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# --------------------------------------------------------
# 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 torch.nn.functional as F
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 kornia.contrib import distance_transform
import matplotlib.pyplot as plt
import cv2
import io
metadata = MetadataCatalog.get('coco_2017_train_panoptic')
from segment_anything import SamAutomaticMaskGenerator
from segment_anything.utils.amg import (
MaskData,
area_from_rle,
batch_iterator,
batched_mask_to_box,
box_xyxy_to_xywh,
build_all_layer_point_grids,
calculate_stability_score,
coco_encode_rle,
generate_crop_boxes,
is_box_near_crop_edge,
mask_to_rle_pytorch,
remove_small_regions,
rle_to_mask,
uncrop_boxes_xyxy,
uncrop_masks,
uncrop_points,
)
def sam_interactive_mask(mask_generator, points, in_points, in_labels, mask_input):
masks, iou_preds, _ = mask_generator.predictor.predict_torch(
in_points,
in_labels,
mask_input=mask_input,
multimask_output=True,
return_logits=True,
)
nm,_,h,w = masks.shape
# Serialize predictions and store in MaskData
data = MaskData(
masks=masks.flatten(0, 1),
iou_preds=iou_preds.flatten(0, 1),
points=torch.as_tensor(points.repeat(masks.shape[1], axis=0)),
)
del masks
# Calculate stability score
data["stability_score"] = calculate_stability_score(
data["masks"], mask_generator.predictor.model.mask_threshold, mask_generator.stability_score_offset
)
masks = data["masks"].reshape(nm, -1, h, w)
scores = (data['iou_preds'] + data['stability_score']).reshape(nm, -1)
index = torch.stack([torch.arange(nm).cuda(), scores.argmax(dim=1)]).tolist()
return masks[index]
def inference_sam_m2m_interactive(model, image, spatial_masks, text_size, label_mode='1', alpha=0.1, anno_mode=['Mask']):
t = []
t.append(transforms.Resize(int(text_size), interpolation=Image.BICUBIC))
transform1 = transforms.Compose(t)
image_ori = transform1(image)
image_ori = np.asarray(image_ori)
images = torch.from_numpy(image_ori.copy()).permute(2,0,1).cuda()
orig_size = images.shape[-2:]
orig_h, orig_w = orig_size
crop_box = [0,0,orig_w,orig_h]
spatial_masks = spatial_masks[:, None].float().cuda()
spatial_masks = F.interpolate(spatial_masks, size=(orig_h, orig_w), mode='bicubic', align_corners=False) > 0
# generate single center point
# n,_,h,w = spatial_masks.shape
# mask_dt = (distance_transform((~F.pad(spatial_masks, pad=(1, 1, 1, 1), mode='constant', value=0)).float())[:,:,1:-1,1:-1]).reshape(n,-1)
# max_xy_idx = torch.stack([torch.arange(n), mask_dt.max(dim=-1)[1].cpu()]).tolist()
# next_mask = torch.zeros(spatial_masks.shape, device=torch.cuda.current_device()).bool()
# next_mask = next_mask.view(n,-1)
# next_mask[max_xy_idx] = True
# next_mask = next_mask.reshape((n,1,h,w))
# points = next_mask.nonzero()[:,2:].flip(dims=[1]).cpu().numpy()
# stack sampled points
acc_points = []
for i in range(len(spatial_masks)):
points = spatial_masks[i:i+1].nonzero()[:,2:].flip(dims=[1]).cpu().numpy()
rand_ids = np.random.choice(points.shape[0], size=40, replace=True)
points = points[rand_ids]
acc_points.append(points)
_np = len(acc_points)
points = np.concatenate(acc_points)
mask_generator = SamAutomaticMaskGenerator(model)
mask_generator.predictor.set_image(image_ori)
im_size = image_ori.shape[:-1]
transformed_points = mask_generator.predictor.transform.apply_coords(points, im_size)
in_points = torch.as_tensor(transformed_points, device=mask_generator.predictor.device).reshape(_np,-1,2).transpose(0,1)
in_labels = torch.ones((in_points.shape[0], _np), dtype=torch.int, device=mask_generator.predictor.device)
masks = sam_interactive_mask(mask_generator, points, in_points.transpose(0,1), in_labels.transpose(0,1), None)
masks = masks > 0.0
iou_preds = torch.ones(masks.shape[0], dtype=torch.float32)
points = torch.zeros((masks.shape[0], 2), dtype=torch.float32)
mask_data = MaskData(
masks=masks,
iou_preds=iou_preds,
points=points,
)
mask_data["stability_score"] = torch.ones(masks.shape[0], dtype=torch.float32)
del masks
mask_data["boxes"] = batched_mask_to_box(mask_data["masks"])
mask_data["crop_boxes"] = torch.tensor([crop_box for _ in range(len(mask_data["boxes"]))])
# Compress to RLE
mask_data["masks"] = uncrop_masks(mask_data["masks"], crop_box, orig_h, orig_w)
mask_data["rles"] = mask_to_rle_pytorch(mask_data["masks"])
del mask_data["masks"]
mask_data["segmentations"] = [rle_to_mask(rle) for rle in mask_data["rles"]]
# Write mask records
outputs = []
for idx in range(len(mask_data["segmentations"])):
ann = {
"segmentation": mask_data["segmentations"][idx],
"area": area_from_rle(mask_data["rles"][idx]),
"bbox": box_xyxy_to_xywh(mask_data["boxes"][idx]).tolist(),
"predicted_iou": mask_data["iou_preds"][idx].item(),
"point_coords": [mask_data["points"][idx].tolist()],
"stability_score": mask_data["stability_score"][idx].item(),
"crop_box": box_xyxy_to_xywh(mask_data["crop_boxes"][idx]).tolist(),
}
outputs.append(ann)
from task_adapter.utils.visualizer import Visualizer
visual = Visualizer(image_ori, metadata=metadata)
sorted_anns = sorted(outputs, key=(lambda x: x['area']), reverse=True)
label = 1
# for ann in sorted_anns:
# mask = ann['segmentation']
# demo = visual.draw_binary_mask_with_number(mask, text=str(label), label_mode=label_mode, alpha=alpha, anno_mode=anno_mode)
# label += 1
# im = demo.get_image()
mask_map = np.zeros(image_ori.shape, dtype=np.uint8)
for i, ann in enumerate(sorted_anns):
mask = ann['segmentation']
color_mask = np.random.random((1, 3)).tolist()[0]
# color_mask = [int(c*255) for c in color_mask]
demo = visual.draw_binary_mask_with_number(mask, text=str(label), label_mode=label_mode, alpha=alpha, anno_mode=anno_mode)
# assign the mask to the mask_map
mask_map[mask == 1] = label
label += 1
im = demo.get_image()
# fig=plt.figure(figsize=(10, 10))
# plt.imshow(image_ori)
# show_anns(outputs)
# fig.canvas.draw()
# im=Image.frombytes('RGB', fig.canvas.get_width_height(), fig.canvas.tostring_rgb())
return im, sorted_anns
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
def show_anns(anns):
if len(anns) == 0:
return
sorted_anns = sorted(anns, key=(lambda x: x['area']), reverse=True)
ax = plt.gca()
ax.set_autoscale_on(False)
polygons = []
color = []
for ann in sorted_anns:
m = ann['segmentation']
img = np.ones((m.shape[0], m.shape[1], 3))
color_mask = np.random.random((1, 3)).tolist()[0]
for i in range(3):
img[:,:,i] = color_mask[i]
ax.imshow(np.dstack((img, m*0.35))) |