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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')
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