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zcmgyu/convert_rico_for_object_detection_based_on_clay.ipynb

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Created February 10, 2022 09:17
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convert_rico_for_object_detection_based_on_clay.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/dusskapark/c59eae6cae2b822bf7d133a2860c4c51/convert_rico_for_object_detection_based_on_clay.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "fxLlwMvtLk1j"
},
"source": [
"# Prepare Training Data\n",
"\n",
"[Rico](https://interactionmining.org/rico) is the largest open repository of mobile app designs to date, created to support five classes of data-driven applications: design search, UI layout generation, UI code generation, user interaction modeling, and user perception prediction. \n",
"\n",
"In this notebook, we will download and rearrange this dataset to fit for TensorFlow Object Detection training based on [CLAY](https://github.com/google-research-datasets/clay) that is used for training and evaluation of the screen layout denoising models\n",
"\n",
"\n",
"We also recommend reading [my blog post on Medium](https://judepark-6960.medium.com/a-design-system-made-with-tensorflow-js-e8646733d6d3) or [this Colab notebook](https://colab.research.google.com/drive/1fjnVbDnHnGnILuvDTv7zAwUwIMntrC7p?usp=sharing) side by side.\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "QcHJuaurS_AO",
"outputId": "28f09654-e823-4e84-a6b2-b9a3914f93d5"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[1;30;43m스트리밍 출력 내용이 길어서 마지막 5000줄이 삭제되었습니다.\u001b[0m\n",
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]
}
],
"source": [
"# Cloning CLAY pipeline\n",
"!git clone https://github.com/google-research-datasets/clay.git\n",
"\n",
"# Downloading data from RICO dataset\n",
"!curl -L \"https://storage.googleapis.com/crowdstf-rico-uiuc-4540/rico_dataset_v0.1/unique_uis.tar.gz\" > jpg.tar.gz; tar -zxvf jpg.tar.gz; rm jpg.tar.gz\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "-8y6pS6EIhy0"
},
"source": [
"# Manipulate RICO dataset\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "EDf9qrUT6bpx"
},
"source": [
"## Scale images \n",
"\n",
"Most of RICO's screenshots are high-resolution images at 1440 × 2560 pixels. If you use these directly, they will use a lot of resources with regards to the GPU and memory within Google Colab's training environment. \n",
"\n",
"So we'll reduce all images to 640px height JPG. Later, we're going to change the value inside annotation files too.\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "3TyY8iiRI6aC",
"outputId": "70c84862-c6c0-48d3-9584-9ab184fc2d18"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"66261\n",
"66261\n"
]
}
],
"source": [
"!mkdir image\n",
"!find combined/. -name \"*.jpg\" -exec mv {} ./image \\;\n",
"\n",
"!find image/. -name \"*.jpg\" | wc -l\n",
"!find combined/. -name \"*.json\" | wc -l"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "dQqxUqXd648m",
"outputId": "cc41adf9-15e8-4c3f-9854-e2abbfc0c01c"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[1;30;43m스트리밍 출력 내용이 길어서 마지막 5000줄이 삭제되었습니다.\u001b[0m\n",
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"end ::: ./jpg/44302.jpg\n"
]
}
],
"source": [
" from PIL import Image\n",
