Initial commit
This commit is contained in:
Ucas-Haoranwei
330
DeepSeek-OCR-master/DeepSeek-OCR-vllm/run_dpsk_ocr_pdf.py
Normal file
330
DeepSeek-OCR-master/DeepSeek-OCR-vllm/run_dpsk_ocr_pdf.py
Normal file
@ -0,0 +1,330 @@
|
||||
import os
|
||||
import fitz
|
||||
import img2pdf
|
||||
import io
|
||||
import re
|
||||
from tqdm import tqdm
|
||||
import torch
|
||||
from concurrent.futures import ThreadPoolExecutor
|
||||
|
||||
|
||||
if torch.version.cuda == '11.8':
|
||||
os.environ["TRITON_PTXAS_PATH"] = "/usr/local/cuda-11.8/bin/ptxas"
|
||||
os.environ['VLLM_USE_V1'] = '0'
|
||||
os.environ["CUDA_VISIBLE_DEVICES"] = '0'
|
||||
|
||||
|
||||
from config import MODEL_PATH, INPUT_PATH, OUTPUT_PATH, PROMPT, SKIP_REPEAT, MAX_CONCURRENCY, NUM_WORKERS, CROP_MODE
|
||||
|
||||
from PIL import Image, ImageDraw, ImageFont
|
||||
import numpy as np
|
||||
from deepseek_ocr import DeepseekOCRForCausalLM
|
||||
|
||||
from vllm.model_executor.models.registry import ModelRegistry
|
||||
|
||||
from vllm import LLM, SamplingParams
|
||||
from process.ngram_norepeat import NoRepeatNGramLogitsProcessor
|
||||
from process.image_process import DeepseekOCRProcessor
|
||||
|
||||
ModelRegistry.register_model("DeepseekOCRForCausalLM", DeepseekOCRForCausalLM)
|
||||
|
||||
|
||||
llm = LLM(
|
||||
model=MODEL_PATH,
|
||||
hf_overrides={"architectures": ["DeepseekOCRForCausalLM"]},
|
||||
block_size=256,
|
||||
enforce_eager=False,
|
||||
trust_remote_code=True,
|
||||
max_model_len=8192,
|
||||
swap_space=0,
|
||||
max_num_seqs=MAX_CONCURRENCY,
|
||||
tensor_parallel_size=1,
|
||||
gpu_memory_utilization=0.9,
|
||||
disable_mm_preprocessor_cache=True
|
||||
)
|
||||
|
||||
logits_processors = [NoRepeatNGramLogitsProcessor(ngram_size=20, window_size=50, whitelist_token_ids= {128821, 128822})] #window for fast；whitelist_token_ids: <td>,</td>
|
||||
|
||||
sampling_params = SamplingParams(
|
||||
temperature=0.0,
|
||||
max_tokens=8192,
|
||||
logits_processors=logits_processors,
|
||||
skip_special_tokens=False,
|
||||
include_stop_str_in_output=True,
|
||||
)
|
||||
|
||||
|
||||
class Colors:
|
||||
RED = '\033[31m'
|
||||
GREEN = '\033[32m'
|
||||
YELLOW = '\033[33m'
|
||||
BLUE = '\033[34m'
|
||||
RESET = '\033[0m'
|
||||
|
||||
def pdf_to_images_high_quality(pdf_path, dpi=144, image_format="PNG"):
|
||||
"""
|
||||
pdf2images
|
||||
"""
|
||||
images = []
|
||||
|
||||
pdf_document = fitz.open(pdf_path)
|
||||
|
||||
zoom = dpi / 72.0
|
||||
matrix = fitz.Matrix(zoom, zoom)
|
||||
|
||||
for page_num in range(pdf_document.page_count):
|
||||
page = pdf_document[page_num]
|
||||
|
||||
pixmap = page.get_pixmap(matrix=matrix, alpha=False)
|
||||
Image.MAX_IMAGE_PIXELS = None
|
||||
|
||||
if image_format.upper() == "PNG":
|
||||
img_data = pixmap.tobytes("png")
|
||||
img = Image.open(io.BytesIO(img_data))
|
||||
else:
|
||||
img_data = pixmap.tobytes("png")
|
||||
img = Image.open(io.BytesIO(img_data))
|
||||
if img.mode in ('RGBA', 'LA'):
|
||||
background = Image.new('RGB', img.size, (255, 255, 255))
|
||||
background.paste(img, mask=img.split()[-1] if img.mode == 'RGBA' else None)
|
||||
img = background
|
||||
|
||||
images.append(img)
|
||||
|
||||
pdf_document.close()
|
||||
return images
|
||||
|
||||
def pil_to_pdf_img2pdf(pil_images, output_path):
|
||||
|
||||
if not pil_images:
|
||||
return
|
||||
|
||||
image_bytes_list = []
|
||||
|
||||
