task_adapter/seem/tasks/inference_seem_pano.py

# --------------------------------------------------------
# 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)))