demo.py

import argparse
import os
import random
from collections import defaultdict

import cv2
import re

import numpy as np
from PIL import Image
import torch
import html
import gradio as gr

import torchvision.transforms as T
import torch.backends.cudnn as cudnn

from vlm4hoi.common.config import Config

from vlm4hoi.common.registry import registry
from vlm4hoi.conversation.conversation import Conversation, SeparatorStyle, Chat

# imports modules for registration
from vlm4hoi.datasets.builders import *
from vlm4hoi.models import *
from vlm4hoi.processors import *
from vlm4hoi.runners import *
from vlm4hoi.tasks import *


def parse_args():
    parser = argparse.ArgumentParser(description="Demo")
    parser.add_argument("--cfg-path", default='eval_configs/vlm4hoi_eval.yaml',
                        help="path to configuration file.")
    parser.add_argument("--gpu-id", type=int, default=0, help="specify the gpu to load the model.")
    parser.add_argument(
        "--options",
        nargs="+",
        help="override some settings in the used config, the key-value pair "
             "in xxx=yyy format will be merged into config file (deprecate), "
             "change to --cfg-options instead.",
    )
    args = parser.parse_args()
    return args


random.seed(42)
np.random.seed(42)
torch.manual_seed(42)

cudnn.benchmark = False
cudnn.deterministic = True

print('Initializing Chat')
args = parse_args()
cfg = Config(args)

device = 'cuda:{}'.format(args.gpu_id)

model_config = cfg.model_cfg
model_config.device_8bit = args.gpu_id
model_cls = registry.get_model_class(model_config.arch)
model = model_cls.from_config(model_config).to(device)
bounding_box_size = 100
vis_processor_cfg = cfg.datasets_cfg.epic_conversation.vis_processor.train
vis_processor = registry.get_processor_class(vis_processor_cfg.name).from_config(vis_processor_cfg)

model = model.eval()

CONV_VISION = Conversation(
    system="",
    roles=(r"<s>[INST] ", r" [/INST]"),
    messages=[],
    offset=2,
    sep_style=SeparatorStyle.SINGLE,
    sep="",
)


def extract_substrings(string):
    # first check if there is no-finished bracket
    index = string.rfind('}')
    if index != -1:
        string = string[:index + 1]

    pattern = r'<p>(.*?)\}(?!<)'
    matches = re.findall(pattern, string)
    substrings = [match for match in matches]

    return substrings


def is_overlapping(rect1, rect2):
    x1, y1, x2, y2 = rect1
    x3, y3, x4, y4 = rect2
    return not (x2 < x3 or x1 > x4 or y2 < y3 or y1 > y4)


def computeIoU(bbox1, bbox2):
    x1, y1, x2, y2 = bbox1
    x3, y3, x4, y4 = bbox2
    intersection_x1 = max(x1, x3)
    intersection_y1 = max(y1, y3)
    intersection_x2 = min(x2, x4)
    intersection_y2 = min(y2, y4)
    intersection_area = max(0, intersection_x2 - intersection_x1 + 1) * max(0, intersection_y2 - intersection_y1 + 1)
    bbox1_area = (x2 - x1 + 1) * (y2 - y1 + 1)
    bbox2_area = (x4 - x3 + 1) * (y4 - y3 + 1)
    union_area = bbox1_area + bbox2_area - intersection_area
    iou = intersection_area / union_area
    return iou


def save_tmp_img(visual_img):
    file_name = "".join([str(random.randint(0, 9)) for _ in range(5)]) + ".jpg"
    file_path = "/tmp/gradio" + file_name
    visual_img.save(file_path)
    return file_path


def mask2bbox(mask):
    if mask is None:
        return ''
    mask = mask.resize([100, 100], resample=Image.NEAREST)
    mask = np.array(mask)[:, :, 0]

    rows = np.any(mask, axis=1)
    cols = np.any(mask, axis=0)

    if rows.sum():
        # Get the top, bottom, left, and right boundaries
        rmin, rmax = np.where(rows)[0][[0, -1]]
        cmin, cmax = np.where(cols)[0][[0, -1]]
        bbox = '{{<{}><{}><{}><{}>}}'.format(cmin, rmin, cmax, rmax)
    else:
        bbox = ''

    return bbox


def escape_markdown(text):
    # List of Markdown special characters that need to be escaped
    md_chars = ['<', '>']

