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	import os
	import torch
	from PIL import Image, ImageOps
	import math
	import time
	import numpy as np
	import uuid
	import cv2

	from transformers import CLIPSegProcessor, CLIPSegForImageSegmentation
	from transformers import pipeline, BlipProcessor, BlipForConditionalGeneration, BlipForQuestionAnswering

	from .utils import seed_everything, gen_new_name, prompts, GLOBAL_SEED
	from diffusers import StableDiffusionPipeline, StableDiffusionInpaintPipeline, StableDiffusionInstructPix2PixPipeline
	from diffusers import EulerAncestralDiscreteScheduler
	from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler
	from controlnet_aux import OpenposeDetector, MLSDdetector, HEDdetector

	from segment_anything.utils.amg import remove_small_regions
	from segment_anything import build_sam, sam_model_registry, SamAutomaticMaskGenerator, SamPredictor
	from torchvision.utils import save_image
	import matplotlib.pyplot as plt

	from omegaconf import OmegaConf
	import yaml

	import matplotlib.pyplot as plt
	# Please DO NOT MOVE THE IMPORT ORDER FOR easyocr.
	import easyocr

	from saicinpainting.evaluation.utils import move_to_device
	from saicinpainting.training.trainers import load_checkpoint
	from saicinpainting.evaluation.data import pad_tensor_to_modulo

	# from .utils import prompts


	# GLOBAL_SEED=19120623


	def HWC3(x):
	assert x.dtype == np.uint8
	if x.ndim == 2:
	x = x[:, :, None]
	assert x.ndim == 3
	H, W, C = x.shape
	assert C == 1 or C == 3 or C == 4
	if C == 3:
	return x
	if C == 1:
	return np.concatenate([x, x, x], axis=2)
	if C == 4:
	color = x[:, :, 0:3].astype(np.float32)
	alpha = x[:, :, 3:4].astype(np.float32) / 255.0
	y = color * alpha + 255.0 * (1.0 - alpha)
	y = y.clip(0, 255).astype(np.uint8)
	return y


	class MaskFormer:
	def __init__(self, device):
	print(f"Initializing MaskFormer to {device}")
	self.device = device
	self.processor = CLIPSegProcessor.from_pretrained("CIDAS/clipseg-rd64-refined")
	self.model = CLIPSegForImageSegmentation.from_pretrained("CIDAS/clipseg-rd64-refined").to(device)

	def inference(self, image_path, text):
	threshold = 0.5
	min_area = 0.02
	padding = 20
	original_image = Image.open(image_path)
	image = original_image.resize((512, 512))
	inputs = self.processor(text=text, images=image, padding="max_length", return_tensors="pt").to(self.device)
	with torch.no_grad():
	outputs = self.model(**inputs)
	mask = torch.sigmoid(outputs[0]).squeeze().cpu().numpy() > threshold
	area_ratio = len(np.argwhere(mask)) / (mask.shape[0] * mask.shape[1])
	if area_ratio < min_area:
	return None
	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
	visual_mask = (mask_array * 255).astype(np.uint8)
	image_mask = Image.fromarray(visual_mask)
	return image_mask.resize(original_image.size)


	class ImageEditing:
	def __init__(self, device):
	print(f"Initializing ImageEditing to {device}")
	self.device = device
	self.mask_former = MaskFormer(device=self.device)
	self.revision = 'fp16' if 'cuda' in device else None
	self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32
	self.inpaint = StableDiffusionInpaintPipeline.from_pretrained(
	"runwayml/stable-diffusion-inpainting", revision=self.revision, torch_dtype=self.torch_dtype).to(device)

