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import gradio as gr
import os
os.system("mim install mmengine")
os.system('mim install "mmcv>=2.0.0"')
os.system("mim install mmdet")
import cv2
from PIL import Image
import numpy as np
from animeinsseg import AnimeInsSeg, AnimeInstances
from animeinsseg.anime_instances import get_color
if not os.path.exists("models"):
os.mkdir("models")
os.system("huggingface-cli lfs-enable-largefiles .")
os.system("git clone https://huggingface.co/dreMaz/AnimeInstanceSegmentation models/AnimeInstanceSegmentation")
ckpt = r'models/AnimeInstanceSegmentation/rtmdetl_e60.ckpt'
mask_thres = 0.3
instance_thres = 0.3
refine_kwargs = {'refine_method': 'refinenet_isnet'} # set to None if not using refinenet
# refine_kwargs = None
net = AnimeInsSeg(ckpt, mask_thr=mask_thres, refine_kwargs=refine_kwargs)
def fn(image):
img = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
instances: AnimeInstances = net.infer(
img,
output_type='numpy',
pred_score_thr=instance_thres
)
drawed = img.copy()
im_h, im_w = img.shape[:2]
# instances.bboxes, instances.masks will be None, None if no obj is detected
for ii, (xywh, mask) in enumerate(zip(instances.bboxes, instances.masks)):
color = get_color(ii)
mask_alpha = 0.5
linewidth = max(round(sum(img.shape) / 2 * 0.003), 2)
# draw bbox
p1, p2 = (int(xywh[0]), int(xywh[1])), (int(xywh[2] + xywh[0]), int(xywh[3] + xywh[1]))
cv2.rectangle(drawed, p1, p2, color, thickness=linewidth, lineType=cv2.LINE_AA)
# draw mask
p = mask.astype(np.float32)
blend_mask = np.full((im_h, im_w, 3), color, dtype=np.float32)
alpha_msk = (mask_alpha * p)[..., None]
alpha_ori = 1 - alpha_msk
drawed = drawed * alpha_ori + alpha_msk * blend_mask
drawed = drawed.astype(np.uint8)
return Image.fromarray(drawed[..., ::-1])
iface = gr.Interface(
fn=fn
inputs=gr.Image(type="numpy"),
outputs=gr.Image(type="pil")
)
iface.launch()
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