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import PIL.Image
import cv2
import gradio as gr
import huggingface_hub
import numpy as np
import onnxruntime as rt
from PIL import ImageOps
from carvekit.trimap.generator import TrimapGenerator
from pymatting import estimate_alpha_cf, estimate_foreground_ml, stack_images, load_image
providers = ['CUDAExecutionProvider', 'CPUExecutionProvider']
model_path = huggingface_hub.hf_hub_download("skytnt/anime-seg", "isnetis.onnx")
rmbg_model = rt.InferenceSession(model_path, providers=providers)
trimapGenerator = TrimapGenerator()
# def custom_background(background, foreground):
# foreground = ImageOps.contain(foreground, background.size)
# x = (background.size[0] - foreground.size[0]) // 2
# y = (background.size[1] - foreground.size[1]) // 2
# background.paste(foreground, (x, y), foreground)
# return background
def custom_background(background: PIL.Image.Image, foreground: np.ndarray):
final_foreground = PIL.Image.fromarray(foreground)
x = (background.size[0] - final_foreground.size[0]) / 2
y = (background.size[1] - final_foreground.size[1]) / 2
box = (x, y, final_foreground.size[0] + x, final_foreground.size[1] + y)
crop = background.crop(box)
final_image = crop.copy()
# put the foreground in the centre of the background
paste_box = (0, final_image.size[1] - final_foreground.size[1], final_image.size[0], final_image.size[1])
final_image.paste(final_foreground, paste_box, mask=final_foreground)
return np.array(final_image)
def get_mask(img, s=1024):
img = (img / 255).astype(np.float32)
h, w = h0, w0 = img.shape[:-1]
h, w = (s, int(s * w / h)) if h > w else (int(s * h / w), s)
ph, pw = s - h, s - w
img_input = np.zeros([s, s, 3], dtype=np.float32)
img_input[ph // 2:ph // 2 + h, pw // 2:pw // 2 + w] = cv2.resize(img, (w, h))
img_input = np.transpose(img_input, (2, 0, 1))
img_input = img_input[np.newaxis, :]
mask = rmbg_model.run(None, {'img': img_input})[0][0]
mask = np.transpose(mask, (1, 2, 0))
mask = mask[ph // 2:ph // 2 + h, pw // 2:pw // 2 + w]
mask = cv2.resize(mask, (w0, h0))[:, :, np.newaxis]
return mask
def change_background_color(image, color="blue"):
mask = get_mask(image)
image = (mask * image + 255 * (1 - mask)).astype(np.uint8)
mask = (mask * 255).astype(np.uint8)
image = np.concatenate([image, mask], axis=2, dtype=np.uint8)
image = PIL.Image.fromarray(image)
background = PIL.Image.new('RGB', image.size, color)
background.paste(image, (0, 0), image)
return background
def generate_trimap(probs, size=7, conf_threshold=0.95):
"""
This function creates a trimap based on simple dilation algorithm
Inputs [3]: an image with probabilities of each pixel being the foreground, size of dilation kernel,
foreground confidence threshold
Output : a trimap
"""
mask = (probs > 0.05).astype(np.uint8) * 255
pixels = 2 * size + 1
kernel = np.ones((pixels, pixels), np.uint8)
dilation = cv2.dilate(mask, kernel, iterations=1)
remake = np.zeros_like(mask)
remake[dilation == 255] = 127 # Set every pixel within dilated region as probably foreground.
remake[probs > conf_threshold] = 255 # Set every pixel with large enough probability as definitely foreground.
return remake
def image2gray(image):
image = PIL.Image.fromarray(image).convert("L")
return np.array(image) / 255.0
def paste(img_orig, alpha):
img_ = img_orig.astype(np.float32) / 255
alpha_ = cv2.resize(alpha, (img_.shape[1], img_.shape[0]), cv2.INTER_LANCZOS4)
fg_alpha = np.concatenate([img_, alpha_[:, :, np.newaxis]], axis=2)
cv2.imwrite("new_back.png", (fg_alpha * 255).astype(np.uint8))
def predict(image, new_background):
mask = get_mask(image)
mask = (mask * 255).astype(np.uint8)
mask = mask.repeat(3, axis=2)
trimap = generate_trimap(mask)
trimap = image2gray(trimap)
# trimap = load_image("images/trimaps/lemur_trimap.png", "GRAY")
original = PIL.Image.fromarray(image)
# mask = image2gray(mask)
mask = PIL.Image.fromarray(mask).convert("L")
trimap = trimapGenerator(original_image=original, mask=mask)
trimap = np.array(trimap) / 255.0
foreground = image / 255
alpha = estimate_alpha_cf(foreground, trimap)
foreground = estimate_foreground_ml(foreground, alpha)
cutout = stack_images(foreground, alpha)
cutout = np.clip(cutout * 255, 0, 255).astype(np.uint8)
if new_background is not None:
return mask, trimap, custom_background(new_background, cutout)
return alpha, trimap, cutout
# contours
def serendipity(image, new_background):
mask = get_mask(image)
mask = 255 - mask
image = (mask * image + 255 * (1 - mask)).astype(np.uint8)
mask = (mask * 255).astype(np.uint8)
image = np.concatenate([image, mask], axis=2, dtype=np.uint8)
return mask, image
def negative(image, new_background):
mask = get_mask(image)
image = (mask * image + 255 * (1 - mask)).astype(np.uint8)
image = 255 - image
mask = (mask * 255).astype(np.uint8)
image = np.concatenate([image, mask], axis=2, dtype=np.uint8)
return mask, image
def checkit(image, new_background):
mask = get_mask(image)
mask = 255 - mask
image = (mask / image - 255 / (1 + mask)).astype(np.uint8)
mask = (mask * 255).astype(np.uint8)
mask = 255 - mask
image = np.concatenate([image, mask], axis=2, dtype=np.uint8)
mask = mask.repeat(3, axis=2)
# if new_background is not None:
# foreground = PIL.Image.fromarray(image)
# return mask, custom_background(new_background, foreground)
return mask, image
footer = r"""
<center>
<b>
Demo based on <a href='https://github.com/SkyTNT/anime-segmentation'>SkyTNT Anime Segmentation</a>
</b>
</center>
"""
with gr.Blocks(title="Face Shine") as app:
gr.HTML("<center><h1>Anime Remove Background</h1></center>")
with gr.Row():
with gr.Column():
input_img = gr.Image(type="numpy", image_mode="RGB", label="Input image")
new_img = gr.Image(type="pil", image_mode="RGBA", label="Custom background")
run_btn = gr.Button(variant="primary")
with gr.Column():
with gr.Accordion(label="Image mask", open=False):
output_mask = gr.Image(type="numpy", label="mask")
output_trimap = gr.Image(type="numpy", label="trimap")
output_img = gr.Image(type="numpy", label="result")
run_btn.click(predict, [input_img, new_img], [output_mask, output_trimap, output_img])
with gr.Row():
examples_data = [[f"examples/{x:02d}.jpg"] for x in range(1, 4)]
examples = gr.Dataset(components=[input_img], samples=examples_data)
examples.click(lambda x: x[0], [examples], [input_img])
with gr.Row():
gr.HTML(footer)
app.launch(share=False, debug=True, enable_queue=True, show_error=True)
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