# Copyright 2022 AI Singapore
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
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"""Superimposes a heat map over an image."""
from typing import Any, Dict
import cv2
import numpy as np
from peekingduck.pipeline.nodes.abstract_node import AbstractNode
[docs]class Node(AbstractNode): # pylint: disable=too-few-public-methods
"""Superimposes a heat map over an image.
The :mod:`draw.heat_map` node helps to identify areas that are more
crowded. Areas that are more crowded are highlighted in red while areas
that are less crowded are highlighted in blue.
Inputs:
|img_data|
|density_map_data|
This is produced by nodes such as :mod:`model.csrnet`.
Outputs:
|img_data|
Configs:
None.
"""
def __init__(self, config: Dict[str, Any] = None, **kwargs: Any) -> None:
super().__init__(config, node_path=__name__, **kwargs)
def run(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
heat_map_img = self._add_heat_map(inputs["density_map"], inputs["img"])
outputs = {"img": heat_map_img}
return outputs
def _add_heat_map(self, density_map: np.ndarray, image: np.ndarray) -> np.ndarray:
"""Superimposes a heat map over an ``image``.
Args:
density_map (np.ndarray): predicted density map.
image (np.ndarray): image in numpy array.
Returns:
image (np.ndarray): image with a heat map superimposed over it.
"""
if np.count_nonzero(density_map) != 0:
density_map = self._norm_min_max(density_map)
heat_map = cv2.applyColorMap(density_map, cv2.COLORMAP_JET)
image = cv2.addWeighted(image, 0.5, heat_map, 0.5, 0)
return image
@staticmethod
def _norm_min_max(src: np.ndarray) -> np.ndarray:
target = None
norm_results = cv2.normalize(
# source array
src,
# destination array
target,
# lower boundary value
alpha=0,
# upper boundary value
beta=255,
# normalization type
norm_type=cv2.NORM_MINMAX,
# data type
dtype=cv2.CV_8U,
)
return norm_results