	@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(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(self, inputs):
	image_path, to_be_replaced_txt, replace_with_txt = inputs.split(",")
	original_image = Image.open(image_path)
	original_size = original_image.size
	mask_image = self.mask_former.inference(image_path, to_be_replaced_txt)
	updated_image = self.inpaint(prompt=replace_with_txt, image=original_image.resize((512, 512)),
	mask_image=mask_image.resize((512, 512))).images[0]
	# updated_image_path = get_new_image_name(image_path, func_name="replace-something")
	updated_image_path = gen_new_name(image_path, f'{type(self).__name__}')
	updated_image = updated_image.resize(original_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 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=None,
	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")
	updated_image_path = gen_new_name(image_path, f'{type(self).__name__}')
	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")
	updated_image_path = gen_new_name(inputs, f'{type(self).__name__}')
	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=None,
	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")
	updated_image_path = gen_new_name(image_path, f'{type(self).__name__}')
	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")
	updated_image_path = gen_new_name(inputs, f'{type(self).__name__}')
	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=None,
	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")
	updated_image_path = gen_new_name(image_path, f'{type(self).__name__}')
	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")
	updated_image_path = gen_new_name(inputs, f'{type(self).__name__}')
	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=None,
	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")
	updated_image_path = gen_new_name(image_path, f'{type(self).__name__}')
	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")
	updated_image_path = gen_new_name(inputs, f'{type(self).__name__}')
	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=None,
	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")
	updated_image_path = gen_new_name(image_path, f'{type(self).__name__}')
	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")
	updated_image_path = gen_new_name(inputs, f'{type(self).__name__}')
	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=None,
	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")
	updated_image_path = gen_new_name(image_path, f'{type(self).__name__}')
	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=None,
	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 a_inference(self, inputs):
	image_path, instruct_text = inputs.split(",")[0], ','.join(inputs.split(',')[1:])
	image = Image.open(image_path)
	seed_everything(GLOBAL_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")
	updated_image_path = gen_new_name(image_path, f'{type(self).__name__}')
	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 ConditionalText2Image:
	template_model = True
	def __init__(self, models):
	self.models = models
	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="Beautify the Image with other Conditions",
	description="useful when you want to generate a new real image from both the user description and condition. "
	"Condition can be one of segmentation, canny, scribble"
	"like: Beautify a image from its segmentation and description "
	"or generate a image from its canny and description. "
	"or generate a real image from scribble and description. "
	"The input to this tool should be a comma separated string of three, "
	"representing the image_path, condition, instruction")
	def inference(self, inputs):
	image_path, condition, instruct_text = inputs.split(",")[0], ','.join(inputs.split(',')[1:])
	# image = Image.open(image_path)
	if 'segmentation' in condition.lower():
	seg_path = self.models['SegmentAnything'].inference(image_path)
	updated_image_path = self.models['SegText2Image'].a_inference(seg_path+","+instruct_text)
	else:
	raise NotImplementedError(condition)

	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")
	updated_image_path = gen_new_name(inputs, f'{type(self).__name__}')
	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=None,
	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")
	updated_image_path = gen_new_name(image_path, f'{type(self).__name__}')
	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")
	updated_image_path = gen_new_name(inputs, f'{type(self).__name__}')
	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=None,
	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")
	updated_image_path = gen_new_name(image_path, f'{type(self).__name__}')
	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 InfinityOutPainting:
	template_model = True # Add this line to show this is a template model.
	def __init__(self, ImageCaptioning, ImageEditing, VisualQuestionAnswering):
	self.llm = OpenAI(temperature=0)
	self.ImageCaption = ImageCaptioning
	self.ImageEditing = ImageEditing
	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.ImageEditing.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).images[0].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")
	updated_image_path = gen_new_name(image_path, f'{type(self).__name__}')
	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


	##################### New Models #####################
	class SegmentAnything:
	def __init__(self, device):
	print(f"Initializing SegmentAnything to {device}")

	sam_checkpoint = "sam_vit_h_4b8939.pth"
	model_type = "vit_h"
	self.sam = sam_model_registry[model_type](checkpoint=sam_checkpoint)
	self.predictor = SamPredictor(self.sam)
	self.sam.to(device=device)
	self.clicked_region = None
	self.img_path = None

	@prompts(name="Segment Anything on Image",
	description="useful when you want to segment anything in the image. "
	"like: segment anything from this image, "
	"The input to this tool should be a string, representing the image_path")
	def inference(self, inputs):
	print("Inputs: ", inputs)
	img_path = inputs.strip()
	self.img_path = img_path
	annos = self.segment_anything(img_path)
	full_img, _ = self.show_annos(annos)
	# full_img = Image.fromarray(full_img)
	# res = Image.fromarray(res)
	# print(os.path.splitext(img_path))
	seg_all_image_path = gen_new_name(img_path, 'SegmentAnything')
	full_img.save(seg_all_image_path, "PNG")

	print(f"\nProcessed SegmentAnything, Input Image: {inputs}, Output Depth: {seg_all_image_path}")
	return seg_all_image_path


