304 lines
9.5 KiB
Python
304 lines
9.5 KiB
Python
import asyncio
|
|
import re
|
|
import os
|
|
|
|
import torch
|
|
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 vllm import AsyncLLMEngine, SamplingParams
|
|
from vllm.engine.arg_utils import AsyncEngineArgs
|
|
from vllm.model_executor.models.registry import ModelRegistry
|
|
import time
|
|
from deepseek_ocr import DeepseekOCRForCausalLM
|
|
from PIL import Image, ImageDraw, ImageFont, ImageOps
|
|
import numpy as np
|
|
from tqdm import tqdm
|
|
from process.ngram_norepeat import NoRepeatNGramLogitsProcessor
|
|
from process.image_process import DeepseekOCRProcessor
|
|
from config import MODEL_PATH, INPUT_PATH, OUTPUT_PATH, PROMPT, CROP_MODE
|
|
|
|
|
|
|
|
ModelRegistry.register_model("DeepseekOCRForCausalLM", DeepseekOCRForCausalLM)
|
|
|
|
def load_image(image_path):
|
|
|
|
try:
|
|
image = Image.open(image_path)
|
|
|
|
corrected_image = ImageOps.exif_transpose(image)
|
|
|
|
return corrected_image
|
|
|
|
except Exception as e:
|
|
print(f"error: {e}")
|
|
try:
|
|
return Image.open(image_path)
|
|
except:
|
|
return None
|
|
|
|
|
|
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):
|
|
|
|
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/{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):
|
|
result_image = draw_bounding_boxes(image, ref_texts)
|
|
return result_image
|
|
|
|
|
|
|
|
|
|
async def stream_generate(image=None, prompt=''):
|
|
|
|
|
|
engine_args = AsyncEngineArgs(
|
|
model=MODEL_PATH,
|
|
hf_overrides={"architectures": ["DeepseekOCRForCausalLM"]},
|
|
block_size=256,
|
|
max_model_len=8192,
|
|
enforce_eager=False,
|
|
trust_remote_code=True,
|
|
tensor_parallel_size=1,
|
|
gpu_memory_utilization=0.75,
|
|
)
|
|
engine = AsyncLLMEngine.from_engine_args(engine_args)
|
|
|
|
logits_processors = [NoRepeatNGramLogitsProcessor(ngram_size=30, window_size=90, whitelist_token_ids= {128821, 128822})] #whitelist: <td>, </td>
|
|
|
|
sampling_params = SamplingParams(
|
|
temperature=0.0,
|
|
max_tokens=8192,
|
|
logits_processors=logits_processors,
|
|
skip_special_tokens=False,
|
|
# ignore_eos=False,
|
|
|
|
)
|
|
|
|
request_id = f"request-{int(time.time())}"
|
|
|
|
printed_length = 0
|
|
|
|
if image and '<image>' in prompt:
|
|
request = {
|
|
"prompt": prompt,
|
|
"multi_modal_data": {"image": image}
|
|
}
|
|
elif prompt:
|
|
request = {
|
|
"prompt": prompt
|
|
}
|
|
else:
|
|
assert False, f'prompt is none!!!'
