yolov7/tools/visualization.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0ab662ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import torch\n",
    "import cv2\n",
    "from torchvision import transforms\n",
    "import numpy as np\n",
    "from utils.datasets import letterbox"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cfd4b844",
   "metadata": {},
   "outputs": [],
   "source": [
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "weigths = torch.load('./weights/yolov7-e6e.pt')\n",
    "model = weigths['model']\n",
    "model = model.half().to(device)\n",
    "_ = model.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1ee054f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "image = cv2.imread('./images/person.jpg')  # 504x378 image\n",
    "image = letterbox(image, 1280, stride=64, auto=True)[0]\n",
    "image_ = image.copy()\n",
    "image = transforms.ToTensor()(image)\n",
    "image = torch.tensor(np.array([image.numpy()]))\n",
    "image = image.to(device)\n",
    "image = image.half()\n",
    "\n",
    "output = model(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7ec9e6ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "obj1 = output[1][0][0, 0, :, :, 4].sigmoid().cpu().numpy()\n",
    "obj2 = output[1][0][0, 1, :, :, 4].sigmoid().cpu().numpy()\n",
    "obj3 = output[1][0][0, 2, :, :, 4].sigmoid().cpu().numpy()\n",
    "obj4 = output[1][1][0, 0, :, :, 4].sigmoid().cpu().numpy()\n",
    "obj5 = output[1][1][0, 1, :, :, 4].sigmoid().cpu().numpy()\n",
    "obj6 = output[1][1][0, 2, :, :, 4].sigmoid().cpu().numpy()\n",
    "obj7 = output[1][2][0, 0, :, :, 4].sigmoid().cpu().numpy()\n",
    "obj8 = output[1][2][0, 1, :, :, 4].sigmoid().cpu().numpy()\n",
    "obj9 = output[1][2][0, 2, :, :, 4].sigmoid().cpu().numpy()\n",
    "obj10 = output[1][3][0, 0, :, :, 4].sigmoid().cpu().numpy()\n",
    "obj11 = output[1][3][0, 1, :, :, 4].sigmoid().cpu().numpy()\n",
    "obj12 = output[1][3][0, 2, :, :, 4].sigmoid().cpu().numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "38878c81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAcwAAAFdCAYAAACO4V1gAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAEAAElEQVR4nOz9x49lW5bmif32PlpcLUwr1+7PnwqREZGiKrMqM5nVlehCN9BNNMFBc8YRwf+GQ4ITorsLxVJNlsgSKUK8FxFPP/fn0rS8Wh19zt4cWLBmlSjGhCDgv9mF2TVcbMDOd9da3/q20Frzjne84x3veMc7/nbk/68/wDve8Y53vOMd///AO8F8xzve8Y53vOO