{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "view-in-github"
},
"source": [
""
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 136
},
"id": "2kfpFqPshj8L",
"outputId": "d1f10813-209e-4226-e54f-39cf1db77ff5"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"tar: Error opening archive: Failed to open 'open-images-bus-trucks.tar.xz'\n",
"'rm' is not recognized as an internal or external command,\n",
"operable program or batch file.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"f:\\Zajecia\\books\\computer_vision\\Modern-Computer-Vision-with-PyTorch-master\\Modern-Computer-Vision-with-PyTorch-master\\Modern-Computer-Vision-with-PyTorch-master\\Chapter08\\ssd-utils\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"fatal: destination path 'ssd-utils' already exists and is not an empty directory.\n"
]
}
],
"source": [
"import os\n",
"if not os.path.exists('open-images-bus-trucks'):\n",
" !pip install -q torch_snippets\n",
" !wget --quiet https://www.dropbox.com/s/agmzwk95v96ihic/open-images-bus-trucks.tar.xz\n",
" !tar -xf open-images-bus-trucks.tar.xz\n",
" !rm open-images-bus-trucks.tar.xz\n",
" !git clone https://github.com/sizhky/ssd-utils/\n",
"%cd ssd-utils"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"id": "s65Jc3jshlwf"
},
"outputs": [
{
"ename": "FileNotFoundError",
"evalue": "[Errno 2] No such file or directory: '../open-images-bus-trucks//df.csv'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_28720\\2147305028.py\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[0mDATA_ROOT\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'../open-images-bus-trucks/'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[0mIMAGE_ROOT\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34mf'{DATA_ROOT}/images'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mDF_RAW\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf'{DATA_ROOT}/df.csv'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 6\u001b[0m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'ImageID'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0misin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'ImageID'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munique\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\util\\_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 309\u001b[0m \u001b[0mstacklevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mstacklevel\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 310\u001b[0m )\n\u001b[1;32m--> 311\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 312\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 313\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers\\readers.py\u001b[0m in \u001b[0;36mread_csv\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[0;32m 676\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkwds_defaults\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 677\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 678\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 679\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 680\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers\\readers.py\u001b[0m in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m 573\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 574\u001b[0m \u001b[1;31m# Create the parser.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 575\u001b[1;33m \u001b[0mparser\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 576\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 577\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers\\readers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m 930\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 931\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhandles\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mIOHandles\u001b[0m \u001b[1;33m|\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 932\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 933\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 934\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers\\readers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[1;34m(self, f, engine)\u001b[0m\n\u001b[0;32m 1214\u001b[0m \u001b[1;31m# \"Union[str, PathLike[str], ReadCsvBuffer[bytes], ReadCsvBuffer[str]]\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1215\u001b[0m \u001b[1;31m# , \"str\", \"bool\", \"Any\", \"Any\", \"Any\", \"Any\", \"Any\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1216\u001b[1;33m self.handles = get_handle( # type: ignore[call-overload]\n\u001b[0m\u001b[0;32m 1217\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1218\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\common.py\u001b[0m in \u001b[0;36mget_handle\u001b[1;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[0;32m 784\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mioargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mencoding\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;34m\"b\"\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mioargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 785\u001b[0m \u001b[1;31m# Encoding\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 786\u001b[1;33m handle = open(\n\u001b[0m\u001b[0;32m 787\u001b[0m \u001b[0mhandle\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 788\u001b[0m \u001b[0mioargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '../open-images-bus-trucks//df.csv'"
]
}
],
"source": [
"from torch_snippets import *\n",
"DATA_ROOT = '../open-images-bus-trucks/'\n",
"IMAGE_ROOT = f'{DATA_ROOT}/images'\n",
"DF_RAW = df = pd.read_csv(f'{DATA_ROOT}/df.csv')\n",
"\n",
"df = df[df['ImageID'].isin(df['ImageID'].unique().tolist())]\n",
"\n",
"label2target = {l:t+1 for t,l in enumerate(DF_RAW['LabelName'].unique())}\n",
"label2target['background'] = 0\n",
"target2label = {t:l for l,t in label2target.items()}\n",
"background_class = label2target['background']\n",
"num_classes = len(label2target)\n",
"\n",
"device = 'cuda' if torch.cuda.is_available() else 'cpu'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "VZrEG_KjhrAc"
},
"outputs": [],
"source": [
"import collections, os, torch\n",
"from PIL import Image\n",
"from torchvision import transforms\n",
"normalize = transforms.Normalize(\n",
" mean=[0.485, 0.456, 0.406],\n",
" std=[0.229, 0.224, 0.225]\n",
")\n",
"denormalize = transforms.Normalize(\n",
" mean=[-0.485/0.229, -0.456/0.224, -0.406/0.255],\n",
" std=[1/0.229, 1/0.224, 1/0.255]\n",
")\n",
"\n",
"def preprocess_image(img):\n",
" img = torch.tensor(img).permute(2,0,1)\n",
" img = normalize(img)\n",
" return img.to(device).float()\n",
" \n",
"class OpenDataset(torch.utils.data.Dataset):\n",
" w, h = 300, 300\n",
" def __init__(self, df, image_dir=IMAGE_ROOT):\n",
" self.image_dir = image_dir\n",
" self.files = glob.glob(self.image_dir+'/*')\n",
" self.df = df\n",
" self.image_infos = df.ImageID.unique()\n",
" logger.info(f'{len(self)} items loaded')\n",
" \n",
" def __getitem__(self, ix):\n",
" # load images and masks\n",
" image_id = self.image_infos[ix]\n",
" img_path = find(image_id, self.files)\n",
" img = Image.open(img_path).convert(\"RGB\")\n",
" img = np.array(img.resize((self.w, self.h), resample=Image.BILINEAR))/255.\n",
" data = df[df['ImageID'] == image_id]\n",
" labels = data['LabelName'].values.tolist()\n",
" data = data[['XMin','YMin','XMax','YMax']].values\n",
" data[:,[0,2]] *= self.w\n",
" data[:,[1,3]] *= self.h\n",
" boxes = data.astype(np.uint32).tolist() # convert to absolute coordinates\n",
" return img, boxes, labels\n",
"\n",
" def collate_fn(self, batch):\n",
" images, boxes, labels = [], [], []\n",
" for item in batch:\n",
" img, image_boxes, image_labels = item\n",
" img = preprocess_image(img)[None]\n",
" images.append(img)\n",
" boxes.append(torch.tensor(image_boxes).float().to(device)/300.)\n",
" labels.append(torch.tensor([label2target[c] for c in image_labels]).long().to(device))\n",
" images = torch.cat(images).to(device)\n",
" return images, boxes, labels\n",
" def __len__(self):\n",
" return len(self.image_infos)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 51
},
"id": "CnaMqB-Ehuc-",
"outputId": "3ec3a031-39e9-4ad7-ef3a-ae73afeea81a"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2020-10-13 10:38:19.093 | INFO | __main__:__init__:25 - 13702 items loaded\n",
"2020-10-13 10:38:19.138 | INFO | __main__:__init__:25 - 1523 items loaded\n"
]
}
],
"source": [
"from sklearn.model_selection import train_test_split\n",
