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omni_bot / swarms /agents /multi_modal_visual_agent.py

Zack Zitting Bradshaw

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	import os
	import random
	import torch
	import cv2
	import re
	import uuid
	from PIL import Image, ImageDraw, ImageOps, ImageFont
	import math
	import numpy as np
	import inspect
	from transformers import pipeline, BlipProcessor, BlipForConditionalGeneration, BlipForQuestionAnswering

	from diffusers import StableDiffusionPipeline, StableDiffusionInpaintPipeline, StableDiffusionInstructPix2PixPipeline
	from diffusers import EulerAncestralDiscreteScheduler
	from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler
	from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker

	from controlnet_aux import OpenposeDetector, MLSDdetector, HEDdetector

	from langchain.agents.initialize import initialize_agent
	from langchain.agents.tools import Tool
	from langchain.chains.conversation.memory import ConversationBufferMemory
	from langchain.llms.openai import OpenAI

	# Grounding DINO
	import groundingdino.datasets.transforms as T
	from groundingdino.models import build_model
	from groundingdino.util.slconfig import SLConfig
	from groundingdino.util.utils import clean_state_dict, get_phrases_from_posmap

	# segment anything
	from segment_anything import build_sam, SamPredictor, SamAutomaticMaskGenerator
	import cv2
	import numpy as np
	import matplotlib.pyplot as plt
	import wget



	#prompts
	VISUAL_AGENT_PREFIX = """
	Worker Multi-Modal Agent is designed to be able to assist with
	a wide range of text and visual related tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics.
	Worker Multi-Modal Agent is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.

	Worker Multi-Modal Agent is able to process and understand large amounts of text and images. As a language model, Worker Multi-Modal Agent can not directly read images, but it has a list of tools to finish different visual tasks. Each image will have a file name formed as "image/xxx.png", and Worker Multi-Modal Agent can invoke different tools to indirectly understand pictures. When talking about images, Worker Multi-Modal Agent is very strict to the file name and will never fabricate nonexistent files. When using tools to generate new image files, Worker Multi-Modal Agent is also known that the image may not be the same as the user's demand, and will use other visual question answering tools or description tools to observe the real image. Worker Multi-Modal Agent is able to use tools in a sequence, and is loyal to the tool observation outputs rather than faking the image content and image file name. It will remember to provide the file name from the last tool observation, if a new image is generated.

	Human may provide new figures to Worker Multi-Modal Agent with a description. The description helps Worker Multi-Modal Agent to understand this image, but Worker Multi-Modal Agent should use tools to finish following tasks, rather than directly imagine from the description.

	Overall, Worker Multi-Modal Agent is a powerful visual dialogue assistant tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics.


	TOOLS:
	------

	Worker Multi-Modal Agent has access to the following tools:"""

	VISUAL_AGENT_FORMAT_INSTRUCTIONS = """To use a tool, please use the following format:

	```
	Thought: Do I need to use a tool? Yes
	Action: the action to take, should be one of [{tool_names}]
	Action Input: the input to the action
	Observation: the result of the action
	```

	When you have a response to say to the Human, or if you do not need to use a tool, you MUST use the format:

	```
	Thought: Do I need to use a tool? No
	{ai_prefix}: [your response here]
	```
	"""

	VISUAL_AGENT_SUFFIX = """You are very strict to the filename correctness and will never fake a file name if it does not exist.
	You will remember to provide the image file name loyally if it's provided in the last tool observation.

	Begin!

	Previous conversation history:
	{chat_history}

	New input: {input}
	Since Worker Multi-Modal Agent is a text language model, Worker Multi-Modal Agent must use tools to observe images rather than imagination.
	The thoughts and observations are only visible for Worker Multi-Modal Agent, Worker Multi-Modal Agent should remember to repeat important information in the final response for Human.
	Thought: Do I need to use a tool? {agent_scratchpad} Let's think step by step.
	"""

	VISUAL_AGENT_PREFIX_CN = """Worker Multi-Modal Agent 旨在能够协助完成范围广泛的文本和视觉相关任务，从回答简单的问题到提供对广泛主题的深入解释和讨论。 Worker Multi-Modal Agent 能够根据收到的输入生成类似人类的文本，使其能够进行听起来自然的对话，并提供连贯且与手头主题相关的响应。

	Worker Multi-Modal Agent 能够处理和理解大量文本和图像。作为一种语言模型，Worker Multi-Modal Agent 不能直接读取图像，但它有一系列工具来完成不同的视觉任务。每张图片都会有一个文件名，格式为“image/xxx.png”，Worker Multi-Modal Agent可以调用不同的工具来间接理解图片。在谈论图片时，Worker Multi-Modal Agent 对文件名的要求非常严格，绝不会伪造不存在的文件。在使用工具生成新的图像文件时，Worker Multi-Modal Agent也知道图像可能与用户需求不一样，会使用其他视觉问答工具或描述工具来观察真实图像。 Worker Multi-Modal Agent 能够按顺序使用工具，并且忠于工具观察输出，而不是伪造图像内容和图像文件名。如果生成新图像，它将记得提供上次工具观察的文件名。

	Human 可能会向 Worker Multi-Modal Agent 提供带有描述的新图形。描述帮助 Worker Multi-Modal Agent 理解这个图像，但 Worker Multi-Modal Agent 应该使用工具来完成以下任务，而不是直接从描述中想象。有些工具将会返回英文描述，但你对用户的聊天应当采用中文。

	总的来说，Worker Multi-Modal Agent 是一个强大的可视化对话辅助工具，可以帮助处理范围广泛的任务，并提供关于范围广泛的主题的有价值的见解和信息。

	工具列表:
	------

	Worker Multi-Modal Agent 可以使用这些工具:"""

	VISUAL_AGENT_FORMAT_INSTRUCTIONS_CN = """用户使用中文和你进行聊天，但是工具的参数应当使用英文。如果要调用工具，你必须遵循如下格式:

	```
	Thought: Do I need to use a tool? Yes
	Action: the action to take, should be one of [{tool_names}]
	Action Input: the input to the action
	Observation: the result of the action
	```

	当你不再需要继续调用工具，而是对观察结果进行总结回复时，你必须使用如下格式：


	```
	Thought: Do I need to use a tool? No
	{ai_prefix}: [your response here]
	```
	"""

	VISUAL_AGENT_SUFFIX_CN = """你对文件名的正确性非常严格，而且永远不会伪造不存在的文件。

	开始!

	因为Worker Multi-Modal Agent是一个文本语言模型，必须使用工具去观察图片而不是依靠想象。
	推理想法和观察结果只对Worker Multi-Modal Agent可见，需要记得在最终回复时把重要的信息重复给用户，你只能给用户返回中文句子。我们一步一步思考。在你使用工具时，工具的参数只能是英文。

	聊天历史:
	{chat_history}

	新输入: {input}
	Thought: Do I need to use a tool? {agent_scratchpad}
	"""

	os.makedirs('image', exist_ok=True)


	def seed_everything(seed):
	random.seed(seed)
	np.random.seed(seed)
	torch.manual_seed(seed)
	torch.cuda.manual_seed_all(seed)
	return seed


	def prompts(name, description):
	def decorator(func):
	func.name = name
	func.description = description
	return func

	return decorator


	def blend_gt2pt(old_image, new_image, sigma=0.15, steps=100):
	new_size = new_image.size
	old_size = old_image.size
	easy_img = np.array(new_image)
	gt_img_array = np.array(old_image)
	pos_w = (new_size[0] - old_size[0]) // 2
	pos_h = (new_size[1] - old_size[1]) // 2

	kernel_h = cv2.getGaussianKernel(old_size[1], old_size[1] * sigma)
	kernel_w = cv2.getGaussianKernel(old_size[0], old_size[0] * sigma)
	kernel = np.multiply(kernel_h, np.transpose(kernel_w))

	kernel[steps:-steps, steps:-steps] = 1
	kernel[:steps, :steps] = kernel[:steps, :steps] / kernel[steps - 1, steps - 1]
	kernel[:steps, -steps:] = kernel[:steps, -steps:] / kernel[steps - 1, -(steps)]
	kernel[-steps:, :steps] = kernel[-steps:, :steps] / kernel[-steps, steps - 1]
	kernel[-steps:, -steps:] = kernel[-steps:, -steps:] / kernel[-steps, -steps]
	kernel = np.expand_dims(kernel, 2)
	kernel = np.repeat(kernel, 3, 2)

	weight = np.linspace(0, 1, steps)
	top = np.expand_dims(weight, 1)
	top = np.repeat(top, old_size[0] - 2 * steps, 1)
	top = np.expand_dims(top, 2)
	top = np.repeat(top, 3, 2)

	weight = np.linspace(1, 0, steps)
	down = np.expand_dims(weight, 1)
	down = np.repeat(down, old_size[0] - 2 * steps, 1)
	down = np.expand_dims(down, 2)
	down = np.repeat(down, 3, 2)

