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basic_app.py
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basic_app.py
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from dataclasses import dataclass
from typing import Any
import cv2
import numpy as np
from regex import W
import streamlit as st
import torch
from PIL import Image
from rich import print
from streamlit_cropper import st_cropper
from streamlit_drawable_canvas import st_canvas
from api.sam_model import SamModel
from utils import seed_everything, draw_red_point_bbox_based_on_center, draw_center_bbox
seed_everything(seed=42)
import os
from collections import defaultdict
import torchvision.transforms as transforms
from diffusers import StableDiffusionInpaintPipeline, DiffusionPipeline
@dataclass
class Size:
width: int = 800
height: int = 250
def __call__(self, *args: Any, **kwds: Any) -> Any:
return (self.width, self.height)
class App:
def __init__(self, device="cuda"):
st.set_page_config(layout="wide", initial_sidebar_state="collapsed")
self.device = device
st.session_state["app"] = (
st.session_state["app"] if "app" in st.session_state else dict()
)
st.session_state["model"] = (
st.session_state["model"] if "model" in st.session_state else dict()
)
# diffusion model
if "pipe" not in st.session_state["model"].keys():
self.pipe = DiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-1",
torch_dtype=torch.float16,
)
self.pipe = self.pipe.to(self.device + ":0")
st.session_state["model"]["pipe"] = self.pipe
else:
self.pipe = st.session_state["model"]["pipe"]
if "inpaint_pipe" not in st.session_state["model"].keys():
self.inpaint_pipe = StableDiffusionInpaintPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-inpainting",
torch_dtype=torch.float16,
)
self.inpaint_pipe = self.inpaint_pipe.to(self.device + ":0")
st.session_state["model"]["inpaint_pipe"] = self.inpaint_pipe
else:
self.inpaint_pipe = st.session_state["model"]["inpaint_pipe"]
# SAM
if "sam" not in st.session_state["model"].keys():
self.sam = SamModel.create_instance(
self.device + ":1", "vit_h", is_hq=True
).setup()
st.session_state["model"]["sam"] = self.sam
else:
self.sam = st.session_state["model"]["sam"]
def run(self):
main_cols = st.columns([2, 1])
#
# Input and buttons
#
with main_cols[0]:
prompt = st.text_input("Prompt", placeholder="Prompt....")
print("prompt", prompt)
btn_cols = st.columns(4)
with btn_cols[0]:
if st.button("Generate") and prompt is not None and prompt != "":
st.session_state["app"].clear()
st.session_state["app"]["image"] = self.pipe(prompt).images[0]
st.session_state["app"]["image"].save("gen.png")
#
# Cropper
#
with btn_cols[1]:
if st.button("Crop"):
image_np = np.array(st.session_state["app"]["image"])
mask, _ = self.sam(
image=image_np,
xyxy=np.array([st.session_state["app"]["xyxy"]]),
)
mask = mask.to(torch.int8)
mask_np = mask[0][0].cpu().numpy()
image_np[mask_np == 0] = 0
st.session_state["app"]["image"] = Image.fromarray(image_np)
#
# Inpaint
#
with btn_cols[2]:
if st.button("Inpaint"):
width, height = (
st.session_state["app"]["image"].width,
st.session_state["app"]["image"].height,
)
#
# SAM to find inpaint area
#
image_np = np.array(st.session_state["app"]["image"])
inpaint_bbox = st.session_state["app"]["xyxy"]
mask, _ = self.sam(
image=image_np,
xyxy=np.array([inpaint_bbox]),
)
mask = mask.to(torch.int8)
mask_np = mask[0][0].cpu().numpy()
mask_np[mask_np == 1] = 255
mask_image = Image.fromarray(mask_np)
mask_image.save("inpaint-mask.png")
image_source_for_inpaint = st.session_state["app"]["image"]
image_mask_for_inpaint = mask_image
inpaint = self.inpaint_pipe(
prompt=prompt,
image=image_source_for_inpaint,
mask_image=image_mask_for_inpaint,
width=width,
height=height,
).images[0]
inpaint = inpaint.resize(
(width, height)
)
inpaint.save("inpaint.png")
st.session_state["app"]["image"] = inpaint
#
# paint
#
with btn_cols[3]:
if st.button("Paint"):
width, height = (
st.session_state["app"]["image"].width,
st.session_state["app"]["image"].height,
)
paint = self.pipe(prompt, width=width, height=height).images[0]
#
# Get center of paint
#
paint_width, paint_height = (width, height)
bbox_width, bbox_height = (
st.session_state["app"]["xywh"]["width"],
st.session_state["app"]["xywh"]["height"],
)
bbox_left, bbox_top = ( # left, top
st.session_state["app"]["xywh"]["left"],
st.session_state["app"]["xywh"]["top"],
)
center = (paint_width // 2, paint_height // 2)
point_coords = np.array([[center]])
point_labels = np.ones(point_coords.shape[1])[None, :]
point_coords = torch.tensor(point_coords).to(self.device + ":1")
point_labels = torch.tensor(point_labels).to(self.device + ":1")
#
# Get bbox based on center
#
center_bbox = (
center[0] - bbox_width // 2,
center[1] - bbox_height // 2,
center[0] + bbox_width // 2,
center[1] + bbox_height // 2,
)
#
# SAM
#
paint_np = np.array(paint)
# draw_center_bbox(paint_np, center, bbox)
Image.fromarray(paint_np).save("paint.png")
mask, _ = self.sam(
image=paint_np,
point_coords=point_coords,
point_labels=point_labels,
xyxy=np.array([center_bbox]),
)
mask = mask.to(torch.int8)
mask_np = mask[0][0].cpu().numpy()
paint_np[mask_np == 0] = 0
# draw_red_point_bbox_based_on_center(paint_np)
paint = Image.fromarray(paint_np)
paint = paint.convert("RGBA")
paint_data = paint.getdata()
new_data = []
for item in paint_data:
if item[0] == 0 and item[1] == 0 and item[2] == 0:
new_data.append((255, 255, 255, 0))
else:
new_data.append(item)
paint.putdata(new_data)
paint = paint.resize((bbox_width, bbox_height))
paint.save("paint-masked.png")
alpha = Image.new("RGBA", paint.size, (255, 255, 255, 0))
alpha.paste(paint, (0, 0), paint)
st.session_state["app"]["image"].paste(
alpha, (bbox_left, bbox_top), alpha
)
#
# Show image
#
with main_cols[1]:
#
# Show current image
#
if (
"image" in st.session_state["app"].keys()
and st.session_state["app"]["image"] is not None
):
st.session_state["app"]["xywh"] = st_cropper(
st.session_state["app"]["image"],
realtime_update=True,
box_color="#0000FF",
return_type="box",
)
st.session_state["app"]["xyxy"] = (
st.session_state["app"]["xywh"]["left"],
st.session_state["app"]["xywh"]["top"],
st.session_state["app"]["xywh"]["width"]
+ st.session_state["app"]["xywh"]["left"],
st.session_state["app"]["xywh"]["height"]
+ st.session_state["app"]["xywh"]["top"],
)
print("app: ", st.session_state["app"])
print("=====================================================================")
if __name__ == "__main__":
app = App(device="cuda" if torch.cuda.is_available() else "cpu")
app.run()