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| 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 numpy as np | |
| import PIL.ImageDraw as ImageDraw | |
| from image_editing_utils import create_bubble_frame | |
| import copy | |
| from tools import mask_painter | |
| from PIL import Image | |
| import os | |
| 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" | |
| download_checkpoint(checkpoint_url, folder, filename) | |
| title = """<h1 align="center">Caption-Anything</h1>""" | |
| 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: https://github.com/ttengwang/Caption-Anything | |
| """ | |
| examples = [ | |
| ["test_img/img2.jpg"], | |
| ["test_img/img5.jpg"], | |
| ["test_img/img12.jpg"], | |
| ["test_img/img14.jpg"], | |
| ] | |
| args = parse_augment() | |
| args.captioner = 'blip2' | |
| args.seg_crop_mode = 'wo_bg' | |
| args.regular_box = True | |
| # args.device = 'cuda:5' | |
| # args.disable_gpt = False | |
| # args.enable_reduce_tokens = True | |
| # args.port=20322 | |
| model = CaptionAnything(args) | |
| def init_openai_api_key(api_key): | |
| os.environ['OPENAI_API_KEY'] = api_key | |
| model.init_refiner() | |
| 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 chat_with_points(chat_input, click_state, state): | |
| if not hasattr(model, "text_refiner"): | |
| response = "Text refiner is not initilzed, please input openai api key." | |
| state = state + [(chat_input, response)] | |
| return 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}\nNow 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] | |
| if len(captions): | |
| prev_visual_context = ', '.join(pos_points) + captions[-1] + '\n' | |
| else: | |
| prev_visual_context = 'no point exists.' | |
| 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 inference_seg_cap(image_input, point_prompt, language, sentiment, factuality, length, state, click_state, evt:gr.SelectData): | |
| if point_prompt == 'Positive': | |
| coordinate = "[[{}, {}, 1]]".format(str(evt.index[0]), str(evt.index[1])) | |
| else: | |
| coordinate = "[[{}, {}, 0]]".format(str(evt.index[0]), str(evt.index[1])) | |
| controls = {'length': length, | |
| 'sentiment': sentiment, | |
| 'factuality': factuality, | |
| 'language': language} | |
| # click_coordinate = "[[{}, {}, 1]]".format(str(evt.index[0]), str(evt.index[1])) | |
| # chat_input = click_coordinate | |
| prompt = get_prompt(coordinate, 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)] | |
| state = state + [("caption: {}".format(out['generated_captions']['raw_caption']), None)] | |
| wiki = out['generated_captions'].get('wiki', "") | |
| click_state[2].append(out['generated_captions']['raw_caption']) | |
| text = out['generated_captions']['raw_caption'] | |
| # draw = ImageDraw.Draw(image_input) | |
| # draw.text((evt.index[0], evt.index[1]), text, textcolor=(0,0,255), text_size=120) | |
| input_mask = np.array(Image.open(out['mask_save_path']).convert('P')) | |
| image_input = mask_painter(np.array(image_input), input_mask) | |
| origin_image_input = image_input | |
| image_input = create_bubble_frame(image_input, text, (evt.index[0], evt.index[1])) | |
| yield state, state, click_state, chat_input, image_input, wiki | |
| if not args.disable_gpt and hasattr(model, "text_refiner"): | |
| refined_caption = model.text_refiner.inference(query=text, controls=controls, context=out['context_captions']) | |
| # new_cap = 'Original: ' + text + '. Refined: ' + refined_caption['caption'] | |
| new_cap = refined_caption['caption'] | |
| refined_image_input = create_bubble_frame(origin_image_input, new_cap, (evt.index[0], evt.index[1])) | |
| yield state, state, click_state, chat_input, refined_image_input, wiki | |
| def upload_callback(image_input, state): | |