" import os\n",
"\n",
" raw_path = './image/' # source image path\n",
" data_path = './jpg/' # Resized image path\n",
"\n",
" # Start resize --------------------\n",
" ## If there is no data_path, create\n",
" if not os.path.exists(data_path):\n",
" os.mkdir(data_path)\n",
"\n",
" ## Specify a list of all images in the source image path\n",
" data_list = os.listdir(raw_path)\n",
" print(len(data_list))\n",
"\n",
" # Save all images after resizing\n",
" for name in data_list:\n",
" im = Image.open(raw_path + name)\n",
" im = im.resize((360, 640))\n",
" im.save(data_path + name)\n",
" print('end ::: ' + data_path + name)\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Ew-gRzQ51eGy"
},
"source": [
"## Convert to XML\n",
"\n",
"We'll extract only the necessary information such as such as the bounding box, filename, and component name from the JSON files and convert them to `xml` format.\n",
"\n",
"We will use the dataset selected by CLAY in this process. By using this we can get a model with much better performance than using RICO directly. Please see more details from the links below: \n",
"\n",
"- CHI'22 Paper: https://t.co/vHXyTmJOsp\n",
"- CLAY dataset: https://t.co/EepAt2PMpM"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "OxCLb3cc8PNs",
"outputId": "04e2e8f5-6b5b-4dd6-d2f7-cc986303ead9"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[1;30;43m스트리밍 출력 내용이 길어서 마지막 5000줄이 삭제되었습니다.\u001b[0m\n",
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"48664\n",
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]
}
],
"source": [
"import os\n",
"import json\n",
"from xml.etree.ElementTree import Element, SubElement\n",
"import csv\n",
"import sys\n",
"\n",
"sys.setrecursionlimit(10**6)\n",
"\n",
"\n",
"def beautify(elem, indent=0):\n",
" \"\"\"\n",
" xml 트리를 문자열로 변환합니다.\n",
" Converts the XML tree to a string.\n",
"\n",
" :param elem: xml element\n",
" :param indent: Indent Level to display in front\n",
" \"\"\"\n",
" result0 = f\"{' ' * indent}<{elem.tag}>\"\n",
" # 값이 있는 태그면 값을 바로 출력\n",
" # If there is a value in the tag, immediately print the value\n",
" if elem.text is not None:\n",
" result0 += elem.text + f\"</{elem.tag}>\\n\"\n",
" # 값이 없고 자식 노드가 있으면 재귀 호출로 출력합니다.\n",
" # If the tag has no value and has child nodes, call the recursive function.\n",
" else:\n",
" result0 += \"\\n\"\n",
" for _child in elem:\n",
" result0 += beautify(_child, indent + 1)\n",
" result0 += f\"{' ' * indent}</{elem.tag}>\\n\"\n",
" return result0\n",
"\n",
"\n",
"def labelinlist(screen_id, name):\n",
" \"\"\"\n",
" clay_csv에 따라서 node를 분류합니다. \n",
" Filter nodes according to clay_csv\n",
" \"\"\"\n",
" global resultdic\n",
"\n",
" for label in resultdic[screen_id]:\n",
" if(label[0] == name):\n",
" return True\n",
" return False\n",
"\n",
"\n",
"def replace_label(screen_id, node):\n",
" \"\"\"\n",
" label의 번호를 리턴합니다. \n",
" Returns the label number.\n",
" \"\"\"\n",
" global resultdic\n",
" global labeldict\n",
" for label in resultdic[screen_id]:\n",
" if(label[0] == node):\n",
" label_map = int(label[1])\n",
" return labeldict[label_map]\n",
" return False\n",
"\n",
"\n",
"def recursive(screen_id, child, result_out):\n",
" \"\"\"\n",
" 원하는 역할을 하기 위해서 재귀호출을 할 수 있는 함수를 생성합니다.\n",
" Create a function that makes a recursive call.\n",
