Buckets:
| { | |
| "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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