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 if instances.bboxes is None: return Image.fromarray(drawed[..., ::-1]) 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( # design titles and text descriptions title="Anime Subject Instance Segmentation", description="Segment image subjects with the proposed model in the paper [*Instance-guided Cartoon Editing with a Large-scale Dataset*](https://cartoonsegmentation.github.io/).", fn=fn, inputs=gr.Image(type="numpy"), outputs=gr.Image(type="pil"), examples=["1562990.jpg", "612989.jpg", "sample_3.jpg"] ) iface.launch()