| state = [] + [('Image size: ' + str(image_input.size), None)] | |
| click_state = [[], [], []] | |
| model.segmenter.image = None | |
| model.segmenter.image_embedding = None | |
| model.segmenter.set_image(image_input) | |
| return state, image_input, click_state | |
| with gr.Blocks( | |
| css=''' | |
| #image_upload{min-height:400px} | |
| #image_upload [data-testid="image"], #image_upload [data-testid="image"] > div{min-height: 600px} | |
| ''' | |
| ) as iface: | |
| state = gr.State([]) | |
| click_state = gr.State([[],[],[]]) | |
| origin_image = gr.State(None) | |
| gr.Markdown(title) | |
| gr.Markdown(description) | |
| with gr.Row(): | |
| with gr.Column(scale=1.0): | |
| image_input = gr.Image(type="pil", interactive=True, elem_id="image_upload") | |
| with gr.Row(scale=1.0): | |
| point_prompt = gr.Radio( | |
| choices=["Positive", "Negative"], | |
| value="Positive", | |
| label="Point Prompt", | |
| interactive=True) | |
| clear_button_clike = gr.Button(value="Clear Clicks", interactive=True) | |
| clear_button_image = gr.Button(value="Clear Image", interactive=True) | |
| with gr.Row(scale=1.0): | |
| language = gr.Dropdown(['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, | |
| ) | |
| with gr.Row(scale=1.0): | |
| factuality = gr.Radio( | |
| choices=["Factual", "Imagination"], | |
| value="Factual", | |
| label="Factuality", | |
| interactive=True, | |
| ) | |
| length = gr.Slider( | |
| minimum=10, | |
| maximum=80, | |
| value=10, | |
| step=1, | |
| interactive=True, | |
| label="Length", | |
| ) | |
| with gr.Column(scale=0.5): | |
| openai_api_key = gr.Textbox( | |
| placeholder="Input your openAI API key and press Enter", | |
| show_label=False, | |
| label = "OpenAI API Key", | |
| lines=1, | |
| type="password" | |
| ) | |
| openai_api_key.submit(init_openai_api_key, inputs=[openai_api_key]) | |
| wiki_output = gr.Textbox(lines=6, label="Wiki") | |
| chatbot = gr.Chatbot(label="Chat about Selected Object",).style(height=450,scale=0.5) | |
| chat_input = gr.Textbox(lines=1, label="Chat Input") | |
| with gr.Row(): | |
| clear_button_text = gr.Button(value="Clear Text", interactive=True) | |
| submit_button_text = gr.Button(value="Submit", interactive=True, variant="primary") | |
| clear_button_clike.click( | |
| lambda x: ([[], [], []], x, ""), | |
| [origin_image], | |
| [click_state, image_input, wiki_output], | |
| queue=False, | |
| show_progress=False | |
| ) | |
| clear_button_image.click( | |
| lambda: (None, [], [], [[], [], []], ""), | |
| [], | |
| [image_input, chatbot, state, click_state, wiki_output], | |
| queue=False, | |
| show_progress=False | |
| ) | |
| clear_button_text.click( | |
| lambda: ([], [], [[], [], []]), | |
| [], | |
| [chatbot, state, click_state], | |
| queue=False, | |
| show_progress=False | |
| ) | |
| image_input.clear( | |
| lambda: (None, [], [], [[], [], []], ""), | |
| [], | |
| [image_input, chatbot, state, click_state, wiki_output], | |
| queue=False, | |
| show_progress=False | |
| ) | |
| examples = gr.Examples( | |
| examples=examples, | |
| inputs=[image_input], | |
| ) | |
| image_input.upload(upload_callback,[image_input, state], [state, origin_image, click_state]) | |
| chat_input.submit(chat_with_points, [chat_input, click_state, state], [chatbot, state]) | |
| # select coordinate | |
| image_input.select(inference_seg_cap, | |
| inputs=[ | |
| origin_image, | |
| point_prompt, | |
| language, | |
| sentiment, | |
| factuality, | |
| length, | |
| state, | |
| click_state | |
| ], | |
| outputs=[chatbot, state, click_state, chat_input, image_input, wiki_output], | |
| show_progress=False, queue=True) | |
| iface.queue(concurrency_count=1, api_open=False, max_size=10) | |
| iface.launch(server_name="0.0.0.0", enable_queue=True) |