331 lines
9.6 KiB
Python
331 lines
9.6 KiB
Python
import os
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import fitz
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import img2pdf
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import io
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import re
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from tqdm import tqdm
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import torch
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from concurrent.futures import ThreadPoolExecutor
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if torch.version.cuda == '11.8':
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os.environ["TRITON_PTXAS_PATH"] = "/usr/local/cuda-11.8/bin/ptxas"
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os.environ['VLLM_USE_V1'] = '0'
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os.environ["CUDA_VISIBLE_DEVICES"] = '0'
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from config import MODEL_PATH, INPUT_PATH, OUTPUT_PATH, PROMPT, SKIP_REPEAT, MAX_CONCURRENCY, NUM_WORKERS, CROP_MODE
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from PIL import Image, ImageDraw, ImageFont
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import numpy as np
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from deepseek_ocr import DeepseekOCRForCausalLM
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from vllm.model_executor.models.registry import ModelRegistry
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from vllm import LLM, SamplingParams
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from process.ngram_norepeat import NoRepeatNGramLogitsProcessor
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from process.image_process import DeepseekOCRProcessor
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ModelRegistry.register_model("DeepseekOCRForCausalLM", DeepseekOCRForCausalLM)
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llm = LLM(
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model=MODEL_PATH,
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hf_overrides={"architectures": ["DeepseekOCRForCausalLM"]},
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block_size=256,
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enforce_eager=False,
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trust_remote_code=True,
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max_model_len=8192,
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swap_space=0,
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max_num_seqs=MAX_CONCURRENCY,
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tensor_parallel_size=1,
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gpu_memory_utilization=0.9,
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disable_mm_preprocessor_cache=True
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)
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logits_processors = [NoRepeatNGramLogitsProcessor(ngram_size=20, window_size=50, whitelist_token_ids= {128821, 128822})] #window for fast；whitelist_token_ids: <td>,</td>
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sampling_params = SamplingParams(
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temperature=0.0,
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max_tokens=8192,
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logits_processors=logits_processors,
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skip_special_tokens=False,
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include_stop_str_in_output=True,
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)
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class Colors:
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RED = '\033[31m'
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GREEN = '\033[32m'
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YELLOW = '\033[33m'
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BLUE = '\033[34m'
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RESET = '\033[0m'
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def pdf_to_images_high_quality(pdf_path, dpi=144, image_format="PNG"):
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"""
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pdf2images
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"""
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images = []
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pdf_document = fitz.open(pdf_path)
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zoom = dpi / 72.0
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matrix = fitz.Matrix(zoom, zoom)
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for page_num in range(pdf_document.page_count):
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page = pdf_document[page_num]
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pixmap = page.get_pixmap(matrix=matrix, alpha=False)
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Image.MAX_IMAGE_PIXELS = None
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if image_format.upper() == "PNG":
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img_data = pixmap.tobytes("png")
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img = Image.open(io.BytesIO(img_data))
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else:
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img_data = pixmap.tobytes("png")
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img = Image.open(io.BytesIO(img_data))
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if img.mode in ('RGBA', 'LA'):
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background = Image.new('RGB', img.size, (255, 255, 255))
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background.paste(img, mask=img.split()[-1] if img.mode == 'RGBA' else None)
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img = background
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images.append(img)
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pdf_document.close()
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return images
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def pil_to_pdf_img2pdf(pil_images, output_path):
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if not pil_images:
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return
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image_bytes_list = []
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for img in pil_images:
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if img.mode != 'RGB':
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img = img.convert('RGB')
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img_buffer = io.BytesIO()
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img.save(img_buffer, format='JPEG', quality=95)
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img_bytes = img_buffer.getvalue()
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image_bytes_list.append(img_bytes)
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try:
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pdf_bytes = img2pdf.convert(image_bytes_list)
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with open(output_path, "wb") as f:
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f.write(pdf_bytes)
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except Exception as e:
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print(f"error: {e}")
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def re_match(text):
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pattern = r'(<\|ref\|>(.*?)<\|/ref\|><\|det\|>(.*?)<\|/det\|>)'
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matches = re.findall(pattern, text, re.DOTALL)
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mathes_image = []
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mathes_other = []
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for a_match in matches:
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if '<|ref|>image<|/ref|>' in a_match[0]:
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mathes_image.append(a_match[0])
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else:
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mathes_other.append(a_match[0])
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return matches, mathes_image, mathes_other
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def extract_coordinates_and_label(ref_text, image_width, image_height):
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try:
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label_type = ref_text[1]
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cor_list = eval(ref_text[2])
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except Exception as e:
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print(e)
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return None
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return (label_type, cor_list)
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def draw_bounding_boxes(image, refs, jdx):
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image_width, image_height = image.size
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img_draw = image.copy()
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draw = ImageDraw.Draw(img_draw)
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overlay = Image.new('RGBA', img_draw.size, (0, 0, 0, 0))
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draw2 = ImageDraw.Draw(overlay)
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# except IOError:
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font = ImageFont.load_default()
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img_idx = 0
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for i, ref in enumerate(refs):
