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import os |
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import os.path as osp |
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import json |
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import random |
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import transformers |
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from tqdm import tqdm |
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from multiprocessing import Pool |
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from functools import partial |
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from copy import deepcopy |
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from data_class import domNode, nodeBbox |
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MAX_TOTAL_LEN = 800 |
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MAX_TOKEN_LEN = 50 |
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MIN_TOKEN_LEN = 0 |
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MAX_IoU = 0 |
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NODE_SEPARATOR_TOKEN = '<node_separator>' |
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TEXT2BBOX_TEMPLATE = [ |
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"Where is the given content?", |
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] |
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BBOX2TEXT_TEMPLATE = [ |
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"Can you give me a description of the region in image?", |
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] |
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class NodewithOCR(domNode): |
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def __init__(self, id, info, children, father=None, tokenizer=None, img_w=None, img_h=None, task=""): |
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super().__init__(id, info, children, father, tokenizer, img_w=img_w, img_h=img_h, task="") |
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self.struct_text: str = self.get_struct_text() |
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def get_struct_text(self) -> str: |
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if self.is_leaf(): |
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struct_text = self.info.text.strip() |
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if self.is_img(): |
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if not struct_text: |
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return "" |
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if self.info.type == 'img[svg]': |
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struct_text = '[svg]' + struct_text |
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else: |
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struct_text = '[image]' + struct_text |
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return struct_text |
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children_text = [] |
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for child in self.children: |
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child_text = child.struct_text |
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if child_text: |
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children_text.append(child.struct_text) |
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return NODE_SEPARATOR_TOKEN.join(children_text) |
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def get_context_text(self, root_text: str) -> str: |
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self.context_text: str = "" |
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cur_node = self |
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for _ in range(3): |
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if root_text.count(cur_node.struct_text) == 1: |
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func = '' |
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if self.info.func == 'type': |
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func += '(input box)' |
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elif self.info.func == 'click': |
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func += '(clickable)' |
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elif self.info.func == 'text': |
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func += '(pure text)' |
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if cur_node == self: |
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context = "" |
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else: |
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context = f'(context: "{cur_node.struct_text}")' |
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self.context_text = self.struct_text + func + context |
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return |
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if not cur_node.father: |
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return |
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cur_node = cur_node.father |
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def bbox2str(self) -> str: |
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x1_rel = round(max(0, (self.info.bbox.x1 / IMG_W)) * 1000) |
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y1_rel = round(max(0, (self.info.bbox.y1 / IMG_H)) * 1000) |
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x2_rel = round(min(0.999, (self.info.bbox.x2 / IMG_W)) * 1000) |
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y2_rel = round(min(0.999, (self.info.bbox.y2 / IMG_H)) * 1000) |
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coords = [x1_rel, y1_rel, x2_rel, y2_rel] |
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bbox_str = ["{:03}".format(coord) for coord in coords] |
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bbox_str = "[" + ", ".join(bbox_str) + "]" |
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return bbox_str |
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def bbox2str_2(self) -> str: |
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x1_rel = round(max(0, (self.info.bbox.x1 / IMG_W)), 2) |
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y1_rel = round(max(0, (self.info.bbox.y1 / IMG_H)), 2) |
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x2_rel = round(min(0.999, (self.info.bbox.x2 / IMG_W)), 2) |
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y2_rel = round(min(0.999, (self.info.bbox.y2 / IMG_H)), 2) |
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coords = [x1_rel, y1_rel, x2_rel, y2_rel] |
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bbox_str = ["{:.2f}".format(coord) for coord in coords] |
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bbox_str = "(" + ",".join(bbox_str) + ")" |
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return bbox_str |
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def bbox2str_ct(self) -> str: |
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cx_rel = round(min(0.999, max(0, (self.info.bbox.cx / IMG_W))) * 1000) |
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cy_rel = round(min(0.999, max(0, (self.info.bbox.cy / IMG_H))) * 1000) |
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w_rel = round(min(0.999, max(0, (self.info.bbox.width / IMG_W))) * 1000) |
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h_rel = round(min(0.999, max(0, (self.info.bbox.height / IMG_H))) * 1000) |
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coords = [cx_rel, cy_rel, w_rel, h_rel] |
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bbox_str = ["{:03}".format(coord) for coord in coords] |
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bbox_str = "[" + ", ".join(bbox_str) + "]" |
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return bbox_str |
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def bbox2str_ct_block(self, block_num_w=2, block_num_h=2) -> str: |
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block_w = IMG_W // block_num_w |
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block_h = IMG_H // block_num_h |
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block_xi = max(0, min(IMG_W - 1, self.info.bbox.cx)) // block_w |
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block_yi = max(0, min(IMG_H - 1, self.info.bbox.cy)) // block_h |
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block_idx = block_yi * block_num_w + block_xi |
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cx_block = self.info.bbox.cx % block_w |
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cy_block = self.info.bbox.cy % block_h |
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cx_rel = round(min(0.999, max(0, (cx_block / block_w))) * 1000) |
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cy_rel = round(min(0.999, max(0, (cy_block / block_h))) * 1000) |
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w_rel = round(min(0.999, max(0, (self.info.bbox.width / block_w))) * 1000) |
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h_rel = round(min(0.999, max(0, (self.info.bbox.height / block_h))) * 1000) |
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coords = [cx_rel, cy_rel, w_rel, h_rel] |
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bbox_str = [str(block_idx)] + ["{:03}".format(coord) for coord in coords] |
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bbox_str = "[" + ", ".join(bbox_str) + "]" |
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return bbox_str |
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def bbox2str_ct_2(self) -> str: |
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cx_rel = round(min(0.999, max(0, (self.info.bbox.cx / IMG_W))), 2) |
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cy_rel = round(min(0.999, max(0, (self.info.bbox.cy / IMG_H))), 2) |
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coords = [cx_rel, cy_rel] |
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bbox_str = ["{:.2f}".format(coord) for coord in coords] |
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bbox_str = "(" + ",".join(bbox_str) + ")" |
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return bbox_str |
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def init_context_text(node: NodewithOCR, root_text: str): |
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node.get_context_text(root_text) |
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for child in node.children: |
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init_context_text(child, root_text) |
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tokenizer = transformers.AutoTokenizer.from_pretrained( |
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"path/to/your/tokenizer", |
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trust_remote_code=True, |
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) |
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tokenizer.add_tokens([NODE_SEPARATOR_TOKEN], special_tokens=True) |
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def get_str_token_len(str_: str) -> int: |
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return len(tokenizer.encode(str_)) |
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def collect_nodes(dom: NodewithOCR, node_list: list[NodewithOCR]) -> list: |
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if dom.is_valid: |
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node_list.append(dom) |
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for child in dom.children: |
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collect_nodes(child, node_list) |
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return node_list |
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def select_node(node, node_text, node_bbox: nodeBbox, selected_node_list: list[domNode], total_len: int) -> tuple[bool, int]: |
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if node.info.func == 'type': |
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if not node.context_text: |
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return False, total_len |
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if not node_text: |
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return False, total_len |
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try: |
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token_len = get_str_token_len(node_text) |
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except: |
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return False, total_len |
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if token_len > MAX_TOKEN_LEN or token_len < MIN_TOKEN_LEN: |
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return False, total_len |
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total_len += token_len + 20 |
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if selected_node_list: |
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selected_bbox_list = [_node.info.bbox for _node in selected_node_list] |
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max_IoUs = node_bbox.get_max_IoU(selected_bbox_list) |
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if max_IoUs > MAX_IoU: |
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return False, total_len |
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return True, total_len |
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def get_bbox_text(node_list: list[NodewithOCR], context=False, is_point=True, is_seeclick=False): |
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input_node_list = [_node for _node in node_list if _node.info.func == 'type'] |
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pointer_node_list = [_node for _node in node_list if _node.info.pointer] |
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click_node_list = [_node for _node in node_list if not _node.info.pointer and _node.info.func == 'click'] |
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text_node_list = [_node for _node in node_list if _node.info.func == 'text'] |
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input_node_list_copy = deepcopy(input_node_list) |
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pointer_node_list_copy = deepcopy(pointer_node_list) |
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click_node_list_copy = deepcopy(click_node_list) |
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text_node_list_copy = deepcopy(text_node_list) |
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random.shuffle(input_node_list_copy) |
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random.shuffle(pointer_node_list_copy) |
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random.shuffle(click_node_list_copy) |
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random.shuffle(text_node_list_copy) |
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node_list_copy = input_node_list_copy + pointer_node_list_copy + click_node_list_copy + text_node_list_copy |
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total_len = 0 |
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selected_node_list = [] |
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for node in node_list_copy: |
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node_text = node.context_text if context else node.struct_text |
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keep, total_len = select_node(node, node_text, node.info.bbox, selected_node_list, total_len) |
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if total_len > MAX_TOTAL_LEN: |
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break |
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if keep: |
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selected_node_list.append(node) |
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random.shuffle(selected_node_list) |
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bbox_str_list = [] |
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text_str_list = [] |
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for i, node in enumerate(selected_node_list): |
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node_text = node.context_text if context else node.struct_text |
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if not is_seeclick: |
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bbox_str_list.append(f"{i+1}. {node.bbox2str_ct_block(block_num_w=BLOCK_NUM_W, block_num_h=BLOCK_NUM_H)}") |
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else: |
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if is_point: |
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bbox_str_list.append(f"{i+1}. {node.bbox2str_ct_2()}") |
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else: |
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bbox_str_list.append(f"{i+1}. {node.bbox2str_2()}") |
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text_str_list.append(f"{i+1}. {node_text}") |
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bbox_str = '\n'.join(bbox_str_list) |
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text_str = '\n'.join(text_str_list) |
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return bbox_str, text_str |
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def make_bbox2text(node_list: list[NodewithOCR], img_path: str, is_point=True, is_seeclick=False): |
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bbox_str, text_str = get_bbox_text(node_list, context=False, is_point=is_point, is_seeclick=is_seeclick) |
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if not bbox_str or not text_str: |
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return |
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prompt = bbox_str + '\n' + random.choice(BBOX2TEXT_TEMPLATE) |
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return { |
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"image": img_path, |
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"conversations": [ |
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{ |
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"from": "human", |
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"value": f"<image>\n{prompt}" |
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}, |
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{ |
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"from": "gpt", |
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"value": text_str |
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} |
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] |
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} |
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def make_text2bbox(node_list: list[NodewithOCR], img_path: str, is_point=True, is_seeclick=False): |
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bbox_str, text_str = get_bbox_text(node_list, context=True, is_point=is_point, is_seeclick=is_seeclick) |
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if not bbox_str or not text_str: |
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return |
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prompt = text_str + '\n' + random.choice(TEXT2BBOX_TEMPLATE) + '(bbox: [block_index, cx, cy, w, h])' |
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return { |
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"image": img_path, |
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"conversations": [ |
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{ |
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"from": "human", |
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"value": f"<image>\n{prompt}" |
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}, |
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{ |
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"from": "gpt", |
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"value": bbox_str |
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} |
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] |
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} |
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def write_ann(ann_list, ann_path, mode='w', save_ratio=1.0): |
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ann_dir = osp.dirname(ann_path) |
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if not osp.exists(ann_dir): |
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os.mkdir(ann_dir) |
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if save_ratio == 1.0 or random.random() < save_ratio: |
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with open(ann_path, mode, encoding='utf-8') as f: |
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for i in range(len(ann_list)): |
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ann_list[i]['id'] = i |
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ann_str = json.dumps(ann_list[i], ensure_ascii=False) |
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f.write(ann_str + '\n') |
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def single_proc(mode, data_dir, language, sub_dir): |
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sub_path = osp.join(data_dir, sub_dir) |
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screen_shot_dir = osp.join(sub_path, 'screenshot') |
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dom_sub_dir = 'dom_svg' if language == 'zh' else 'dom_svg_en' |
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dom_dir = osp.join(sub_path, dom_sub_dir) |
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html_dir = osp.join(sub_path, 'html') |
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html_path = osp.join(html_dir, 'html_0.html') |
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if not osp.exists(html_path): |
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return [] |
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html_content = open(html_path).readline().strip() |
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if html_content in ['https://www.qq.com/', 'https://music.163.com/']: |
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return [] |
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all_annotations = [] |
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for img_file in os.listdir(screen_shot_dir): |
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if 'tmp' in img_file: |
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continue |
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file_ids = img_file.split('.')[0].split('_')[1] |
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json_path = osp.join(dom_dir, f'dom_{file_ids}.json') |
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img_path = osp.join(screen_shot_dir, img_file) |
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if not osp.exists(json_path): |
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continue |
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dom_data = json.load(open(json_path)) |
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try: |
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dom_node = NodewithOCR(tokenizer=tokenizer, img_w=IMG_W, img_h=IMG_H, task=mode, **dom_data) |
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except ValueError as e: |
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print(f"Json data error: {e}\n{json_path}") |
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continue |
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init_context_text(dom_node, dom_node.struct_text) |
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node_list = collect_nodes(dom_node, []) |
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if mode == 'bbox2text': |
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ann = make_bbox2text(node_list, img_path, is_seeclick=False, is_point=False) |
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elif mode == 'text2bbox': |
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ann = make_text2bbox(node_list, img_path, is_seeclick=False, is_point=False) |
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elif mode == 'text2bbox_seeclick': |
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ann = make_text2bbox(node_list, img_path, is_seeclick=True, is_point=False) |
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elif mode == 'bbox2text_seeclick': |
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ann = make_bbox2text(node_list, img_path, is_seeclick=True, is_point=False) |
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elif mode == 'text2point_seeclick': |
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ann = make_text2bbox(node_list, img_path, is_seeclick=True, is_point=True) |
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elif mode == 'point2text_seeclick': |
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ann = make_bbox2text(node_list, img_path, is_seeclick=True, is_point=True) |
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else: |
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assert 0 |
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if ann is not None: |
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all_annotations.append(ann) |
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return all_annotations |
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def main_multi(data_dir, dst_path, mode, language='zh', save_ratio=1.0): |
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pool = Pool(processes=16) |
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sub_dir_list = os.listdir(data_dir) |
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single_proc_partial = partial(single_proc, mode, data_dir, language) |
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for res in tqdm(pool.imap_unordered(single_proc_partial, sub_dir_list), total=len(sub_dir_list)): |
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write_ann(res, dst_path, mode='a', save_ratio=save_ratio) |
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if __name__ == '__main__': |
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mode = 'text2bbox' |
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BLOCK_NUM_W = 2 |
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BLOCK_NUM_H = 2 |
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IMG_W = 1120 |
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IMG_H = 1120 |
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data_dir = 'data_20240617' |
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dst_path = f'xxx.jsonl' |
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BLOCK_NUM_W = 3 |
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BLOCK_NUM_H = 2 |
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IMG_W = 1920 |
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IMG_H = 1080 |
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data_dir = 'data_20240624' |
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dst_path = f'xxx.jsonl' |
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main_multi(data_dir, dst_path, mode, language='en') |
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