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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os, cv2\n",
"import string\n",
"import os.path as osp\n",
"import numpy as np\n",
"from collections import defaultdict\n",
"from vlmeval.smp import ls, load, dump, download_file, encode_image_file_to_base64, md5, mrlines\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import multiprocessing as mp\n",
"from PIL import Image, ImageFont, ImageDraw\n",
"\n",
"font_URL = 'http://opencompass.openxlab.space/utils/Fonts/timesb.ttf'\n",
"font_file = 'timesb.ttf'\n",
"if not osp.exists(font_file):\n",
" download_file(font_URL)\n",
" \n",
"test_split_URL = 'https://s3-us-east-2.amazonaws.com/prior-datasets/ai2d_test_ids.csv'\n",
"test_split_file = 'ai2d_test_ids.csv'\n",
"if not osp.exists(test_split_file):\n",
" download_file(test_split_URL)\n",
" \n",
"test_ids = set(mrlines(test_split_file))\n",
" \n",
"def proper_font_size(font_file, wh, text, ratio=1):\n",
" font_size = 2\n",
" while True:\n",
" font = ImageFont.truetype(font_file, font_size)\n",
" real_box = font.getbbox(text)\n",
" real_wh = (real_box[2] - real_box[0], real_box[3] - real_box[1])\n",
" if real_wh[0] > wh[0] * ratio or real_wh[1] > wh[1] * ratio:\n",
" break\n",
" font_size += 1\n",
" return font_size\n",
"\n",
"def cover_image(ann_path):\n",
" data = load(ann_path)\n",
" texts = list(data['text'].values())\n",
" raw_img = ann_path.replace('annotations', 'images').replace('.json', '')\n",
" tgt_img = raw_img.replace('images', 'images_abc')\n",
" img = Image.open(raw_img)\n",
" draw = ImageDraw.Draw(img)\n",
" for text in texts:\n",
" st, ed = tuple(text['rectangle'][0]), tuple(text['rectangle'][1])\n",
" T = text['replacementText']\n",
" draw.rectangle((st, ed), fill='white')\n",
" font_size = proper_font_size(font_file, (ed[0] - st[0], ed[1] - st[1]), T, ratio=1)\n",
" font = ImageFont.truetype(font_file, font_size)\n",
" text_box = font.getbbox(T)\n",
" text_wh = (text_box[2] - text_box[0], text_box[3] - text_box[1])\n",
" cx, cy = (st[0] + ed[0]) // 2, st[1]\n",
" stx = cx - text_wh[0] // 2\n",
" sty = cy - text_wh[1] // 2\n",
" draw.text((stx, sty), T, font=font, fill='black')\n",
" img.save(tgt_img) "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Process for no mask images\n",
"test_ids = set(mrlines(test_split_file))\n",
"\n",
"def detect_image_color(image):\n",
" gray_image = image.convert('L')\n",
" mean_brightness = np.mean(np.array(gray_image))\n",
" if mean_brightness < 127:\n",
" return 'white'\n",
" else:\n",
" return 'black'\n",
"\n",
"def cover_image(ann_path):\n",
" data = load(ann_path)\n",
" texts = list(data['text'].values())\n",
" raw_img = ann_path.replace('annotations', 'images').replace('.json', '')\n",
" tgt_img = raw_img.replace('images', 'images_abc')\n",
" img = Image.open(raw_img)\n",
" draw = ImageDraw.Draw(img)\n",
" color = detect_image_color(img)\n",
" font_size = 0\n",
" for text in texts:\n",
" st, ed = tuple(text['rectangle'][0]), tuple(text['rectangle'][1])\n",
" font_size += (ed[1] - st[1])\n",
" if len(texts) != 0:\n",
" font_size /= len(texts)\n",
" else:\n",
" font_size = 2\n",
" for text in texts:\n",
" st, ed = tuple(text['rectangle'][0]), tuple(text['rectangle'][1])\n",
" T = text['replacementText']\n",
" for i in range(2):\n",
" draw.rectangle(\n",
" [(st[0] - i, st[1] - i), (ed[0] + i, ed[1] + i)],\n",
" outline=color\n",
" )\n",
" font = ImageFont.truetype(font_file, font_size)\n",
" text_box = font.getbbox(T)\n",
" text_wh = (text_box[2] - text_box[0], text_box[3] - text_box[1])\n",
" cx, cy = (st[0] + ed[0]) // 2, st[1]\n",
" stx = cx - text_wh[0] // 2\n",
" sty = cy - text_wh[1] * 1.5\n",
" if sty < 0:\n",
" sty = cy + text_wh[1] * 1.3\n",
" draw.text((stx, sty), T, font=font, fill=color)\n",