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try:
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result = extract_coordinates_and_label(ref, image_width, image_height)
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if result:
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label_type, points_list = result
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color = (np.random.randint(0, 200), np.random.randint(0, 200), np.random.randint(0, 255))
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color_a = color + (20, )
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for points in points_list:
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x1, y1, x2, y2 = points
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x1 = int(x1 / 999 * image_width)
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y1 = int(y1 / 999 * image_height)
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x2 = int(x2 / 999 * image_width)
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y2 = int(y2 / 999 * image_height)
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if label_type == 'image':
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try:
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cropped = image.crop((x1, y1, x2, y2))
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cropped.save(f"{OUTPUT_PATH}/images/{jdx}_{img_idx}.jpg")
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except Exception as e:
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print(e)
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pass
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img_idx += 1
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try:
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if label_type == 'title':
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draw.rectangle([x1, y1, x2, y2], outline=color, width=4)
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draw2.rectangle([x1, y1, x2, y2], fill=color_a, outline=(0, 0, 0, 0), width=1)
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else:
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draw.rectangle([x1, y1, x2, y2], outline=color, width=2)
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draw2.rectangle([x1, y1, x2, y2], fill=color_a, outline=(0, 0, 0, 0), width=1)
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text_x = x1
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text_y = max(0, y1 - 15)
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text_bbox = draw.textbbox((0, 0), label_type, font=font)
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text_width = text_bbox[2] - text_bbox[0]
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text_height = text_bbox[3] - text_bbox[1]
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draw.rectangle([text_x, text_y, text_x + text_width, text_y + text_height],
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fill=(255, 255, 255, 30))
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draw.text((text_x, text_y), label_type, font=font, fill=color)
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except:
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pass
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except:
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continue
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img_draw.paste(overlay, (0, 0), overlay)
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return img_draw
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def process_image_with_refs(image, ref_texts, jdx):
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result_image = draw_bounding_boxes(image, ref_texts, jdx)
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return result_image
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def process_single_image(image):
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"""single image"""
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prompt_in = prompt
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cache_item = {
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"prompt": prompt_in,
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"multi_modal_data": {"image": DeepseekOCRProcessor().tokenize_with_images(images = [image], bos=True, eos=True, cropping=CROP_MODE)},
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}
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return cache_item
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if __name__ == "__main__":
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os.makedirs(OUTPUT_PATH, exist_ok=True)
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os.makedirs(f'{OUTPUT_PATH}/images', exist_ok=True)
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print(f'{Colors.RED}PDF loading .....{Colors.RESET}')
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images = pdf_to_images_high_quality(INPUT_PATH)
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prompt = PROMPT
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# batch_inputs = []
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with ThreadPoolExecutor(max_workers=NUM_WORKERS) as executor:
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batch_inputs = list(tqdm(
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executor.map(process_single_image, images),
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total=len(images),
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desc="Pre-processed images"
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))
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# for image in tqdm(images):
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# prompt_in = prompt
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# cache_list = [
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# {
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# "prompt": prompt_in,
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# "multi_modal_data": {"image": DeepseekOCRProcessor().tokenize_with_images(images = [image], bos=True, eos=True, cropping=CROP_MODE)},
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# }
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# ]
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# batch_inputs.extend(cache_list)
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outputs_list = llm.generate(
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batch_inputs,
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sampling_params=sampling_params
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)
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output_path = OUTPUT_PATH
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os.makedirs(output_path, exist_ok=True)
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mmd_det_path = output_path + '/' + INPUT_PATH.split('/')[-1].replace('.pdf', '_det.mmd')
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mmd_path = output_path + '/' + INPUT_PATH.split('/')[-1].replace('pdf', 'mmd')
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pdf_out_path = output_path + '/' + INPUT_PATH.split('/')[-1].replace('.pdf', '_layouts.pdf')
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contents_det = ''
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contents = ''
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draw_images = []
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jdx = 0
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for output, img in zip(outputs_list, images):
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content = output.outputs[0].text
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if '<｜end▁of▁sentence｜>' in content: # repeat no eos
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content = content.replace('<｜end▁of▁sentence｜>', '')
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else:
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if SKIP_REPEAT:
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continue
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page_num = f'\n<--- Page Split --->'
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contents_det += content + f'\n{page_num}\n'
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image_draw = img.copy()
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matches_ref, matches_images, mathes_other = re_match(content)
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# print(matches_ref)
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result_image = process_image_with_refs(image_draw, matches_ref, jdx)
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draw_images.append(result_image)
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for idx, a_match_image in enumerate(matches_images):
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content = content.replace(a_match_image, f' + '_' + str(idx) + '.jpg)\n')
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for idx, a_match_other in enumerate(mathes_other):
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content = content.replace(a_match_other, '').replace('\\coloneqq', ':=').replace('\\eqqcolon', '=:').replace('\n\n\n\n', '\n\n').replace('\n\n\n', '\n\n')
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contents += content + f'\n{page_num}\n'
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jdx += 1
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with open(mmd_det_path, 'w', encoding='utf-8') as afile:
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afile.write(contents_det)
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with open(mmd_path, 'w', encoding='utf-8') as afile:
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afile.write(contents)
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pil_to_pdf_img2pdf(draw_images, pdf_out_path)
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