使用派生的映射函数修改s？

发布于 2025-01-29 02:25:55 字数 905 浏览 3 评论 0原文

我已经多次阅读了整篇论文，并且找到了一种使所有内容都在Python中制作的方法...除此之外（第二条第C条）。他们希望我以旧钟的2.589倍的2.589倍的新最小值和旧最大的0.9倍。我该如何实现？

编辑：感谢FMW42，我现在有一个大致执行应有的代码，但是当我使用DTW值“'tuple'对象没有属性'dtype'时，它会引发错误：

hsv_img = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
h, s, v = cv2.split(hsv_img)
clahe = cv2.createCLAHE(clipLimit=2.6, tileGridSize=(3,3))
clahe_v = clahe.apply(v)
dwts = pywt.dwt(s, 'bior1.3')

smin = np.amin(dwts)
smax = np.amax(dwts)
print("smin:", smin, "smax:", smax)
sleep(2)
smin_new = 2.589 * smin
smax_new = 0.9 * smax
print("smin_new:", smin_new, "smax_new:", smax_new)
sleep(2)

magic_s = skimage.exposure.rescale_intensity(dwts, in_range=(smin,smax), out_range=(smin_new,smax_new)).astype(np.uint8)

对Google链接btw：抱歉。（（文章链接：[https://www.google.com/url?sa = t＆amp; rct=j＆p =＆q =＆p =＆amp； 2F％2FCore.AC.UK％2FDownload％2FPDF％2F2228552574.PDF＆amp; usg = aovvaw15wem7_gvgvgvmvzhmhntksn] [1]

原文

I've read this entire paper multiple times, and I've found a way to make everything in python... Except this (Article II, section C). They want me to upscale the saturation values with a new min of 2.589 times the old min, and a new max of 0.9 times the old max. How do i achieve this?

Edit: Thanks to fmw42 i now have a code that roughly does what it should, but it throws an error when i use my dtw value "'tuple' object has no attribute 'dtype'" My code:

hsv_img = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
h, s, v = cv2.split(hsv_img)
clahe = cv2.createCLAHE(clipLimit=2.6, tileGridSize=(3,3))
clahe_v = clahe.apply(v)
dwts = pywt.dwt(s, 'bior1.3')

smin = np.amin(dwts)
smax = np.amax(dwts)
print("smin:", smin, "smax:", smax)
sleep(2)
smin_new = 2.589 * smin
smax_new = 0.9 * smax
print("smin_new:", smin_new, "smax_new:", smax_new)
sleep(2)

magic_s = skimage.exposure.rescale_intensity(dwts, in_range=(smin,smax), out_range=(smin_new,smax_new)).astype(np.uint8)

sorry for the google link btw :(
Article link: [https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&ved=2ahUKEwjx2_Cay9z3AhWDyYsKHdeFCn8QFnoECAoQAQ&url=https%3A%2F%2Fcore.ac.uk%2Fdownload%2Fpdf%2F228552574.pdf&usg=AOvVaw15wEM7_gVgvmVZhmHNTksN][1]

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感性不性感 2025-02-05 02:25:55

在Python/opencv/numpy/spimage中，您可以使用np.amin（）和np.amax（）获取图像中的最小值和最大值。然后，从“旧最小的2.589倍的2.589倍”中计算新的最小值，而新的最大最高为0.9倍旧的最大值”。然后，使用skimage.spure.Rescale_intsentys（）进行从输入值到新输出值的处理。

这是一个示例，使用旧的最小值和0.5时间的5倍，以使更改更明显。

输入：

import cv2
import numpy as np
import skimage.exposure

# read image
img = cv2.imread("lena.png")

# convert bgr to hsv
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)

# extract channels
h,s,v = cv2.split(hsv)

# get min and max of saturation channel
smin = np.amin(s)
smax = np.amax(s)
print("smin:", smin, "smax:", smax)

# compute new saturation values
#smin_new = 2.589 * smin
#smax_new = 0.9 * smax
smin_new = 5 * smin
smax_new = 0.5 * smax
print("smin_new:", smin_new, "smax_new:", smax_new)

# modify saturation
s_new = skimage.exposure.rescale_intensity(s, in_range=(smin,smax), out_range=(smin_new,smax_new)).astype(np.uint8)

# merge hsv channels
hsv_new = cv2.merge([h,s_new,v])

# convert hsv to bgr
result = cv2.cvtColor(hsv_new, cv2.COLOR_HSV2BGR)

# save result
cv2.imwrite('lena_modified_saturation.png', result)

cv2.imshow('saturation', s)
cv2.imshow('saturation_modified', s_new)
cv2.imshow('result', result)
cv2.imshow('result', result)
cv2.waitKey(0)
cv2.destroyAllWindows()

计算：

Smin：10 Smax：255
smin_new：50 smax_new：127.5

结果：

In Python/OpenCV/Numpy/Skimage, you can use np.amin() and np.amax() to get the min and max values in the image. Then compute the new min and max from your " 2.589 times the old min, and a new max of 0.9 times the old max" in Python simple math. Then use skimage.exposure.rescale_intensity() to do the processing from input values to new output values.

Here is an example using 5 times the old min and 0.5 time the old max so that the change is more visible.

Input:

import cv2
import numpy as np
import skimage.exposure

# read image
img = cv2.imread("lena.png")

# convert bgr to hsv
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)

# extract channels
h,s,v = cv2.split(hsv)

# get min and max of saturation channel
smin = np.amin(s)
smax = np.amax(s)
print("smin:", smin, "smax:", smax)

# compute new saturation values
#smin_new = 2.589 * smin
#smax_new = 0.9 * smax
smin_new = 5 * smin
smax_new = 0.5 * smax
print("smin_new:", smin_new, "smax_new:", smax_new)

# modify saturation
s_new = skimage.exposure.rescale_intensity(s, in_range=(smin,smax), out_range=(smin_new,smax_new)).astype(np.uint8)

# merge hsv channels
hsv_new = cv2.merge([h,s_new,v])

# convert hsv to bgr
result = cv2.cvtColor(hsv_new, cv2.COLOR_HSV2BGR)

# save result
cv2.imwrite('lena_modified_saturation.png', result)

cv2.imshow('saturation', s)
cv2.imshow('saturation_modified', s_new)
cv2.imshow('result', result)
cv2.imshow('result', result)
cv2.waitKey(0)
cv2.destroyAllWindows()

Calculations:

smin: 10 smax: 255
smin_new: 50 smax_new: 127.5

Result: