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import gc
import gradio as gr
from fastapi import FastAPI, Body,Header,status
from gradio import components,Blocks,Row
import json
from PIL import Image
import torchvision.transforms as transforms
import torch
from pathlib import Path
import os
import requests
from segment_anything import sam_model_registry, SamPredictor
import numpy as np
from modules.safe import unsafe_torch_load, load
from modules.devices import device, torch_gc, cpu
from modules.processing import process_images
import modules.scripts as scripts
UNIT_DEBUG=False
def import_or_install(package,pip_name=None):
import importlib
import subprocess
if pip_name is None:
pip_name=package
try:
importlib.import_module(package)
print(f"{package} is already installed")
except ImportError:
print(f"{package} is not installed, installing now...")
subprocess.call(['pip', 'install', package])
print(f"{package} has been installed")
import_or_install("segment_anything","git+https://github.com/facebookresearch/segment-anything.git")
class InteractiveImageSegmentor:
def download_file_if_not_exists(file_url, file_name):
if not os.path.isfile(file_name):
response = requests.get(file_url)
if response.status_code == 200:
with open(file_name, 'wb') as file:
file.write(response.content)
print("File downloaded successfully!")
else:
print("Failed to download the file.")
def load_model(self,model_choice="sam_vit_b"):
sam_checkpoint=f"{model_choice}.pth"
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.device = device
if model_choice=="sam_vit_b":InteractiveImageSegmentor.download_file_if_not_exists("https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth",sam_checkpoint)
elif model_choice=="sam_vit_l":InteractiveImageSegmentor.download_file_if_not_exists("https://dl.fbaipublicfiles.com/segment_anything/sam_vit_l_0b3195.pth",sam_checkpoint)
elif model_choice=="sam_vit_h":InteractiveImageSegmentor.download_file_if_not_exists("https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth",sam_checkpoint)
model_type=model_choice.replace("sam_","")
if model_type not in sam_model_registry:
model_type="default"
print(f"Loading model {model_type} from {sam_checkpoint}")
torch.load = unsafe_torch_load
self.sam = sam_model_registry[model_type](checkpoint=sam_checkpoint)
self.sam.to(self.device)
self.predictor = SamPredictor(self.sam)
torch.load = load
def clear_sam_cache(self):
self.sam.unload_model()
gc.collect()
torch_gc()
def mask2image_multi(self,mask:torch.Tensor):
# print(mask.shape)
if mask.dim() == 3 and mask.shape[-1] == 3:
mask = mask.permute(2, 0, 1)
elif mask.dim() == 3 and mask.shape[0] == 3:
pass
else:
print(mask.shape)
raise ValueError("Mask tensor has an unexpected shape.")
color = torch.Tensor([255/255, 155/255, 114/255, 0.6]).to(self.device)
binary_mask = mask[0, :, :]
h, w = binary_mask.shape
mask_image = binary_mask.reshape(h, w, 1) * color.reshape(1, 1, -1)
return mask_image.permute(2, 0, 1)
def mask2image(self, mask: torch.Tensor):
if mask.dim() == 3 and mask.shape[0] == 1:
binary_mask = mask.squeeze(0)
elif mask.dim() == 2:
binary_mask = mask
else:
print(mask.shape)
raise ValueError("Mask tensor has an unexpected shape.")
h, w = binary_mask.shape
rgb_image = binary_mask.repeat(3, 1, 1)
alpha_channel = torch.full((1, h, w), 0.6).to(self.device)
rgba_image = torch.cat((rgb_image, alpha_channel), dim=0)
color = torch.Tensor([255/255, 155/255, 114/255]).to(self.device).reshape(3, 1, 1)
return rgba_image
def preview_segment(self,image:Image,points:list[list[float]]=None,bbox=None,labels:list[int]=None):
pil_2_tensor = transforms.PILToTensor()
rgba_image = image.convert("RGBA")
image_tensor = pil_2_tensor(rgba_image).cuda()
result_tensor=image_tensor.clone()
mask_tensor=self.segment(points,bbox,labels)
mask_image_tensor=self.mask2image(mask_tensor)
mask_image=transforms.ToPILImage()(mask_image_tensor)
result_image:Image = transforms.ToPILImage()(result_tensor)
result_image=Image.alpha_composite(result_image,mask_image)
return result_image
def segment(self,points:list[list[float]]=None,bbox=None,labels:list[int]=None)->torch.Tensor:
if len(points)==0:points=None
if len(labels)==0:labels=None
if len(bbox)==0:bbox=None
if points is not None:points = torch.Tensor(np.array(points)).to(self.device).unsqueeze(0)
if labels is not None:labels = torch.Tensor(np.array(labels)).to(self.device).unsqueeze(0)
if bbox is not None:bbox = torch.Tensor(np.array(bbox)).to(self.device)
print(points,labels,bbox)
masks, scores, logits = self.predictor.predict_torch(
point_coords=points,
point_labels=labels,
boxes=bbox,
multimask_output=False,
)
return masks[0]
def remove_selected(self,image:Image,points:list[list[float]]=None,boxes=None,labels:list[int]=None):
pil_2_tensor = transforms.PILToTensor()
rgba_image = image.convert("RGBA")
image_tensor = pil_2_tensor(rgba_image).cuda()
mask_tensor = image_segmentor.segment(points=points,bbox=boxes,labels=labels)
result_tensor=image_tensor*(1-mask_tensor)
result_image:Image = transforms.ToPILImage()(result_tensor)
return result_image
def remove_unselected(self,image:Image,points:list[list[float]]=None,boxes=None,labels:list[int]=None):
pil_2_tensor = transforms.PILToTensor()
rgba_image = image.convert("RGBA")
image_tensor = pil_2_tensor(rgba_image).cuda()
mask_tensor = image_segmentor.segment(points=points,bbox=boxes,labels=labels)
print(image_tensor.shape,mask_tensor.shape)
result_tensor=image_tensor*mask_tensor
result_image:Image = transforms.ToPILImage()(result_tensor)
return result_image
pass
def reset_image(image:Image):
global image_segmentor
if image_segmentor is None:
image_segmentor=InteractiveImageSegmentor()
image_segmentor.load_model()
image_segmentor.predictor.reset_image()
image_array = np.array(image)
image_segmentor.predictor.set_image(image_array)
return image
def on_image_changed(image:Image):
global points,labels,box_cache,boxes
points=[]
labels=[]
boxes=[]
box_cache=[]
reset_image(image)
return image
def on_image_clicked(image:Image,choice,input_type,event_data:gr.SelectData):
global box_cache,boxes,points,labels
if isinstance(choice,str):
if choice=="Select":choice=1
elif choice=="Deselect":choice=0
if input_type=="Point":
points.append(event_data.index)
labels.append(choice)
return image_segmentor.preview_segment(image,points=points,bbox=boxes,labels=labels)
elif input_type=="Box":
box_cache.extend(event_data.index)
if len(box_cache)==4:
boxes.append(box_cache)
box_cache=[]
return image_segmentor.preview_segment(image,points=points,bbox=boxes,labels=labels)
return image
def on_remove_btn_clicked(image:Image,remove_type:str):
global points,labels,box_cache,boxes
if remove_type=="Selected":
return image_segmentor.remove_selected(image,points=points,boxes=boxes,labels=labels)
elif remove_type=="Unselected":
return image_segmentor.remove_unselected(image,points=points,boxes=boxes,labels=labels)
return image
class Script(scripts.Script):
def title(self):
return "Interactive Image Segmentor"
def show(self, is_img2img):
return is_img2img
def ui(self, is_img2img):
if not is_img2img: return
with Blocks():
with Row(equal_height=True):
choice=components.Radio(choices=["Select","Deselect"],value="Select",label="Selection Type")
input_type=components.Radio(choices=["Point","Box"],value="Point",label="Input Type")
remove_type=components.Radio(choices=["Selected","Unselected"],value="Selected",label="Remove Type")
with Row(equal_height=True):
image=components.Image(type="pil",interactive=True,image_mode="RGB")
resulting_image=components.Image(type="pil",image_mode="RGBA")
image.change(on_image_changed,inputs=[image],outputs=[resulting_image])
image.select(on_image_clicked,inputs=[image,choice,input_type],outputs=[resulting_image])
with Row(equal_height=True):
remove_btn = components.Button(value="Preview Remove Effect")
remove_btn.click(on_remove_btn_clicked,inputs=[image,remove_type],outputs=[resulting_image])
pass
return [image,points,labels,boxes]
def run(self,p,image,points,labels,boxes):
if image is None:
image=p.init_images[0]
image_segmentor.predictor.set_image(np.array(image))
mask=image_segmentor.predictor.predict_torch(points,labels,boxes)
p.image_mask=mask
proc = process_images(p)
proc.images.append(mask)
return proc
pass
def interactive_image_segmentor_api(_: Blocks, app: FastAPI):
@app.post("/figma/interactive_image_segmentor/upload_image")
async def upload_image(image_str:str = Body(...)):
import base64
import io
image_bytes = base64.b64decode(image_str)
image = Image.open(io.BytesIO(image_bytes),formats=["PNG"])
image_segmentor.predictor.reset_image()
image_segmentor.predictor.set_image(np.array(image))
return image
@app.post("/figma/interactive_image_segmentor/image_x_mask")
async def remove_selected(image_str:str = Body(...),points:list[list[float]]=Body(...),\
boxes:list[list[float]]=Body(...),labels:list[int]=Body(...), remove_type:bool=Body(...)):
import base64
import io
image_bytes = base64.b64decode(image_str)
image = Image.open(io.BytesIO(image_bytes),formats=["PNG"])
if remove_type=="Selected":
image= image_segmentor.remove_selected(image,points=points,boxes=boxes,labels=labels)
elif remove_type=="Unselected":
image= image_segmentor.remove_unselected(image,points=points,boxes=boxes,labels=labels)
return image
pass
points=[]
labels=[]
box_cache:list=[]
boxes=[]
image_segmentor=None
# with Blocks() as demo:
# with Row(equal_height=True):
# choice=components.Radio(label="Choice",choices=["Select","Deselect"],value="Select")
# input_type=components.Radio(label="Input Type",choices=["Point","Box"],value="Point")
# remove_type=components.Radio(choices=["Selected","Unselected"],value="Selected")
# with Row(equal_height=True):
# image=components.Image(type="pil",interactive=True,image_mode="RGB")
# resulting_image=components.Image(type="pil",image_mode="RGBA")
# image.change(on_image_changed,inputs=[image],outputs=[resulting_image])
# image.select(on_image_clicked,inputs=[image,choice,input_type],outputs=[resulting_image])
# with Row(equal_height=True):
# remove_btn = components.Button(value="Preview Remove Effect")
# remove_btn.click(on_remove_btn_clicked,inputs=[image,remove_type],outputs=[resulting_image])
# pass
# demo.queue()
# demo.launch()
try:
import modules.script_callbacks as script_callbacks
script_callbacks.on_app_started(interactive_image_segmentor_api)
except:
pass
pass |