	weight = np.linspace(0, 1, steps)
	left = np.expand_dims(weight, 0)
	left = np.repeat(left, old_size[1] - 2 * steps, 0)
	left = np.expand_dims(left, 2)
	left = np.repeat(left, 3, 2)

	weight = np.linspace(1, 0, steps)
	right = np.expand_dims(weight, 0)
	right = np.repeat(right, old_size[1] - 2 * steps, 0)
	right = np.expand_dims(right, 2)
	right = np.repeat(right, 3, 2)

	kernel[:steps, steps:-steps] = top
	kernel[-steps:, steps:-steps] = down
	kernel[steps:-steps, :steps] = left
	kernel[steps:-steps, -steps:] = right

	pt_gt_img = easy_img[pos_h:pos_h + old_size[1], pos_w:pos_w + old_size[0]]
	gaussian_gt_img = kernel * gt_img_array + (1 - kernel) * pt_gt_img # gt img with blur img
	gaussian_gt_img = gaussian_gt_img.astype(np.int64)
	easy_img[pos_h:pos_h + old_size[1], pos_w:pos_w + old_size[0]] = gaussian_gt_img
	gaussian_img = Image.fromarray(easy_img)
	return gaussian_img


	def cut_dialogue_history(history_memory, keep_last_n_words=500):
	if history_memory is None or len(history_memory) == 0:
	return history_memory
	tokens = history_memory.split()
	n_tokens = len(tokens)
	print(f"history_memory:{history_memory}, n_tokens: {n_tokens}")
	if n_tokens < keep_last_n_words:
	return history_memory
	paragraphs = history_memory.split('\n')
	last_n_tokens = n_tokens
	while last_n_tokens >= keep_last_n_words:
	last_n_tokens -= len(paragraphs[0].split(' '))
	paragraphs = paragraphs[1:]
	return '\n' + '\n'.join(paragraphs)


	def get_new_image_name(org_img_name, func_name="update"):
	head_tail = os.path.split(org_img_name)
	head = head_tail[0]
	tail = head_tail[1]
	name_split = tail.split('.')[0].split('_')
	this_new_uuid = str(uuid.uuid4())[:4]
	if len(name_split) == 1:
	most_org_file_name = name_split[0]
	else:
	assert len(name_split) == 4
	most_org_file_name = name_split[3]
	recent_prev_file_name = name_split[0]
	new_file_name = f'{this_new_uuid}_{func_name}_{recent_prev_file_name}_{most_org_file_name}.png'
	return os.path.join(head, new_file_name)

	class InstructPix2Pix:
	def __init__(self, device):
	print(f"Initializing InstructPix2Pix to {device}")
	self.device = device
	self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32

	self.pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained("timbrooks/instruct-pix2pix",
	safety_checker=StableDiffusionSafetyChecker.from_pretrained('CompVis/stable-diffusion-safety-checker'),
	torch_dtype=self.torch_dtype).to(device)
	self.pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(self.pipe.scheduler.config)

	@prompts(name="Instruct Image Using Text",
	description="useful when you want to the style of the image to be like the text. "
	"like: make it look like a painting. or make it like a robot. "
	"The input to this tool should be a comma separated string of two, "
	"representing the image_path and the text. ")
	def inference(self, inputs):
	"""Change style of image."""
	print("===>Starting InstructPix2Pix Inference")
	image_path, text = inputs.split(",")[0], ','.join(inputs.split(',')[1:])
	original_image = Image.open(image_path)
	image = self.pipe(text, image=original_image, num_inference_steps=40, image_guidance_scale=1.2).images[0]
	updated_image_path = get_new_image_name(image_path, func_name="pix2pix")
	image.save(updated_image_path)
	print(f"\nProcessed InstructPix2Pix, Input Image: {image_path}, Instruct Text: {text}, "
	f"Output Image: {updated_image_path}")
	return updated_image_path


	class Text2Image:
	def __init__(self, device):
	print(f"Initializing Text2Image to {device}")
	self.device = device
	self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32
	self.pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5",
	torch_dtype=self.torch_dtype)
	self.pipe.to(device)
	self.a_prompt = 'best quality, extremely detailed'
	self.n_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, ' \
	'fewer digits, cropped, worst quality, low quality'

	@prompts(name="Generate Image From User Input Text",
	description="useful when you want to generate an image from a user input text and save it to a file. "
	"like: generate an image of an object or something, or generate an image that includes some objects. "
	"The input to this tool should be a string, representing the text used to generate image. ")
	def inference(self, text):
	image_filename = os.path.join('image', f"{str(uuid.uuid4())[:8]}.png")
	prompt = text + ', ' + self.a_prompt
	image = self.pipe(prompt, negative_prompt=self.n_prompt).images[0]
	image.save(image_filename)
	print(
	f"\nProcessed Text2Image, Input Text: {text}, Output Image: {image_filename}")
	return image_filename


	class ImageCaptioning:
	def __init__(self, device):
	print(f"Initializing ImageCaptioning to {device}")
	self.device = device
	self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32
	self.processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
	self.model = BlipForConditionalGeneration.from_pretrained(
	"Salesforce/blip-image-captioning-base", torch_dtype=self.torch_dtype).to(self.device)

	@prompts(name="Get Photo Description",
	description="useful when you want to know what is inside the photo. receives image_path as input. "
	"The input to this tool should be a string, representing the image_path. ")
	def inference(self, image_path):
	inputs = self.processor(Image.open(image_path), return_tensors="pt").to(self.device, self.torch_dtype)
	out = self.model.generate(**inputs)
	captions = self.processor.decode(out[0], skip_special_tokens=True)
	print(f"\nProcessed ImageCaptioning, Input Image: {image_path}, Output Text: {captions}")
	return captions


	class Image2Canny:
	def __init__(self, device):
	print("Initializing Image2Canny")
	self.low_threshold = 100
	self.high_threshold = 200

	@prompts(name="Edge Detection On Image",
	description="useful when you want to detect the edge of the image. "
	"like: detect the edges of this image, or canny detection on image, "
	"or perform edge detection on this image, or detect the canny image of this image. "
	"The input to this tool should be a string, representing the image_path")
	def inference(self, inputs):
	image = Image.open(inputs)
	image = np.array(image)
	canny = cv2.Canny(image, self.low_threshold, self.high_threshold)
	canny = canny[:, :, None]
	canny = np.concatenate([canny, canny, canny], axis=2)
	canny = Image.fromarray(canny)
	updated_image_path = get_new_image_name(inputs, func_name="edge")
	canny.save(updated_image_path)
	print(f"\nProcessed Image2Canny, Input Image: {inputs}, Output Text: {updated_image_path}")
	return updated_image_path


	class CannyText2Image:
	def __init__(self, device):
	print(f"Initializing CannyText2Image to {device}")
	self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32
	self.controlnet = ControlNetModel.from_pretrained("fusing/stable-diffusion-v1-5-controlnet-canny",
	torch_dtype=self.torch_dtype)
	self.pipe = StableDiffusionControlNetPipeline.from_pretrained(
	"runwayml/stable-diffusion-v1-5", controlnet=self.controlnet, safety_checker=StableDiffusionSafetyChecker.from_pretrained('CompVis/stable-diffusion-safety-checker'),
	torch_dtype=self.torch_dtype)
	self.pipe.scheduler = UniPCMultistepScheduler.from_config(self.pipe.scheduler.config)
	self.pipe.to(device)
	self.seed = -1
	self.a_prompt = 'best quality, extremely detailed'
	self.n_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, ' \
	'fewer digits, cropped, worst quality, low quality'

	@prompts(name="Generate Image Condition On Canny Image",
	description="useful when you want to generate a new real image from both the user description and a canny image."
	" like: generate a real image of a object or something from this canny image,"
	" or generate a new real image of a object or something from this edge image. "
	"The input to this tool should be a comma separated string of two, "
	"representing the image_path and the user description. ")
	def inference(self, inputs):
	image_path, instruct_text = inputs.split(",")[0], ','.join(inputs.split(',')[1:])
	image = Image.open(image_path)
	self.seed = random.randint(0, 65535)
	seed_everything(self.seed)
	prompt = f'{instruct_text}, {self.a_prompt}'
	image = self.pipe(prompt, image, num_inference_steps=20, eta=0.0, negative_prompt=self.n_prompt,
	guidance_scale=9.0).images[0]
	updated_image_path = get_new_image_name(image_path, func_name="canny2image")
	image.save(updated_image_path)
	print(f"\nProcessed CannyText2Image, Input Canny: {image_path}, Input Text: {instruct_text}, "
	f"Output Text: {updated_image_path}")
	return updated_image_path


	class Image2Line:
	def __init__(self, device):
	print("Initializing Image2Line")
	self.detector = MLSDdetector.from_pretrained('lllyasviel/ControlNet')