for img in pil_images:
|
||||
if img.mode != 'RGB':
|
||||
img = img.convert('RGB')
|
||||
|
||||
img_buffer = io.BytesIO()
|
||||
img.save(img_buffer, format='JPEG', quality=95)
|
||||
img_bytes = img_buffer.getvalue()
|
||||
image_bytes_list.append(img_bytes)
|
||||
|
||||
try:
|
||||
pdf_bytes = img2pdf.convert(image_bytes_list)
|
||||
with open(output_path, "wb") as f:
|
||||
f.write(pdf_bytes)
|
||||
|
||||
except Exception as e:
|
||||
print(f"error: {e}")
|
||||
|
||||
|
||||
|
||||
def re_match(text):
|
||||
pattern = r'(<\|ref\|>(.*?)<\|/ref\|><\|det\|>(.*?)<\|/det\|>)'
|
||||
matches = re.findall(pattern, text, re.DOTALL)
|
||||
|
||||
|
||||
mathes_image = []
|
||||
mathes_other = []
|
||||
for a_match in matches:
|
||||
if '<|ref|>image<|/ref|>' in a_match[0]:
|
||||
mathes_image.append(a_match[0])
|
||||
else:
|
||||
mathes_other.append(a_match[0])
|
||||
return matches, mathes_image, mathes_other
|
||||
|
||||
|
||||
def extract_coordinates_and_label(ref_text, image_width, image_height):
|
||||
|
||||
|
||||
try:
|
||||
label_type = ref_text[1]
|
||||
cor_list = eval(ref_text[2])
|
||||
except Exception as e:
|
||||
print(e)
|
||||
return None
|
||||
|
||||
return (label_type, cor_list)
|
||||
|
||||
|
||||
def draw_bounding_boxes(image, refs, jdx):
|
||||
|
||||
image_width, image_height = image.size
|
||||
img_draw = image.copy()
|
||||
draw = ImageDraw.Draw(img_draw)
|
||||
|
||||
overlay = Image.new('RGBA', img_draw.size, (0, 0, 0, 0))
|
||||
draw2 = ImageDraw.Draw(overlay)
|
||||
|
||||
# except IOError:
|
||||
font = ImageFont.load_default()
|
||||
|
||||
img_idx = 0
|
||||
|
||||
for i, ref in enumerate(refs):
|
||||
try:
|
||||
result = extract_coordinates_and_label(ref, image_width, image_height)
|
||||
if result:
|
||||
label_type, points_list = result
|
||||
|
||||
color = (np.random.randint(0, 200), np.random.randint(0, 200), np.random.randint(0, 255))
|
||||
|
||||
color_a = color + (20, )
|
||||
for points in points_list:
|
||||
x1, y1, x2, y2 = points
|
||||
|
||||
x1 = int(x1 / 999 * image_width)
|
||||
y1 = int(y1 / 999 * image_height)
|
||||
|
||||
x2 = int(x2 / 999 * image_width)
|
||||
y2 = int(y2 / 999 * image_height)
|
||||
|
||||
if label_type == 'image':
|
||||
try:
|
||||
cropped = image.crop((x1, y1, x2, y2))
|
||||
cropped.save(f"{OUTPUT_PATH}/images/{jdx}_{img_idx}.jpg")
|
||||
except Exception as e:
|
||||
print(e)
|
||||
pass
|
||||
img_idx += 1
|
||||
|
||||
try:
|
||||
if label_type == 'title':
|
||||
draw.rectangle([x1, y1, x2, y2], outline=color, width=4)
|
||||
draw2.rectangle([x1, y1, x2, y2], fill=color_a, outline=(0, 0, 0, 0), width=1)
|
||||
else:
|
||||
draw.rectangle([x1, y1, x2, y2], outline=color, width=2)
|
||||
draw2.rectangle([x1, y1, x2, y2], fill=color_a, outline=(0, 0, 0, 0), width=1)
|
||||
|
||||
text_x = x1
|
||||
text_y = max(0, y1 - 15)
|
||||
|
||||
text_bbox = draw.textbbox((0, 0), label_type, font=font)
|
||||
text_width = text_bbox[2] - text_bbox[0]
|
||||
text_height = text_bbox[3] - text_bbox[1]
|
||||
draw.rectangle([text_x, text_y, text_x + text_width, text_y + text_height],
|
||||
fill=(255, 255, 255, 30))
|
||||
|
||||
draw.text((text_x, text_y), label_type, font=font, fill=color)
|
||||
except:
|
||||
pass
|
||||
except:
|
||||
continue
|
||||
img_draw.paste(overlay, (0, 0), overlay)
|
||||
return img_draw
|
||||
|
||||
|
||||
def process_image_with_refs(image, ref_texts, jdx):
|
||||
