{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Balerinki: 6.47%\n",
      "Botki: 3.45%\n",
      "Creepersy: 7.02%\n",
      "Czolenka: 3.83%\n",
      "Domowe: 11.32%\n",
      "Espadryle: 5.27%\n",
      "Glany: 4.10%\n",
      "Kalosze: 1.63%\n",
      "Klapki: 8.62%\n",
      "Kozaki: 0.00%\n",
      "Mokasyny: 8.28%\n",
      "Polbuty: 5.79%\n",
      "Pozostale: 6.90%\n",
      "Sandaly: 7.30%\n",
      "Sniegowce: 3.05%\n",
      "Sportowe: 6.44%\n",
      "Tenisowki: 6.18%\n",
      "Trekkingowe: 4.36%\n",
      "biel: 3.80%\n",
      "czern: 13.46%\n",
      "inny-kolor: 10.42%\n",
      "odcienie-brazu-i-bezu: 6.71%\n",
      "odcienie-czerwieni: 2.75%\n",
      "odcienie-fioletu: 8.17%\n",
      "odcienie-granatowego: 13.61%\n",
      "odcienie-niebieskiego: 11.62%\n",
      "odcienie-pomaranczowego: 4.03%\n",
      "odcienie-rozu: 0.00%\n",
      "odcienie-szarosci-i-srebra: 9.14%\n",
      "odcienie-zieleni: 5.95%\n",
      "odcienie-zoltego-i-zlota: 1.45%\n",
      "wielokolorowy: 8.89%\n",
      "Domowe odcienie-granatowego\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Balerinki: 11.13%\n",
      "Botki: 10.93%\n",
      "Creepersy: 1.87%\n",
      "Czolenka: 16.84%\n",
      "Domowe: 0.63%\n",
      "Espadryle: 1.21%\n",
      "Glany: 6.07%\n",
      "Kalosze: 1.64%\n",
      "Klapki: 0.00%\n",
      "Kozaki: 2.64%\n",
      "Mokasyny: 12.56%\n",
      "Polbuty: 8.10%\n",
      "Pozostale: 7.79%\n",
      "Sandaly: 4.03%\n",
      "Sniegowce: 1.48%\n",
      "Sportowe: 3.63%\n",
      "Tenisowki: 5.98%\n",
      "Trekkingowe: 3.48%\n",
      "biel: 5.27%\n",
      "czern: 8.17%\n",
      "inny-kolor: 9.55%\n",
      "odcienie-brazu-i-bezu: 0.62%\n",
      "odcienie-czerwieni: 2.27%\n",
      "odcienie-fioletu: 11.20%\n",
      "odcienie-granatowego: 13.46%\n",
      "odcienie-niebieskiego: 14.54%\n",
      "odcienie-pomaranczowego: 7.65%\n",
      "odcienie-rozu: 3.07%\n",
      "odcienie-szarosci-i-srebra: 5.34%\n",
      "odcienie-zieleni: 7.19%\n",
      "odcienie-zoltego-i-zlota: 0.00%\n",
      "wielokolorowy: 11.67%\n",
      "Czolenka odcienie-niebieskiego\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Balerinki: 6.88%\n",
      "Botki: 1.42%\n",
      "Creepersy: 9.72%\n",
      "Czolenka: 2.11%\n",
      "Domowe: 7.20%\n",
      "Espadryle: 5.38%\n",
      "Glany: 4.77%\n",
      "Kalosze: 0.89%\n",
      "Klapki: 7.30%\n",
      "Kozaki: 0.00%\n",
      "Mokasyny: 5.78%\n",
      "Polbuty: 9.16%\n",
      "Pozostale: 6.55%\n",
      "Sandaly: 5.41%\n",
      "Sniegowce: 2.76%\n",
      "Sportowe: 9.59%\n",
      "Tenisowki: 7.45%\n",
      "Trekkingowe: 7.62%\n",
      "biel: 4.53%\n",
      "czern: 7.76%\n",
      "inny-kolor: 9.83%\n",
      "odcienie-brazu-i-bezu: 0.31%\n",
      "odcienie-czerwieni: 1.39%\n",
      "odcienie-fioletu: 12.41%\n",
      "odcienie-granatowego: 14.03%\n",
      "odcienie-niebieskiego: 14.15%\n",
      "odcienie-pomaranczowego: 8.37%\n",
      "odcienie-rozu: 3.32%\n",
      "odcienie-szarosci-i-srebra: 4.68%\n",
      "odcienie-zieleni: 6.95%\n",
      "odcienie-zoltego-i-zlota: 0.00%\n",
      "wielokolorowy: 12.26%\n",
      "Creepersy odcienie-niebieskiego\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Balerinki: 3.03%\n",
      "Botki: 10.12%\n",