	@prompts(name="Line Detection On Image",
	description="useful when you want to detect the straight line of the image. "
	"like: detect the straight lines of this image, or straight line detection on image, "
	"or perform straight line detection on this image, or detect the straight line image of this image. "
	"The input to this tool should be a string, representing the image_path")
	def inference(self, inputs):
	image = Image.open(inputs)
	mlsd = self.detector(image)
	updated_image_path = get_new_image_name(inputs, func_name="line-of")
	mlsd.save(updated_image_path)
	print(f"\nProcessed Image2Line, Input Image: {inputs}, Output Line: {updated_image_path}")
	return updated_image_path


	class LineText2Image:
	def __init__(self, device):
	print(f"Initializing LineText2Image to {device}")
	self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32
	self.controlnet = ControlNetModel.from_pretrained("fusing/stable-diffusion-v1-5-controlnet-mlsd",
	torch_dtype=self.torch_dtype)
	self.pipe = StableDiffusionControlNetPipeline.from_pretrained(
	"runwayml/stable-diffusion-v1-5", controlnet=self.controlnet, safety_checker=StableDiffusionSafetyChecker.from_pretrained('CompVis/stable-diffusion-safety-checker'),
	torch_dtype=self.torch_dtype
	)
	self.pipe.scheduler = UniPCMultistepScheduler.from_config(self.pipe.scheduler.config)
	self.pipe.to(device)
	self.seed = -1
	self.a_prompt = 'best quality, extremely detailed'
	self.n_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, ' \
	'fewer digits, cropped, worst quality, low quality'

	@prompts(name="Generate Image Condition On Line Image",
	description="useful when you want to generate a new real image from both the user description "
	"and a straight line image. "
	"like: generate a real image of a object or something from this straight line image, "
	"or generate a new real image of a object or something from this straight lines. "
	"The input to this tool should be a comma separated string of two, "
	"representing the image_path and the user description. ")
	def inference(self, inputs):
	image_path, instruct_text = inputs.split(",")[0], ','.join(inputs.split(',')[1:])
	image = Image.open(image_path)
	self.seed = random.randint(0, 65535)
	seed_everything(self.seed)
	prompt = f'{instruct_text}, {self.a_prompt}'
	image = self.pipe(prompt, image, num_inference_steps=20, eta=0.0, negative_prompt=self.n_prompt,
	guidance_scale=9.0).images[0]
	updated_image_path = get_new_image_name(image_path, func_name="line2image")
	image.save(updated_image_path)
	print(f"\nProcessed LineText2Image, Input Line: {image_path}, Input Text: {instruct_text}, "
	f"Output Text: {updated_image_path}")
	return updated_image_path


	class Image2Hed:
	def __init__(self, device):
	print("Initializing Image2Hed")
	self.detector = HEDdetector.from_pretrained('lllyasviel/ControlNet')

	@prompts(name="Hed Detection On Image",
	description="useful when you want to detect the soft hed boundary of the image. "
	"like: detect the soft hed boundary of this image, or hed boundary detection on image, "
	"or perform hed boundary detection on this image, or detect soft hed boundary image of this image. "
	"The input to this tool should be a string, representing the image_path")
	def inference(self, inputs):
	image = Image.open(inputs)
	hed = self.detector(image)
	updated_image_path = get_new_image_name(inputs, func_name="hed-boundary")
	hed.save(updated_image_path)
	print(f"\nProcessed Image2Hed, Input Image: {inputs}, Output Hed: {updated_image_path}")
	return updated_image_path


	class HedText2Image:
	def __init__(self, device):
	print(f"Initializing HedText2Image to {device}")
	self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32
	self.controlnet = ControlNetModel.from_pretrained("fusing/stable-diffusion-v1-5-controlnet-hed",
	torch_dtype=self.torch_dtype)
	self.pipe = StableDiffusionControlNetPipeline.from_pretrained(
	"runwayml/stable-diffusion-v1-5", controlnet=self.controlnet, safety_checker=StableDiffusionSafetyChecker.from_pretrained('CompVis/stable-diffusion-safety-checker'),
	torch_dtype=self.torch_dtype
	)
	self.pipe.scheduler = UniPCMultistepScheduler.from_config(self.pipe.scheduler.config)
	self.pipe.to(device)
	self.seed = -1
	self.a_prompt = 'best quality, extremely detailed'
	self.n_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, ' \
	'fewer digits, cropped, worst quality, low quality'

	@prompts(name="Generate Image Condition On Soft Hed Boundary Image",
	description="useful when you want to generate a new real image from both the user description "
	"and a soft hed boundary image. "
	"like: generate a real image of a object or something from this soft hed boundary image, "
	"or generate a new real image of a object or something from this hed boundary. "
	"The input to this tool should be a comma separated string of two, "
	"representing the image_path and the user description")
	def inference(self, inputs):
	image_path, instruct_text = inputs.split(",")[0], ','.join(inputs.split(',')[1:])
	image = Image.open(image_path)
	self.seed = random.randint(0, 65535)
	seed_everything(self.seed)
	prompt = f'{instruct_text}, {self.a_prompt}'
	image = self.pipe(prompt, image, num_inference_steps=20, eta=0.0, negative_prompt=self.n_prompt,
	guidance_scale=9.0).images[0]
	updated_image_path = get_new_image_name(image_path, func_name="hed2image")
	image.save(updated_image_path)
	print(f"\nProcessed HedText2Image, Input Hed: {image_path}, Input Text: {instruct_text}, "
	f"Output Image: {updated_image_path}")
	return updated_image_path


	class Image2Scribble:
	def __init__(self, device):
	print("Initializing Image2Scribble")
	self.detector = HEDdetector.from_pretrained('lllyasviel/ControlNet')

	@prompts(name="Sketch Detection On Image",
	description="useful when you want to generate a scribble of the image. "
	"like: generate a scribble of this image, or generate a sketch from this image, "
	"detect the sketch from this image. "
	"The input to this tool should be a string, representing the image_path")
	def inference(self, inputs):
	image = Image.open(inputs)
	scribble = self.detector(image, scribble=True)
	updated_image_path = get_new_image_name(inputs, func_name="scribble")
	scribble.save(updated_image_path)
	print(f"\nProcessed Image2Scribble, Input Image: {inputs}, Output Scribble: {updated_image_path}")
	return updated_image_path


	class ScribbleText2Image:
	def __init__(self, device):
	print(f"Initializing ScribbleText2Image to {device}")
	self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32
	self.controlnet = ControlNetModel.from_pretrained("fusing/stable-diffusion-v1-5-controlnet-scribble",
	torch_dtype=self.torch_dtype)
	self.pipe = StableDiffusionControlNetPipeline.from_pretrained(
	"runwayml/stable-diffusion-v1-5", controlnet=self.controlnet, safety_checker=StableDiffusionSafetyChecker.from_pretrained('CompVis/stable-diffusion-safety-checker'),
	torch_dtype=self.torch_dtype
	)
	self.pipe.scheduler = UniPCMultistepScheduler.from_config(self.pipe.scheduler.config)
	self.pipe.to(device)
	self.seed = -1
	self.a_prompt = 'best quality, extremely detailed'
	self.n_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, ' \
	'fewer digits, cropped, worst quality, low quality'

	@prompts(name="Generate Image Condition On Sketch Image",
	description="useful when you want to generate a new real image from both the user description and "
	"a scribble image or a sketch image. "
	"The input to this tool should be a comma separated string of two, "
	"representing the image_path and the user description")
	def inference(self, inputs):
	image_path, instruct_text = inputs.split(",")[0], ','.join(inputs.split(',')[1:])
	image = Image.open(image_path)
	self.seed = random.randint(0, 65535)
	seed_everything(self.seed)
	prompt = f'{instruct_text}, {self.a_prompt}'
	image = self.pipe(prompt, image, num_inference_steps=20, eta=0.0, negative_prompt=self.n_prompt,
	guidance_scale=9.0).images[0]
	updated_image_path = get_new_image_name(image_path, func_name="scribble2image")
	image.save(updated_image_path)
	print(f"\nProcessed ScribbleText2Image, Input Scribble: {image_path}, Input Text: {instruct_text}, "
	f"Output Image: {updated_image_path}")
	return updated_image_path


	class Image2Pose:
	def __init__(self, device):
	print("Initializing Image2Pose")
	self.detector = OpenposeDetector.from_pretrained('lllyasviel/ControlNet')

	@prompts(name="Pose Detection On Image",
	description="useful when you want to detect the human pose of the image. "
	"like: generate human poses of this image, or generate a pose image from this image. "
	"The input to this tool should be a string, representing the image_path")
	def inference(self, inputs):
	image = Image.open(inputs)
	pose = self.detector(image)
	updated_image_path = get_new_image_name(inputs, func_name="human-pose")
	pose.save(updated_image_path)
	print(f"\nProcessed Image2Pose, Input Image: {inputs}, Output Pose: {updated_image_path}")
	return updated_image_path


	class PoseText2Image:
	def __init__(self, device):
	print(f"Initializing PoseText2Image to {device}")
	self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32
	self.controlnet = ControlNetModel.from_pretrained("fusing/stable-diffusion-v1-5-controlnet-openpose",
	torch_dtype=self.torch_dtype)
	self.pipe = StableDiffusionControlNetPipeline.from_pretrained(
	"runwayml/stable-diffusion-v1-5", controlnet=self.controlnet, safety_checker=StableDiffusionSafetyChecker.from_pretrained('CompVis/stable-diffusion-safety-checker'),
	torch_dtype=self.torch_dtype)
	self.pipe.scheduler = UniPCMultistepScheduler.from_config(self.pipe.scheduler.config)
	self.pipe.to(device)
	self.num_inference_steps = 20
	self.seed = -1
	self.unconditional_guidance_scale = 9.0
	self.a_prompt = 'best quality, extremely detailed'
	self.n_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit,' \
	' fewer digits, cropped, worst quality, low quality'

