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import gradio as gr
import cv2
import numpy as np
from PIL import Image
import base64
from io import BytesIO
from models.image_text_transformation import ImageTextTransformation
import argparse
import torch
parser = argparse.ArgumentParser()
parser.add_argument('--gpt_version', choices=['gpt-3.5-turbo', 'gpt4'], default='gpt-3.5-turbo')
parser.add_argument('--image_caption', action='store_true', dest='image_caption', default=True, help='Set this flag to True if you want to use BLIP2 Image Caption')
parser.add_argument('--dense_caption', action='store_true', dest='dense_caption', default=True, help='Set this flag to True if you want to use Dense Caption')
parser.add_argument('--semantic_segment', action='store_true', dest='semantic_segment', default=False, help='Set this flag to True if you want to use semantic segmentation')
parser.add_argument('--image_caption_device', choices=['cuda', 'cpu'], default='cpu', help='Select the device: cuda or cpu, gpu memory larger than 14G is recommended')
parser.add_argument('--dense_caption_device', choices=['cuda', 'cpu'], default='cpu', help='Select the device: cuda or cpu, < 6G GPU is not recommended>')
parser.add_argument('--semantic_segment_device', choices=['cuda', 'cpu'], default='cpu', help='Select the device: cuda or cpu, gpu memory larger than 14G is recommended')
parser.add_argument('--contolnet_device', choices=['cuda', 'cpu'], default='cpu', help='Select the device: cuda or cpu, <6G GPU is not recommended>')
args = parser.parse_args()
# device = "cuda" if torch.cuda.is_available() else "cpu"
device = "cpu"
if device == "cuda":
args.image_caption_device = "cuda"
args.dense_caption_device = "cuda"
args.semantic_segment_device = "cuda"
args.contolnet_device = "cuda"
else:
args.image_caption_device = "cpu"
args.dense_caption_device = "cpu"
args.semantic_segment_device = "cpu"
args.contolnet_device = "cpu"
def pil_image_to_base64(image):
buffered = BytesIO()
image.save(buffered, format="JPEG")
img_str = base64.b64encode(buffered.getvalue()).decode()
return img_str
def add_logo():
with open("examples/logo.png", "rb") as f:
logo_base64 = base64.b64encode(f.read()).decode()
return logo_base64
def process_image(image_src, options, processor):
processor.args.semantic_segment = "Semantic Segment" in options
image_generation_status = "Image Generation" in options
image_caption, dense_caption, region_semantic, gen_text = processor.image_to_text(image_src)
if image_generation_status:
gen_image = processor.text_to_image(gen_text)
gen_image_str = pil_image_to_base64(gen_image)
# Combine the outputs into a single HTML output
custom_output = f'''
<h2>Image->Text:</h2>
<div style="display: flex; flex-wrap: wrap;">
<div style="flex: 1;">
<h3>Image Caption</h3>
<p>{image_caption}</p>
</div>
<div style="flex: 1;">
<h3>Dense Caption</h3>
<p>{dense_caption}</p>
</div>
<div style="flex: 1;">
<h3>Region Semantic</h3>
<p>{region_semantic}</p>
</div>
<div style="flex: 1;">
<h3>GPT4 Reasoning:</h3>
<p>{gen_text}</p>
</div>
</div>
'''
if image_generation_status:
custom_output += f'''
<h2>Text->Image:</h2>
<div style="display: flex; flex-wrap: wrap;">
<div style="flex: 1;">
<h3>Generated Image</h3>
<img src="data:image/jpeg;base64,{gen_image_str}" width="400" style="vertical-align: middle;">
</div>
</div>
'''
return custom_output
processor = ImageTextTransformation(args)
# Create Gradio input and output components
image_input = gr.inputs.Image(type='filepath', label="Input Image")
semantic_segment_checkbox = gr.inputs.Checkbox(label="Semantic Segment", default=False)
image_generation_checkbox = gr.inputs.Checkbox(label="Image Generation", default=False)
logo_base64 = add_logo()
# Create the title with the logo
title_with_logo = f'<img src="data:image/jpeg;base64,{logo_base64}" width="400" style="vertical-align: middle;"> Understanding Image with Text'
# Create Gradio interface
interface = gr.Interface(
fn=lambda image, options: process_image(image, options, processor),
inputs=[image_input,
gr.CheckboxGroup(
label="Options",
choices=["Semantic Segment", "Image Generation"],
),
],
outputs=gr.outputs.HTML(),
title=title_with_logo,
description="""
This code support image to text transformation. Then the generated text can do retrieval, question answering et al to conduct zero-shot.
\n Since GPU is expensive, we use CPU for demo. Run code local with gpu or google colab we provided for fast speed.
\n Semantic segment is very slow in cpu(~8m).
\n Ttext2image model is controlnet is also very slow in cpu(~2m), which used canny edge as reference.
"""
)
# Launch the interface
interface.launch() |