	@prompts(name="Segment the Clicked Region",
	description="useful when you want to segment the masked region or block in the image. "
	"like: segment the masked region in this image, "
	"or segment the clicked region in this image, "
	"The input to this tool should be None.")
	def inference_by_mask(self, inputs=None):
	# mask = np.array(Image.open(mask_path).convert('L'))
	res_mask = self.segment_by_mask(self.clicked_region)
	filaname = gen_new_name(self.img_path, 'SegmentAnything')
	mask_img = Image.fromarray(res_mask.astype(np.uint8)*255)
	mask_img.save(filaname, "PNG")
	return filaname

	def segment_by_mask(self, mask=None):
	import random
	random.seed(GLOBAL_SEED)
	if mask is None:
	mask = self.clicked_region
	idxs = np.nonzero(mask)
	# print(idxs)
	num_points = min(max(1, int(len(idxs[0]) * 0.01)), 16)
	sampled_idx = random.sample(range(0, len(idxs[0])), num_points)
	new_mask = []
	for i in range(len(idxs)):
	new_mask.append(idxs[i][sampled_idx])
	points = np.array(new_mask).reshape(2, -1).transpose(1, 0)[:, ::-1]
	labels = np.array([1] * num_points)

	res_masks, scores, _ = self.predictor.predict(
	point_coords=points,
	point_labels=labels,
	multimask_output=True,
	)

	return res_masks[np.argmax(scores), :, :]


	def segment_anything(self, img_path):
	img = cv2.imread(img_path)
	img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

	mask_generator = SamAutomaticMaskGenerator(self.sam)
	annos = mask_generator.generate(img)
	return annos

	def get_detection_map(self, img_path):
	annos = self.segment_anything(img_path)
	_, detection_map = self.show_anns(annos)

	return detection_map

	def preprocess(self, img, img_path):
	self.predictor.set_image(img)
	self.img_path = img_path

	def reset(self):
	self.predictor.reset_image()
	self.clicked_region = None
	self.img_path = None

	def show_annos(self, anns):
	# From https://github.com/sail-sg/EditAnything/blob/main/sam2image.py#L91
	if len(anns) == 0:
	return
	sorted_anns = sorted(anns, key=(lambda x: x['area']), reverse=True)
	full_img = None

	# for ann in sorted_anns:
	for i in range(len(sorted_anns)):
	ann = anns[i]
	m = ann['segmentation']
	if full_img is None:
	full_img = np.zeros((m.shape[0], m.shape[1], 3))
	map = np.zeros((m.shape[0], m.shape[1]), dtype=np.uint16)
	map[m != 0] = i + 1
	color_mask = np.random.random((1, 3)).tolist()[0]
	full_img[m != 0] = color_mask
	full_img = full_img * 255
	# anno encoding from https://github.com/LUSSeg/ImageNet-S
	res = np.zeros((map.shape[0], map.shape[1], 3))
	res[:, :, 0] = map % 256
	res[:, :, 1] = map // 256
	res.astype(np.float32)
	full_img = Image.fromarray(np.uint8(full_img))
	return full_img, res

	def segment_by_points(self, img, points, lables):
	# TODO
	# masks, _, _ = self.predictor.predict(
	# point_coords=np.array(points[-1]),
	# point_labels=np.array(lables[-1]),
	# # mask_input=mask_input[-1],
	# multimask_output=True, # SAM outputs 3 masks, we choose the one with highest score
	# )
	# # return masks_[np.argmax(scores_), :, :]
	# return masks
	pass


	class RemoveClickedAnything:
	"""
	prepare:
	```
	curl -L $(yadisk-direct https://disk.yandex.ru/d/ouP6l8VJ0HpMZg) -o big-lama.zip
	unzip big-lama.zip
	```
	"""
	def __init__(self, device):
	print(f"Initializing RemoveClickedAnything to {device}")
	self.device=device
	sam_checkpoint = "sam_vit_h_4b8939.pth"
	model_type = "vit_h"
	self.sam = sam_model_registry[model_type](checkpoint=sam_checkpoint)
	self.sam.to(device=device)
	self.click_point_state = None

