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import copy
import os
import sys
from typing import Any, Dict, List, Union
import cv2
import numpy as np
import torch
from PIL import Image
from tqdm import tqdm
inpa_basedir = os.path.normpath(os.path.join(os.path.dirname(__file__), ".."))
if inpa_basedir not in sys.path:
sys.path.append(inpa_basedir)
from ia_file_manager import ia_file_manager # noqa: E402
from ia_get_dataset_colormap import create_pascal_label_colormap # noqa: E402
from ia_logging import ia_logging # noqa: E402
from ia_sam_manager import check_bfloat16_support, get_sam_mask_generator # noqa: E402
from ia_ui_items import get_sam_model_ids # noqa: E402
def get_all_sam_ids() -> List[str]:
"""Get all SAM IDs.
Returns:
List[str]: SAM IDs
"""
return get_sam_model_ids()
def sam_file_path(sam_id: str) -> str:
"""Get SAM file path.
Args:
sam_id (str): SAM ID
Returns:
str: SAM file path
"""
return os.path.join(ia_file_manager.models_dir, sam_id)
def sam_file_exists(sam_id: str) -> bool:
"""Check if SAM file exists.
Args:
sam_id (str): SAM ID
Returns:
bool: True if SAM file exists else False
"""
sam_checkpoint = sam_file_path(sam_id)
return os.path.isfile(sam_checkpoint)
def get_available_sam_ids() -> List[str]:
"""Get available SAM IDs.
Returns:
List[str]: available SAM IDs
"""
all_sam_ids = get_all_sam_ids()
for sam_id in all_sam_ids.copy():
if not sam_file_exists(sam_id):
all_sam_ids.remove(sam_id)
return all_sam_ids
def check_inputs_generate_sam_masks(
input_image: Union[np.ndarray, Image.Image],
sam_id: str,
anime_style_chk: bool = False,
) -> None:
"""Check generate SAM masks inputs.
Args:
input_image (Union[np.ndarray, Image.Image]): input image
sam_id (str): SAM ID
anime_style_chk (bool): anime style check
Returns:
None
"""
if input_image is None or not isinstance(input_image, (np.ndarray, Image.Image)):
raise ValueError("Invalid input image")
if sam_id is None or not isinstance(sam_id, str):
raise ValueError("Invalid SAM ID")
if anime_style_chk is None or not isinstance(anime_style_chk, bool):
raise ValueError("Invalid anime style check")
def convert_input_image(input_image: Union[np.ndarray, Image.Image]) -> np.ndarray:
"""Convert input image.
Args:
input_image (Union[np.ndarray, Image.Image]): input image
Returns:
np.ndarray: converted input image
"""
if isinstance(input_image, Image.Image):
input_image = np.array(input_image)
if input_image.ndim == 2:
input_image = input_image[:, :, np.newaxis]
if input_image.shape[2] == 1:
input_image = np.concatenate([input_image] * 3, axis=-1)
return input_image
def generate_sam_masks(
input_image: Union[np.ndarray, Image.Image],
sam_id: str,
anime_style_chk: bool = False,
) -> List[Dict[str, Any]]:
"""Generate SAM masks.
Args:
input_image (Union[np.ndarray, Image.Image]): input image
sam_id (str): SAM ID
anime_style_chk (bool): anime style check
Returns:
List[Dict[str, Any]]: SAM masks
"""
check_inputs_generate_sam_masks(input_image, sam_id, anime_style_chk)
input_image = convert_input_image(input_image)
sam_checkpoint = sam_file_path(sam_id)
sam_mask_generator = get_sam_mask_generator(sam_checkpoint, anime_style_chk)
ia_logging.info(f"{sam_mask_generator.__class__.__name__} {sam_id}")
if "sam2_" in sam_id:
device = "cuda" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.bfloat16 if check_bfloat16_support() else torch.float16
with torch.inference_mode(), torch.autocast(device, dtype=torch_dtype):
sam_masks = sam_mask_generator.generate(input_image)
else:
sam_masks = sam_mask_generator.generate(input_image)
if anime_style_chk:
for sam_mask in sam_masks:
sam_mask_seg = sam_mask["segmentation"]
sam_mask_seg = cv2.morphologyEx(sam_mask_seg.astype(np.uint8), cv2.MORPH_CLOSE, np.ones((5, 5), np.uint8))
sam_mask_seg = cv2.morphologyEx(sam_mask_seg.astype(np.uint8), cv2.MORPH_OPEN, np.ones((5, 5), np.uint8))
sam_mask["segmentation"] = sam_mask_seg.astype(bool)
ia_logging.info("sam_masks: {}".format(len(sam_masks)))
sam_masks = copy.deepcopy(sam_masks)
return sam_masks
def sort_masks_by_area(
sam_masks: List[Dict[str, Any]],
) -> List[Dict[str, Any]]:
"""Sort mask by area.
