import gradio as gr
from transformers import AutoProcessor, AutoModelForCausalLM
from PIL import Image
import requests
import copy
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import random
import numpy as np
model_id = 'microsoft/Florence-2-large'
model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True).eval()
processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
def run_example(task_prompt, image, text_input=None):
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):
if task_prompt == '<CAPTION>':
result = run_example(task_prompt, image)
return result
elif task_prompt == '<DETAILED_CAPTION>':
result = run_example(task_prompt, image)
return result
elif task_prompt == '<MORE_DETAILED_CAPTION>':
result = run_example(task_prompt, image)
return result
elif task_prompt == '<OD>':
results = run_example(task_prompt, image)
fig = plot_bbox(image, results['<OD>'])
return fig
elif task_prompt == '<DENSE_REGION_CAPTION>':
results = run_example(task_prompt, image)
fig = plot_bbox(image, results['<DENSE_REGION_CAPTION>'])
return fig
elif task_prompt == '<REGION_PROPOSAL>':
results = run_example(task_prompt, image)
fig = plot_bbox(image, results['<REGION_PROPOSAL>'])
return fig
elif task_prompt == '<CAPTION_TO_PHRASE_GROUNDING>':
results = run_example(task_prompt, image, text_input)
fig = plot_bbox(image, results['<CAPTION_TO_PHRASE_GROUNDING>'])
return fig
elif task_prompt == '<REFERRING_EXPRESSION_SEGMENTATION>':
results = run_example(task_prompt, image, text_input)
output_image = copy.deepcopy(image)
output_image = draw_polygons(output_image, results['<REFERRING_EXPRESSION_SEGMENTATION>'], fill_mask=True)
return output_image
elif task_prompt == '<REGION_TO_SEGMENTATION>':
results = run_example(task_prompt, image, text_input)
output_image = copy.deepcopy(image)
output_image = draw_polygons(output_image, results['<REGION_TO_SEGMENTATION>'], fill_mask=True)
return output_image
elif task_prompt == '<OPEN_VOCABULARY_DETECTION>':
results = run_example(task_prompt, image, text_input)
bbox_results = convert_to_od_format(results['<OPEN_VOCABULARY_DETECTION>'])
fig = plot_bbox(image, bbox_results)
return fig
elif task_prompt == '<REGION_TO_CATEGORY>':
results = run_example(task_prompt, image, text_input)
return results
elif task_prompt == '<REGION_TO_DESCRIPTION>':
results = run_example(task_prompt, image, text_input)
return results
elif task_prompt == '<OCR>':
result = run_example(task_prompt, image)
return result
elif task_prompt == '<OCR_WITH_REGION>':
results = run_example(task_prompt, image)
output_image = copy.deepcopy(image)
output_image = draw_ocr_bboxes(output_image, results['<OCR_WITH_REGION>'])
return output_image
css = """
#output {
height: 500px;
overflow: auto;
border: 1px solid #ccc;
}
"""
with gr.Blocks(css=css) as demo:
gr.HTML("<h1><center>Florence-2 Demo<center><h1>")
with gr.Tab(label="Florence-2 Image Captioning"):
with gr.Row():
with gr.Column():
input_img = gr.Image(label="Input Picture")
task_prompt = gr.Dropdown(choices=[
'<CAPTION>', '<DETAILED_CAPTION>', '<MORE_DETAILED_CAPTION>', '<OD>',
'<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>'
], label="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", '<CAPTION>'],
["image1.jpg", '<OD>'],
["image1.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], [output_text, output_img])
demo.launch(debug=True)