from io import BytesIO
import string
import gradio as gr
import requests
from caption_anything import CaptionAnything
import torch
import json
import sys
import argparse
from caption_anything import parse_augment
import os
# download sam checkpoint if not downloaded
def download_checkpoint(url, folder, filename):
os.makedirs(folder, exist_ok=True)
filepath = os.path.join(folder, filename)
if not os.path.exists(filepath):
response = requests.get(url, stream=True)
with open(filepath, "wb") as f:
for chunk in response.iter_content(chunk_size=8192):
if chunk:
f.write(chunk)
return filepath
checkpoint_url = "https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth"
folder = "segmenter"
filename = "sam_vit_h_4b8939.pth"
title = """Caption-Anything
"""
description = """Gradio demo for Caption Anything, image to dense captioning generation with various language styles. To use it, simply upload your image, or click one of the examples to load them.
Code: GitHub repo:
"""
examples = [
["test_img/img2.jpg", "[[1000, 700, 1]]"]
]
args = parse_augment()
def get_prompt(chat_input, click_state):
points = click_state[0]
labels = click_state[1]
inputs = json.loads(chat_input)
for input in inputs:
points.append(input[:2])
labels.append(input[2])
prompt = {
"prompt_type":["click"],
"input_point":points,
"input_label":labels,
"multimask_output":"True",
}
return prompt
def inference_seg_cap(image_input, chat_input, language, sentiment, factuality, length, state, click_state):
controls = {'length': length,
'sentiment': sentiment,
'factuality': factuality,
'language': language}
prompt = get_prompt(chat_input, click_state)
print('prompt: ', prompt, 'controls: ', controls)
out = model.inference(image_input, prompt, controls)
state = state + [(None, "Image point: {}, Input label: {}".format(prompt["input_point"], prompt["input_label"]))]
for k, v in out['generated_captions'].items():
state = state + [(f'{k}: {v}', None)]
click_state[2].append(out['generated_captions']['raw_caption'])
image_output_mask = out['mask_save_path']
image_output_crop = out['crop_save_path']
return state, state, click_state, image_output_mask, image_output_crop
def upload_callback(image_input, state):
state = state + [('Image size: ' + str(image_input.size), None)]
return state
# get coordinate in format [[x,y,positive/negative]]
def get_select_coords(image_input, point_prompt, language, sentiment, factuality, length, state, click_state, evt: gr.SelectData):
print("point_prompt: ", point_prompt)
if point_prompt == 'Positive Point':
coordinate = "[[{}, {}, 1]]".format(str(evt.index[0]), str(evt.index[1]))
else:
coordinate = "[[{}, {}, 0]]".format(str(evt.index[0]), str(evt.index[1]))
return (coordinate,) + inference_seg_cap(image_input, coordinate, language, sentiment, factuality, length, state, click_state)
def chat_with_points(chat_input, click_state, state):
points, labels, captions = click_state
# point_chat_prompt = "I want you act as a chat bot in terms of image. I will give you some points (w, h) in the image and tell you what happed on the point in natural language. Note that (0, 0) refers to the top-left corner of the image, w refers to the width and h refers the height. You should chat with me based on the fact in the image instead of imagination. Now I tell you the points with their visual description:\n{points_with_caps}\n. Now begin chatting! Human: {chat_input}\nAI: "