Args:
sam_masks (List[Dict[str, Any]]): SAM masks
Returns:
List[Dict[str, Any]]: sorted SAM masks
"""
return sorted(sam_masks, key=lambda x: np.sum(x.get("segmentation").astype(np.uint32)))
def get_seg_colormap() -> np.ndarray:
"""Get segmentation colormap.
Returns:
np.ndarray: segmentation colormap
"""
cm_pascal = create_pascal_label_colormap()
seg_colormap = cm_pascal
seg_colormap = np.array([c for c in seg_colormap if max(c) >= 64], dtype=np.uint8)
return seg_colormap
def insert_mask_to_sam_masks(
sam_masks: List[Dict[str, Any]],
insert_mask: Dict[str, Any],
) -> List[Dict[str, Any]]:
"""Insert mask to SAM masks.
Args:
sam_masks (List[Dict[str, Any]]): SAM masks
insert_mask (Dict[str, Any]): insert mask
Returns:
List[Dict[str, Any]]: SAM masks
"""
if insert_mask is not None and isinstance(insert_mask, dict) and "segmentation" in insert_mask:
if (len(sam_masks) > 0 and
sam_masks[0]["segmentation"].shape == insert_mask["segmentation"].shape and
np.any(insert_mask["segmentation"])):
sam_masks.insert(0, insert_mask)
ia_logging.info("insert mask to sam_masks")
return sam_masks
def create_seg_color_image(
input_image: Union[np.ndarray, Image.Image],
sam_masks: List[Dict[str, Any]],
) -> np.ndarray:
"""Create segmentation color image.
Args:
input_image (Union[np.ndarray, Image.Image]): input image
sam_masks (List[Dict[str, Any]]): SAM masks
Returns:
np.ndarray: segmentation color image
"""
input_image = convert_input_image(input_image)
seg_colormap = get_seg_colormap()
sam_masks = sam_masks[:len(seg_colormap)]
with tqdm(total=len(sam_masks), desc="Processing segments") as progress_bar:
canvas_image = np.zeros((*input_image.shape[:2], 1), dtype=np.uint8)
for idx, seg_dict in enumerate(sam_masks[0:min(255, len(sam_masks))]):
seg_mask = np.expand_dims(seg_dict["segmentation"].astype(np.uint8), axis=-1)
canvas_mask = np.logical_not(canvas_image.astype(bool)).astype(np.uint8)
seg_color = np.array([idx+1], dtype=np.uint8) * seg_mask * canvas_mask
canvas_image = canvas_image + seg_color
progress_bar.update(1)
seg_colormap = np.insert(seg_colormap, 0, [0, 0, 0], axis=0)
temp_canvas_image = np.apply_along_axis(lambda x: seg_colormap[x[0]], axis=-1, arr=canvas_image)
if len(sam_masks) > 255:
canvas_image = canvas_image.astype(bool).astype(np.uint8)
for idx, seg_dict in enumerate(sam_masks[255:min(509, len(sam_masks))]):
seg_mask = np.expand_dims(seg_dict["segmentation"].astype(np.uint8), axis=-1)
canvas_mask = np.logical_not(canvas_image.astype(bool)).astype(np.uint8)
seg_color = np.array([idx+2], dtype=np.uint8) * seg_mask * canvas_mask
canvas_image = canvas_image + seg_color
progress_bar.update(1)
seg_colormap = seg_colormap[256:]
seg_colormap = np.insert(seg_colormap, 0, [0, 0, 0], axis=0)
seg_colormap = np.insert(seg_colormap, 0, [0, 0, 0], axis=0)
canvas_image = np.apply_along_axis(lambda x: seg_colormap[x[0]], axis=-1, arr=canvas_image)
canvas_image = temp_canvas_image + canvas_image
else:
canvas_image = temp_canvas_image
ret_seg_image = canvas_image.astype(np.uint8)
return ret_seg_image
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