import gradio as gr
from transformers import AutoProcessor, AutoModelForCausalLM
import requests
import copy
from PIL import Image, ImageDraw, ImageFont
import io
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import random
import numpy as np
import os
import subprocess
from unittest.mock import patch
from transformers.dynamic_module_utils import get_imports
def fixed_get_imports(filename: str | os.PathLike) -> list[str]:
if not str(filename).endswith("modeling_florence2.py"):
return get_imports(filename)
imports = get_imports(filename)
imports.remove("flash_attn")
return imports
with patch("transformers.dynamic_module_utils.get_imports", fixed_get_imports):
models = {
'microsoft/Florence-2-base-ft': AutoModelForCausalLM.from_pretrained('microsoft/Florence-2-base-ft', trust_remote_code=True, device_map='cpu'),
'microsoft/Florence-2-base': AutoModelForCausalLM.from_pretrained('microsoft/Florence-2-base', trust_remote_code=True, device_map='cpu'),
}
processors = {
'microsoft/Florence-2-base-ft': AutoProcessor.from_pretrained('microsoft/Florence-2-base-ft', trust_remote_code=True),
'microsoft/Florence-2-base': AutoProcessor.from_pretrained('microsoft/Florence-2-base', trust_remote_code=True),
}
DESCRIPTION = "# [Florence-2 Demo](https://huggingface.co/microsoft/Florence-2-base)"
colormap = ['blue','orange','green','purple','brown','pink','gray','olive','cyan','red',
'lime','indigo','violet','aqua','magenta','coral','gold','tan','skyblue']
def fig_to_pil(fig):
buf = io.BytesIO()
fig.savefig(buf, format='png')
buf.seek(0)
return Image.open(buf)
def run_example(task_prompt, image, text_input=None, model_id='microsoft/Florence-2-base'):
model = models[model_id]
processor = processors[model_id]
if text_input is None:
prompt = task_prompt
else:
prompt = task_prompt + text_input
inputs = processor(text=prompt, images=image, return_tensors="pt")
generated_ids = model.generate(
input_ids=inputs["input_ids"],
pixel_values=inputs["pixel_values"],
max_new_tokens=1024,
early_stopping=False,
do_sample=False,
num_beams=3,
)
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
parsed_answer = processor.post_process_generation(
generated_text,
task=task_prompt,
image_size=(image.width, image.height)
)
return parsed_answer
def plot_bbox(image, data):
fig, ax = plt.subplots()
ax.imshow(image)
for bbox, label in zip(data['bboxes'], data['labels']):
x1, y1, x2, y2 = bbox
rect = patches.Rectangle((x1, y1), x2-x1, y2-y1, linewidth=1, edgecolor='r', facecolor='none')
ax.add_patch(rect)
plt.text(x1, y1, label, color='white', fontsize=8, bbox=dict(facecolor='red', alpha=0.5))
ax.axis('off')
return fig
def draw_polygons(image, prediction, fill_mask=False):
draw = ImageDraw.Draw(image)
scale = 1
for polygons, label in zip(prediction['polygons'], prediction['labels']):
color = random.choice(colormap)
fill_color = random.choice(colormap) if fill_mask else None
for _polygon in polygons:
_polygon = np.array(_polygon).reshape(-1, 2)
if len(_polygon) < 3:
print('Invalid polygon:', _polygon)
continue
_polygon = (_polygon * scale).reshape(-1).tolist()
if fill_mask:
draw.polygon(_polygon, outline=color, fill=fill_color)
else:
draw.polygon(_polygon, outline=color)
draw.text((_polygon[0] + 8, _polygon[1] + 2), label, fill=color)
return image
def convert_to_od_format(data):
bboxes = data.get('bboxes', [])
labels = data.get('bboxes_labels', [])
od_results = {
'bboxes': bboxes,
'labels': labels
}
return od_results