    # Escape each special character
    for char in md_chars:
        text = text.replace(char, '\\' + char)

    return text


def reverse_escape(text):
    md_chars = ['\\<', '\\>']

    for char in md_chars:
        text = text.replace(char, char[1:])

    return text


colors = [
    (255, 0, 0),
    (0, 255, 0),
    (0, 0, 255),
    (210, 210, 0),
    (255, 0, 255),
    (0, 255, 255),
    (114, 128, 250),
    (0, 165, 255),
    (0, 128, 0),
    (144, 238, 144),
    (238, 238, 175),
    (255, 191, 0),
    (0, 128, 0),
    (226, 43, 138),
    (255, 0, 255),
    (0, 215, 255),
]

color_map = {
    f"{color_id}": f"#{hex(color[2])[2:].zfill(2)}{hex(color[1])[2:].zfill(2)}{hex(color[0])[2:].zfill(2)}" for
    color_id, color in enumerate(colors)
}

used_colors = colors


def visualize_all_bbox_together(image, generation):
    if image is None:
        return None, ''

    generation = html.unescape(generation)

    image_width, image_height = image.size
    image = image.resize([500, int(500 / image_width * image_height)])
    image_width, image_height = image.size

    string_list = extract_substrings(generation)
    if string_list:  # it is grounding or detection
        mode = 'all'
        entities = defaultdict(list)
        i = 0
        j = 0
        for string in string_list:
            try:
                obj, string = string.split('</p>')
            except ValueError:
                print('wrong string: ', string)
                continue
            bbox_list = string.split('<delim>')
            flag = False
            for bbox_string in bbox_list:
                integers = re.findall(r'-?\d+', bbox_string)
                if len(integers) == 4:
                    x0, y0, x1, y1 = int(integers[0]), int(integers[1]), int(integers[2]), int(integers[3])
                    left = x0 / bounding_box_size * image_width
                    bottom = y0 / bounding_box_size * image_height
                    right = x1 / bounding_box_size * image_width
                    top = y1 / bounding_box_size * image_height

                    entities[obj].append([left, bottom, right, top])

                    j += 1
                    flag = True
            if flag:
                i += 1
    else:
        integers = re.findall(r'-?\d+', generation)

        if len(integers) == 4:  # it is refer
            mode = 'single'

            entities = list()
            x0, y0, x1, y1 = int(integers[0]), int(integers[1]), int(integers[2]), int(integers[3])
            left = x0 / bounding_box_size * image_width
            bottom = y0 / bounding_box_size * image_height
            right = x1 / bounding_box_size * image_width
            top = y1 / bounding_box_size * image_height
            entities.append([left, bottom, right, top])
        else:
            # don't detect any valid bbox to visualize
            return None, ''

    if len(entities) == 0:
        return None, ''

    if isinstance(image, Image.Image):
        image_h = image.height
        image_w = image.width
        image = np.array(image)

    elif isinstance(image, str):
        if os.path.exists(image):
            pil_img = Image.open(image).convert("RGB")
            image = np.array(pil_img)[:, :, [2, 1, 0]]
            image_h = pil_img.height
            image_w = pil_img.width
        else:
            raise ValueError(f"invaild image path, {image}")
    elif isinstance(image, torch.Tensor):

        image_tensor = image.cpu()
        reverse_norm_mean = torch.tensor([0.48145466, 0.4578275, 0.40821073])[:, None, None]
        reverse_norm_std = torch.tensor([0.26862954, 0.26130258, 0.27577711])[:, None, None]
        image_tensor = image_tensor * reverse_norm_std + reverse_norm_mean
        pil_img = T.ToPILImage()(image_tensor)
        image_h = pil_img.height
        image_w = pil_img.width
        image = np.array(pil_img)[:, :, [2, 1, 0]]
    else:
        raise ValueError(f"invaild image format, {type(image)} for {image}")

    indices = list(range(len(entities)))

    new_image = image.copy()

    previous_bboxes = []
    # size of text
    text_size = 0.5
    # thickness of text
    text_line = 1  # int(max(1 * min(image_h, image_w) / 512, 1))
    box_line = 2
    (c_width, text_height), _ = cv2.getTextSize("F", cv2.FONT_HERSHEY_COMPLEX, text_size, text_line)
    base_height = int(text_height * 0.675)
    text_offset_original = text_height - base_height
    text_spaces = 2