"trn_ids, val_ids = train_test_split(df.ImageID.unique(), test_size=0.1, random_state=99)\n",
"trn_df, val_df = df[df['ImageID'].isin(trn_ids)], df[df['ImageID'].isin(val_ids)]\n",
"len(trn_df), len(val_df)\n",
"\n",
"train_ds = OpenDataset(trn_df)\n",
"test_ds = OpenDataset(val_df)\n",
"\n",
"train_loader = DataLoader(train_ds, batch_size=4, collate_fn=train_ds.collate_fn, drop_last=True)\n",
"test_loader = DataLoader(test_ds, batch_size=4, collate_fn=test_ds.collate_fn, drop_last=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "H4QhRAB7hu8x"
},
"outputs": [],
"source": [
"def train_batch(inputs, model, criterion, optimizer):\n",
" model.train()\n",
" N = len(train_loader)\n",
" images, boxes, labels = inputs\n",
" _regr, _clss = model(images)\n",
" loss = criterion(_regr, _clss, boxes, labels)\n",
" optimizer.zero_grad()\n",
" loss.backward()\n",
" optimizer.step()\n",
" return loss\n",
" \n",
"@torch.no_grad()\n",
"def validate_batch(inputs, model, criterion):\n",
" model.eval()\n",
" images, boxes, labels = inputs\n",
" _regr, _clss = model(images)\n",
" loss = criterion(_regr, _clss, boxes, labels)\n",
" return loss"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "aPNylBebhxIa"
},
"outputs": [],
"source": [
"from model import SSD300, MultiBoxLoss\n",
"from detect import *"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 188,
"referenced_widgets": [
"c7a9a84353ba44ccbe0ca74f6e28d2d7",
"481a843c371648cca13253e801b33674",
"a89e2f1de7c64a2c867e3e7242215570",
"f9bf1840e56249a8a4cbe9cf0517d323",
"303a9f6960464ebaaed9f0b6db332872",
"3f43740c77a94596bf44c3e27edef3a3",
"570b8c5491894c5fa4e6085ac3b2c1c7",
"d759286449f046d9ad7a229459a7a436"
]
},
"id": "YSmdnGBShyL6",
"outputId": "8fe9eb28-ff8f-4df5-9c76-737049bf3d05"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Downloading: \"https://download.pytorch.org/models/vgg16-397923af.pth\" to /root/.cache/torch/hub/checkpoints/vgg16-397923af.pth\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c7a9a84353ba44ccbe0ca74f6e28d2d7",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"HBox(children=(FloatProgress(value=0.0, max=553433881.0), HTML(value='')))"
]
},
"metadata": {
"tags": []
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"Loaded base model.\n",
"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/dist-packages/torch/nn/_reduction.py:44: UserWarning: size_average and reduce args will be deprecated, please use reduction='none' instead.\n",
" warnings.warn(warning.format(ret))\n"
]
}
],
"source": [
"n_epochs = 3\n",
"\n",
"model = SSD300(num_classes, device)\n",
"optimizer = torch.optim.AdamW(model.parameters(), lr=1e-4, weight_decay=1e-5)\n",
"criterion = MultiBoxLoss(priors_cxcy=model.priors_cxcy, device=device)\n",
"\n",
"log = Report(n_epochs=n_epochs)\n",
"logs_to_print = 5"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "7ex1zZl4h3Po"
},
"outputs": [],
"source": [
"for epoch in range(n_epochs):\n",
" _n = len(train_loader)\n",
" for ix, inputs in enumerate(train_loader):\n",
" loss = train_batch(inputs, model, criterion, optimizer)\n",
" pos = (epoch + (ix+1)/_n)\n",
" log.record(pos, trn_loss=loss.item(), end='\\r')\n",
"\n",
" _n = len(test_loader)\n",
" for ix,inputs in enumerate(test_loader):\n",
" loss = validate_batch(inputs, model, criterion)\n",
" pos = (epoch + (ix+1)/_n)\n",
" log.record(pos, val_loss=loss.item(), end='\\r')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"id": "YpM7pTH3h5Iw",
"outputId": "f64e4832-cbe2-4aa0-fc69-b436d1269036"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2020-10-13 10:39:28.949 | INFO | torch_snippets.loader:Glob:178 - 15225 files found at ../open-images-bus-trucks//images/*\n"
]
}
],
"source": [
"image_paths = Glob(f'{DATA_ROOT}/images/*')\n",
"image_id = choose(test_ds.image_infos)\n",
"img_path = find(image_id, test_ds.files)\n",
"original_image = Image.open(img_path, mode='r')\n",
"original_image = original_image.convert('RGB')"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 743
},
"id": "_LdQ9g5rh-F6",
"outputId": "336b962c-3113-4242-c43e-a19595252bf4"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[35, 34, 212, 123]] ['Truck @ 1.00']\n"
]
},
{
"data": {
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\n",
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