如何从图像中过滤特定图像坐标

Question

我正在读取图像，获取具有特定亮度值的对象，然后将 X 和 Y 坐标绘制到图像上。

但是，有一组巨大的异常值，它们都位于图像的矩形部分，它的X和Y坐标是1110-1977（宽度）和1069-1905（身高）。从这里开始，我将遍历图像的这个小方块部分，并从我预先创建的 x 和 y 数组中删除任何具有与此处所示相同坐标的值。

但是，这会删除更多坐标，例如，X 在 1110-1977 范围内。所以当我只想过滤中心的正方形时，最终结果是交叉模式过滤。我该怎么做？

代码

from PIL import Image, ImageDraw
import numpy as np
from math import sqrt
imag = Image.open("Centaurus_A-DeNoiseAI-denoise.jpg")
imag = imag.convert ('RGB')
x=[]
y=[]
imag2=Image.open("Cen_A_cropped.jpg")
imag2=imag2.convert('RGB')
r=[]
g=[]
b=[]
width2, height2=imag2.size
for count2 in range(width2):
    for i2 in range(height2):
        X,Y=count2,i2
        (R,G,B)=imag2.getpixel((X,Y))
        r.append(R)
        g.append(G)
        b.append(B)
average_r=sum(r)/len(r)
average_g=sum(g)/len(g)
average_b=sum(b)/len(b)
brightness_average=sqrt(0.299*(average_r**2) + 0.587*(average_g**2) + 0.114*(average_b**2))
print("Avg. brightness "+str(brightness_average))
def calculate_brightness(galaxy,ref_clus,clus_mag):
    delta_b=(galaxy/ref_clus)
    bright=delta_b**2
    mag=np.log(bright)/np.log(2.512)
    return mag+clus_mag
count=0
X,Y = 1556,1568
(R,G,B) = imag.getpixel((X,Y))
width, height=imag.size
brightness = sqrt(0.299*(R**2) + 0.587*(G**2) + 0.114*(B**2))
print("Magnitude: "+str((calculate_brightness(13050, 15.79,3.7))))
reference=brightness_average/(calculate_brightness(13050, 15.79,3.7)/6.84)
print("Reference: "+str(reference))
for count in range(width):
    for i in range(height):
        X,Y = count,i
        (R,G,B) = imag.getpixel((X,Y))
        brightness = sqrt(0.299*(R**2) + 0.587*(G**2) + 0.114*(B**2))
        if(reference<=brightness<=reference+3):
            x.append(X)
            y.append(Y)

#post processing----------------------------------------------------------------------------------------------------
for x2 in range(1110, 1977):
    for y2 in range(1069, 1905):
        X,Y=x2,y2
        if(X in x and Y in y):
            x.remove(X)
            y.remove(Y)
#-------------------------------------------------------------------------------------------------------------------
with imag as im:
    delta = 19
    draw = ImageDraw.Draw(im)
    for i in range(len(x)):
        draw.rectangle([x[i-delta],y[i-delta],x[i-delta],y[i-delta]], fill=(0,255,0))

    im.save("your_image.png")

Centaurus_A-DeNoiseAI-denoise.jpg

Cen_A_cropped.jpg

Answer 1

您的 post- 处理逻辑有缺陷。你去掉了一堆1110-1977范围内的X值，没有检查它对应的Y值是否也在框的范围内。而是删除此代码部分，并在您第一次循环收集 x 和 y 坐标时添加该逻辑。

for count in range(width):
    for i in range(height):
        X,Y = count,i
        if 1110 <= X < 1977 and 1069 <= Y < 1905:    # add these
            continue                                 # two lines
        (R,G,B) = imag.getpixel((X,Y))

但是，有一种更好的方法可以通过使用 numpy 数组来完成完全相同的事情。您可以向量化大量计算，而不是编写显式循环。

import numpy as np
from PIL import Image, ImageDraw

image = Image.open('Centaurus_A-DeNoiseAI-denoise.jpg').convert('RGB')
img1 = np.array(image)
img2 = np.array(Image.open('Cen_A_cropped.jpg').convert('RGB'))

coeffs = np.array([.299, .587, .114])
average = img2.mean(axis=(0, 1))
brightness_average = np.sqrt(np.sum(average**2 * coeffs))
reference = brightness_average / (calculate_brightness(13050, 15.79,3.7) / 6.84)
print(f'Avg. brightness: {brightness_average}')
print(f'Reference: {reference}')

brightness = np.sqrt(np.sum(img1.astype(int)**2 * coeffs, axis=-1))
accepted_brightness = (brightness >= reference) * (brightness <= reference + 3)
pixels_used = np.ones((img1.shape[:2]), dtype=bool)
pixels_used[1069:1905,1110:1977] = False
rows, cols = np.where(accepted_brightness * pixels_used)

with image as im:
    draw = ImageDraw.Draw(im)
    draw.point(list(zip(cols, rows)), fill=(0, 255, 0))
    image.save('out.png')

这里使用的主要技巧在行

rows, cols = np.where(accepted_brightness * pixels_used)

accepted_brightess 是每个像素的二维数组，无论其亮度是否在您的首选范围内，都有一个布尔值。 pixels_used 是另一个二维布尔数组，其中每个像素都是 True，除了您想要忽略的靠近中心的框中的像素。这两者的组合为您提供了具有正确亮度且不在中心正方形中的像素坐标。

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