" img.save(tgt_img) "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"download_file('https://ai2-public-datasets.s3.amazonaws.com/diagrams/ai2d-all.zip')\n",
"os.system('unzip -o ai2d-all.zip')\n",
"\n",
"images = ls('ai2d/images/')\n",
"questions = ls('ai2d/questions/')\n",
"annotations = ls('ai2d/annotations/')\n",
"cates = load('ai2d/categories.json')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"pool = mp.Pool(32)\n",
"pool.map(cover_image, annotations)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def puncproc(inText):\n",
" import re\n",
" outText = inText\n",
" punct = [\n",
" ';', r'/', '[', ']', '\"', '{', '}', '(', ')', '=', '+', '\\\\', '_', '-',\n",
" '>', '<', '@', '`', ',', '?', '!'\n",
" ]\n",
" commaStrip = re.compile('(\\d)(,)(\\d)') # noqa: W605\n",
" periodStrip = re.compile('(?!<=\\d)(\\.)(?!\\d)') # noqa: W605\n",
" for p in punct:\n",
" if (p + ' ' in inText or ' ' + p in inText) or (re.search(commaStrip, inText) is not None):\n",
" outText = outText.replace(p, '')\n",
" else:\n",
" outText = outText.replace(p, ' ')\n",
" outText = periodStrip.sub('', outText, re.UNICODE)\n",
" return outText\n",
"\n",
"def check_choices(line):\n",
" def ischar(s):\n",
" s = str(s)\n",
" if s in ['{}', 'Both', 'None of above']:\n",
" return True\n",
" elif s.startswith('Stage ') and ischar(s[6:]):\n",
" return True\n",
" elif ' and ' in s and np.all([ischar(x) for x in s.split(' and ')]):\n",
" return True\n",
" elif len(s) <= 2:\n",
" return True\n",
" elif len(puncproc(s).split()) > 1:\n",
" return np.all([ischar(x) for x in puncproc(s).split()])\n",
" return False\n",
" n_char = sum([ischar(line[x]) for x in 'ABCD'])\n",
" return n_char >= 3\n",
"\n",
"def check_question(question):\n",
" words = puncproc(question).split()\n",
" for ch in string.ascii_lowercase + string.ascii_uppercase:\n",
" if ch in words:\n",
" return True\n",
" return False\n",
"\n",
"def is_abc(abc, choices, question):\n",
" if abc == 0:\n",
" return False\n",
" if check_choices(choices):\n",
" return True\n",
" if check_question(question):\n",
" return True\n",
" return False"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"data_all = defaultdict(list)\n",
"for qfile in questions:\n",
" data = load(qfile)\n",
" idx = data['imageName'].split('.')[0]\n",
" if idx not in test_ids:\n",
" continue\n",
" image_pth = qfile.replace('questions', 'images').replace('.json', '')\n",
" cate = cates[image_pth.split('/')[-1]]\n",
" for q, qmeta in data['questions'].items():\n",
" assert '.png-' in qmeta['questionId']\n",
" main, sub = qmeta['questionId'].split('.png-')\n",
" idx = int(main) * 100 + int(sub)\n",
" \n",
" answers = qmeta['answerTexts']\n",
" correct = qmeta['correctAnswer']\n",
" \n",
" data_all['index'].append(idx)\n",
" data_all['question'].append(q)\n",
" assert len(answers) == 4\n",
" for c, a in zip('ABCD', answers):\n",
" data_all[c].append(a)\n",
" data_all['answer'].append('ABCD'[qmeta['correctAnswer']])\n",
" data_all['category'].append(cate)\n",
" data_all['abcLabel'].append(qmeta['abcLabel'])\n",
" abc = is_abc(qmeta['abcLabel'], {x: data_all[x][-1] for x in 'ABCD'}, q)\n",
" # if qmeta['abcLabel'] and not abc:\n",
" # print(qmeta['abcLabel'], {x: data_all[x][-1] for x in 'ABCD'}, q)\n",
" data_all['image_path'].append(image_pth.replace('images', 'images_abc') if abc else image_pth)\n",
"data = pd.DataFrame(data_all)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"images = []\n",
"image_seen = {}\n",
"for idx, pth in zip(data['index'], data['image_path']):\n",
" images.append(encode_image_file_to_base64(pth))\n",
"\n",
"data['image'] = images\n",
"dump(data, 'AI2D_TEST.tsv')\n",
"print(md5('AI2D_TEST.tsv'))"
]
}
],
"metadata": {
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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