      "Creepersy: 5.18%\n",
      "Czolenka: 4.62%\n",
      "Domowe: 2.23%\n",
      "Espadryle: 0.16%\n",
      "Glany: 8.46%\n",
      "Kalosze: 12.42%\n",
      "Klapki: 2.96%\n",
      "Kozaki: 11.97%\n",
      "Mokasyny: 2.46%\n",
      "Polbuty: 2.84%\n",
      "Pozostale: 8.41%\n",
      "Sandaly: 4.87%\n",
      "Sniegowce: 11.06%\n",
      "Sportowe: 0.00%\n",
      "Tenisowki: 4.89%\n",
      "Trekkingowe: 4.31%\n",
      "biel: 8.37%\n",
      "czern: 4.25%\n",
      "inny-kolor: 8.15%\n",
      "odcienie-brazu-i-bezu: 0.00%\n",
      "odcienie-czerwieni: 1.89%\n",
      "odcienie-fioletu: 9.10%\n",
      "odcienie-granatowego: 7.77%\n",
      "odcienie-niebieskiego: 14.16%\n",
      "odcienie-pomaranczowego: 7.08%\n",
      "odcienie-rozu: 7.18%\n",
      "odcienie-szarosci-i-srebra: 5.30%\n",
      "odcienie-zieleni: 10.11%\n",
      "odcienie-zoltego-i-zlota: 3.08%\n",
      "wielokolorowy: 13.57%\n",
      "Kalosze odcienie-niebieskiego\n"
     ]
    }
   ],
   "source": [
    "import requests\n",
    "from torch.autograd import Variable\n",
    "import torchvision.transforms as transforms\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from PIL import Image\n",
    "import torch\n",
    "\n",
    "import numpy as np\n",
    "import os.path\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# functions to show an image\n",
    "\n",
    "\n",
    "def imshow(img):\n",
    "    img = img / 2 + 0.5     # unnormalize\n",
    "    npimg = img.numpy()\n",
    "    plt.imshow(np.transpose(npimg, (1, 2, 0)))\n",
    "    plt.show()\n",
    "\n",
    "transform = transforms.Compose(\n",
    "    [  transforms.Resize(32),\n",
    "     transforms.Pad(10, fill=255),\n",
    "     transforms.CenterCrop((32, 32)),\n",
    "     transforms.ToTensor(),\n",
    "     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n",
    "    ]\n",
    ")\n",
    "\n",
    "class Net(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Net, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(3, 6, 5)\n",
    "        self.pool = nn.MaxPool2d(2, 2)\n",
    "        self.conv2 = nn.Conv2d(6, 16, 5)\n",
    "        self.fc1 = nn.Linear(16 * 5 * 5, 120)\n",
    "        self.fc2 = nn.Linear(120, 84)\n",
    "        self.fc3 = nn.Linear(84, 18)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.pool(F.relu(self.conv1(x)))\n",
    "        x = self.pool(F.relu(self.conv2(x)))\n",
    "        x = x.view(-1, 16 * 5 * 5)\n",
    "        x = F.relu(self.fc1(x))\n",
    "        x = F.relu(self.fc2(x))\n",
    "        x = self.fc3(x)\n",
    "        return x\n",
    "\n",
    "class NetBoss(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(NetBoss, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(3, 6, 5)\n",
    "        self.pool = nn.MaxPool2d(2, 2)\n",
    "        self.conv2 = nn.Conv2d(6, 16, 5)\n",
    "        self.fc1 = nn.Linear(16 * 5 * 5, 120)\n",
    "        self.fc2 = nn.Linear(120, 84)\n",
    "        self.fc3 = nn.Linear(84, 14)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.pool(F.relu(self.conv1(x)))\n",
    "        x = self.pool(F.relu(self.conv2(x)))\n",
    "        x = x.view(-1, 16 * 5 * 5)\n",
    "        x = F.relu(self.fc1(x))\n",
    "        x = F.relu(self.fc2(x))\n",
    "        x = self.fc3(x)\n",
