問(wèn)題描述
我有一個(gè) (540, 960, 1) 形狀的圖像，其值范圍為黑白的 [0..255].我需要將其轉(zhuǎn)換為熱圖"表示.例如，具有 255 的像素應(yīng)該是最熱的，而具有 0 的像素應(yīng)該是最不熱的.其他介于兩者之間.我還需要將熱圖作為 Numpy 數(shù)組返回，以便稍后將它們合并到視頻中.有沒(méi)有辦法做到這一點(diǎn)?
這里有兩種方法，一種使用Matplotlib，一種只使用OpenCV
方法#1: OpenCV +
我們可以使用 OpenCV 內(nèi)置的熱圖功能.這是使用 cv2.COLORMAP_HOT 熱圖
代碼
導(dǎo)入 cv2圖像 = cv2.imread('frame.png', 0)熱圖 = cv2.applyColorMap(圖像，cv2.COLORMAP_HOT)cv2.imshow('熱圖', 熱圖)cv2.waitKey()注意:雖然 OpenCV 的內(nèi)置實(shí)現(xiàn)簡(jiǎn)短快捷，但我建議使用方法 #1，因?yàn)橛懈蟮念伾珗D選擇.Matplotlib 有 數(shù)百種不同的顏色圖并允許您在 OpenCV 時(shí)創(chuàng)建自己的自定義顏色圖只有12個(gè)可供選擇.這是內(nèi)置的 OpenCV 顏色圖選擇:
I have a (540, 960, 1) shaped image with values ranging from [0..255] which is black and white. I need to convert it to a "heatmap" representation. As an example, pixels with 255 should be of most heat and pixels with 0 should be with least heat. Others in-between. I also need to return the heat maps as Numpy arrays so I can later merge them to a video. Is there a way to achieve this?
Here are two methods, one using Matplotlib and one using only OpenCV
Method #1: OpenCV + matplotlib.pyplot.get_cmap
To implement a grayscale (1-channel) -> heatmap (3-channel) conversion, we first load in the image as grayscale. By default, OpenCV reads in an image as 3-channel, 8-bit BGR.
We can directly load in an image as grayscale using cv2.imread() with the cv2.IMREAD_GRAYSCALE parameter or use cv2.cvtColor() to convert a BGR image to grayscale with the cv2.COLOR_BGR2GRAY parameter. Once we load in the image, we throw this grayscale image into Matplotlib to obtain our heatmap image. Matplotlib returns a RGB format so we must convert back to Numpy format and switch to BGR colorspace for use with OpenCV. Here's a example using a scientific infrared camera image as input with the inferno colormap. See choosing color maps in Matplotlib for available built-in colormaps depending on your desired use case.
Input image:
Output heatmap image:
Code
import matplotlib.pyplot as plt
import numpy as np
import cv2
image = cv2.imread('frame.png', 0)
colormap = plt.get_cmap('inferno')
heatmap = (colormap(image) * 2**16).astype(np.uint16)[:,:,:3]
heatmap = cv2.cvtColor(heatmap, cv2.COLOR_RGB2BGR)
cv2.imshow('image', image)
cv2.imshow('heatmap', heatmap)
cv2.waitKey()
Method #2: cv2.applyColorMap()
We can use OpenCV's built in heatmap function. Here's the result using the cv2.COLORMAP_HOT heatmap
Code
import cv2
image = cv2.imread('frame.png', 0)
heatmap = cv2.applyColorMap(image, cv2.COLORMAP_HOT)
cv2.imshow('heatmap', heatmap)
cv2.waitKey()
Note: Although OpenCV's built-in implementation is short and quick, I recommend using Method #1 since there is a larger colormap selection. Matplotlib has hundreds of various colormaps and allows you to create your own custom color maps while OpenCV only has 12 to choose from. Here's the built in OpenCV colormap selection:
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