	@prompts(name="Remove the clicked object",
	description="useful when you want to remove an object by clicking in the image. "
	"like: remove object by clicking, "
	"The input to this tool should be a string, representing the image_path")
	def inference_replace(self, inputs):
	if self.click_point_state is None:
	print(f"The current point state is None, return the input path {inputs}")
	return inputs

	print("Inputs: ", inputs)
	image_path = inputs
	img = np.array(Image.open(image_path))

	predictor = SamPredictor(self.sam)
	predictor.set_image(img)
	point_coords = [self.click_point_state]
	point_labels = [1]
	point_coords = np.array(point_coords)
	point_labels = np.array(point_labels)

	masks, scores, logits = predictor.predict(
	point_coords=point_coords,
	point_labels=point_labels,
	multimask_output=True,
	)
	# '''
	for i, (mask, score) in enumerate(zip(masks, scores)):
	plt.figure(figsize=(10,10))
	plt.imshow(img)
	show_mask(mask, plt.gca())
	show_points(point_coords, point_labels, plt.gca())
	plt.title(f"Mask {i+1}, Score: {score:.3f}", fontsize=18)
	plt.axis('off')
	plt.savefig("show_mask{}.jpg".format(i))
	# '''

	masks = masks.astype(np.uint8) * 255
	masks = [self.dilate_mask(mask) for mask in masks]

	imgs = []
	for idx, mask in enumerate(masks):
	img_inpainted = self.inpaint_img_with_lama(
	img, mask, "./lama/configs/prediction/default.yaml", "big-lama")
	img_inpainted = img_inpainted.astype(np.uint8)
	each = resize_image(img_inpainted, 512)
	imgs.append(torch.DoubleTensor(each))
	image_list = torch.stack(imgs).permute(0, 3, 1, 2)

	updated_image_path = gen_new_name(inputs, f'{type(self).__name__}')
	save_image(image_list, updated_image_path, nrow=3, # requires data type: torch.float64
	normalize=True, value_range=(0, 255))
	print(f"\nProcessed SegmentAnything, Input Image: {inputs}, Output Depth: {updated_image_path}")
	return updated_image_path


	def inpaint_img_with_lama(self, img, mask, config_p,
	ckpt_p: str="./lama/configs/prediction/default.yaml",
	mod=8):
	assert len(mask.shape) == 2
	if np.max(mask) == 1:
	mask = mask * 255
	img = torch.from_numpy(img).float().div(255.)
	mask = torch.from_numpy(mask).float()
	predict_config = OmegaConf.load(config_p)
	predict_config.model.path = ckpt_p
	device = torch.device(predict_config.device)

	train_config_path = os.path.join(
	predict_config.model.path, 'config.yaml')

	with open(train_config_path, 'r') as f:
	train_config = OmegaConf.create(yaml.safe_load(f))

	train_config.training_model.predict_only = True
	train_config.visualizer.kind = 'noop'

	checkpoint_path = os.path.join(
	predict_config.model.path, 'models',
	predict_config.model.checkpoint
	)
	model = load_checkpoint(
	train_config, checkpoint_path, strict=False, map_location='cpu')
	model.freeze()
	if not predict_config.get('refine', False):
	model.to(device)

	batch = {}
	batch['image'] = img.permute(2, 0, 1).unsqueeze(0)
	batch['mask'] = mask[None, None]
	unpad_to_size = [batch['image'].shape[2], batch['image'].shape[3]]
	batch['image'] = pad_tensor_to_modulo(batch['image'], mod)
	batch['mask'] = pad_tensor_to_modulo(batch['mask'], mod)
	batch = move_to_device(batch, device)
	batch['mask'] = (batch['mask'] > 0) * 1

	batch = model(batch)
	cur_res = batch[predict_config.out_key][0].permute(1, 2, 0)
	cur_res = cur_res.detach().cpu().numpy()

	if unpad_to_size is not None:
	orig_height, orig_width = unpad_to_size
	cur_res = cur_res[:orig_height, :orig_width]