    # num_bboxes = sum(len(x[-1]) for x in entities)
    used_colors = colors  # random.sample(colors, k=num_bboxes)

    color_id = -1
    for entity_idx, entity_name in enumerate(entities):
        if mode == 'single' or mode == 'identify':
            bboxes = entity_name
            bboxes = [bboxes]
        else:
            bboxes = entities[entity_name]
        color_id += 1
        for bbox_id, (x1_norm, y1_norm, x2_norm, y2_norm) in enumerate(bboxes):
            skip_flag = False
            orig_x1, orig_y1, orig_x2, orig_y2 = int(x1_norm), int(y1_norm), int(x2_norm), int(y2_norm)

            color = used_colors[entity_idx % len(used_colors)]  # tuple(np.random.randint(0, 255, size=3).tolist())
            new_image = cv2.rectangle(new_image, (orig_x1, orig_y1), (orig_x2, orig_y2), color, box_line)

            if mode == 'all':
                l_o, r_o = box_line // 2 + box_line % 2, box_line // 2 + box_line % 2 + 1

                x1 = orig_x1 - l_o
                y1 = orig_y1 - l_o

                if y1 < text_height + text_offset_original + 2 * text_spaces:
                    y1 = orig_y1 + r_o + text_height + text_offset_original + 2 * text_spaces
                    x1 = orig_x1 + r_o

                # add text background
                (text_width, text_height), _ = cv2.getTextSize(f"  {entity_name}", cv2.FONT_HERSHEY_COMPLEX, text_size,
                                                               text_line)
                text_bg_x1, text_bg_y1, text_bg_x2, text_bg_y2 = x1, y1 - (
                            text_height + text_offset_original + 2 * text_spaces), x1 + text_width, y1

                for prev_bbox in previous_bboxes:
                    if computeIoU((text_bg_x1, text_bg_y1, text_bg_x2, text_bg_y2), prev_bbox['bbox']) > 0.95 and \
                            prev_bbox['phrase'] == entity_name:
                        skip_flag = True
                        break
                    while is_overlapping((text_bg_x1, text_bg_y1, text_bg_x2, text_bg_y2), prev_bbox['bbox']):
                        text_bg_y1 += (text_height + text_offset_original + 2 * text_spaces)
                        text_bg_y2 += (text_height + text_offset_original + 2 * text_spaces)
                        y1 += (text_height + text_offset_original + 2 * text_spaces)

                        if text_bg_y2 >= image_h:
                            text_bg_y1 = max(0, image_h - (text_height + text_offset_original + 2 * text_spaces))
                            text_bg_y2 = image_h
                            y1 = image_h
                            break
                if not skip_flag:
                    alpha = 0.5
                    for i in range(text_bg_y1, text_bg_y2):
                        for j in range(text_bg_x1, text_bg_x2):
                            if i < image_h and j < image_w:
                                if j < text_bg_x1 + 1.35 * c_width:
                                    # original color
                                    bg_color = color
                                else:
                                    # white
                                    bg_color = [255, 255, 255]
                                new_image[i, j] = (alpha * new_image[i, j] + (1 - alpha) * np.array(bg_color)).astype(
                                    np.uint8)

                    cv2.putText(
                        new_image, f"  {entity_name}", (x1, y1 - text_offset_original - 1 * text_spaces),
                        cv2.FONT_HERSHEY_COMPLEX, text_size, (0, 0, 0), text_line, cv2.LINE_AA
                    )

                    previous_bboxes.append(
                        {'bbox': (text_bg_x1, text_bg_y1, text_bg_x2, text_bg_y2), 'phrase': entity_name})

    if mode == 'all':
        def color_iterator(colors):
            while True:
                for color in colors:
                    yield color

        color_gen = color_iterator(colors)

        # Add colors to phrases and remove <p></p>
        def colored_phrases(match):
            phrase = match.group(1)
            color = next(color_gen)
            return f'<span style="color:rgb{color}">{phrase}</span>'

        generation = re.sub(r'{<\d+><\d+><\d+><\d+>}|<delim>', '', generation)
        generation_colored = re.sub(r'<p>(.*?)</p>', colored_phrases, generation)
    else:
        generation_colored = ''

    pil_image = Image.fromarray(new_image)
    return pil_image, generation_colored


def gradio_reset(chat_state, img_list):
    if chat_state is not None:
        chat_state.messages = []
    if img_list is not None:
        img_list = []
    return None, gr.update(value=None, interactive=True), gr.update(placeholder='Upload your image and chat',
                                                                    interactive=True), chat_state, img_list


def image_upload_trigger(upload_flag, replace_flag, img_list):
    # set the upload flag to true when receive a new image.
    # if there is an old image (and old conversation), set the replace flag to true to reset the conv later.
    upload_flag = 1
    if img_list:
        replace_flag = 1
    return upload_flag, replace_flag