def draw_ocr_bboxes(image, prediction):
scale = 1
draw = ImageDraw.Draw(image)
bboxes, labels = prediction['quad_boxes'], prediction['labels']
for box, label in zip(bboxes, labels):
color = random.choice(colormap)
new_box = (np.array(box) * scale).tolist()
draw.polygon(new_box, width=3, outline=color)
draw.text((new_box[0]+8, new_box[1]+2),
"{}".format(label),
align="right",
fill=color)
return image
def process_image(image, task_prompt, text_input=None, model_id='microsoft/Florence-2-base'):
image = Image.fromarray(image) # Convert NumPy array to PIL Image
if task_prompt == 'Caption':
task_prompt = '<CAPTION>'
results = run_example(task_prompt, image, model_id=model_id)
return results, None
elif task_prompt == 'Detailed Caption':
task_prompt = '<DETAILED_CAPTION>'
results = run_example(task_prompt, image, model_id=model_id)
return results, None
elif task_prompt == 'More Detailed Caption':
task_prompt = '<MORE_DETAILED_CAPTION>'
results = run_example(task_prompt, image, model_id=model_id)
return results, None
elif task_prompt == 'Caption + Grounding':
task_prompt = '<CAPTION>'
results = run_example(task_prompt, image, model_id=model_id)
text_input = results[task_prompt]
task_prompt = '<CAPTION_TO_PHRASE_GROUNDING>'
results = run_example(task_prompt, image, text_input, model_id)
results['<CAPTION>'] = text_input
fig = plot_bbox(image, results['<CAPTION_TO_PHRASE_GROUNDING>'])
return results, fig_to_pil(fig)
elif task_prompt == 'Detailed Caption + Grounding':
task_prompt = '<DETAILED_CAPTION>'
results = run_example(task_prompt, image, model_id=model_id)
text_input = results[task_prompt]
task_prompt = '<CAPTION_TO_PHRASE_GROUNDING>'
results = run_example(task_prompt, image, text_input, model_id)
results['<DETAILED_CAPTION>'] = text_input
fig = plot_bbox(image, results['<CAPTION_TO_PHRASE_GROUNDING>'])
return results, fig_to_pil(fig)
elif task_prompt == 'More Detailed Caption + Grounding':
task_prompt = '<MORE_DETAILED_CAPTION>'
results = run_example(task_prompt, image, model_id=model_id)
text_input = results[task_prompt]
task_prompt = '<CAPTION_TO_PHRASE_GROUNDING>'
results = run_example(task_prompt, image, text_input, model_id)
results['<MORE_DETAILED_CAPTION>'] = text_input
fig = plot_bbox(image, results['<CAPTION_TO_PHRASE_GROUNDING>'])
return results, fig_to_pil(fig)
elif task_prompt == 'Object Detection':
task_prompt = '<OD>'
results = run_example(task_prompt, image, model_id=model_id)
fig = plot_bbox(image, results['<OD>'])
return results, fig_to_pil(fig)
elif task_prompt == 'Dense Region Caption':
task_prompt = '<DENSE_REGION_CAPTION>'
results = run_example(task_prompt, image, model_id=model_id)
fig = plot_bbox(image, results['<DENSE_REGION_CAPTION>'])
return results, fig_to_pil(fig)
elif task_prompt == 'Region Proposal':
task_prompt = '<REGION_PROPOSAL>'
results = run_example(task_prompt, image, model_id=model_id)
fig = plot_bbox(image, results['<REGION_PROPOSAL>'])
return results, fig_to_pil(fig)
elif task_prompt == 'Caption to Phrase Grounding':
task_prompt = '<CAPTION_TO_PHRASE_GROUNDING>'
results = run_example(task_prompt, image, text_input, model_id)
fig = plot_bbox(image, results['<CAPTION_TO_PHRASE_GROUNDING>'])
return results, fig_to_pil(fig)
elif task_prompt == 'Referring Expression Segmentation':
task_prompt = '<REFERRING_EXPRESSION_SEGMENTATION>'
results = run_example(task_prompt, image, text_input, model_id)
output_image = copy.deepcopy(image)