/gHeC+Y53vOMd73jHfwHvBPMd73jHO97xjv8C3gnmO97xjne84x3/BbwTzHe84x3veMc7/gsw/7Yf/t//r/8nLW8WBP0G8WzBqCr41V/+mnFZEQ8nNAIDUZMM4hy/Mul6Pmt7a1yfj/FTQVq4HA6HrFJNLE0soyRwLPqNOu2NgE4/ZO9Ok+baY1arhOnNJR++/4TNvS5Nz8IK6ixXY8plDZwFvmMQeC1eHL5hMU8QY5v50iVsrhPutfEbI2bLM5bDgJu3xzx9fJ/ee7/DP/9X/5ThyRt+8NEPubmK+P3fv8/+vsCwLb598Yz5oKATNGn36yyTJfH1BXuPH3Mxznj+N8eo2Q3h5h2++uYNW3fvsPu4xt5uE99y8RKF36wRmxXfvX7D1XjF9Szm5qriR+/9HVobbZ59/i+4uDxGWJJuYLC3e8DB3U3SYobKLFbLORsbLRrtjzg5ecvB3S4vn7/kV588xzRs+t0drm6uqZKE7nqDBVNEAV7zET/6+COs4ppllHE9XZKUAxancz54/zE7D6Bd67CKcnLqTKohi9GQDV9xMZ1hqB79tT7JcsTafgcPh9H5OZXrcnx2yjK12N/Y52JwiRJrbDTh8Xv7TEcpwrDxHJur0Smr0QKTHa7GGaHTZmfvAdIPSSrFt5/+a/LpWwK/BkVOv7/N1uMPWMxe4NkVaZSAVJgBvH75miTKaLQbtPx1PvzeP2Qy/4759Yitez/gevCS6OKG/l2Dm+kIaWjmiymW4ePXt2nVHlB3PC4vX9Bu+7S6HvF8ielYCEcgtMN0MiRVCckiR1eS/k6XyU1EVU4YTxcYrsNkMqHum5hOgFsEPP3ox9j+FXGRkc5KIpUwuJxSz3Y5nb+gt9bkenBGUhTo3GFzax9LKharG4K6TaO5y/nhglrTphkGCEvSC1yi0ubs7XMqx6AdHjC9/IbLScTu/kcYQU5VrJjOjlAqZ34Tk2eKes9lHi0RGNimiW8G3H/wEZs7e0ynJ0xmMzb3d0FYXN6cY1Vw9uwao2HR29yiKARSWsymFnu7v0s9jBmPhwTeA66Gh2ztbnB1ec3R4IKD7gNsmbFcfEmzq3HEBmlpIu2c9f2/g50IiukNpxcn6GCCaSnGN2eowqbR7VOWkqRcYbo+hvs+nltHZRpRrSjMCVk24u7BH2IJk/Hp39DptMEsma6OyZcrzt4OceolNWeP6fUVjd4ahrdBGZ8znd/gWG0ajRCn0ScwK1TmMClD+r2HDE6PsDyb0G/x/JN/TTK4REoDy5ZcjxK+Pn/Df/WPPuLRk3vcXGe0djqIdMF0OqbZMnAcj8++fs3FeMGPPj7A0zX+b//Lv+To4pC1oMcf/9kP+A//7jmD8RvSUvM77/8uu+s76PmKz7/6kq7fZRSljMtrFknM9kaXTq3Fhx9/xCc/+4pZFoOt2dhc4/17a/zFv/2SttdlOhrw7eERLgo8myp3cGo2hamhgiIpMOwcsRSUlclP/uAp3Wab//BXnxLNZlRCIQwL0zIpy4I00WCBUZZIx8AwDAzHhFKz3nYJagH5suTtaMGjey1uTpdMVyvSvEJIAykUlmVhCIESDgfbm5xcXOIGBnmUkKcZZSlQ2qRWC/ECg+vLEZZvIYRG5QohoKoMbMvCESWzvMAsNKZtolRFgQatAY1jWEhDoKoSLTRZKigqheUIDKFQAjBNlK6QSmBaGq0gKzS6FKgKhCWhAttWSCnIcpBCg5KUUmNqjRQaIQWiBAyBlhoqTVlITFcym+Ti/2vBfPhgE7XZxeo0Gd4MqCcWNorPf/6SF3rFTRohE48is0kdTWplHF5OwA5odOHpvQM+WPT5/KtL8nmG1j6rvGA2nTNeLrg8czh9c0XgniF8A5TBeJHTe+mzv73Bxx99iC0N4vSC4ViRhAkOCb9+dgrDAR+8/5juZo+qMjk5PSZeHDGPX/Oof5++71Jra/LkW/7g6Q43zQ6jyCGdLlld2BynMbI7JV6kiNwi6HhklYFhOqzv3afITO7s2dxdf0qZP+R6Kjk6XyCrirPjBW2vS33XpnHXYpkbfPHFNzhI9roBjpGzuhmxnByRKEWpNnFtC0OfsdZroZRGKg9VZSwnEboyUMoiT8aYZcQ337zFpUGn7nM1KrlcLWk0QoRnMZ1KLkYxu1suIprx8rvn3N/2abU7TJcKI7PR9ZAkSaiUxzQ9xbGaHL0cMI+GOL7B1LTo1Fr4lkHuLvDNJiYVi2qJtk1836Db6fDqb15wfnpDu+5wd6NFp92FKiKvpmx0tlAqQmc5yawicCtC91YstvfuMIumZLNL7h/ssmzMyBONF/gIr83FYE7oCGzPR5kC16gYLYZIT1ILAuotjzyKWMVjkniGcAVldk29IRBlyGo1o+n6LFYJeVSinAwjXjArLshdGy90CNohFQUpEBo2y+WYKM/RUUaVL5BVl/FCMVu+oRl6xKuKIjeYLSOKQlFYOabw2NzbIVNXjEdTOn4by1qSziOsokZn+w6pFSOIobII7A6jaMF0PmKju4lSHrBBVkCjaZOkMVko6IQHWEHI9PqYtYNtbNMlLiVqJOg3WuTpjGQ5xqgE09mcbtNnsxMySxYI16CcSYTI2NvZ4eHeU/LCZDWN0YXLchbx8tVXuLILmYG35bC+W2c8XHL64ohG7xFBe4eD3TtoAmzTIC0WbO32SfII2+xglGPU9JSoVeA19+m5XU7OXrLe3MUILZRYsZyOCI0AYWowC6JpxioaYZomttNFKQ/L66DymIurK9Df0Kr3yYSkKAv06oz+Zo9c24iipCp8lvMxppcwnVwTJyDtAFkZZJVmlUfsrwtc18CQB3xgP6ASS1ZLjWU4NBtdluYmjapLWkHnrslwdkklhli2Q2qYCNuiEiZKJkhRoYuSMl+xt73PYDVG6Bjb0azmCafTC85en9Bttaimc745eoXMI+qWyw9/58eMLgs2G02KuM31ZMHrw9fUmy77O7tc/yJm/Z6POMlgmmKUJeNFzH6/x68+/YLEKTkej2kUBrKIefv8NelC4m0kXIxGmEIRBA7CspiritLSlJWgXOZYhqZYVlS5JvB9jo5u+GL8Bp1X5BXgOdhSoKRGS4VlCxrNPvFiRmkWCOFSb9Y5WN+m2wg4ub4AJ6Hu++iqwhMGc3UrlLarMTBwQxvykjhLeXtyjSElnbDB+SQFaaB1hSsEdaviaryiVvMoypRCVcjKQFsSTAW6JMsK6qbk3lqDy9WCca6xgUpJlKEQpkBrTSUUVWVQKYXrGEhbIw2BaQtEVVLmgrQQSMMgzxRVrnAsE8PXlGi0ELcCWAFagNAoXWEAjmMgKoUWmkoIKg0oUEqAqZF/iyr+rYI5Klzqpst0cE2t3cCy63ieZq2/TvOnX/HNF88YJZqkKki0Bmng6Yy645Mkgpdvzvn7f/wjVn4NNRlhhQFbgYWlNBdnJS++O2cyXnFdxeRSURmC/PgGaVrUQo+/+vVrvv+kS1jzOT4dcTUdUkYJdb/JZDim0YTOzpL5UpJeKzo1E+EHOE2b7XsbXJUlFz//GrfVZ31/j+HrIVv7T2iubZBmYz79yzN6nRoHmx5WQzKNrxmOZ7TDGm67QxJ75GlKURRUvkOr7fC9Bw/I7ArfMXj+4pBuvEacLLg4u+RsNOT3f/h79HsP0XtdDHuLUabpe3f5kz/9B7wc/AIju+BOp8v6VoerqwnbD/tgmZxdLhDeEmN
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1152x864 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "plt.figure(figsize=(8,8))\n",
    "plt.axis('off')\n",
    "plt.imshow(image_[:,:,[2,1,0]])\n",
    "plt.show()\n",
    "fig, ax = plt.subplots(4,3,figsize=(16,12))\n",
    "#[ax_.axis('off') for ax_ in ax.ravel()]\n",
    "[ax_.set_xticklabels([]) for ax_ in ax.ravel()]\n",
    "[ax_.set_yticklabels([]) for ax_ in ax.ravel()]\n",
    "ax.ravel()[0].imshow(obj1)\n",
    "ax.ravel()[1].imshow(obj2)\n",
    "ax.ravel()[2].imshow(obj3)\n",
    "ax.ravel()[3].imshow(obj4)\n",
    "ax.ravel()[4].imshow(obj5)\n",
    "ax.ravel()[5].imshow(obj6)\n",
    "ax.ravel()[6].imshow(obj7)\n",
    "ax.ravel()[7].imshow(obj8)\n",
    "ax.ravel()[8].imshow(obj9)\n",
    "ax.ravel()[9].imshow(obj10)\n",
    "ax.ravel()[10].imshow(obj11)\n",
    "ax.ravel()[11].imshow(obj12)\n",
    "plt.subplots_adjust(wspace=-0.52, hspace=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8536ecc8",
   "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}