def example_trigger(text_input, image, upload_flag, replace_flag, img_list):
    # set the upload flag to true when receive a new image.
    # if there is an old image (and old conversation), set the replace flag to true to reset the conv later.
    upload_flag = 1
    if img_list or replace_flag == 1:
        replace_flag = 1

    return upload_flag, replace_flag


def gradio_ask(user_message, chatbot, chat_state, gr_img, img_list, upload_flag, replace_flag):
    if len(user_message) == 0:
        text_box_show = 'Input should not be empty!'
    else:
        text_box_show = ''

    if isinstance(gr_img, dict):
        gr_img, mask = gr_img['image'], gr_img['mask']
    else:
        mask = None

    if '[identify]' in user_message:
        # check if user provide bbox in the text input
        integers = re.findall(r'-?\d+', user_message)
        if len(integers) != 4:  # no bbox in text
            bbox = mask2bbox(mask)
            user_message = user_message + bbox

    if chat_state is None:
        chat_state = CONV_VISION.copy()

    if upload_flag:
        if replace_flag:
            chat_state = CONV_VISION.copy()  # new image, reset everything
            replace_flag = 0
            chatbot = []
        img_list = []
        llm_message = chat.upload_img(gr_img, chat_state, img_list)
        upload_flag = 0

    chat.ask(user_message, chat_state)

    chatbot = chatbot + [[user_message, None]]

    if '[identify]' in user_message:
        visual_img, _ = visualize_all_bbox_together(gr_img, user_message)
        if visual_img is not None:
            file_path = save_tmp_img(visual_img)
            chatbot = chatbot + [[(file_path,), None]]

    return text_box_show, chatbot, chat_state, img_list, upload_flag, replace_flag


def gradio_answer(chatbot, chat_state, img_list, temperature):
    llm_message = chat.answer(conv=chat_state,
                              img_list=img_list,
                              temperature=temperature,
                              max_new_tokens=500,
                              max_length=2000)[0]
    chatbot[-1][1] = llm_message
    return chatbot, chat_state


def gradio_stream_answer(chatbot, chat_state, img_list, temperature):
    if len(img_list) > 0:
        if not isinstance(img_list[0], torch.Tensor):
            chat.encode_img(img_list)
    streamer = chat.stream_answer(conv=chat_state,
                                  img_list=img_list,
                                  temperature=temperature,
                                  max_new_tokens=500,
                                  max_length=2000)
    output = ''
    for new_output in streamer:
        escapped = escape_markdown(new_output)
        output += escapped
        chatbot[-1][1] = output
        yield chatbot, chat_state
    chat_state.messages[-1][1] = '</s>'
    return chatbot, chat_state


def gradio_visualize(chatbot, gr_img):
    if isinstance(gr_img, dict):
        gr_img, mask = gr_img['image'], gr_img['mask']

    unescaped = reverse_escape(chatbot[-1][1])
    visual_img, generation_color = visualize_all_bbox_together(gr_img, unescaped)
    if visual_img is not None:
        if len(generation_color):
            chatbot[-1][1] = generation_color
        file_path = save_tmp_img(visual_img)
        chatbot = chatbot + [[None, (file_path,)]]

    return chatbot


def gradio_taskselect(idx):
    prompt_list = [
        '[grounding] Describe the ongoing action in this image',
        '[refer] ',
        '[detection] ',
        '[identify] what is this ',
        '[vqa] '
    ]
    instruct_list = [
        '**Hint:** Send the command to generate a grounded image description',
        '**Hint:** Type in a phrase about an object in the image and send the command',
        '**Hint:** Type in a caption or phrase, and see object locations in the image',
        '**Hint:** Draw a bounding box on the uploaded image then send the command. Click the "clear" botton on the top right of the image before redraw',
        '**Hint:** Send a question to get a short answer',
    ]
    return prompt_list[idx], instruct_list[idx]




chat = Chat(model, vis_processor, device=device)

title = """<h1 align="center">VLM4HOI Demo</h1>"""
description = 'Welcome to Our VLM4HOI Demo!'
# TODO: Add link to our webpage and then uncomment this line
# article = """<p><a href=''><img src='https://img.shields.io/badge/Project-Page-Green'></a></p>"""

introduction = '''
VLM4HOI enables hand object interaction referral in images from egocentric videos. Use the following __task tags__ to interact with the model:
1. Grounding: Generate a grounded image description around the action happening in the image.
2. Refer: As about hand object relationship. For example, "[refer] which hand is holding the __object name__ in". 
3. Detection: Give an object's name as input and the model will generate its location in the image. 
4. Identify: Draw the bounding box on the uploaded image window and CLICK **Send** to generate the name of the object under the bounding box. (CLICK "clear" button before re-drawing next time).
5. VQA: Ask a visual question to the model. For example, "[vqa] What is the object in the left/right hand of the person?" 
'''

