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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
metadata = MetadataCatalog.get('coco_2017_train_panoptic')
class SemanticSAMPredictor:
def __init__(self, model, thresh=0.5, text_size=640, hole_scale=100, island_scale=100):
"""
thresh: iou thresh to filter low confidence objects
text_size: resize the input image short edge for the model to process
hole_scale: fill in small holes as in SAM
island_scale: remove small regions as in SAM
"""
self.model = model
self.thresh = thresh
self.text_size = hole_scale
self.hole_scale = hole_scale
self.island_scale = island_scale
self.point = None
def predict(self, image_ori, image, point=None):
"""
produce up to 6 prediction results for each click
"""
width = image_ori.shape[0]
height = image_ori.shape[1]
data = {"image": image, "height": height, "width": width}
# import ipdb; ipdb.set_trace()
if point is None:
point = torch.tensor([[0.5, 0.5, 0.006, 0.006]]).cuda()
else:
point = torch.tensor(point).cuda()
point_ = point
point = point_.clone()
point[0, 0] = point_[0, 0]
point[0, 1] = point_[0, 1]
# point = point[:, [1, 0]]
point = torch.cat([point, point.new_tensor([[0.005, 0.005]])], dim=-1)
self.point = point[:, :2].clone()*(torch.tensor([width, height]).to(point))
data['targets'] = [dict()]
data['targets'][0]['points'] = point
data['targets'][0]['pb'] = point.new_tensor([0.])
batch_inputs = [data]
masks, ious = self.model.model.evaluate_demo(batch_inputs)
return masks, ious
def process_multi_mask(self, masks, ious, image_ori):
pred_masks_poses = masks
reses = []
ious = ious[0, 0]
ids = torch.argsort(ious, descending=True)
text_res = ''
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 < self.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, _ = self.remove_small_regions(mask, int(self.hole_scale), mode="holes")
mask, _ = self.remove_small_regions(mask, int(self.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)
res = demo.get_image()
point_x0 = max(0, int(self.point[0, 0]) - 3)
point_x1 = min(image_ori.shape[1], int(self.point[0, 0]) + 3)
point_y0 = max(0, int(self.point[0, 1]) - 3)
point_y1 = min(image_ori.shape[0], int(self.point[0, 1]) + 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 predict_masks(self, image_ori, image, point=None):
masks, ious = self.predict(image_ori, image, point)
return self.process_multi_mask(masks, ious, image_ori)
@staticmethod
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
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