" \"\"\"\n",
" obj = Element(\"object\")\n",
"\n",
" # Set bounds\n",
" bounds = child['bounds']\n",
"\n",
" # Set difficult\n",
" SubElement(obj, \"difficult\").text = '0'\n",
"\n",
" # Set bndbox\n",
" bndbox = SubElement(obj, \"bndbox\")\n",
" xmin = bounds[0]\n",
" ymin = bounds[1]\n",
" xmax = bounds[2]\n",
" ymax = bounds[3]\n",
"\n",
" SubElement(bndbox, \"xmin\").text = str(round(xmin/4))\n",
" SubElement(bndbox, \"ymin\").text = str(round(ymin/4))\n",
" SubElement(bndbox, \"xmax\").text = str(round(xmax/4))\n",
" SubElement(bndbox, \"ymax\").text = str(round(ymax/4))\n",
"\n",
" name = child['pointer']\n",
" if(labelinlist(screen_id, name)):\n",
" label_map = replace_label(screen_id, name)\n",
"\n",
" # Set name\n",
" SubElement(obj, \"name\").text = label_map\n",
" result_out.append(beautify(obj))\n",
"\n",
" # 생성한 object 태그를 문자열로 변환해서 추가합니다.\n",
" # Convert the created object tag to a string and add it.\n",
" if 'children' not in child:\n",
" return\n",
"\n",
" # 자식 노드가 있는 경우 자식 노드에 대해 재귀 호출을 수행합니다.\n",
" # If there is a child node, make a recursive call to the child node.\n",
" for ch in child.get('children', []):\n",
" if(ch != None):\n",
" recursive(screen_id, ch, result_out)\n",
" else:\n",
" print(screen_id)\n",
" print(ch,sep=\"\\n\")\n",
" \n",
"\n",
"def json2xml(screen_id, infile, outfile):\n",
" \"\"\"\n",
" json2xml function\n",
" param infile :\n",
" ourfile :\n",
" \"\"\"\n",
"\n",
" result_out = []\n",
" imgName = infile.replace(\"./combined/\", \"\")\n",
" imgName = imgName.replace(\"json\", \"jpg\")\n",
"\n",
" # Read the file\n",
" with open(infile, \"r\", encoding=\"UTF-8\") as f:\n",
" data = json.load(f)\n",
"\n",
" # 자식 노드에 대해 재귀 호출을 수행합니다.\n",
" # Make a recursive call on child nodes.\n",
" for child in data['activity']['root'].get('children', []):\n",
" recursive(screen_id, child, result_out)\n",
"\n",
" # 그리고 해당 결과를 파일로 저장합니다.\n",
" # And save the result to the XML file.\n",
" with open(outfile, \"w\", encoding=\"UTF-8\") as f:\n",
" f.write(\"\".join(\"<annotation><folder />\"))\n",
" f.write(\"\".join(\"<filename>\" + imgName + \"</filename>\\n\"\n",
" \"<path>\" + imgName + \"</path>\\n\" +\n",
" \"<source><database>RICO</database></source><size><width>360</width><height>640</height><depth>3</depth></size><segmented>0</segmented>\"))\n",
" f.write(\"\".join(result_out))\n",
" f.write(\"\".join(\"</annotation>\"))\n",
"\n",
"\n",
"def readcsv(filename):\n",
" \"\"\"\n",
" {screen_id:[[name0,id0]...],}\n",
" \"\"\"\n",
" result = []\n",
" with open(filename, newline='') as csvfile:\n",
" reader = csv.reader(csvfile, delimiter=',', quotechar='|')\n",
"\n",
" for row in reader:\n",
" result.append(row)\n",
"\n",
" # print(row)\n",
" del result[0]\n",
"\n",
" resultdic = {}\n",
" for row in result:\n",
" if row[0] in resultdic.keys():\n",
" resultdic[row[0]].append([row[1], row[2]])\n",
" else:\n",
" resultdic[row[0]] = [[row[1], row[2]]]\n",
"\n",
" return resultdic\n",
"\n",
"\n",
"def readtxt(filename):\n",
" \"\"\"\n",
" [{num}][{label}]\n",
" \"\"\"\n",
" result = {}\n",
" with open(filename, newline='') as f:\n",
" while True:\n",
" line = f.readline()\n",
" if(not line):\n",
" break\n",
" num, label = line.split(':')\n",
" num = int(num)\n",
" label = label.strip()\n",
" # print(f'[{num}][{label}]')\n",
