Source code for flash.image.segmentation.viz
# Copyright The PyTorch Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Any, Dict, List, Tuple, Union
import numpy as np
import torch
from torch import Tensor
from flash.core.data.base_viz import BaseVisualization
from flash.core.data.io.input import DataKeys
from flash.core.utilities.imports import _MATPLOTLIB_AVAILABLE, Image, requires
from flash.core.utilities.stages import RunningStage
from flash.image.segmentation.output import SegmentationLabelsOutput
if _MATPLOTLIB_AVAILABLE:
import matplotlib.pyplot as plt
else:
plt = None
[docs]class SegmentationMatplotlibVisualization(BaseVisualization):
"""Process and show the image batch and its associated label using matplotlib."""
def __init__(self, labels_map: Dict[int, Tuple[int, int, int]]):
super().__init__()
self.max_cols: int = 4 # maximum number of columns we accept
self.block_viz_window: bool = True # parameter to allow user to block visualisation windows
self.labels_map: Dict[int, Tuple[int, int, int]] = labels_map
@staticmethod
@requires("image")
def _to_numpy(img: Union[Tensor, Image.Image]) -> np.ndarray:
out: np.ndarray
if isinstance(img, np.ndarray):
out = img
elif isinstance(img, Image.Image):
out = np.array(img)
elif isinstance(img, Tensor):
out = img.squeeze(0).permute(1, 2, 0).cpu().numpy()
else:
raise TypeError(f"Unknown image type. Got: {type(img)}.")
return out
@requires("matplotlib")
def _show_images_and_labels(
self,
data: List[Any],
num_samples: int,
title: str,
limit_nb_samples: int = None,
figsize: Tuple[int, int] = (6.4, 4.8),
):
num_samples = max(1, min(num_samples, limit_nb_samples))
# define the image grid
cols: int = min(num_samples, self.max_cols)
rows: int = num_samples // cols
# create figure and set title
fig, axs = plt.subplots(rows, cols, figsize=figsize)
fig.suptitle(title)
if not isinstance(axs, np.ndarray):
axs = np.array(axs)
axs = axs.flatten()
for i, ax in enumerate(axs):
# unpack images and labels
sample = data[i]
if isinstance(sample, dict):
image = sample[DataKeys.INPUT]
label = sample[DataKeys.TARGET]
elif isinstance(sample, tuple):
image = sample[0]
label = sample[1]
else:
raise TypeError(f"Unknown data type. Got: {type(data)}.")
# convert images and labels to numpy and stack horizontally
image_vis: np.ndarray = self._to_numpy(image)
label_tmp: Tensor = SegmentationLabelsOutput.labels_to_image(
torch.as_tensor(label).squeeze(), self.labels_map
)
label_vis: np.ndarray = self._to_numpy(label_tmp)
img_vis = np.hstack((image_vis, label_vis))
# send to visualiser
ax.imshow(img_vis)
ax.axis("off")
plt.show(block=self.block_viz_window)
def show_load_sample(
self,
samples: List[Any],
running_stage: RunningStage,
limit_nb_samples: int,
figsize: Tuple[int, int] = (6.4, 4.8),
):
win_title: str = f"{running_stage} - show_load_sample"
self._show_images_and_labels(samples, len(samples), win_title, limit_nb_samples, figsize)
def show_per_sample_transform(
self,
samples: List[Any],
running_stage: RunningStage,
limit_nb_samples: int,
figsize: Tuple[int, int] = (6.4, 4.8),
):
win_title: str = f"{running_stage} - show_per_sample_transform"
self._show_images_and_labels(samples, len(samples), win_title, limit_nb_samples, figsize)