	cur_res = np.clip(cur_res * 255, 0, 255).astype('uint8')
	return cur_res

	def dilate_mask(self, mask, dilate_factor=15):
	# dilate mask
	mask = mask.astype(np.uint8)
	mask = cv2.dilate(mask, np.ones((15, 15), np.uint8), iterations=1)

	return mask


	class RemovesMaskedAnything:
	"""
	prepare:
	```
	curl -L $(yadisk-direct https://disk.yandex.ru/d/ouP6l8VJ0HpMZg) -o big-lama.zip
	unzip big-lama.zip
	```
	"""
	template_model=True # Add this line to show this is a template model.
	def __init__(self, SegmentAnything):
	self.SegmentAnything = SegmentAnything
	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="Remove the masked object",
	description="useful when you want to remove an object by masking the region in the image. "
	"like: remove object by the masked region"
	"The input to this tool should be a comma separated string of two, "
	"representing the image_path and seg_path")
	def inference_replace(self, inputs):
	print("Inputs: ", inputs)
	image_path, seg_mask_path = inputs.split(',')
	image_path = image_path.strip()
	seg_mask_path = seg_mask_path.strip()
	img = np.array(Image.open(image_path))
	seg_mask = Image.open(seg_mask_path).convert('L')
	seg_mask = np.array(seg_mask)

	seg_mask = self.dilate_mask(seg_mask)
	inpainted_img = self.inpaint_img_with_lama(
	img, seg_mask, "./lama/configs/prediction/default.yaml", "big-lama")

	inpainted_img_path = gen_new_name(image_path, "RemovesMaskedAnything")
	Image.fromarray(inpainted_img).save(inpainted_img_path, 'PNG')

	print(f"\nProcessed SegmentAnything, Input Image: {inputs}, Output Image: {inpainted_img_path}")
	return inpainted_img_path


	def preprocess_img(self, img):
	self.SegmentAnything.set_image(img)

	def inpaint_img_with_lama(self, img, mask, config_p,
	ckpt_p: str="./lama/configs/prediction/default.yaml",
	mod=8):
	assert len(mask.shape) == 2
	if np.max(mask) == 1:
	mask = mask * 255
	img = torch.from_numpy(img).float().div(255.)
	mask = torch.from_numpy(mask).float()
	predict_config = OmegaConf.load(config_p)
	predict_config.model.path = ckpt_p
	device = torch.device(predict_config.device)

	train_config_path = os.path.join(
	predict_config.model.path, 'config.yaml')

	with open(train_config_path, 'r') as f:
	train_config = OmegaConf.create(yaml.safe_load(f))

	train_config.training_model.predict_only = True
	train_config.visualizer.kind = 'noop'

	checkpoint_path = os.path.join(
	predict_config.model.path, 'models',
	predict_config.model.checkpoint
	)
	model = load_checkpoint(
	train_config, checkpoint_path, strict=False, map_location='cpu')
	model.freeze()
	if not predict_config.get('refine', False):
	model.to(device)

	batch = {}
	batch['image'] = img.permute(2, 0, 1).unsqueeze(0)
	batch['mask'] = mask[None, None]
	unpad_to_size = [batch['image'].shape[2], batch['image'].shape[3]]
	batch['image'] = pad_tensor_to_modulo(batch['image'], mod)
	batch['mask'] = pad_tensor_to_modulo(batch['mask'], mod)
	batch = move_to_device(batch, device)
	batch['mask'] = (batch['mask'] > 0) * 1

	batch = model(batch)
	cur_res = batch[predict_config.out_key][0].permute(1, 2, 0)
	cur_res = cur_res.detach().cpu().numpy()

	if unpad_to_size is not None:
	orig_height, orig_width = unpad_to_size
	cur_res = cur_res[:orig_height, :orig_width]

	cur_res = np.clip(cur_res * 255, 0, 255).astype('uint8')
	return cur_res

	def dilate_mask(self, mask, dilate_factor=15):
	# dilate mask
	mask = mask.astype(np.uint8)
	mask = cv2.dilate(mask, np.ones((15, 15), np.uint8), iterations=1)