" result[num] = label\n",
" return result\n",
"\n",
"\n",
"def main():\n",
" \"\"\"\n",
" 메인 함수\n",
" The main function \n",
" \"\"\"\n",
" global resultdic\n",
" global labeldict\n",
" # print(files)\n",
"\n",
" resultdic = readcsv(\"./clay/clay_labels.csv\")\n",
" labeldict = readtxt('./clay/label_map.txt')\n",
"\n",
" data_path = './xml/'\n",
" if not os.path.exists(data_path):\n",
" os.mkdir(data_path)\n",
"\n",
" files = os.listdir('./combined')\n",
"\n",
" for infile in files:\n",
" screen_id = infile.replace('.json', '').strip()\n",
" selected_screen = resultdic.get(screen_id)\n",
" #print(selected_screen)\n",
" if selected_screen == None:\n",
" print(screen_id+\" is not exist!\") \n",
" \n",
" else:\n",
" infile = f'./combined/{infile}'\n",
" outfile = infile.replace(\"combined\", \"xml\").replace(\"json\", \"xml\")\n",
" #print(infile, outfile)\n",
" json2xml(screen_id, infile, outfile)\n",
"\n",
"\n",
"if __name__ == \"__main__\":\n",
" main()\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "6WBbbQW3KyVi"
},
"source": [
"## Generate Label Map\n",
"\n",
"Next, we should generate the `label_map.pbtxt` based on label_map text file from CLAY. \n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"id": "FCs4uLnOLbC-",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "187166cb-2c14-4633-c087-8155bd012699"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"item {\n",
" name: \"PAGER_INDICATOR\",\n",
" id: 1\n",
"}\n",
"\n",
"item {\n",
" name: \"NAVIGATION_BAR\",\n",
" id: 2\n",
"}\n",
"\n",
"item {\n",
" name: \"TEXT_INPUT\",\n",
" id: 3\n",
"}\n",
"\n",
"item {\n",
" name: \"MAP\",\n",
" id: 4\n",
"}\n",
"\n",
"item {\n",
" name: \"ADVERTISEMENT\",\n",
" id: 5\n",
"}\n",
"\n",
"item {\n",
" name: \"LIST_ITEM\",\n",
" id: 6\n",
"}\n",
"\n",
"item {\n",
" name: \"RADIO_BUTTON\",\n",
" id: 7\n",
"}\n",
"\n",
"item {\n",
" name: \"DATE_PICKER\",\n",
" id: 8\n",
"}\n",
"\n",
"item {\n",
" name: \"TOOLBAR\",\n",
" id: 9\n",
"}\n",
"\n",
"item {\n",
" name: \"TEXT\",\n",
" id: 10\n",
"}\n",
"\n",
"item {\n",
" name: \"CONTAINER\",\n",
" id: 11\n",
"}\n",
"\n",
"item {\n",
" name: \"IMAGE\",\n",
" id: 12\n",
"}\n",
"\n",
"item {\n",
" name: \"PICTOGRAM\",\n",
" id: 13\n",
"}\n",
"\n",
"item {\n",
" name: \"BUTTON\",\n",
" id: 14\n",
"}\n",
"\n",
"item {\n",
" name: \"SWITCH\",\n",
" id: 15\n",
"}\n",
"\n",
"item {\n",
" name: \"SPINNER\",\n",
" id: 16\n",
"}\n",
"\n",
"item {\n",
" name: \"CHECK_BOX\",\n",
" id: 17\n",
"}\n",
"\n",
"item {\n",
" name: \"NUMBER_STEPPER\",\n",
" id: 18\n",
"}\n",
"\n",
"item {\n",
" name: \"DRAWER\",\n",
" id: 19\n",
"}\n",
"\n",
"item {\n",
" name: \"CARD_VIEW\",\n",
" id: 20\n",
"}\n",
"\n",
"item {\n",
" name: \"PROGRESS_BAR\",\n",
" id: 21\n",
"}\n",
"\n",
"item {\n",
" name: \"BACKGROUND\",\n",
" id: 22\n",
"}\n",
"\n",
"item {\n",
" name: \"SLIDER\",\n",
" id: 23\n",
"}\n",
"\n",
"item {\n",
" name: \"LABEL\",\n",
" id: 24\n",
"}\n",
"\n",
"\n"
]
}
],
"source": [
"import os\n",
"import xml.etree.ElementTree as ET\n",
"\n",
"obj = []\n",
"\n",
"for filename in os.listdir(\"./xml/\"):\n",
" # with open(os.path.join(\"xml\", filename), 'r') as f:\n",
" tree = ET.parse(os.path.join(\"./xml/\", filename))\n",
" root = tree.getroot()\n",
" object = root.findall(\"object\")\n",
