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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df_obj = pd.read_parquet('object_annotations.parquet')\n",
"df_obj"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# create a new column bbox with a list of the bounding box coordinates\n",
"df_obj['bbox'] = df_obj.apply(lambda row: [row['col_x'], row['row_y'], row['width'], row['height']], axis=1)\n",
"df_obj"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df_obj = df_obj.drop(columns=['col_x', 'row_y', 'width', 'height', 'caption', 'source'])\n",
"df_obj "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# group by filenmae and aggregate the bbox, confidence, label columns into a list\n",
"df_obj = df_obj.groupby('filename').agg({'bbox': list, 'confidence': list, 'label': list}).reset_index()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df_obj"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# create a new column 'labels' with a dict of bbox, confidence, label\n",
"df_obj['labels'] = df_obj.apply(lambda row: [{'bbox': bbox, 'confidence': confidence, 'label': label} for bbox, confidence, label in zip(row['bbox'], row['confidence'], row['label'])], axis=1)\n",
"df_obj"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# all columns except filename and labels\n",
"df_obj = df_obj[['filename', 'labels']]\n",
"df_obj"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# plot the first row 'filename' and 'labels' column using matplotlib\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.patches as patches\n",
"import numpy as np\n",
"from PIL import Image\n",
"\n",
"row = df_obj.iloc[0]\n",
"filename = row['filename']\n",
"labels = row['labels']\n",
"\n",
"labels\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# load the image\n",
"img = Image.open(f'{filename}')\n",
"img = np.array(img)\n",
"\n",
"# create figure and axes\n",
"fig, ax = plt.subplots(1)\n",
"\n",
"# display the image\n",
"ax.imshow(img)\n",
"\n",
"# plot bbox on the image along with labels and confidence\n",
"for label in labels:\n",
" bbox = label['bbox']\n",
" rect = patches.Rectangle((bbox[0], bbox[1]), bbox[2], bbox[3], linewidth=1, edgecolor='r', facecolor='none')\n",
" ax.add_patch(rect)\n",
" ax.text(bbox[0], bbox[1], f\"{label['label']} {label['confidence']:.2f}\", color='r')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_parquet('image_annotations.parquet')\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# drop all columns except filename, caption, label, class id\n",
"df = df[['filename', 'caption', 'label']]\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# merge with df_obj on filename\n",
"df = df.merge(df_obj, on='filename')\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# rename label to image_label, class_id to image_class_id\n",
"df.rename(columns={'label': 'image_labels', 'labels': 'objects'}, inplace=True)\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df.to_parquet('annotations.parquet', index=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.10.12"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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