output_image = draw_polygons(output_image, results['<REFERRING_EXPRESSION_SEGMENTATION>'], fill_mask=True)
return results, output_image
elif task_prompt == 'Region to Segmentation':
task_prompt = '<REGION_TO_SEGMENTATION>'
results = run_example(task_prompt, image, text_input, model_id)
output_image = copy.deepcopy(image)
output_image = draw_polygons(output_image, results['<REGION_TO_SEGMENTATION>'], fill_mask=True)
return results, output_image
elif task_prompt == 'Open Vocabulary Detection':
task_prompt = '<OPEN_VOCABULARY_DETECTION>'
results = run_example(task_prompt, image, text_input, model_id)
bbox_results = convert_to_od_format(results['<OPEN_VOCABULARY_DETECTION>'])
fig = plot_bbox(image, bbox_results)
return results, fig_to_pil(fig)
elif task_prompt == 'Region to Category':
task_prompt = '<REGION_TO_CATEGORY>'
results = run_example(task_prompt, image, text_input, model_id)
return results, None
elif task_prompt == 'Region to Description':
task_prompt = '<REGION_TO_DESCRIPTION>'
results = run_example(task_prompt, image, text_input, model_id)
return results, None
elif task_prompt == 'OCR':
task_prompt = '<OCR>'
results = run_example(task_prompt, image, model_id=model_id)
return results, None
elif task_prompt == 'OCR with Region':
task_prompt = '<OCR_WITH_REGION>'
results = run_example(task_prompt, image, model_id=model_id)
output_image = copy.deepcopy(image)
output_image = draw_ocr_bboxes(output_image, results['<OCR_WITH_REGION>'])
return results, output_image
else:
return "", None # Return empty string and None for unknown task prompts
css = """
#output {
height: 500px;
overflow: auto;
border: 1px solid #ccc;
}
"""
single_task_list =[
'Caption', 'Detailed Caption', 'More Detailed Caption', 'Object Detection',
'Dense Region Caption', 'Region Proposal', 'Caption to Phrase Grounding',
'Referring Expression Segmentation', 'Region to Segmentation',
'Open Vocabulary Detection', 'Region to Category', 'Region to Description',
'OCR', 'OCR with Region'
]
cascased_task_list =[
'Caption + Grounding', 'Detailed Caption + Grounding', 'More Detailed Caption + Grounding'
]
def update_task_dropdown(choice):
if choice == 'Cascased task':
return gr.Dropdown(choices=cascased_task_list, value='Caption + Grounding')
else:
return gr.Dropdown(choices=single_task_list, value='Caption')
with gr.Blocks(css=css) as demo:
gr.Markdown(DESCRIPTION)
with gr.Tab(label="Florence-2 Image Captioning"):
with gr.Row():
with gr.Column():
input_img = gr.Image(label="Input Picture")
model_selector = gr.Dropdown(choices=list(models.keys()), label="Model", value='microsoft/Florence-2-base')
task_type = gr.Radio(choices=['Single task', 'Cascased task'], label='Task type selector', value='Single task')
task_prompt = gr.Dropdown(choices=single_task_list, label="Task Prompt", value="Caption")
task_type.change(fn=update_task_dropdown, inputs=task_type, outputs=task_prompt)
text_input = gr.Textbox(label="Text Input (optional)")
submit_btn = gr.Button(value="Submit")
with gr.Column():
output_text = gr.Textbox(label="Output Text")
output_img = gr.Image(label="Output Image")
gr.Examples(
examples=[
["image1.jpg", 'Object Detection'],
["image2.jpg", 'OCR with Region']
],
inputs=[input_img, task_prompt],
outputs=[output_text, output_img],
fn=process_image,
cache_examples=True,
label='Try examples'
)
submit_btn.click(process_image, [input_img, task_prompt, text_input, model_selector], [output_text, output_img])
demo.launch(debug=True)