" name = [x.findtext(\"name\") for x in object]\n",
"\n",
" for i in name:\n",
" obj.append(i)\n",
"\n",
"obj_unique = list(set(obj))\n",
"# print(obj_unique)\n",
"\n",
"pbtxt = \"\"\n",
"for i in range(len(obj_unique)):\n",
" pbtxt += \"item {\\n name: \\\"\"+obj_unique[i]+\"\\\",\\n id: \"+str(i+1)+\"\\n}\\n\"+\"\\n\"\n",
" \n",
"with open(\"label_map.pbtxt\", \"w\", encoding=\"utf-8\") as f:\n",
" f.write(pbtxt)\n",
"\n",
"print(pbtxt)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "6_mh4tID4CiE"
},
"source": [
"## Partition the dataset\n",
"\n",
"Next, We're going to split our dataset into the desired training and testing subsets. Typically, the ratio is 9:1. 90% of the images are used for training and the rest 10% is maintained for testing, but you can chose whatever ratio suits your needs.\n",
"\n",
"[Lyudmil Vladimirov](https://github.com/sglvladi) has published a great code example on [splitting the dataset](https://tensorflow-object-detection-api-tutorial.readthedocs.io/en/latest/training.html#partition-the-dataset)."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"id": "3YzxY3keSIYo",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "ef4672b6-2746-4a3d-e34a-0d55bd03c2af"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[1;30;43m스트리밍 출력 내용이 길어서 마지막 5000줄이 삭제되었습니다.\u001b[0m\n",
"./xml/67719.xml ./train/67719.xml\n",
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"./xml/9781.xml ./train/9781.xml\n",
"./xml/9782.xml ./train/9782.xml\n",
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"./xml/996.xml ./train/996.xml\n",
"./xml/9960.xml ./train/9960.xml\n",
"./xml/9961.xml ./train/9961.xml\n",
"./xml/9962.xml ./train/9962.xml\n",
"./xml/9963.xml ./train/9963.xml\n",
"./xml/9964.xml ./train/9964.xml\n",
"./xml/9965.xml ./train/9965.xml\n",
"./xml/9966.xml ./train/9966.xml\n",
"./xml/9967.xml ./train/9967.xml\n",
"./xml/9968.xml ./train/9968.xml\n",
"./xml/9969.xml ./train/9969.xml\n",
"./xml/997.xml ./train/997.xml\n",
"./xml/998.xml ./train/998.xml\n",
"./xml/9982.xml ./train/9982.xml\n",
"./xml/9983.xml ./train/9983.xml\n",
"./xml/9984.xml ./train/9984.xml\n",
"./xml/9985.xml ./train/9985.xml\n",
"./xml/9986.xml ./train/9986.xml\n",
"./xml/9987.xml ./train/9987.xml\n",
"./xml/9988.xml ./train/9988.xml\n",
"./xml/999.xml ./train/999.xml\n",
"./xml/9990.xml ./train/9990.xml\n",
"./xml/9991.xml ./train/9991.xml\n",
"./xml/9992.xml ./train/9992.xml\n",
"./xml/9999.xml ./train/9999.xml\n"
]
}
],
"source": [
"import os\n",
"import shutil\n",
"\n",
"\n",
"def readtxt(filename):\n",
" result = []\n",
" with open(filename, newline='') as f:\n",
" while True:\n",
" screen_id = f.readline()\n",
" if(not screen_id):\n",
" break\n",
" num = int(screen_id)\n",
" result.append(num)\n",
"\n",
" return result\n",
"\n",
"\n",
"def split(folder):\n",
" data_path = f'./{folder}/'\n",
" if not os.path.exists(data_path):\n",
" os.mkdir(data_path)\n",
" list = readtxt(f'./clay/split_{folder}_id.txt')\n",
"\n",
" for infile in list:\n",
" outfile = f'{data_path}{infile}.xml'\n",
" outimgfile = f'{data_path}{infile}.jpg'\n",
" \n",
" inimgfile = f'./jpg/{infile}.jpg'\n",
" infile = f'./xml/{infile}.xml'\n",
" \n",
" print(infile, outfile)\n",
" shutil.copyfile(infile, outfile)\n",
" shutil.copyfile(inimgfile, outimgfile)\n",
"\n",
"\n",
