import copy
import os
import subprocess
import typing
from pathlib import Path
from typing import Optional
import numpy as np
import scipy.ndimage
import skimage
from skimage import img_as_ubyte
from skimage.morphology import binary_erosion
from arthropod_describer.common.common import Info
from arthropod_describer.common.label_image import RegionProperty, PropertyType
from arthropod_describer.common.photo import Photo
from arthropod_describer.common.plugin import PropertyComputation
from arthropod_describer.common.regions_cache import RegionsCache, Region
from arthropod_describer.common.units import Value
from arthropod_describer.common.user_params import UserParam
from arthropod_describer.plugins.test_plugin.properties.geodesic_utils import compute_longest_geodesic, \
find_shortest_path
[docs]class MeanWidth(PropertyComputation):
"""
GROUP: Basic properties
NAME: Mean width (cpp)
DESCRIPTION: Mean width of a region (px or mm)
KEY: mean_width_cpp
"""
def __init__(self, info: Optional[Info] = None):
super().__init__(info)
def __call__(self, photo: Photo, region_labels: typing.List[int], regions_cache: RegionsCache) -> \
typing.List[RegionProperty]:
lab_img = photo['Labels']
props: typing.List[RegionProperty] = []
for label in region_labels:
if label not in regions_cache.regions:
continue
bin_img = regions_cache.regions[label].mask
if not np.any(bin_img):
continue
region_obj = regions_cache.regions[label]
bin_path = Path(__file__).parent / 'bin/geodesic_length.exe'
reg_path = Path(__file__).parent / f'body_region_{photo.image_name}.png'
skimage.io.imsave(str(reg_path), img_as_ubyte(region_obj.mask), check_contrast=False)
out_path = Path(__file__).parent / f'result_{photo.image_name}.txt'
args = [
str(bin_path),
str(reg_path),
str(out_path)
]
return_obj = subprocess.run(args, cwd=str(bin_path.parent))
if return_obj.returncode != 0:
os.remove(reg_path)
os.remove(out_path)
continue
geodesic_xx, geodesic_yy = [], []
with open(out_path) as f:
lines = f.readlines()
# length = float(lines[0].strip())
src_split = lines[1].strip().split(',')
# src_pixel = (int(src_split[0]), int(src_split[1]))
dst_split = lines[2].strip().split(',')
# dst_pixel = (int(dst_split[0]), int(dst_split[1]))
geodesic_pixels_str = lines[3].split(';')
for pixel_str in geodesic_pixels_str:
if pixel_str == '':
continue
spl = pixel_str.split(',')
x, y = int(spl[0]), int(spl[1])
geodesic_xx.append(x)
geodesic_yy.append(y)
outline = np.logical_and(bin_img, binary_erosion(bin_img, footprint=np.ones((3, 3), dtype=np.uint8)))
dst: np.ndarray = scipy.ndimage.distance_transform_edt(outline)
mean_width = np.mean(2.0 * dst[geodesic_yy, geodesic_xx])
if np.isnan(mean_width):
# TODO inspect `get_longest_geodesic2` function
mean_width = -42.0
# io.imsave(f'C:\\Users\\radoslav\\Desktop\\mean_width\\{label}_bin_roi.png', bin_roi, check_contrast=False)
# io.imsave(f'C:\\Users\\radoslav\\Desktop\\mean_width\\{label}_outline.png', outline, check_contrast=False)
# io.imsave(f'C:\\Users\\radoslav\\Desktop\\mean_width\\{label}_dst.png',
# (255.0 * (dst / (np.max(dst) + 1e-6))).astype(np.uint8), check_contrast=False)
prop = RegionProperty()
prop.info = copy.deepcopy(self.info)
prop.prop_type = PropertyType.Scalar
prop.label = label
if photo.image_scale is not None:
prop.value = Value(float(mean_width), self._px_unit) / photo.image_scale
# prop.unit = 'mm'
else:
prop.value = Value(float(mean_width), self._px_unit)
# prop.unit = 'px'
prop.num_vals = 1
prop.val_names = ['Mean width']
props.append(prop)
os.remove(reg_path)
os.remove(out_path)
return props
@property
def user_params(self) -> typing.List[UserParam]:
return super().user_params
@property
def region_restricted(self) -> bool:
return super().region_restricted
@property
def computes(self) -> typing.Dict[str, Info]:
return {self.info.key: self.info}
[docs] def example(self, prop_name: str) -> RegionProperty:
prop = RegionProperty()
prop.label = 0
prop.info = copy.deepcopy(self.info)
prop.value = None
prop.num_vals = 1
prop.prop_type = PropertyType.Scalar
prop.val_names = []
return prop
[docs] def target_worksheet(self, prop_name: str) -> str:
return super(MeanWidth, self).target_worksheet(self.info.key)
@property
def group(self) -> str:
return super().group