result_image = draw_bounding_boxes(image, ref_texts, jdx)
|
||||
return result_image
|
||||
|
||||
|
||||
def process_single_image(image):
|
||||
"""single image"""
|
||||
prompt_in = prompt
|
||||
cache_item = {
|
||||
"prompt": prompt_in,
|
||||
"multi_modal_data": {"image": DeepseekOCRProcessor().tokenize_with_images(images = [image], bos=True, eos=True, cropping=CROP_MODE)},
|
||||
}
|
||||
return cache_item
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
os.makedirs(OUTPUT_PATH, exist_ok=True)
|
||||
os.makedirs(f'{OUTPUT_PATH}/images', exist_ok=True)
|
||||
|
||||
print(f'{Colors.RED}PDF loading .....{Colors.RESET}')
|
||||
|
||||
|
||||
images = pdf_to_images_high_quality(INPUT_PATH)
|
||||
|
||||
|
||||
prompt = PROMPT
|
||||
|
||||
# batch_inputs = []
|
||||
|
||||
with ThreadPoolExecutor(max_workers=NUM_WORKERS) as executor:
|
||||
batch_inputs = list(tqdm(
|
||||
executor.map(process_single_image, images),
|
||||
total=len(images),
|
||||
desc="Pre-processed images"
|
||||
))
|
||||
|
||||
|
||||
# for image in tqdm(images):
|
||||
|
||||
# prompt_in = prompt
|
||||
# cache_list = [
|
||||
# {
|
||||
# "prompt": prompt_in,
|
||||
# "multi_modal_data": {"image": DeepseekOCRProcessor().tokenize_with_images(images = [image], bos=True, eos=True, cropping=CROP_MODE)},
|
||||
# }
|
||||
# ]
|
||||
# batch_inputs.extend(cache_list)
|
||||
|
||||
|
||||
outputs_list = llm.generate(
|
||||
batch_inputs,
|
||||
sampling_params=sampling_params
|
||||
)
|
||||
|
||||
|
||||
output_path = OUTPUT_PATH
|
||||
|
||||
os.makedirs(output_path, exist_ok=True)
|
||||
|
||||
|
||||
mmd_det_path = output_path + '/' + INPUT_PATH.split('/')[-1].replace('.pdf', '_det.mmd')
|
||||
mmd_path = output_path + '/' + INPUT_PATH.split('/')[-1].replace('pdf', 'mmd')
|
||||
pdf_out_path = output_path + '/' + INPUT_PATH.split('/')[-1].replace('.pdf', '_layouts.pdf')
|
||||
contents_det = ''
|
||||
contents = ''
|
||||
draw_images = []
|
||||
jdx = 0
|
||||
for output, img in zip(outputs_list, images):
|
||||
content = output.outputs[0].text
|
||||
|
||||
if '<｜end▁of▁sentence｜>' in content: # repeat no eos
|
||||
content = content.replace('<｜end▁of▁sentence｜>', '')
|
||||
else:
|
||||
if SKIP_REPEAT:
|
||||
continue
|
||||
|
||||
|
||||
page_num = f'\n<--- Page Split --->'
|
||||
|
||||
contents_det += content + f'\n{page_num}\n'
|
||||
|
||||
image_draw = img.copy()
|
||||
|
||||
matches_ref, matches_images, mathes_other = re_match(content)
|
||||
# print(matches_ref)
|
||||
result_image = process_image_with_refs(image_draw, matches_ref, jdx)
|
||||
|
||||
|
||||
draw_images.append(result_image)
|
||||
|
||||
|
||||
for idx, a_match_image in enumerate(matches_images):
|
||||
content = content.replace(a_match_image, f' + '_' + str(idx) + '.jpg)\n')
|
||||
|
||||
for idx, a_match_other in enumerate(mathes_other):
|
||||
content = content.replace(a_match_other, '').replace('\\coloneqq', ':=').replace('\\eqqcolon', '=:').replace('\n\n\n\n', '\n\n').replace('\n\n\n', '\n\n')
|
||||
|
||||
|
||||
contents += content + f'\n{page_num}\n'
|
||||
|
||||
|
||||
jdx += 1
|
||||
|
||||
with open(mmd_det_path, 'w', encoding='utf-8') as afile:
|
||||
afile.write(contents_det)
|
||||
|
||||
with open(mmd_path, 'w', encoding='utf-8') as afile:
|
||||
afile.write(contents)
|
||||
|
||||
|
||||
pil_to_pdf_img2pdf(draw_images, pdf_out_path)
|
||||
|
||||
Reference in New Issue
Block a user