from typing import Dict, Optional, Tuple, List
from dataclasses import dataclass
import os
import sys
proj_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
sys.path.append(os.path.join(proj_dir))
import time
import cv2
import gradio as gr
import numpy as np
import torch
import PIL
from PIL import Image
import rembg
from rembg import remove
rembg_session = rembg.new_session()
from segment_anything import sam_model_registry, SamPredictor
import craftsman
from craftsman.systems.base import BaseSystem
from craftsman.utils.config import ExperimentConfig, load_config
parent_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
def check_input_image(input_image):
if input_image is None:
raise gr.Error("No image uploaded!")
def load_model(
ckpt_path: str,
config_path: str,
device = "cuda"
):
cfg: ExperimentConfig
cfg = load_config(config_path)
if 'pretrained_model_name_or_path' not in cfg.system.condition_model or cfg.system.condition_model.pretrained_model_name_or_path is None:
cfg.system.condition_model.config_path = config_path.replace("config.yaml", "clip_config.json")
system: BaseSystem = craftsman.find(cfg.system_type)(
cfg.system,
)
print(f"Restoring states from the checkpoint path at {ckpt_path} with config {cfg}")
system.load_state_dict(torch.load(ckpt_path, map_location=torch.device('cpu'))['state_dict'])
system = system.to(device).eval()
return system
class RMBG(object):
def __init__(self, device):
sam = sam_model_registry["vit_h"](checkpoint=f"{parent_dir}/ckpts/SAM/sam_vit_h_4b8939.pth").to(device)
self.predictor = SamPredictor(sam)
def rmbg_sam(self, input_image):
def _sam_segment(predictor, input_image, *bbox_coords):
bbox = np.array(bbox_coords)
image = np.asarray(input_image)
start_time = time.time()
predictor.set_image(image)
masks_bbox, scores_bbox, logits_bbox = predictor.predict(box=bbox, multimask_output=True)
print(f"SAM Time: {time.time() - start_time:.3f}s")
out_image = np.zeros((image.shape[0], image.shape[1], 4), dtype=np.uint8)
out_image[:, :, :3] = image
out_image_bbox = out_image.copy()
out_image_bbox[:, :, 3] = masks_bbox[-1].astype(np.uint8) * 255
torch.cuda.empty_cache()
return Image.fromarray(out_image_bbox, mode='RGBA')
RES = 1024
input_image.thumbnail([RES, RES], Image.Resampling.LANCZOS)
image_rem = input_image.convert('RGBA')
image_nobg = remove(image_rem, alpha_matting=True)
arr = np.asarray(image_nobg)[:, :, -1]
x_nonzero = np.nonzero(arr.sum(axis=0))
y_nonzero = np.nonzero(arr.sum(axis=1))
x_min = int(x_nonzero[0].min())
y_min = int(y_nonzero[0].min())
x_max = int(x_nonzero[0].max())
y_max = int(y_nonzero[0].max())
return _sam_segment(self.predictor, input_image.convert('RGB'), x_min, y_min, x_max, y_max)
def rmbg_rembg(self, input_image):
def _rembg_remove(
image: PIL.Image.Image,
rembg_session = None,
force: bool = False,
**rembg_kwargs,
) -> PIL.Image.Image:
do_remove = True
if image.mode == "RGBA" and image.getextrema()[3][0] < 255:
# explain why current do not rm bg
print("alhpa channl not enpty, skip remove background, using alpha channel as mask")
background = Image.new("RGBA", image.size, (0, 0, 0, 0))
image = Image.alpha_composite(background, image)
do_remove = False
do_remove = do_remove or force
if do_remove:
image = rembg.remove(image, session=rembg_session, **rembg_kwargs)
return image
return _rembg_remove(input_image, rembg_session, force_remove=True)
def run(self, rm_type, image, foreground_ratio, background_choice, backgroud_color):
# image = cv2.resize(np.array(image), (crop_size, crop_size))
# image = Image.fromarray(image)
if background_choice == "Alpha as mask":
background = Image.new("RGBA", image.size, (backgroud_color[0], backgroud_color[1], backgroud_color[2], 0))
return Image.alpha_composite(background, image)
elif "Remove" in background_choice:
if rm_type.upper() == "SAM":
image = self.rmbg_sam(image)
elif rm_type.upper() == "REMBG":
image = self.rmbg_rembg(image)
else:
return -1
image = do_resize_content(image, foreground_ratio)
image = expand_to_square(image)
# image = add_background(image, backgroud_color)
# return image.convert("RGB")
return image
elif "Original" in background_choice:
return image
else:
return -1
def do_resize_content(original_image: Image, scale_rate):
# resize image content wile retain the original image size
if scale_rate != 1:
# Calculate the new size after rescaling
new_size = tuple(int(dim * scale_rate) for dim in original_image.size)
# Resize the image while maintaining the aspect ratio
resized_image = original_image.resize(new_size)
# Create a new image with the original size and black background
padded_image = Image.new("RGBA", original_image.size, (0, 0, 0, 0))
paste_position = ((original_image.width - resized_image.width) // 2, (original_image.height - resized_image.height) // 2)
padded_image.paste(resized_image, paste_position)
return padded_image
else:
return original_image
def expand2square(pil_img, background_color):
width, height = pil_img.size
if width == height:
return pil_img
elif width > height:
result = Image.new(pil_img.mode, (width, width), background_color)
result.paste(pil_img, (0, (width - height) // 2))
return result
else:
result = Image.new(pil_img.mode, (height, height), background_color)
result.paste(pil_img, ((height - width) // 2, 0))
return result
def expand_to_square(image, bg_color=(0, 0, 0, 0)):
# expand image to 1:1
width, height = image.size
if width == height:
return image
new_size = (max(width, height), max(width, height))
new_image = Image.new("RGBA", new_size, bg_color)
paste_position = ((new_size[0] - width) // 2, (new_size[1] - height) // 2)
new_image.paste(image, paste_position)
return new_image
def add_background(image, bg_color=(255, 255, 255)):
# given an RGBA image, alpha channel is used as mask to add background color
background = Image.new("RGBA", image.size, bg_color)
return Image.alpha_composite(background, image)