text_input = gr.Textbox(placeholder='Upload your image and chat', interactive=True, show_label=False, container=False,
                        scale=8)
with gr.Blocks() as demo:
    gr.Markdown(title)
    # gr.Markdown(description)
    # TODO: Will add the article link after the project page is up
    # gr.Markdown(article)

    with gr.Row():
        with gr.Column(scale=0.5):
            image = gr.Image(type="pil", tool='sketch', brush_radius=20)

            temperature = gr.Slider(
                minimum=0.1,
                maximum=1.5,
                value=0.6,
                step=0.1,
                interactive=True,
                label="Temperature",
            )

            clear = gr.Button("Restart")

            gr.Markdown(introduction)

        with gr.Column():
            chat_state = gr.State(value=None)
            img_list = gr.State(value=[])
            chatbot = gr.Chatbot(label='VLM4HOI')

            dataset = gr.Dataset(
                components=[gr.Textbox(visible=False)],
                samples=[['Grounding'], ['Refer'], ['Detection'], ['Identify'], ['VQA']],
                type="index",
                label='Task Tags',
            )
            task_inst = gr.Markdown('Upload your image from an egocentric video and chat')
            with gr.Row():
                text_input.render()
                send = gr.Button("Send", variant='primary', size='sm', scale=1)

    upload_flag = gr.State(value=0)
    replace_flag = gr.State(value=0)
    image.upload(image_upload_trigger, [upload_flag, replace_flag, img_list], [upload_flag, replace_flag])

    with gr.Row():
        with gr.Column():
            gr.Examples(examples=[
                ["examples/frame_0000108904.jpg", "[refer] where is the left hand of the person", upload_flag, replace_flag, img_list],
                ["examples/frame_0000006305.jpg", "[vqa] what is in the right hand of the person", upload_flag, replace_flag, img_list],
                ["examples/frame_0000002150.jpg", "[refer] Which hand has the supplement box", upload_flag, replace_flag,
                 img_list],
                ["examples/videoframe_29225.png", "[refer] left hand", upload_flag,
                 replace_flag, img_list],
            ], inputs=[image, text_input, upload_flag, replace_flag, img_list], fn=example_trigger,
                outputs=[upload_flag, replace_flag])
        with gr.Column():
            gr.Examples(examples=[
                ["examples/videoframe_377388.png", "[identify] what is this {<1><46><42><87>}",
                 upload_flag, replace_flag, img_list],
                ["examples/videoframe_1032599.png", "[refer] locate the object being manipuated", upload_flag, replace_flag, img_list],
                ["examples/videoframe_55236.png", "[detection] shoes", upload_flag, replace_flag, img_list],
                ["examples/videoframe_394773.png", "[refer] locate the object being manipulated by left hand", upload_flag,
                 replace_flag, img_list],
            ], inputs=[image, text_input, upload_flag, replace_flag, img_list], fn=example_trigger,
                outputs=[upload_flag, replace_flag])

    dataset.click(
        gradio_taskselect,
        inputs=[dataset],
        outputs=[text_input, task_inst],
        show_progress="hidden",
        postprocess=False,
        queue=False,
    )

    text_input.submit(
        gradio_ask,
        [text_input, chatbot, chat_state, image, img_list, upload_flag, replace_flag],
        [text_input, chatbot, chat_state, img_list, upload_flag, replace_flag], queue=False
    ).success(
        gradio_stream_answer,
        [chatbot, chat_state, img_list, temperature],
        [chatbot, chat_state]
    ).success(
        gradio_visualize,
        [chatbot, image],
        [chatbot],
        queue=False,
    )

    send.click(
        gradio_ask,
        [text_input, chatbot, chat_state, image, img_list, upload_flag, replace_flag],
        [text_input, chatbot, chat_state, img_list, upload_flag, replace_flag], queue=False
    ).success(
        gradio_stream_answer,
        [chatbot, chat_state, img_list, temperature],
        [chatbot, chat_state]
    ).success(
        gradio_visualize,
        [chatbot, image],
        [chatbot],
        queue=False,
    )

    clear.click(gradio_reset, [chat_state, img_list], [chatbot, image, text_input, chat_state, img_list], queue=False)

demo.launch(share=True, enable_queue=True)