font-weight: bold\"> Output Shape      </span>┃<span style=\"font-weight: bold\">    Param # </span>┃<span style=\"font-weight: bold\"> Connected to      </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n",
       "│ input_img           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                 │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)                │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ validation_img      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                 │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)                │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ embedding           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4096</span>)      │ <span style=\"color: #00af00; text-decoration-color: #00af00\">38,960,448</span> │ input_img[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],  │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Functional</span>)        │                   │            │ validation_img[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ l1_dist (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">L1Dist</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4096</span>)      │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ embedding[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],  │\n",
       "│                     │                   │            │ embedding[<span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ dense_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)         │      <span style=\"color: #00af00; text-decoration-color: #00af00\">4,097</span> │ l1_dist[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]     │\n",
       "└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)       \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape     \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m   Param #\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mConnected to     \u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n",
       "│ input_img           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m100\u001b[0m,  │          \u001b[38;5;34m0\u001b[0m │ -                 │\n",
       "│ (\u001b[38;5;33mInputLayer\u001b[0m)        │ \u001b[38;5;34m3\u001b[0m)                │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ validation_img      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m100\u001b[0m,  │          \u001b[38;5;34m0\u001b[0m │ -                 │\n",
       "│ (\u001b[38;5;33mInputLayer\u001b[0m)        │ \u001b[38;5;34m3\u001b[0m)                │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ embedding           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4096\u001b[0m)      │ \u001b[38;5;34m38,960,448\u001b[0m │ input_img[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],  │\n",
       "│ (\u001b[38;5;33mFunctional\u001b[0m)        │                   │            │ validation_img[\u001b[38;5;34m0\u001b[0m… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ l1_dist (\u001b[38;5;33mL1Dist\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4096\u001b[0m)      │          \u001b[38;5;34m0\u001b[0m │ embedding[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],  │\n",
       "│                     │                   │            │ embedding[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ dense_2 (\u001b[38;5;33mDense\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)         │      \u001b[38;5;34m4,097\u001b[0m │ l1_dist[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]     │\n",
       "└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">38,964,545</span> (148.64 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m38,964,545\u001b[0m (148.64 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">38,964,545</span> (148.64 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m38,964,545\u001b[0m (148.64 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# View model summary\n",
    "siamese_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2f11caa2-cfa6-414a-9ec8-9595cd69f9cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setup paths\n",
    "POS_PATH = os.path.join('data', 'positive')\n",
    "NEG_PATH = os.path.join('data', 'negative')\n",
    "APP_PATH = os.path.join('data', 'app')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0b08fa9d-2d6b-4a62-8f00-621ce652dbb8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make fresh app directory\n",
    "shutil.rmtree(APP_PATH, ignore_errors=True)\n",
    "os.makedirs(APP_PATH, exist_ok=True)\n",
    "\n",
    "positive = os.listdir(POS_PATH)\n",
    "random.shuffle(positive)\n",
    "\n",
    "# Take this many random positive images\n",
    "for img in positive[:35]:\n",
    "    shutil.copy(\n",
    "        os.path.join(POS_PATH, img),\n",
    "        APP_PATH,\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d5d3a0fd-d1a6-4a36-a643-a55137c2029c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 334ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 159ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 159ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 151ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 131ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 157ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 132ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 150ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 160ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n"
     ]
    }
   ],
   "source": [
    "model_img = preprocess_file(os.path.join(APP_PATH, random.choice(os.listdir(APP_PATH))))\n",
    "input_img = preprocess_file(os.path.join(NEG_PATH, random.choice(os.listdir(NEG_PATH))))\n",
    "results, verified = verify(input_img, siamese_model, APP_PATH, 0.7, 0.6)\n",
    "\n",
    "# Set plot size \n",
    "plt.figure(figsize=(10,8))\n",
    "\n",
    "# Set first subplot\n",
    "plt.subplot(1,2,1)\n",
    "plt.imshow(model_img)\n",
    "\n",
    "# Set second subplot\n",
    "plt.subplot(1,2,2)\n",
    "plt.imshow(input_img)\n",
    "\n",
    "# Renders cleanly\n",
    "plt.show()\n",
    "print(verified)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a16a9bcf-7cfe-4519-bdd5-147a78435788",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 217ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 156ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 150ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 161ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 174ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 153ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 160ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 189ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 131ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "cap = cv2.VideoCapture(0)\n",
    "while cap.isOpened():\n",
    "    ret, frame = cap.read()\n",
    "    frame = cut_frame(frame)\n",
    "    cv2.imshow('Verification', frame)\n",
    "\n",
    "    key = cv2.waitKey(1) & 0XFF\n",
    "    match key:\n",
    "        # Verification trigger on v\n",
    "        case 118:\n",
    "            #cv2.imwrite('input.jpg', frame)\n",
    "            #img = preprocess_file('input.jpg')\n",
    "            img = preprocess_np(frame)\n",
    "\n",
    "            results, verified = verify(img, siamese_model, APP_PATH, 0.7, 0.6)\n",
    "            print(verified)\n",
    "        # Break gracefully on q\n",
    "        case 113: break\n",
    "\n",
    "cap.release()\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d221204b-5d2a-4fd1-ad7f-a299f088a93e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "94.28571428571428"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Percentage of images that identify at least 70%\n",
    "np.sum(np.squeeze(results) > 0.7) / len(results) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2d7d1d8f-ba82-4cb4-b209-aa5dda617d92",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([[1.]], dtype=float32),\n",
       " array([[1.]], dtype=float32),\n",
       " array([[1.]], dtype=float32),\n",
       " array([[1.]], dtype=float32),\n",
       " array([[1.]], dtype=float32),\n",
       " array([[0.6797763]], dtype=float32),\n",
       " array([[1.]], dtype=float32),\n",
       " array([[0.99279684]], dtype=float32),\n",
       " array([[0.99782217]], dtype=float32),\n",
       " array([[1.]], dtype=float32),\n",
       " array([[1.]], dtype=float32),\n",
       " array([[0.9998843]], dtype=float32),\n",
       " array([[0.9990202]], dtype=float32),\n",
       " array([[0.99977523]], dtype=float32),\n",
       " array([[0.99754024]], dtype=float32),\n",
       " array([[0.9830968]], dtype=float32),\n",
       " array([[1.]], dtype=float32),\n",
       " array([[1.]], dtype=float32),\n",
       " array([[0.53368264]], dtype=float32),\n",
       " array([[0.99999624]], dtype=float32),\n",
       " array([[0.9999999]], dtype=float32),\n",
       " array([[0.99999803]], dtype=float32),\n",
       " array([[1.]], dtype=float32),\n",
       " array([[0.9999949]], dtype=float32),\n",
       " array([[0.99999994]], dtype=float32),\n",
       " array([[1.]], dtype=float32),\n",
       " array([[0.99987924]], dtype=float32),\n",
       " array([[0.9999253]], dtype=float32),\n",
       " array([[1.]], dtype=float32),\n",
       " array([[0.9998433]], dtype=float32),\n",
       " array([[0.9771014]], dtype=float32),\n",
       " array([[1.]], dtype=float32),\n",
       " array([[0.99875355]], dtype=float32),\n",
       " array([[1.]], dtype=float32),\n",
       " array([[1.]], dtype=float32)]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "06f9a01d-7b5b-4504-bf2d-9c0087a15cdf",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}