    "        return x\n",
    "    \n",
    "\n",
    "# loading exisiting NN's\n",
    "if os.path.isfile(\"nn-state-dict.pth\"):\n",
    "    net_kind = Net()\n",
    "    net_kind.load_state_dict(torch.load(\"nn-state-dict.pth\"))\n",
    "    net_kind.eval()\n",
    "    \n",
    "\n",
    "if os.path.isfile(\"nn-col-state-dict.pth\"):\n",
    "    net_col = NetBoss()\n",
    "    net_col.load_state_dict(torch.load(\"nn-col-state-dict.pth\"))\n",
    "    net_col.eval()\n",
    "    \n",
    "\n",
    "url1 = \"https://chillizet-static.hitraff.pl/uploads/productfeeds/images/99/dd/house-klapki-friends-czarny.jpg\"\n",
    "url2 = \"https://e-obuwniczy.pl/pol_pl_POLBUTY-BUT-BAL-VENETTO-635-SKORA-LICOWA-CZARNY-2551_5.jpg\"\n",
    "url3 = \"https://bhp-nord.pl/33827-thickbox_default/but-s1p-portwest-steelite-tove-ft15.jpg\"\n",
    "url4 = \"https://www.sklepmartes.pl/174554-thickbox_default/dzieciece-kalosze-cosy-wellies-kids-2076-victoria-blue-bejo.jpg\"\n",
    "urls = [url1, url2, url3, url4]\n",
    "\n",
    "# dictionary with names of shoe kinds & shoe colours\n",
    "import pickle\n",
    "kind = open(\"class-shoe.pkl\", \"rb\")\n",
    "colour = open(\"class-col.pkl\", \"rb\")\n",
    "shoe_kinds = pickle.load(kind)\n",
    "shoe_colours = pickle.load(colour)\n",
    "\n",
    "for url in urls:\n",
    "    img = Image.open(requests.get(url, stream=True).raw)\n",
    "\n",
    "    image_tensor = transform(img).float()\n",
    "    imshow(image_tensor)\n",
    "    image_tensor = image_tensor.unsqueeze_(0)\n",
    "    inputi = Variable(image_tensor)\n",
    "\n",
    "    \n",
    "    # calculating the % probability of the shoe kind\n",
    "    output_kind = net_kind(inputi)\n",
    "    \n",
    "    _, predicted_1 = torch.max(output_kind.data, 1)\n",
    "    _, predicted_1m = torch.min(output_kind.data, 1)\n",
    "    \n",
    "    output_kind.data.add_(-output_kind.data[0][int(predicted_1m)])\n",
    "    \n",
    "    percentage_kind = output_kind.data.div(torch.sum(output_kind.data)) * 100\n",
    "    for i in range(0, len(percentage_kind[0])):\n",
    "        print(shoe_kinds[i] + \":\", '%.2f' % float(percentage_kind[0][i]) + \"%\")\n",
    "    \n",
    "    # calculating the % probability of the shoe colour\n",
    "    output_col = net_col(inputi)\n",
    "    #print(output_col)\n",
    "    _, predicted_2 = torch.max(output_col.data, 1)\n",
    "    _, predicted_2m = torch.min(output_col.data, 1)\n",
    "    \n",
    "    # problem: colour net wrongly built on a net that outputs 18 classes instead of 14; needs fixing\n",
    "\n",
    "    output_col.data.add_(-output_col.data[0][int(predicted_2m)])\n",
    "    \n",
    "    percentage_colour = output_col.data.div(torch.sum(output_col.data)) * 100\n",
    "    #print(hoh)\n",
    "    #print(torch.sum(hoh[:14]))\n",
    "    for i in range(0, len(percentage_colour[0])):\n",
    "        print(shoe_colours[i] +  \":\", '%.2f' % float(percentage_colour[0][i]) + \"%\")\n",
    "     \n",
    "    # printing the most probable shoe type and colur\n",
    "    print(shoe_names[int(predicted_1)], shoe_colours[int(predicted_2)])\n",
    "\n",
    "    \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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