	@prompts(name="Generate Image Condition On Pose Image",
	description="useful when you want to generate a new real image from both the user description "
	"and a human pose image. "
	"like: generate a real image of a human from this human pose image, "
	"or generate a new real image of a human from this pose. "
	"The input to this tool should be a comma separated string of two, "
	"representing the image_path and the user description")
	def inference(self, inputs):
	image_path, instruct_text = inputs.split(",")[0], ','.join(inputs.split(',')[1:])
	image = Image.open(image_path)
	self.seed = random.randint(0, 65535)
	seed_everything(self.seed)
	prompt = f'{instruct_text}, {self.a_prompt}'
	image = self.pipe(prompt, image, num_inference_steps=20, eta=0.0, negative_prompt=self.n_prompt,
	guidance_scale=9.0).images[0]
	updated_image_path = get_new_image_name(image_path, func_name="pose2image")
	image.save(updated_image_path)
	print(f"\nProcessed PoseText2Image, Input Pose: {image_path}, Input Text: {instruct_text}, "
	f"Output Image: {updated_image_path}")
	return updated_image_path

	class SegText2Image:
	def __init__(self, device):
	print(f"Initializing SegText2Image to {device}")
	self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32
	self.controlnet = ControlNetModel.from_pretrained("fusing/stable-diffusion-v1-5-controlnet-seg",
	torch_dtype=self.torch_dtype)
	self.pipe = StableDiffusionControlNetPipeline.from_pretrained(
	"runwayml/stable-diffusion-v1-5", controlnet=self.controlnet, safety_checker=StableDiffusionSafetyChecker.from_pretrained('CompVis/stable-diffusion-safety-checker'),
	torch_dtype=self.torch_dtype)
	self.pipe.scheduler = UniPCMultistepScheduler.from_config(self.pipe.scheduler.config)
	self.pipe.to(device)
	self.seed = -1
	self.a_prompt = 'best quality, extremely detailed'
	self.n_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit,' \
	' fewer digits, cropped, worst quality, low quality'

	@prompts(name="Generate Image Condition On Segmentations",
	description="useful when you want to generate a new real image from both the user description and segmentations. "
	"like: generate a real image of a object or something from this segmentation image, "
	"or generate a new real image of a object or something from these segmentations. "
	"The input to this tool should be a comma separated string of two, "
	"representing the image_path and the user description")
	def inference(self, inputs):
	image_path, instruct_text = inputs.split(",")[0], ','.join(inputs.split(',')[1:])
	image = Image.open(image_path)
	self.seed = random.randint(0, 65535)
	seed_everything(self.seed)
	prompt = f'{instruct_text}, {self.a_prompt}'
	image = self.pipe(prompt, image, num_inference_steps=20, eta=0.0, negative_prompt=self.n_prompt,
	guidance_scale=9.0).images[0]
	updated_image_path = get_new_image_name(image_path, func_name="segment2image")
	image.save(updated_image_path)
	print(f"\nProcessed SegText2Image, Input Seg: {image_path}, Input Text: {instruct_text}, "
	f"Output Image: {updated_image_path}")
	return updated_image_path


	class Image2Depth:
	def __init__(self, device):
	print("Initializing Image2Depth")
	self.depth_estimator = pipeline('depth-estimation')

	@prompts(name="Predict Depth On Image",
	description="useful when you want to detect depth of the image. like: generate the depth from this image, "
	"or detect the depth map on this image, or predict the depth for this image. "
	"The input to this tool should be a string, representing the image_path")
	def inference(self, inputs):
	image = Image.open(inputs)
	depth = self.depth_estimator(image)['depth']
	depth = np.array(depth)
	depth = depth[:, :, None]
	depth = np.concatenate([depth, depth, depth], axis=2)
	depth = Image.fromarray(depth)
	updated_image_path = get_new_image_name(inputs, func_name="depth")
	depth.save(updated_image_path)
	print(f"\nProcessed Image2Depth, Input Image: {inputs}, Output Depth: {updated_image_path}")
	return updated_image_path


	class DepthText2Image:
	def __init__(self, device):
	print(f"Initializing DepthText2Image to {device}")
	self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32
	self.controlnet = ControlNetModel.from_pretrained(
	"fusing/stable-diffusion-v1-5-controlnet-depth", torch_dtype=self.torch_dtype)
	self.pipe = StableDiffusionControlNetPipeline.from_pretrained(
	"runwayml/stable-diffusion-v1-5", controlnet=self.controlnet, safety_checker=StableDiffusionSafetyChecker.from_pretrained('CompVis/stable-diffusion-safety-checker'),
	torch_dtype=self.torch_dtype)
	self.pipe.scheduler = UniPCMultistepScheduler.from_config(self.pipe.scheduler.config)
	self.pipe.to(device)
	self.seed = -1
	self.a_prompt = 'best quality, extremely detailed'
	self.n_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit,' \
	' fewer digits, cropped, worst quality, low quality'

	@prompts(name="Generate Image Condition On Depth",
	description="useful when you want to generate a new real image from both the user description and depth image. "
	"like: generate a real image of a object or something from this depth image, "
	"or generate a new real image of a object or something from the depth map. "
	"The input to this tool should be a comma separated string of two, "
	"representing the image_path and the user description")
	def inference(self, inputs):
	image_path, instruct_text = inputs.split(",")[0], ','.join(inputs.split(',')[1:])
	image = Image.open(image_path)
	self.seed = random.randint(0, 65535)
	seed_everything(self.seed)
	prompt = f'{instruct_text}, {self.a_prompt}'
	image = self.pipe(prompt, image, num_inference_steps=20, eta=0.0, negative_prompt=self.n_prompt,
	guidance_scale=9.0).images[0]
	updated_image_path = get_new_image_name(image_path, func_name="depth2image")
	image.save(updated_image_path)
	print(f"\nProcessed DepthText2Image, Input Depth: {image_path}, Input Text: {instruct_text}, "
	f"Output Image: {updated_image_path}")
	return updated_image_path


	class Image2Normal:
	def __init__(self, device):
	print("Initializing Image2Normal")
	self.depth_estimator = pipeline("depth-estimation", model="Intel/dpt-hybrid-midas")
	self.bg_threhold = 0.4

	@prompts(name="Predict Normal Map On Image",
	description="useful when you want to detect norm map of the image. "
	"like: generate normal map from this image, or predict normal map of this image. "
	"The input to this tool should be a string, representing the image_path")
	def inference(self, inputs):
	image = Image.open(inputs)
	original_size = image.size
	image = self.depth_estimator(image)['predicted_depth'][0]
	image = image.numpy()
	image_depth = image.copy()
	image_depth -= np.min(image_depth)
	image_depth /= np.max(image_depth)
	x = cv2.Sobel(image, cv2.CV_32F, 1, 0, ksize=3)
	x[image_depth < self.bg_threhold] = 0
	y = cv2.Sobel(image, cv2.CV_32F, 0, 1, ksize=3)
	y[image_depth < self.bg_threhold] = 0
	z = np.ones_like(x) * np.pi * 2.0
	image = np.stack([x, y, z], axis=2)
	image /= np.sum(image 2.0, axis=2, keepdims=True) 0.5
	image = (image * 127.5 + 127.5).clip(0, 255).astype(np.uint8)
	image = Image.fromarray(image)
	image = image.resize(original_size)
	updated_image_path = get_new_image_name(inputs, func_name="normal-map")
	image.save(updated_image_path)
	print(f"\nProcessed Image2Normal, Input Image: {inputs}, Output Depth: {updated_image_path}")
	return updated_image_path


	class NormalText2Image:
	def __init__(self, device):
	print(f"Initializing NormalText2Image to {device}")
	self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32
	self.controlnet = ControlNetModel.from_pretrained(
	"fusing/stable-diffusion-v1-5-controlnet-normal", torch_dtype=self.torch_dtype)
	self.pipe = StableDiffusionControlNetPipeline.from_pretrained(
	"runwayml/stable-diffusion-v1-5", controlnet=self.controlnet, safety_checker=StableDiffusionSafetyChecker.from_pretrained('CompVis/stable-diffusion-safety-checker'),
	torch_dtype=self.torch_dtype)
	self.pipe.scheduler = UniPCMultistepScheduler.from_config(self.pipe.scheduler.config)
	self.pipe.to(device)
	self.seed = -1
	self.a_prompt = 'best quality, extremely detailed'
	self.n_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit,' \
	' fewer digits, cropped, worst quality, low quality'