# "The image is of width {width} and height {height}."
point_chat_prompt = "a) Revised prompt: I am an AI trained to chat with you about an image based on specific points (w, h) you provide, along with their visual descriptions. Please note that (0, 0) refers to the top-left corner of the image, w refers to the width, and h refers to the height. Here are the points and their descriptions you've given me: {points_with_caps}. Now, let's chat! Human: {chat_input} AI:"
prev_visual_context = ""
pos_points = [f"{points[i][0]}, {points[i][1]}" for i in range(len(points)) if labels[i] == 1]
prev_visual_context = ', '.join(pos_points) + captions[-1] + '\n'
chat_prompt = point_chat_prompt.format(**{"points_with_caps": prev_visual_context, "chat_input": chat_input})
response = model.text_refiner.llm(chat_prompt)
state = state + [(chat_input, response)]
return state, state
def init_openai_api_key(api_key):
# os.environ['OPENAI_API_KEY'] = api_key
global model
model = CaptionAnything(args, api_key)
css='''
#image_upload{min-height:200px}
#image_upload [data-testid="image"], #image_upload [data-testid="image"] > div{min-height: 200px}
'''
with gr.Blocks(css=css) as iface:
state = gr.State([])
click_state = gr.State([[],[],[]])
caption_state = gr.State([[]])
gr.Markdown(title)
gr.Markdown(description)
with gr.Column():
openai_api_key = gr.Textbox(
placeholder="Input your openAI API key and press Enter",
show_label=False,
lines=1,
type="password",
)
openai_api_key.submit(init_openai_api_key, inputs=[openai_api_key])
with gr.Row():
with gr.Column(scale=0.7):
image_input = gr.Image(type="pil", interactive=True, label="Image", elem_id="image_upload").style(height=260,scale=1.0)
with gr.Row(scale=0.7):
point_prompt = gr.Radio(
choices=["Positive Point", "Negative Point"],
value="Positive Point",
label="Points",
interactive=True,
)
# with gr.Row():
language = gr.Radio(
choices=["English", "Chinese", "French", "Spanish", "Arabic", "Portuguese","Cantonese"],
value="English",
label="Language",
interactive=True,
)
sentiment = gr.Radio(
choices=["Positive", "Natural", "Negative"],
value="Natural",
label="Sentiment",
interactive=True,
)
factuality = gr.Radio(
choices=["Factual", "Imagination"],
value="Factual",
label="Factuality",
interactive=True,
)
length = gr.Slider(
minimum=5,
maximum=100,
value=10,
step=1,
interactive=True,
label="Length",
)
with gr.Column(scale=1.5):
with gr.Row():
image_output_mask= gr.Image(type="pil", interactive=False, label="Mask").style(height=260,scale=1.0)
image_output_crop= gr.Image(type="pil", interactive=False, label="Cropped Image by Mask", show_progress=False).style(height=260,scale=1.0)
chatbot = gr.Chatbot(label="Chat Output",).style(height=450,scale=0.5)
with gr.Row():
with gr.Column(scale=0.7):
prompt_input = gr.Textbox(lines=1, label="Input Prompt (A list of points like : [[100, 200, 1], [200,300,0]])")
prompt_input.submit(
inference_seg_cap,
[
image_input,
prompt_input,
language,
sentiment,
factuality,
length,
state,
click_state
],
[chatbot, state, click_state, image_output_mask, image_output_crop],
show_progress=False
)
image_input.upload(
upload_callback,
[image_input, state],
[chatbot]
)
with gr.Row():
clear_button = gr.Button(value="Clear Click", interactive=True)
clear_button.click(
lambda: ("", [[], [], []], None, None),
[],
[prompt_input, click_state, image_output_mask, image_output_crop],
queue=False,
show_progress=False
)
clear_button = gr.Button(value="Clear", interactive=True)
clear_button.click(
lambda: ("", [], [], [[], [], []], None, None),
[],
[prompt_input, chatbot, state, click_state, image_output_mask, image_output_crop],
queue=False,
show_progress=False
)
submit_button = gr.Button(
value="Submit", interactive=True, variant="primary"
)
submit_button.click(
inference_seg_cap,
[
image_input,
prompt_input,
language,
sentiment,
factuality,
length,
state,
click_state
],
[chatbot, state, click_state, image_output_mask, image_output_crop],
show_progress=False
)
# select coordinate
image_input.select(
get_select_coords,
inputs=[image_input,point_prompt,language,sentiment,factuality,length,state,click_state],
outputs=[prompt_input, chatbot, state, click_state, image_output_mask, image_output_crop],
show_progress=False
)
image_input.change(
lambda: ("", [], [[], [], []]),
[],
[chatbot, state, click_state],
queue=False,
)
with gr.Column(scale=1.5):
chat_input = gr.Textbox(lines=1, label="Chat Input")
chat_input.submit(chat_with_points, [chat_input, click_state, state], [chatbot, state])
examples = gr.Examples(
examples=examples,
inputs=[image_input, prompt_input],
)
iface.queue(concurrency_count=1, api_open=False, max_size=10)
iface.launch(server_name="0.0.0.0", enable_queue=True, server_port=args.port, share=args.gradio_share)