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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 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
import matplotlib.pyplot as plt
import cv2
import io
from .automatic_mask_generator import SeemAutomaticMaskGenerator
metadata = MetadataCatalog.get('coco_2017_train_panoptic')
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 inference_seem_pano(model, image, 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]
data = {"image": images, "height": orig_h, "width": orig_w}
batch_inputs = [data]
model.model.metadata = metadata
outputs = model.model.evaluate(batch_inputs)
pano_mask = outputs[0]['panoptic_seg'][0]
pano_info = outputs[0]['panoptic_seg'][1]
masks = []
for seg_info in pano_info:
masks += [pano_mask == seg_info['id']]
masks = torch.stack(masks, dim=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)
# create a full zero image as the image_orig
sorted_anns = sorted(outputs, key=(lambda x: x['area']), reverse=True)
label = 1
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))) |