	@prompts(name="Generate Image Condition On Normal Map",
	description="useful when you want to generate a new real image from both the user description and normal map. "
	"like: generate a real image of a object or something from this normal map, "
	"or generate a new real image of a object or something from the normal map. "
	"The input to this tool should be a comma separated string of two, "
	"representing the image_path and the user description")
	def inference(self, inputs):
	image_path, instruct_text = inputs.split(",")[0], ','.join(inputs.split(',')[1:])
	image = Image.open(image_path)
	self.seed = random.randint(0, 65535)
	seed_everything(self.seed)
	prompt = f'{instruct_text}, {self.a_prompt}'
	image = self.pipe(prompt, image, num_inference_steps=20, eta=0.0, negative_prompt=self.n_prompt,
	guidance_scale=9.0).images[0]
	updated_image_path = get_new_image_name(image_path, func_name="normal2image")
	image.save(updated_image_path)
	print(f"\nProcessed NormalText2Image, Input Normal: {image_path}, Input Text: {instruct_text}, "
	f"Output Image: {updated_image_path}")
	return updated_image_path


	class VisualQuestionAnswering:
	def __init__(self, device):
	print(f"Initializing VisualQuestionAnswering to {device}")
	self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32
	self.device = device
	self.processor = BlipProcessor.from_pretrained("Salesforce/blip-vqa-base")
	self.model = BlipForQuestionAnswering.from_pretrained(
	"Salesforce/blip-vqa-base", torch_dtype=self.torch_dtype).to(self.device)

	@prompts(name="Answer Question About The Image",
	description="useful when you need an answer for a question based on an image. "
	"like: what is the background color of the last image, how many cats in this figure, what is in this figure. "
	"The input to this tool should be a comma separated string of two, representing the image_path and the question")
	def inference(self, inputs):
	image_path, question = inputs.split(",")[0], ','.join(inputs.split(',')[1:])
	raw_image = Image.open(image_path).convert('RGB')
	inputs = self.processor(raw_image, question, return_tensors="pt").to(self.device, self.torch_dtype)
	out = self.model.generate(**inputs)
	answer = self.processor.decode(out[0], skip_special_tokens=True)
	print(f"\nProcessed VisualQuestionAnswering, Input Image: {image_path}, Input Question: {question}, "
	f"Output Answer: {answer}")
	return answer


	class Segmenting:
	def __init__(self, device):
	print(f"Inintializing Segmentation to {device}")
	self.device = device
	self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32
	self.model_checkpoint_path = os.path.join("checkpoints","sam")

	self.download_parameters()
	self.sam = build_sam(checkpoint=self.model_checkpoint_path).to(device)
	self.sam_predictor = SamPredictor(self.sam)
	self.mask_generator = SamAutomaticMaskGenerator(self.sam)

	self.saved_points = []
	self.saved_labels = []

	def download_parameters(self):
	url = "https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth"
	if not os.path.exists(self.model_checkpoint_path):
	wget.download(url,out=self.model_checkpoint_path)


	def show_mask(self, mask: np.ndarray,image: np.ndarray,
	random_color: bool = False, transparency=1) -> np.ndarray:

	"""Visualize a mask on top of an image.
	Args:
	mask (np.ndarray): A 2D array of shape (H, W).
	image (np.ndarray): A 3D array of shape (H, W, 3).
	random_color (bool): Whether to use a random color for the mask.
	Outputs:
	np.ndarray: A 3D array of shape (H, W, 3) with the mask
	visualized on top of the image.
	transparenccy: the transparency of the segmentation mask
	"""

	if random_color:
	color = np.concatenate([np.random.random(3)], axis=0)
	else:
	color = np.array([30 / 255, 144 / 255, 255 / 255])
	h, w = mask.shape[-2:]
	mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1) * 255

	image = cv2.addWeighted(image, 0.7, mask_image.astype('uint8'), transparency, 0)


	return image

	def show_box(self, box, ax, label):
	x0, y0 = box[0], box[1]
	w, h = box[2] - box[0], box[3] - box[1]
	ax.add_patch(plt.Rectangle((x0, y0), w, h, edgecolor='green', facecolor=(0,0,0,0), lw=2))
	ax.text(x0, y0, label)


	def get_mask_with_boxes(self, image_pil, image, boxes_filt):

	size = image_pil.size
	H, W = size[1], size[0]
	for i in range(boxes_filt.size(0)):
	boxes_filt[i] = boxes_filt[i] * torch.Tensor([W, H, W, H])
	boxes_filt[i][:2] -= boxes_filt[i][2:] / 2
	boxes_filt[i][2:] += boxes_filt[i][:2]

	boxes_filt = boxes_filt.cpu()
	transformed_boxes = self.sam_predictor.transform.apply_boxes_torch(boxes_filt, image.shape[:2]).to(self.device)

	masks, _, _ = self.sam_predictor.predict_torch(
	point_coords = None,
	point_labels = None,
	boxes = transformed_boxes.to(self.device),
	multimask_output = False,
	)
	return masks

	def segment_image_with_boxes(self, image_pil, image_path, boxes_filt, pred_phrases):

	image = cv2.imread(image_path)
	image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
	self.sam_predictor.set_image(image)

	masks = self.get_mask_with_boxes(image_pil, image, boxes_filt)

	# draw output image

	for mask in masks:
	image = self.show_mask(mask[0].cpu().numpy(), image, random_color=True, transparency=0.3)

	updated_image_path = get_new_image_name(image_path, func_name="segmentation")

	new_image = Image.fromarray(image)
	new_image.save(updated_image_path)

	return updated_image_path

	def set_image(self, img) -> None:
	"""Set the image for the predictor."""
	with torch.cuda.amp.autocast():
	self.sam_predictor.set_image(img)

	def show_points(self, coords: np.ndarray, labels: np.ndarray,
	image: np.ndarray) -> np.ndarray:
	"""Visualize points on top of an image.

	Args:
	coords (np.ndarray): A 2D array of shape (N, 2).
	labels (np.ndarray): A 1D array of shape (N,).
	image (np.ndarray): A 3D array of shape (H, W, 3).
	Returns:
	np.ndarray: A 3D array of shape (H, W, 3) with the points
	visualized on top of the image.
	"""
	pos_points = coords[labels == 1]
	neg_points = coords[labels == 0]
	for p in pos_points:
	image = cv2.circle(
	image, p.astype(int), radius=3, color=(0, 255, 0), thickness=-1)
	for p in neg_points:
	image = cv2.circle(
	image, p.astype(int), radius=3, color=(255, 0, 0), thickness=-1)
	return image


	def segment_image_with_click(self, img, is_positive: bool):

	self.sam_predictor.set_image(img)
	# self.saved_points.append([evt.index[0], evt.index[1]])
	self.saved_labels.append(1 if is_positive else 0)
	input_point = np.array(self.saved_points)
	input_label = np.array(self.saved_labels)

	# Predict the mask
	with torch.cuda.amp.autocast():
	masks, scores, logits = self.sam_predictor.predict(
	point_coords=input_point,
	point_labels=input_label,
	multimask_output=False,
	)

	img = self.show_mask(masks[0], img, random_color=False, transparency=0.3)

	img = self.show_points(input_point, input_label, img)

	return img

	def segment_image_with_coordinate(self, img, is_positive: bool,
	coordinate: tuple):
	'''
	Args:
	img (numpy.ndarray): the given image, shape: H x W x 3.
	is_positive: whether the click is positive, if want to add mask use True else False.
	coordinate: the position of the click
	If the position is (x,y), means click at the x-th column and y-th row of the pixel matrix.
	So x correspond to W, and y correspond to H.
	Output:
	img (PLI.Image.Image): the result image
	result_mask (numpy.ndarray): the result mask, shape: H x W

	Other parameters:
	transparency (float): the transparenccy of the mask
	to control he degree of transparency after the mask is superimposed.
	if transparency=1, then the masked part will be completely replaced with other colors.
	'''
	self.sam_predictor.set_image(img)
	self.saved_points.append([coordinate[0], coordinate[1]])
	self.saved_labels.append(1 if is_positive else 0)
	input_point = np.array(self.saved_points)
	input_label = np.array(self.saved_labels)