|
|
async for request_output in engine.generate(
|
|
request, sampling_params, request_id
|
|
):
|
|
if request_output.outputs:
|
|
full_text = request_output.outputs[0].text
|
|
new_text = full_text[printed_length:]
|
|
print(new_text, end='', flush=True)
|
|
printed_length = len(full_text)
|
|
final_output = full_text
|
|
print('\n')
|
|
|
|
return final_output
|
|
|
|
|
|
|
|
|
|
if __name__ == "__main__":
|
|
|
|
os.makedirs(OUTPUT_PATH, exist_ok=True)
|
|
os.makedirs(f'{OUTPUT_PATH}/images', exist_ok=True)
|
|
|
|
image = load_image(INPUT_PATH).convert('RGB')
|
|
|
|
|
|
if '<image>' in PROMPT:
|
|
|
|
image_features = DeepseekOCRProcessor().tokenize_with_images(images = [image], bos=True, eos=True, cropping=CROP_MODE)
|
|
else:
|
|
image_features = ''
|
|
|
|
prompt = PROMPT
|
|
|
|
result_out = asyncio.run(stream_generate(image_features, prompt))
|
|
|
|
|
|
save_results = 1
|
|
|
|
if save_results and '<image>' in prompt:
|
|
print('='*15 + 'save results:' + '='*15)
|
|
|
|
image_draw = image.copy()
|
|
|
|
outputs = result_out
|
|
|
|
with open(f'{OUTPUT_PATH}/result_ori.mmd', 'w', encoding = 'utf-8') as afile:
|
|
afile.write(outputs)
|
|
|
|
matches_ref, matches_images, mathes_other = re_match(outputs)
|
|
# print(matches_ref)
|
|
result = process_image_with_refs(image_draw, matches_ref)
|
|
|
|
|
|
for idx, a_match_image in enumerate(tqdm(matches_images, desc="image")):
|
|
outputs = outputs.replace(a_match_image, f' + '.jpg)\n')
|
|
|
|
for idx, a_match_other in enumerate(tqdm(mathes_other, desc="other")):
|
|
outputs = outputs.replace(a_match_other, '').replace('\\coloneqq', ':=').replace('\\eqqcolon', '=:')
|
|
|
|
# if 'structural formula' in conversation[0]['content']:
|
|
# outputs = '<smiles>' + outputs + '</smiles>'
|
|
with open(f'{OUTPUT_PATH}/result.mmd', 'w', encoding = 'utf-8') as afile:
|
|
afile.write(outputs)
|
|
|
|
if 'line_type' in outputs:
|
|
import matplotlib.pyplot as plt
|
|
from matplotlib.patches import Circle
|
|
lines = eval(outputs)['Line']['line']
|
|
|
|
line_type = eval(outputs)['Line']['line_type']
|
|
# print(lines)
|
|
|
|
endpoints = eval(outputs)['Line']['line_endpoint']
|
|
|
|
fig, ax = plt.subplots(figsize=(3,3), dpi=200)
|
|
ax.set_xlim(-15, 15)
|
|
ax.set_ylim(-15, 15)
|
|
|
|
for idx, line in enumerate(lines):
|
|
try:
|
|
p0 = eval(line.split(' -- ')[0])
|
|
p1 = eval(line.split(' -- ')[-1])
|
|
|
|
if line_type[idx] == '--':
|
|
ax.plot([p0[0], p1[0]], [p0[1], p1[1]], linewidth=0.8, color='k')
|
|
else:
|
|
ax.plot([p0[0], p1[0]], [p0[1], p1[1]], linewidth = 0.8, color = 'k')
|
|
|
|
ax.scatter(p0[0], p0[1], s=5, color = 'k')
|
|
ax.scatter(p1[0], p1[1], s=5, color = 'k')
|
|
except:
|
|
pass
|
|
|
|
for endpoint in endpoints:
|
|
|
|
label = endpoint.split(': ')[0]
|
|
(x, y) = eval(endpoint.split(': ')[1])
|
|
ax.annotate(label, (x, y), xytext=(1, 1), textcoords='offset points',
|
|
fontsize=5, fontweight='light')
|
|
|
|
try:
|
|
if 'Circle' in eval(outputs).keys():
|
|
circle_centers = eval(outputs)['Circle']['circle_center']
|
|
radius = eval(outputs)['Circle']['radius']
|
|
|
|
for center, r in zip(circle_centers, radius):
|
|
center = eval(center.split(': ')[1])
|
|
circle = Circle(center, radius=r, fill=False, edgecolor='black', linewidth=0.8)
|
|
ax.add_patch(circle)
|
|
except:
|
|
pass
|
|
|
|
|
|
plt.savefig(f'{OUTPUT_PATH}/geo.jpg')
|
|
plt.close()
|
|
|
|
result.save(f'{OUTPUT_PATH}/result_with_boxes.jpg')
|