	return mask


	class InpaintMaskedAnything:
	"""
	prepare:
	```
	curl -L $(yadisk-direct https://disk.yandex.ru/d/ouP6l8VJ0HpMZg) -o big-lama.zip
	unzip big-lama.zip
	```
	"""
	template_model=True # Add this line to show this is a template model.
	def __init__(self, SegmentAnything):
	self.SegmentAnything = SegmentAnything
	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="Inpaint the Masked Object",
	description="useful when you want to remove an object by masking the region in the image. "
	"like: inpaint the masked region. "
	"The input to this tool should be a comma separated string of two, "
	"representing the image_path and seg_path")
	def inference_replace(self, inputs):
	print("Inputs: ", inputs)
	image_path, seg_mask_path = inputs.split(',')
	image_path = image_path.strip()
	seg_mask_path = seg_mask_path.strip()
	img = np.array(Image.open(image_path))
	seg_mask = Image.open(seg_mask_path).convert('L')
	seg_mask = np.array(seg_mask)

	seg_mask = self.dilate_mask(seg_mask)
	inpainted_img = self.inpaint_img_with_lama(
	img, seg_mask, "./lama/configs/prediction/default.yaml", "big-lama")

	time_stamp = int(time.time())
	inpainted_img_path = gen_new_name(image_path, "InpaintMaskedAnything")
	Image.fromarray(inpainted_img).save(inpainted_img_path, 'PNG')

	print(f"\nProcessed InpaintMaskedAnything, Input Image: {inputs}, Output Image: {inpainted_img_path}")
	return inpainted_img_path


	def preprocess_img(self, img):
	self.SegmentAnything.set_image(img)

	def inpaint_img_with_lama(self, img, mask, config_p,
	ckpt_p: str="./lama/configs/prediction/default.yaml",
	mod=8):
	assert len(mask.shape) == 2
	if np.max(mask) == 1:
	mask = mask * 255
	img = torch.from_numpy(img).float().div(255.)
	mask = torch.from_numpy(mask).float()
	predict_config = OmegaConf.load(config_p)
	predict_config.model.path = ckpt_p
	device = torch.device(predict_config.device)

	train_config_path = os.path.join(
	predict_config.model.path, 'config.yaml')

	with open(train_config_path, 'r') as f:
	train_config = OmegaConf.create(yaml.safe_load(f))

	train_config.training_model.predict_only = True
	train_config.visualizer.kind = 'noop'

	checkpoint_path = os.path.join(
	predict_config.model.path, 'models',
	predict_config.model.checkpoint
	)
	model = load_checkpoint(
	train_config, checkpoint_path, strict=False, map_location='cpu')
	model.freeze()
	if not predict_config.get('refine', False):
	model.to(device)

	batch = {}
	batch['image'] = img.permute(2, 0, 1).unsqueeze(0)
	batch['mask'] = mask[None, None]
	unpad_to_size = [batch['image'].shape[2], batch['image'].shape[3]]
	batch['image'] = pad_tensor_to_modulo(batch['image'], mod)
	batch['mask'] = pad_tensor_to_modulo(batch['mask'], mod)
	batch = move_to_device(batch, device)
	batch['mask'] = (batch['mask'] > 0) * 1

	batch = model(batch)
	cur_res = batch[predict_config.out_key][0].permute(1, 2, 0)
	cur_res = cur_res.detach().cpu().numpy()

	if unpad_to_size is not None:
	orig_height, orig_width = unpad_to_size
	cur_res = cur_res[:orig_height, :orig_width]

	cur_res = np.clip(cur_res * 255, 0, 255).astype('uint8')
	return cur_res

	def dilate_mask(self, mask, dilate_factor=15):
	# dilate mask
	mask = mask.astype(np.uint8)
	mask = cv2.dilate(mask, np.ones((15, 15), np.uint8), iterations=1)