	# Predict the mask
	with torch.cuda.amp.autocast():
	masks, scores, logits = self.sam_predictor.predict(
	point_coords=input_point,
	point_labels=input_label,
	multimask_output=False,
	)


	img = self.show_mask(masks[0], img, random_color=False, transparency=0.3)

	img = self.show_points(input_point, input_label, img)

	img = Image.fromarray(img)

	result_mask = masks[0]

	return img, result_mask

	@prompts(name="Segment the Image",
	description="useful when you want to segment all the part of the image, but not segment a certain object."
	"like: segment all the object in this image, or generate segmentations on this image, "
	"or segment the image,"
	"or perform segmentation on this image, "
	"or segment all the object in this image."
	"The input to this tool should be a string, representing the image_path")
	def inference_all(self,image_path):
	image = cv2.imread(image_path)
	image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
	masks = self.mask_generator.generate(image)
	plt.figure(figsize=(20,20))
	plt.imshow(image)
	if len(masks) == 0:
	return
	sorted_anns = sorted(masks, key=(lambda x: x['area']), reverse=True)
	ax = plt.gca()
	ax.set_autoscale_on(False)
	for ann in sorted_anns:
	m = ann['segmentation']
	img = np.ones((m.shape[0], m.shape[1], 3))
	color_mask = np.random.random((1, 3)).tolist()[0]
	for i in range(3):
	img[:,:,i] = color_mask[i]
	ax.imshow(np.dstack((img, m)))

	updated_image_path = get_new_image_name(image_path, func_name="segment-image")
	plt.axis('off')
	plt.savefig(
	updated_image_path,
	bbox_inches="tight", dpi=300, pad_inches=0.0
	)
	return updated_image_path

	class Text2Box:
	def __init__(self, device):
	print(f"Initializing ObjectDetection to {device}")
	self.device = device
	self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32
	self.model_checkpoint_path = os.path.join("checkpoints","groundingdino")
	self.model_config_path = os.path.join("checkpoints","grounding_config.py")
	self.download_parameters()
	self.box_threshold = 0.3
	self.text_threshold = 0.25
	self.grounding = (self.load_model()).to(self.device)

	def download_parameters(self):
	url = "https://github.com/IDEA-Research/GroundingDINO/releases/download/v0.1.0-alpha/groundingdino_swint_ogc.pth"
	if not os.path.exists(self.model_checkpoint_path):
	wget.download(url,out=self.model_checkpoint_path)
	config_url = "https://raw.githubusercontent.com/IDEA-Research/GroundingDINO/main/groundingdino/config/GroundingDINO_SwinT_OGC.py"
	if not os.path.exists(self.model_config_path):
	wget.download(config_url,out=self.model_config_path)
	def load_image(self,image_path):
	# load image
	image_pil = Image.open(image_path).convert("RGB") # load image

	transform = T.Compose(
	[
	T.RandomResize([512], max_size=1333),
	T.ToTensor(),
	T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
	]
	)
	image, _ = transform(image_pil, None) # 3, h, w
	return image_pil, image

	def load_model(self):
	args = SLConfig.fromfile(self.model_config_path)
	args.device = self.device
	model = build_model(args)
	checkpoint = torch.load(self.model_checkpoint_path, map_location="cpu")
	load_res = model.load_state_dict(clean_state_dict(checkpoint["model"]), strict=False)
	print(load_res)
	_ = model.eval()
	return model

	def get_grounding_boxes(self, image, caption, with_logits=True):
	caption = caption.lower()
	caption = caption.strip()
	if not caption.endswith("."):
	caption = caption + "."
	image = image.to(self.device)
	with torch.no_grad():
	outputs = self.grounding(image[None], captions=[caption])
	logits = outputs["pred_logits"].cpu().sigmoid()[0] # (nq, 256)
	boxes = outputs["pred_boxes"].cpu()[0] # (nq, 4)
	logits.shape[0]

	# filter output
	logits_filt = logits.clone()
	boxes_filt = boxes.clone()
	filt_mask = logits_filt.max(dim=1)[0] > self.box_threshold
	logits_filt = logits_filt[filt_mask] # num_filt, 256
	boxes_filt = boxes_filt[filt_mask] # num_filt, 4
	logits_filt.shape[0]

	# get phrase
	tokenlizer = self.grounding.tokenizer
	tokenized = tokenlizer(caption)
	# build pred
	pred_phrases = []
	for logit, box in zip(logits_filt, boxes_filt):
	pred_phrase = get_phrases_from_posmap(logit > self.text_threshold, tokenized, tokenlizer)
	if with_logits:
	pred_phrases.append(pred_phrase + f"({str(logit.max().item())[:4]})")
	else:
	pred_phrases.append(pred_phrase)

	return boxes_filt, pred_phrases

	def plot_boxes_to_image(self, image_pil, tgt):
	H, W = tgt["size"]
	boxes = tgt["boxes"]
	labels = tgt["labels"]
	assert len(boxes) == len(labels), "boxes and labels must have same length"

	draw = ImageDraw.Draw(image_pil)
	mask = Image.new("L", image_pil.size, 0)
	mask_draw = ImageDraw.Draw(mask)

	# draw boxes and masks
	for box, label in zip(boxes, labels):
	# from 0..1 to 0..W, 0..H
	box = box * torch.Tensor([W, H, W, H])
	# from xywh to xyxy
	box[:2] -= box[2:] / 2
	box[2:] += box[:2]
	# random color
	color = tuple(np.random.randint(0, 255, size=3).tolist())
	# draw
	x0, y0, x1, y1 = box
	x0, y0, x1, y1 = int(x0), int(y0), int(x1), int(y1)

	draw.rectangle([x0, y0, x1, y1], outline=color, width=6)
	# draw.text((x0, y0), str(label), fill=color)

	font = ImageFont.load_default()
	if hasattr(font, "getbbox"):
	bbox = draw.textbbox((x0, y0), str(label), font)
	else:
	w, h = draw.textsize(str(label), font)
	bbox = (x0, y0, w + x0, y0 + h)
	# bbox = draw.textbbox((x0, y0), str(label))
	draw.rectangle(bbox, fill=color)
	draw.text((x0, y0), str(label), fill="white")

	mask_draw.rectangle([x0, y0, x1, y1], fill=255, width=2)

	return image_pil, mask

	@prompts(name="Detect the Give Object",
	description="useful when you only want to detect or find out given objects in the picture"
	"The input to this tool should be a comma separated string of two, "
	"representing the image_path, the text description of the object to be found")
	def inference(self, inputs):
	image_path, det_prompt = inputs.split(",")
	print(f"image_path={image_path}, text_prompt={det_prompt}")
	image_pil, image = self.load_image(image_path)

	boxes_filt, pred_phrases = self.get_grounding_boxes(image, det_prompt)

	size = image_pil.size
	pred_dict = {
	"boxes": boxes_filt,
	"size": [size[1], size[0]], # H,W
	"labels": pred_phrases,}

	image_with_box = self.plot_boxes_to_image(image_pil, pred_dict)[0]

	updated_image_path = get_new_image_name(image_path, func_name="detect-something")
	updated_image = image_with_box.resize(size)
	updated_image.save(updated_image_path)
	print(
	f"\nProcessed ObejectDetecting, Input Image: {image_path}, Object to be Detect {det_prompt}, "
	f"Output Image: {updated_image_path}")
	return updated_image_path


	class Inpainting:
	def __init__(self, device):
	self.device = device
	self.revision = 'fp16' if 'cuda' in self.device else None
	self.torch_dtype = torch.float16 if 'cuda' in self.device else torch.float32

	self.inpaint = StableDiffusionInpaintPipeline.from_pretrained(
	"runwayml/stable-diffusion-inpainting", revision=self.revision, torch_dtype=self.torch_dtype,safety_checker=StableDiffusionSafetyChecker.from_pretrained('CompVis/stable-diffusion-safety-checker')).to(device)
	def __call__(self, prompt, image, mask_image, height=512, width=512, num_inference_steps=50):
	update_image = self.inpaint(prompt=prompt, image=image.resize((width, height)),
	mask_image=mask_image.resize((width, height)), height=height, width=width, num_inference_steps=num_inference_steps).images[0]
	return update_image

	class InfinityOutPainting:
	template_model = True # Add this line to show this is a template model.
	def __init__(self, ImageCaptioning, Inpainting, VisualQuestionAnswering):
	self.llm = OpenAI(temperature=0)
	self.ImageCaption = ImageCaptioning
	self.inpaint = Inpainting
	self.ImageVQA = VisualQuestionAnswering
	self.a_prompt = 'best quality, extremely detailed'
	self.n_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, ' \
	'fewer digits, cropped, worst quality, low quality'

	def get_BLIP_vqa(self, image, question):
	inputs = self.ImageVQA.processor(image, question, return_tensors="pt").to(self.ImageVQA.device,
	self.ImageVQA.torch_dtype)
	out = self.ImageVQA.model.generate(**inputs)
	answer = self.ImageVQA.processor.decode(out[0], skip_special_tokens=True)
	print(f"\nProcessed VisualQuestionAnswering, Input Question: {question}, Output Answer: {answer}")
	return answer

	def get_BLIP_caption(self, image):
	inputs = self.ImageCaption.processor(image, return_tensors="pt").to(self.ImageCaption.device,
	self.ImageCaption.torch_dtype)
	out = self.ImageCaption.model.generate(**inputs)
	BLIP_caption = self.ImageCaption.processor.decode(out[0], skip_special_tokens=True)
	return BLIP_caption

	def check_prompt(self, prompt):
	check = f"Here is a paragraph with adjectives. " \
	f"{prompt} " \
	f"Please change all plural forms in the adjectives to singular forms. "
	return self.llm(check)

