Spaces:
Runtime error
Runtime error
File size: 5,526 Bytes
caa89c0 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 |
# --------------------------------------------------------
# Semantic-SAM: Segment and Recognize Anything at Any Granularity
# Copyright (c) 2023 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Hao Zhang ([email protected])
# --------------------------------------------------------
import torch
import numpy as np
from torchvision import transforms
from task_adapter.utils.visualizer import Visualizer
from typing import Tuple
from PIL import Image
from detectron2.data import MetadataCatalog
from detectron2.structures import BitMasks
from semantic_sam.utils import box_ops
metadata = MetadataCatalog.get('coco_2017_train_panoptic')
def interactive_infer_image_box(model, image,all_classes,all_parts, thresh,text_size,hole_scale,island_scale,semantic, refimg=None, reftxt=None, audio_pth=None, video_pth=None):
t = []
t.append(transforms.Resize(int(text_size), interpolation=Image.BICUBIC))
transform1 = transforms.Compose(t)
image_ori = transform1(image['image'])
mask_ori = transform1(image['mask'])
width = image_ori.size[0]
height = image_ori.size[1]
image_ori = np.asarray(image_ori)
images = torch.from_numpy(image_ori.copy()).permute(2,0,1).cuda()
all_classes, all_parts=all_classes.strip().strip("\"[]").split(':'),all_parts.strip().strip("\"[]").split(':')
data = {"image": images, "height": height, "width": width}
mask_ori = np.asarray(mask_ori)[:,:,0:1].copy()
mask_ori = torch.from_numpy(mask_ori).permute(2,0,1)[0]
flaten_mask = mask_ori.unsqueeze(0)
# import ipdb; ipdb.set_trace()
points=mask_ori.nonzero().float().to(images.device)
if len(points)==0:
point_=point=points.new_tensor([[0.5,0.5,0.5,0.5]])
else:
mean_point=points.mean(0)[None]
box_xyxy = BitMasks(flaten_mask > 0).get_bounding_boxes().tensor
h = mask_ori.shape[0]
w = mask_ori.shape[1]
box_xywh = (box_ops.box_xyxy_to_cxcywh(box_xyxy) / torch.as_tensor([w, h, w, h])).cuda()
# point_=points.mean(0)[None]
# point=point_.clone()
# point[0, 0] = point_[0, 0] / mask_ori.shape[0]
# point[0, 1] = point_[0, 1] / mask_ori.shape[1]
# point = point[:, [1, 0]]
point=box_xywh
data['targets'] = [dict()]
data['targets'][0]['points']=point
data['targets'][0]['pb']=point.new_tensor([1.])
batch_inputs = [data]
masks,ious = model.model.evaluate_demo(batch_inputs,all_classes,all_parts, task='demo_box')
pred_masks_poses = masks
reses=[]
ious=ious[0,0]
ids=torch.argsort(ious,descending=True)
text_res=''
try:
thresh=float(thresh)
except Exception:
thresh=0.0
mask_ls=[]
ious_res=[]
areas=[]
for i,(pred_masks_pos,iou) in enumerate(zip(pred_masks_poses[ids],ious[ids])):
iou=round(float(iou),2)
texts=f'{iou}'
mask=(pred_masks_pos>0.0).cpu().numpy()
area=mask.sum()
conti=False
if iou<thresh:
conti=True
for m in mask_ls:
if np.logical_and(mask,m).sum()/np.logical_or(mask,m).sum()>0.95:
conti=True
break
if i == len(pred_masks_poses[ids])-1 and mask_ls==[]:
conti=False
if conti:
continue
ious_res.append(iou)
mask_ls.append(mask)
areas.append(area)
mask,_=remove_small_regions(mask,int(hole_scale),mode="holes")
mask,_=remove_small_regions(mask,int(island_scale),mode="islands")
mask=(mask).astype(np.float)
out_txt = texts
visual = Visualizer(image_ori, metadata=metadata)
color=[0.,0.,1.0]
demo = visual.draw_binary_mask(mask, color=color, text=texts)
demo = visual.draw_box(box_xyxy[0])
res = demo.get_image()
# point_x0=max(0,int(point_[0, 1])-3)
# point_x1=min(mask_ori.shape[1],int(point_[0, 1])+3)
# point_y0 = max(0, int(point_[0, 0]) - 3)
# point_y1 = min(mask_ori.shape[0], int(point_[0, 0]) + 3)
# res[point_y0:point_y1,point_x0:point_x1,0]=255
# res[point_y0:point_y1,point_x0:point_x1,1]=0
# res[point_y0:point_y1,point_x0:point_x1,2]=0
reses.append(Image.fromarray(res))
text_res=text_res+';'+out_txt
ids=list(torch.argsort(torch.tensor(areas),descending=False))
ids = [int(i) for i in ids]
torch.cuda.empty_cache()
return reses,[reses[i] for i in ids]
def remove_small_regions(
mask: np.ndarray, area_thresh: float, mode: str
) -> Tuple[np.ndarray, bool]:
"""
Removes small disconnected regions and holes in a mask. Returns the
mask and an indicator of if the mask has been modified.
"""
import cv2 # type: ignore
assert mode in ["holes", "islands"]
correct_holes = mode == "holes"
working_mask = (correct_holes ^ mask).astype(np.uint8)
n_labels, regions, stats, _ = cv2.connectedComponentsWithStats(working_mask, 8)
sizes = stats[:, -1][1:] # Row 0 is background label
small_regions = [i + 1 for i, s in enumerate(sizes) if s < area_thresh]
if len(small_regions) == 0:
return mask, False
fill_labels = [0] + small_regions
if not correct_holes:
fill_labels = [i for i in range(n_labels) if i not in fill_labels]
# If every region is below threshold, keep largest
if len(fill_labels) == 0:
fill_labels = [int(np.argmax(sizes)) + 1]
mask = np.isin(regions, fill_labels)
return mask, True |