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Running
on
Zero
File size: 3,454 Bytes
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import gradio as gr
from transformers import AutoModelForCausalLM, AutoProcessor
from PIL import Image, ImageDraw
import requests
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import numpy as np
import random
# Load model and processor
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):
prompt = task_prompt if text_input is None else 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))
plt.axis('off')
plt.show()
def draw_polygons(image, prediction, fill_mask=False):
draw = ImageDraw.Draw(image)
colormap = ['blue', 'orange', 'green', 'purple', 'brown', 'pink', 'gray', 'olive', 'cyan', 'red']
for polygons, label in zip(prediction['polygons'], prediction['labels']):
color = random.choice(colormap)
fill_color = color if fill_mask else None
for polygon in polygons:
draw.polygon(polygon, outline=color, fill=fill_color)
draw.text((polygon[0][0], polygon[0][1]), label, fill=color)
image.show()
def gradio_interface(image, task_prompt, text_input):
result = run_example(task_prompt, image, text_input)
if task_prompt in ['<OD>', '<OPEN_VOCABULARY_DETECTION>']:
plot_bbox(image, result)
elif task_prompt in ['<REFERRING_EXPRESSION_SEGMENTATION>', '<REGION_TO_SEGMENTATION>']:
draw_polygons(image, result, fill_mask=True)
return result
with gr.Blocks() as demo:
gr.Markdown("## Florence Model Advanced Tasks")
with gr.Row():
image_input = gr.Image(type="pil")
task_input = gr.Dropdown(label="Select Task", 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>'
])
text_input = gr.Textbox(label="Optional Text Input", placeholder="Enter text here if required by the task")
submit_btn = gr.Button("Run Task")
output = gr.Textbox(label="Output")
submit_btn.click(fn=gradio_interface, inputs=[image_input, task_input, text_input], outputs=output)
demo.launch() |