"def main():\n",
" \"\"\"\n",
" 메인 함수\n",
" The main function \n",
" \"\"\"\n",
" split('dev')\n",
" split('test')\n",
" split('train')\n",
"\n",
"\n",
"if __name__ == \"__main__\":\n",
" main()\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "JDVA0Vc9oDEC",
"outputId": "ba036fb4-f88e-46a5-c482-d201bc5e5200"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"44629\n",
"8719\n",
"6207\n"
]
}
],
"source": [
"\n",
"!find train/. -name \"*.jpg\" | wc -l\n",
"!find test/. -name \"*.jpg\" | wc -l\n",
"!find dev/. -name \"*.jpg\" | wc -l\n",
"\n",
"#%rm -rf xml combined image"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "oHMDd0zW1OdA"
},
"source": [
"## Downloading data by mounting Google drive\n",
"\n",
"> Congrats!\n",
"\n",
"Hope you enjoyed this!"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "v77Lv1mV1zfH",
"outputId": "63d0b973-4d1e-4214-a4e8-91bcf7fdf731"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Mounted at /content/drive\n"
]
}
],
"source": [
"from google.colab import drive\n",
"drive.mount('/content/drive')"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ijVFyomcQlwt"
},
"source": [
"## Convert XML to TFrecord\n",
"\n",
"it is time to convert VOC XML into TFRecord files by using [Lyudmil Vladimirov](https://github.com/sglvladi)'s [code example](https://tensorflow-object-detection-api-tutorial.readthedocs.io/en/latest/training.html#create-tensorflow-records). \n",
"\n",
"However, his `generate_tfrecord.py` file presumes the user is only working with a single class. A slight modification is required. So, I folked his script and use it as below:"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ZwBcTRuUPwjG",
"outputId": "8ba9befb-e2ec-4af4-e4e9-bdf694c1a51f"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"--2022-02-04 13:21:21-- https://raw.githubusercontent.com/dusskapark/TensorFlowObjectDetectionTutorial/master/docs/source/scripts/generate_tfrecord.py\n",
"Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
"Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 6437 (6.3K) [text/plain]\n",
"Saving to: ‘generate_tfrecord.py’\n",
"\n",
"generate_tfrecord.p 100%[===================>] 6.29K --.-KB/s in 0s \n",
"\n",
"2022-02-04 13:21:22 (69.8 MB/s) - ‘generate_tfrecord.py’ saved [6437/6437]\n",
"\n",
"2022-02-04 13:21:25.262013: E tensorflow/stream_executor/cuda/cuda_driver.cc:271] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected\n",
"Successfully created the TFRecord file: train.record\n",
"2022-02-04 13:24:16.102638: E tensorflow/stream_executor/cuda/cuda_driver.cc:271] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected\n",
"Successfully created the TFRecord file: test.record\n",
"2022-02-04 13:24:50.790502: E tensorflow/stream_executor/cuda/cuda_driver.cc:271] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected\n",
"Successfully created the TFRecord file: dev.record\n"
]
}
],
"source": [
"%cd /content/\n",
"\n",
"# Download utils\n",
"!wget \"https://raw.githubusercontent.com/dusskapark/TensorFlowObjectDetectionTutorial/master/docs/source/scripts/generate_tfrecord.py\"\n",
"\n",
"# Generate TFrecords \n",
"!python generate_tfrecord.py -x train -l label_map.pbtxt -o train.record\n",
"!python generate_tfrecord.py -x test -l label_map.pbtxt -o test.record\n",
