【python】热力图绘制： intensity_heatmap，density_heatmap
在使用深度学习时，尤其生成模型，我们想知道生成误差或者重构误差出现在什么地方，这时我们就需要用热力图来可视化。在可视化中，除了要显示损失的heatmap以外，我们还要与原始图像进行一个加权，使我们清楚地看到重构误差损失出现在什么位置。
本文提供两个函数解决这个问题：intensity_heatmap和density_heatmap
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intensity_heatmap:损失强度热力图。像素值的亮暗显示损失的强与弱。
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density_heatmap:损失密度热力图。只统计损失强度超过一定阈值的点，并且按阈值二值化。然后对这些强损失点添加半径，可视化强损失点的密度。
代码如下：
首先，需要安装三个函数包 cv2,PIL,pyheatmap,其中pyheatmap的安装如下：
pip install pyheatmap
# conda install pyheatmap 会报错，conda中不存在这个程序包热力图的定义，使用代码如下：
import cv2
from PIL import Image
from pyheatmap.heatmap import HeatMap
def intensity_heatmap(background_img, intensity_map, blue_mask_weight=0.3, heat_map_weight=0.5):
"""
:param background_img: 背景图
:param intensity_map: 损失强度图
:param blue_mask_weight:
:param heat_map_weight:
:return:
"""
assert background_img.ndim == 3
assert intensity_map.ndim == 2 or intensity_map.ndim == 3
assert background_img.shape[0:2] == intensity_map.shape[0:2]
background_img_norm = ((background_img - background_img.min()) / (background_img.max() - background_img.min())
* 255).astype(np.uint8)
intensity_map_norm = ((intensity_map - intensity_map.min()) / (intensity_map.max() - intensity_map.min())
* 255).astype(np.uint8)
# 背景图需要加一个蓝色掩码，以更好的显示热力图
blue_mask = cv2.rectangle(background_img.copy(), (0, 0), background_img.shape[0:2], (0, 0, 255), -1).astype(np.uint8)
heatmap_base = cv2.addWeighted(blue_mask, blue_mask_weight, background_img_norm, 1 - blue_mask_weight, 0).astype(np.uint8)
# 获取热力图
hotmap_blue = np.array(cv2.applyColorMap(intensity_map_norm, cv2.COLORMAP_HOT)) # 是蓝色的3通道heatmap
hotmap_red = cv2.cvtColor(hotmap_blue, cv2.COLOR_RGB2BGR) # 由蓝色heatmap转为红色heatmap
intensity_hotmap_img = cv2.addWeighted(hotmap_red, heat_map_weight, heatmap_base, 1 - heat_map_weight, 0)
return intensity_hotmap_img
def density_heatmap(background_img, intensity_map, intensity_thr=50, density_radius=5, heat_map_weight=0.5):
"""
:param background_img: 背景图
:param intensity_map: 损失map
:param intensity_thr: 强损失阈值
:param density_radius: 密度半径
:param heat_map_weight:
:return:
"""
assert background_img.ndim == 3
assert intensity_map.ndim == 2 or intensity_map.ndim == 3
assert background_img.shape[0:2] == intensity_map.shape[0:2]
background_img_norm = ((background_img - background_img.min()) / (background_img.max() - background_img.min())
* 255).astype(np.uint8)
intensity_map_norm = ((intensity_map - intensity_map.min()) / (intensity_map.max() - intensity_map.min())
* 255).astype(np.uint8)
hotmap_points = []
for i in range(intensity_map.shape[0]): ##获取异常点
for j in range(intensity_map.shape[1]):
if intensity_map_norm[i, j] > intensity_thr:
hotmap_points.append([j, i])
background = Image.new("RGB", (intensity_map.shape[1], intensity_map.shape[0]), color=0) ## heatmap基底
hm = HeatMap(hotmap_points)
density_heatmap_img = np.array(hm.heatmap(base=background, r=density_radius))
density_heatmap_img = cv2.addWeighted(density_heatmap_img, heat_map_weight, background_img_norm,
1 - heat_map_weight, 0)
return density_heatmap_img
if __name__ == "__main__":
img_path = "gt_img.jpg"
loss_map_path = "loss_map.npy"
gt_img = cv2.imread(img_path) # shape = [h,w,c]
loss_map = np.load(loss_map_path) # shape = [h,w,1] 或 shape = [h,w]
density_heatmap_img = density_heatmap(gt_img,loss_map,intensity_thr=50,density_radius=10,heat_map_weight=0.5)
intensity_hotmap_img = intensity_heatmap(gt_img,loss_map,blue_mask_weight = 0.1,heat_map_weight = 0.6)
plt.subplot(121)
plt.imshow(density_heatmap_img)
plt.subplot(122)
plt.imshow(intensity_hotmap_img)
plt.show()以下使用示例引用自：https://blog.csdn.net/weixin_43289135/article/details/104651047
# -*- coding: utf-8 -*-
from pyheatmap.heatmap import HeatMap
import numpy as np
x = [10,20,30,80,50,40,70,90,60,30,40,50]
y = [50,40,20,30,60,90,60,20,60,10,70,50]
data = []
for i in range(0,11):
temperature = [int(x[i]), int(y[i]),1]# 设置每个像素点处的温度
data.append(temperature)
heat = HeatMap(data)
heat.clickmap(save_as="1.png") #点击图
heat.heatmap(save_as="2.png") #热图