	def get_imagine_caption(self, image, imagine):
	BLIP_caption = self.get_BLIP_caption(image)
	background_color = self.get_BLIP_vqa(image, 'what is the background color of this image')
	style = self.get_BLIP_vqa(image, 'what is the style of this image')
	imagine_prompt = f"let's pretend you are an excellent painter and now " \
	f"there is an incomplete painting with {BLIP_caption} in the center, " \
	f"please imagine the complete painting and describe it" \
	f"you should consider the background color is {background_color}, the style is {style}" \
	f"You should make the painting as vivid and realistic as possible" \
	f"You can not use words like painting or picture" \
	f"and you should use no more than 50 words to describe it"
	caption = self.llm(imagine_prompt) if imagine else BLIP_caption
	caption = self.check_prompt(caption)
	print(f'BLIP observation: {BLIP_caption}, ChatGPT imagine to {caption}') if imagine else print(
	f'Prompt: {caption}')
	return caption

	def resize_image(self, image, max_size=1000000, multiple=8):
	aspect_ratio = image.size[0] / image.size[1]
	new_width = int(math.sqrt(max_size * aspect_ratio))
	new_height = int(new_width / aspect_ratio)
	new_width, new_height = new_width - (new_width % multiple), new_height - (new_height % multiple)
	return image.resize((new_width, new_height))

	def dowhile(self, original_img, tosize, expand_ratio, imagine, usr_prompt):
	old_img = original_img
	while (old_img.size != tosize):
	prompt = self.check_prompt(usr_prompt) if usr_prompt else self.get_imagine_caption(old_img, imagine)
	crop_w = 15 if old_img.size[0] != tosize[0] else 0
	crop_h = 15 if old_img.size[1] != tosize[1] else 0
	old_img = ImageOps.crop(old_img, (crop_w, crop_h, crop_w, crop_h))
	temp_canvas_size = (expand_ratio * old_img.width if expand_ratio * old_img.width < tosize[0] else tosize[0],
	expand_ratio * old_img.height if expand_ratio * old_img.height < tosize[1] else tosize[
	1])
	temp_canvas, temp_mask = Image.new("RGB", temp_canvas_size, color="white"), Image.new("L", temp_canvas_size,
	color="white")
	x, y = (temp_canvas.width - old_img.width) // 2, (temp_canvas.height - old_img.height) // 2
	temp_canvas.paste(old_img, (x, y))
	temp_mask.paste(0, (x, y, x + old_img.width, y + old_img.height))
	resized_temp_canvas, resized_temp_mask = self.resize_image(temp_canvas), self.resize_image(temp_mask)
	image = self.inpaint(prompt=prompt, image=resized_temp_canvas, mask_image=resized_temp_mask,
	height=resized_temp_canvas.height, width=resized_temp_canvas.width,
	num_inference_steps=50).resize(
	(temp_canvas.width, temp_canvas.height), Image.ANTIALIAS)
	image = blend_gt2pt(old_img, image)
	old_img = image
	return old_img

	@prompts(name="Extend An Image",
	description="useful when you need to extend an image into a larger image."
	"like: extend the image into a resolution of 2048x1024, extend the image into 2048x1024. "
	"The input to this tool should be a comma separated string of two, representing the image_path and the resolution of widthxheight")
	def inference(self, inputs):
	image_path, resolution = inputs.split(',')
	width, height = resolution.split('x')
	tosize = (int(width), int(height))
	image = Image.open(image_path)
	image = ImageOps.crop(image, (10, 10, 10, 10))
	out_painted_image = self.dowhile(image, tosize, 4, True, False)
	updated_image_path = get_new_image_name(image_path, func_name="outpainting")
	out_painted_image.save(updated_image_path)
	print(f"\nProcessed InfinityOutPainting, Input Image: {image_path}, Input Resolution: {resolution}, "
	f"Output Image: {updated_image_path}")
	return updated_image_path



	class ObjectSegmenting:
	template_model = True # Add this line to show this is a template model.
	def __init__(self, Text2Box:Text2Box, Segmenting:Segmenting):
	# self.llm = OpenAI(temperature=0)
	self.grounding = Text2Box
	self.sam = Segmenting


	@prompts(name="Segment the given object",
	description="useful when you only want to segment the certain objects in the picture"
	"according to the given text"
	"like: segment the cat,"
	"or can you segment an obeject for me"
	"The input to this tool should be a comma separated string of two, "
	"representing the image_path, the text description of the object to be found")
	def inference(self, inputs):
	image_path, det_prompt = inputs.split(",")
	print(f"image_path={image_path}, text_prompt={det_prompt}")
	image_pil, image = self.grounding.load_image(image_path)

	boxes_filt, pred_phrases = self.grounding.get_grounding_boxes(image, det_prompt)
	updated_image_path = self.sam.segment_image_with_boxes(image_pil,image_path,boxes_filt,pred_phrases)
	print(
	f"\nProcessed ObejectSegmenting, Input Image: {image_path}, Object to be Segment {det_prompt}, "
	f"Output Image: {updated_image_path}")
	return updated_image_path

	def merge_masks(self, masks):
	'''
	Args:
	mask (numpy.ndarray): shape N x 1 x H x W
	Outputs:
	new_mask (numpy.ndarray): shape H x W
	'''
	if type(masks) == torch.Tensor:
	x = masks
	elif type(masks) == np.ndarray:
	x = torch.tensor(masks,dtype=int)
	else:
	raise TypeError("the type of the input masks must be numpy.ndarray or torch.tensor")
	x = x.squeeze(dim=1)
	value, _ = x.max(dim=0)
	new_mask = value.cpu().numpy()
	new_mask.astype(np.uint8)
	return new_mask

	def get_mask(self, image_path, text_prompt):

	print(f"image_path={image_path}, text_prompt={text_prompt}")
	# image_pil (PIL.Image.Image) -> size: W x H
	# image (numpy.ndarray) -> H x W x 3
	image_pil, image = self.grounding.load_image(image_path)

	boxes_filt, pred_phrases = self.grounding.get_grounding_boxes(image, text_prompt)
	image = cv2.imread(image_path)
	image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
	self.sam.sam_predictor.set_image(image)

	# masks (torch.tensor) -> N x 1 x H x W
	masks = self.sam.get_mask_with_boxes(image_pil, image, boxes_filt)

	# merged_mask -> H x W
	merged_mask = self.merge_masks(masks)
	# draw output image

	for mask in masks:
	image = self.sam.show_mask(mask[0].cpu().numpy(), image, random_color=True, transparency=0.3)


	Image.fromarray(merged_mask)

	return merged_mask


	class ImageEditing:
	template_model = True
	def __init__(self, Text2Box:Text2Box, Segmenting:Segmenting, Inpainting:Inpainting):
	print("Initializing ImageEditing")
	self.sam = Segmenting
	self.grounding = Text2Box
	self.inpaint = Inpainting

	def pad_edge(self,mask,padding):
	#mask Tensor [H,W]
	mask = mask.numpy()
	true_indices = np.argwhere(mask)
	mask_array = np.zeros_like(mask, dtype=bool)
	for idx in true_indices:
	padded_slice = tuple(slice(max(0, i - padding), i + padding + 1) for i in idx)
	mask_array[padded_slice] = True
	new_mask = (mask_array * 255).astype(np.uint8)
	#new_mask
	return new_mask

	@prompts(name="Remove Something From The Photo",
	description="useful when you want to remove and object or something from the photo "
	"from its description or location. "
	"The input to this tool should be a comma separated string of two, "
	"representing the image_path and the object need to be removed. ")
	def inference_remove(self, inputs):
	image_path, to_be_removed_txt = inputs.split(",")[0], ','.join(inputs.split(',')[1:])
	return self.inference_replace_sam(f"{image_path},{to_be_removed_txt},background")

	@prompts(name="Replace Something From The Photo",
	description="useful when you want to replace an object from the object description or "
	"location with another object from its description. "
	"The input to this tool should be a comma separated string of three, "
	"representing the image_path, the object to be replaced, the object to be replaced with ")
	def inference_replace_sam(self,inputs):
	image_path, to_be_replaced_txt, replace_with_txt = inputs.split(",")

	print(f"image_path={image_path}, to_be_replaced_txt={to_be_replaced_txt}")
	image_pil, image = self.grounding.load_image(image_path)
	boxes_filt, pred_phrases = self.grounding.get_grounding_boxes(image, to_be_replaced_txt)
	image = cv2.imread(image_path)
	image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
	self.sam.sam_predictor.set_image(image)
	masks = self.sam.get_mask_with_boxes(image_pil, image, boxes_filt)
	mask = torch.sum(masks, dim=0).unsqueeze(0)
	mask = torch.where(mask > 0, True, False)
	mask = mask.squeeze(0).squeeze(0).cpu() #tensor

	mask = self.pad_edge(mask,padding=20) #numpy
	mask_image = Image.fromarray(mask)

	updated_image = self.inpaint(prompt=replace_with_txt, image=image_pil,
	mask_image=mask_image)
	updated_image_path = get_new_image_name(image_path, func_name="replace-something")
	updated_image = updated_image.resize(image_pil.size)
	updated_image.save(updated_image_path)
	print(
	f"\nProcessed ImageEditing, Input Image: {image_path}, Replace {to_be_replaced_txt} to {replace_with_txt}, "
	f"Output Image: {updated_image_path}")
	return updated_image_path

	class BackgroundRemoving:
	'''
	using to remove the background of the given picture
	'''
	template_model = True
	def __init__(self,VisualQuestionAnswering:VisualQuestionAnswering, Text2Box:Text2Box, Segmenting:Segmenting):
	self.vqa = VisualQuestionAnswering
	self.obj_segmenting = ObjectSegmenting(Text2Box,Segmenting)

