import itertools
from typing import Dict, Sequence, Tuple
import numpy
from matplotlib import pyplot
from warg import Number
__all__ = [
"plot_errors",
"masks_to_color_img",
"plot_prediction",
"bounding_box_from_mask",
]
[docs]def plot_errors(results_dict: Dict, title: str) -> None:
"""
Args:
results_dict:
title:
"""
markers = itertools.cycle(("+", "x", "o"))
pyplot.title(f"{title}")
for label, result in sorted(results_dict.items()):
pyplot.plot(result, marker=next(markers), label=label)
pyplot.ylabel("dice_coef")
pyplot.xlabel("epoch")
pyplot.legend(loc=3, bbox_to_anchor=(1, 0))
pyplot.show()
[docs]def masks_to_color_img(masks: numpy.ndarray) -> numpy.ndarray:
"""
Args:
masks:
Returns:
"""
height, width, mask_channels = masks.shape
color_channels = 3
color_image = numpy.zeros((height, width, color_channels), dtype=numpy.uint8) * 255
for y in range(height):
for x in range(width):
for mc in range(mask_channels):
color_image[y, x, mc % color_channels] = masks[y, x, mc]
return color_image.astype(numpy.uint8)
[docs]def plot_prediction(
img_array: numpy.ndarray,
labels: Sequence,
max_pred: Sequence,
pred: Sequence,
n_col: int = 3,
) -> None:
"""
Args:
img_array:
labels:
max_pred:
pred:
n_col:
"""
n_row = len(img_array) // n_col
f, plots = pyplot.subplots(
n_row, n_col, sharex="all", sharey="all", figsize=(n_col * 4, n_row * 4)
)
for i in range(len(img_array)):
plots[i // n_col, i % n_col].imshow(img_array[i])
plots[i // n_col, i % n_col].set_title(
f"truth:{labels[i]},\n max_pred:{max_pred[i]},\n prediction:{pred[i]}",
fontsize=8,
)
[docs]def bounding_box_from_mask(
hard_mask: numpy.ndarray,
) -> Tuple[Number, Number, Number, Number]:
"""
Args:
hard_mask:
Returns:
"""
nz = numpy.nonzero(hard_mask)
return numpy.min(nz[0]), numpy.min(nz[1]), numpy.max(nz[0]), numpy.max(nz[1])