"!python generate_tfrecord.py -x dev -l label_map.pbtxt -o dev.record\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "aD7BAB7aVDkM"
},
"source": [
"# Download outputs\n",
"\n",
"> Congrats!\n",
"\n",
"Hope you enjoyed this!\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 68
},
"id": "DtHA9cIA2oBN",
"outputId": "e5f941b8-432f-4f07-d661-72e0310cd267"
},
"outputs": [
{
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"data": {
"application/javascript": [
"download(\"download_2a0c0404-b8d8-44ac-b185-8397a4c2fff0\", \"test.record\", 178477156)"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/javascript": [
"\n",
" async function download(id, filename, size) {\n",
" if (!google.colab.kernel.accessAllowed) {\n",
" return;\n",
" }\n",
" const div = document.createElement('div');\n",
" const label = document.createElement('label');\n",
" label.textContent = `Downloading \"${filename}\": `;\n",
" div.appendChild(label);\n",
" const progress = document.createElement('progress');\n",
" progress.max = size;\n",
" div.appendChild(progress);\n",
" document.body.appendChild(div);\n",
"\n",
" const buffers = [];\n",
" let downloaded = 0;\n",
"\n",
" const channel = await google.colab.kernel.comms.open(id);\n",
" // Send a message to notify the kernel that we're ready.\n",
" channel.send({})\n",
"\n",
" for await (const message of channel.messages) {\n",
" // Send a message to notify the kernel that we're ready.\n",
" channel.send({})\n",
" if (message.buffers) {\n",
" for (const buffer of message.buffers) {\n",
" buffers.push(buffer);\n",
" downloaded += buffer.byteLength;\n",
" progress.value = downloaded;\n",
" }\n",
" }\n",
" }\n",
" const blob = new Blob(buffers, {type: 'application/binary'});\n",
" const a = document.createElement('a');\n",
" a.href = window.URL.createObjectURL(blob);\n",
" a.download = filename;\n",
" div.appendChild(a);\n",
" a.click();\n",
" div.remove();\n",
" }\n",
" "
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/javascript": [
"download(\"download_2ec6c00c-2fbf-421e-8387-2fbd09fadf47\", \"label_map.pbtxt\", 1091)"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from google.colab import files\n",
"files.download(\"/content/train.record\")\n",
"files.download(\"/content/test.record\")\n",
"files.download(\"/content/dev.record\")\n",
"files.download(\"/content/label_map.pbtxt\")"
]
},
{
"cell_type": "code",
"source": [
"!mv dev.record /content/drive/MyDrive\n",
"!mv test.record /content/drive/MyDrive\n",
"!mv train.record /content/drive/MyDrive\n",
"!mv label_map.pbtxt /content/drive/MyDrive"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "G1aboGiZ7CTd",
"outputId": "a03c3b80-d717-403d-cfb0-461840298065"
},
"execution_count": 26,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"mv: cannot stat 'dev.record': No such file or directory\n",
"mv: cannot stat 'test.record': No such file or directory\n",
"mv: cannot stat 'train.record': No such file or directory\n"
]
}
]
}
],
"metadata": {
"colab": {
"collapsed_sections": [],
"name": "Convert_RICO_for_Object_Detection_based_on_CLAY.ipynb",
"provenance": [],
"include_colab_link": true
},
"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": "3.8.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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