	@prompts(name="Remove the background",
	description="useful when you want to extract the object or remove the background,"
	"the input should be a string image_path"
	)
	def inference(self, image_path):
	'''
	given a image, return the picture only contains the extracted main object
	'''
	updated_image_path = None

	mask = self.get_mask(image_path)

	image = Image.open(image_path)
	mask = Image.fromarray(mask)
	image.putalpha(mask)

	updated_image_path = get_new_image_name(image_path, func_name="detect-something")
	image.save(updated_image_path)

	return updated_image_path

	def get_mask(self, image_path):
	'''
	Description:
	given an image path, return the mask of the main object.
	Args:
	image_path (string): the file path of the image
	Outputs:
	mask (numpy.ndarray): H x W
	'''
	vqa_input = f"{image_path}, what is the main object in the image?"
	text_prompt = self.vqa.inference(vqa_input)

	mask = self.obj_segmenting.get_mask(image_path,text_prompt)

	return mask


	class MultiModalVisualAgent:
	def __init__(
	self,
	load_dict,
	prefix: str = VISUAL_AGENT_PREFIX,
	format_instructions: str = VISUAL_AGENT_FORMAT_INSTRUCTIONS,
	suffix: str = VISUAL_AGENT_SUFFIX
	):
	print(f"Initializing MultiModalVisualAgent, load_dict={load_dict}")

	if 'ImageCaptioning' not in load_dict:
	raise ValueError("You have to load ImageCaptioning as a basic function for MultiModalVisualAgent")

	self.models = {}

	for class_name, device in load_dict.items():
	self.models[class_name] = globals()[class_name](device=device)

	for class_name, module in globals().items():
	if getattr(module, 'template_model', False):
	template_required_names = {
	k for k in inspect.signature(module.__init__).parameters.keys() if k!='self'
	}

	loaded_names = set([type(e).__name__ for e in self.models.values()])

	if template_required_names.issubset(loaded_names):
	self.models[class_name] = globals()[class_name](
	**{name: self.models[name] for name in template_required_names})

	print(f"All the Available Functions: {self.models}")

	self.tools = []
	for instance in self.models.values():
	for e in dir(instance):
	if e.startswith('inference'):
	func = getattr(instance, e)
	self.tools.append(
	Tool(name=func.name, description=func.description, func=func)
	)

	self.llm = OpenAI(temperature=0)
	self.memory = ConversationBufferMemory(
	memory_key="chat_history",
	output_key='output'
	)

	def init_agent(self, lang):
	self.memory.clear()

	agent_prefix = self.prefix
	agent_suffix = self.suffix
	agent_format_instructions = self.format_instructions

	if lang=='English':
	PREFIX, FORMAT_INSTRUCTIONS, SUFFIX = agent_prefix, agent_format_instructions, agent_suffix
	else:
	PREFIX, FORMAT_INSTRUCTIONS, SUFFIX = VISUAL_AGENT_PREFIX_CN, VISUAL_AGENT_FORMAT_INSTRUCTIONS_CN, VISUAL_AGENT_SUFFIX_CN

	self.agent = initialize_agent(
	self.tools,
	self.llm,
	agent="conversational-react-description",
	verbose=True,
	memory=self.memory,
	return_intermediate_steps=True,
	agent_kwargs={
	'prefix': PREFIX,
	'format_instructions': FORMAT_INSTRUCTIONS,
	'suffix': SUFFIX
	},
	)

	def run_text(self, text):
	self.agent.memory.buffer = cut_dialogue_history(
	self.agent.memory.buffer,
	keep_last_n_words=500
	)

	res = self.agent({"input": text.strip()})
	res['output'] = res['output'].replace("\\", "/")
	response = re.sub('(image/[-\w].png)', lambda m: f'![](file={m.group(0)}){m.group(0)}*', res['output'])

	print(f"\nProcessed run_text, Input text: {text}\n"
	f"Current Memory: {self.agent.memory.buffer}")

	return response

	def run_image(self, image, lang):
	image_filename = os.path.join('image', f"{str(uuid.uuid4())[:8]}.png")

	img = Image.open(image)
	width, height = img.size
	ratio = min(512 / width, 512 / height)

	width_new, height_new = (round(width * ratio), round(height * ratio))
	width_new = int(np.round(width_new / 64.0)) * 64
	height_new = int(np.round(height_new / 64.0)) * 64

	img = img.resize((width_new, height_new))
	img = img.convert('RGB')
	img.save(image_filename, "PNG")

	description = self.models['ImageCaptioning'].inference(image_filename)

	if lang == 'Chinese':
	Human_prompt = f'\nHuman: 提供一张名为 {image_filename}的图片。它的描述是: {description}。这些信息帮助你理解这个图像，但是你应该使用工具来完成下面的任务，而不是直接从我的描述中想象。如果你明白了, 说 \"收到\". \n'
	AI_prompt = "收到。 "
	else:
	Human_prompt = f'\nHuman: provide a figure named {image_filename}. The description is: {description}. This information helps you to understand this image, but you should use tools to finish following tasks, rather than directly imagine from my description. If you understand, say \"Received\". \n'
	AI_prompt = "Received. "

	self.agent.memory.buffer = self.agent.memory.buffer + Human_prompt + 'AI: ' + AI_prompt

	print(f"\nProcessed run_image, Input image: {image_filename}\n"
	f"Current Memory: {self.agent.memory.buffer}")

	return AI_prompt

	def clear_memory(self):
	self.memory.clear()




	###### usage
	from swarms.agents.message import Message


	class MultiModalAgent:
	"""
	A user-friendly abstraction over the MultiModalVisualAgent that provides a simple interface
	to process both text and images.

	Initializes the MultiModalAgent.

	Architecture:


	Parameters:
	load_dict (dict, optional): Dictionary of class names and devices to load.
	Defaults to a basic configuration.

	temperature (float, optional): Temperature for the OpenAI model. Defaults to 0.

	default_language (str, optional): Default language for the agent.
	Defaults to "English".

	Usage
	--------------
	For chats:
	------------
	agent = MultiModalAgent()
	agent.chat("Hello")

	-----------

	Or just with text
	------------
	agent = MultiModalAgent()
	agent.run_text("Hello")


	"""
	def __init__(
	self,
	load_dict,
	temperature: int = 0.1,
	language: str = "english"
	):
	self.load_dict = load_dict
	self.temperature = temperature
	self.langigage = language

	# if load_dict is None:
	# self.load_dict = {
	# "ImageCaptioning": "default_device"
	# }

	self.agent = MultiModalVisualAgent(
	load_dict,
	temperature
	)
	self.language = language
	self.history = []


	def run_text(
	self,
	text: str = None,
	language = "english"
	):
	"""Run text through the model"""

	if language is None:
	language = self.language

	try:
	self.agent.init_agent(language)
	return self.agent.run_text(text)
	except Exception as e:
	return f"Error processing text: {str(e)}"

	def run_img(
	self,
	image_path: str,
	language = "english"
	):
	"""If language is None"""
	if language is None:
	language = self.default_language

	try:
	return self.agent.run_image(
	image_path,
	language
	)
	except Exception as error:
	return f"Error processing image: {str(error)}"

	def chat(
	self,
	msg: str = None,
	language: str = "english",
	streaming: bool = False
	):
	"""
	Run chat with the multi-modal agent

	Args:
	msg (str, optional): Message to send to the agent. Defaults to None.
	language (str, optional): Language to use. Defaults to None.
	streaming (bool, optional): Whether to stream the response. Defaults to False.

	Returns:
	str: Response from the agent

	Usage:
	--------------
	agent = MultiModalAgent()
	agent.chat("Hello")

	"""
	if language is None:
	language = self.default_language

	#add users message to the history
	self.history.append(
	Message(
	"User",
	msg
	)
	)

	#process msg
	try:
	self.agent.init_agent(language)
	response = self.agent.run_text(msg)

	#add agent's response to the history
	self.history.append(
	Message(
	"Agent",
	response
	)
	)

	#if streaming is = True
	if streaming:
	return self._stream_response(response)
	else:
	response

	except Exception as error:
	error_message = f"Error processing message: {str(error)}"

	#add error to history
	self.history.append(
	Message(
	"Agent",
	error_message
	)
	)
	return error_message

	def _stream_response(
	self,
	response: str = None
	):
	"""
	Yield the response token by token (word by word)

	Usage:
	--------------
	for token in _stream_response(response):
	print(token)

	"""
	for token in response.split():
	yield token

	def clear(self):
	"""Clear agent's memory"""
	try:
	self.agent.clear_memory()
	except Exception as e:
	return f"Error cleaning memory: {str(e)}"