	return mask


	class ExtractMaskedAnything:
	template_model=True # Add this line to show this is a template model.
	def __init__(self, SegmentAnything):
	self.SegmentAnything = SegmentAnything
	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="Extract the masked anything",
	# description="useful when you want to extract the masked region in the image. "
	# "like: extract the masked region or keep the masked region in the image. "
	# "The input to this tool should be a comma separated string of two, "
	# "representing the image_path and mask_path")
	@prompts(name="Extract the Masked anything",
	description="useful when you want to extract the masked region in the image. "
	"like: extract the masked region in the image or "
	"keep the masked region in the image. "
	"The input to this tool should be a string, "
	"representing the image_path")
	def inference(self, inputs):
	print("Inputs: ", inputs)
	image_path = inputs.strip()
	seg_mask_path = self.SegmentAnything.inference_by_mask()
	# if len(inputs.split(',')) == 1:
	# seg_mask_path = self.SegmentAnything.inference_by_mask()
	# image_path = inputs.strip()
	# else:
	# image_path, seg_mask_path = inputs.split(',')
	# image_path = image_path.strip()
	seg_mask_path = seg_mask_path.strip()
	img = np.array(Image.open(image_path).convert("RGB"))
	seg_mask = Image.open(seg_mask_path).convert('RGB')
	seg_mask = np.array(seg_mask, dtype=np.uint8)
	new_img = img * (seg_mask // 255)
	print(new_img.shape)
	print(seg_mask.shape)
	rgba_img = np.concatenate((new_img, seg_mask[:, :, :1]), axis=-1).astype(np.uint8)
	rgba_img = Image.fromarray(rgba_img).convert("RGBA")
	new_name = gen_new_name(image_path, "ExtractMaskedAnything")
	rgba_img.save(new_name, 'PNG')

	print(f"\nProcessed ExtractMaskedAnything, Input Image: {inputs}, Output Image: {new_name}")
	return new_name


	class ReplaceClickedAnything:
	def __init__(self, device):
	print(f"Initializing ReplaceClickedAnything to {device}")
	self.device=device
	sam_checkpoint = "sam_vit_h_4b8939.pth"
	model_type = "vit_h"
	self.sam = sam_model_registry[model_type](checkpoint=sam_checkpoint)
	self.sam.to(device=device)
	self.click_point_state = None
	self.revision = 'fp16' if 'cuda' in device else None
	self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32
	self.inpaint = StableDiffusionInpaintPipeline.from_pretrained(
	"runwayml/stable-diffusion-inpainting", revision=self.revision, torch_dtype=self.torch_dtype).to(device)


	@prompts(name="Replace the clicked object",
	description="useful when you want to replace an object by clicking in the image. "
	"like: replace object by clicking, "
	"The input to this tool should be a string, representing the image_path")
	def inference_replace(self, inputs):
	if self.click_point_state is None:
	print(f"The current point state is None, return the input path {inputs}")
	return inputs

	print("Inputs: ", inputs)
	image_path = inputs
	img = np.array(Image.open(image_path))

	predictor = SamPredictor(self.sam)
	predictor.set_image(img)
	point_coords = [self.click_point_state]
	point_labels = [1]
	point_coords = np.array(point_coords)
	point_labels = np.array(point_labels)

	masks, scores, logits = predictor.predict(
	point_coords=point_coords,
	point_labels=point_labels,
	multimask_output=True,
	)
	# '''
	for i, (mask, score) in enumerate(zip(masks, scores)):
	plt.figure(figsize=(10,10))
	plt.imshow(img)
	show_mask(mask, plt.gca())
	show_points(point_coords, point_labels, plt.gca())
	plt.title(f"Mask {i+1}, Score: {score:.3f}", fontsize=18)
	plt.axis('off')
	plt.savefig("show_mask{}.jpg".format(i))
	# '''

	imgs = []
	for mask in masks:
	mask_pil = Image.fromarray(mask)
	image_pil = Image.fromarray(img)

	image_pil = image_pil.resize((512, 512))
	mask_pil = mask_pil.resize((512, 512))
	each = self.inpaint(prompt='cute dog', image=image_pil, mask_image=mask_pil).images[0]
	each = resize_image(np.array(each), 512)
	imgs.append(torch.DoubleTensor(each))
	image_list = torch.stack(imgs).permute(0, 3, 1, 2)

	# updated_image_path = get_new_image_name(inputs, func_name="clickreplace")
	updated_image_path = gen_new_name(inputs, f'{type(self).__name__}')
	save_image(image_list, updated_image_path, nrow=3, # requires data type: torch.float64
	normalize=True, value_range=(0, 255))
	print(f"\nProcessed ReplaceClickedAnything, Input Image: {inputs}, Output Depth: {updated_image_path}")
	return updated_image_path


	class ReplaceMaskedAnything:
	def __init__(self, device):
	print(f"Initializing ReplaceClickedAnything to {device}")
	self.device=device
	self.revision = 'fp16' if 'cuda' in device else None
	self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32
	self.inpaint = StableDiffusionInpaintPipeline.from_pretrained(
	"runwayml/stable-diffusion-inpainting", revision=self.revision, torch_dtype=self.torch_dtype).to(device)


	@prompts(name="Replace the masked object",
	description="useful when you want to replace an object by clicking in the image. "
	"like: replace the masked object"
	"The input to this tool should be a comma separated string of Three, "
	"representing the image_path and the seg_path and the prompt")
	def inference_replace(self, inputs):
	print("Inputs: ", inputs)
	image_path, seg_path = inputs.split(',')[:2]
	image_path = image_path.strip()
	seg_path = seg_path.strip()
	prompt = ','.join(inputs.split(',')[2:]).strip()
	img = Image.open(image_path)
	original_shape = img.size
	img = img.resize((512, 512))
	seg_img = Image.open(seg_path).convert("L").resize((512, 512))

	gen_img = self.inpaint(prompt=prompt, image=img, mask_image=seg_img).images[0]
	# gen_img = resize_image(np.array(gen_img), 512)
	gen_img = gen_img.resize(original_shape)
	gen_img_path = gen_new_name(image_path, 'ReplaceMaskedAnything')
	gen_img.save(gen_img_path, 'PNG')
	print(f"\nProcessed ReplaceMaskedAnything, Input Image: {inputs}, Output Depth: {gen_img_path}.")
	return gen_img_path


	class ImageOCRRecognition:
	def __init__(self, device):
	print(f"Initializing ImageOCRRecognition to {device}")
	self.device = device
	self.reader = easyocr.Reader(['ch_sim', 'en'], gpu=device) # this needs to run only once to load the model into memory
	self.result = None
	self.image_path=None
	self.clicked_region = None


	@prompts(name="recognize the optical characters in the image",
	description="useful when you want to recognize the characters or words in the clicked region of image. "
	"like: recognize the characters or words in the clicked region."
	"The input to this tool should be a comma separated string of two, "
	"The input to this tool should be None.")
	def inference_by_mask(self, inputs=None):
	mask = self.clicked_region
	inds =np.where(mask != 0)
	coord = []
	for ind_per_dim in inds:
	coord.append(int(ind_per_dim.mean()))

	if self.image_path is None or len(inds[0]) == 0:
	# self.result = self.reader.readtext(image_path)
	return 'No characters in the image'

	# stat = [100, 595] # todo

	# reader = easyocr.Reader(['ch_sim', 'en', 'fr', 'it', 'ja', 'ko', 'ru', 'de', 'pt']) # this needs to run only once to load the model into memory
	orc_text = self.search((coord[1], coord[0]))
	if orc_text is None or len(orc_text) == 0:
	orc_text = 'No characters in the image'

	print(
	f"\nProcessed ImageOCRRecognition, Input Image: {self.image_path}, "
	f"Output Text: {orc_text}.")
	return orc_text

	@prompts(name="recognize all optical characters in the image",
	description="useful when you want to recognize all characters or words in the image. "
	"like: recognize all characters and words in the image."
	"The input to this tool should be a string, "
	"representing the image_path.")
	def inference(self, inputs):
	image_path = inputs.strip()
	if self.image_path != image_path:
	self.result = self.reader.readtext(image_path)
	self.image_path = image_path
	# print(self.result)
	res_text = []
	for item in self.result:
	# ([[x, y], [x, y], [x, y], [x, y]], text, confidence)
	res_text.append(item[1])
	print(
	f"\nProcessed ImageOCRRecognition, Input Image: {self.image_path}, "
	f"Output Text: {res_text}")
	return res_text

	def preprocess(self, img, img_path):
	self.image_path = img_path
	self.result = self.reader.readtext(self.image_path)

	def search(self, coord):
	for item in self.result:
	left_top = item[0][0]
	right_bottom=item[0][-2]
	if (coord[0] >= left_top[0] and coord[1] >= left_top[1]) and \
	(coord[0] <= right_bottom[0] and coord[1] <= right_bottom[1]):
	return item[1]

	return None

	def reset(self):
	self.image_path = None
	self.result = None
	self.mask = None