JustinSDK

import cv2
import numpy as np

f_range = 50 # 低頻範圍
# 圖片來源：https://openhome.cc/Gossip/images/caterpillar.jpg
img = cv2.imread('caterpillar.jpg', cv2.IMREAD_GRAYSCALE)
rows, cols = img.shape
cy, cx = int(rows / 2), int(cols/2)

x = np.arange(-cx, cx)

JustinSDK

import cv2
import numpy as np

f_range = 15 # 低頻範圍
# 圖片來源：https://openhome.cc/Gossip/images/caterpillar.jpg
img = cv2.imread('caterpillar.jpg', cv2.IMREAD_GRAYSCALE)
rows, cols = img.shape
cy, cx = int(rows / 2), int(cols/2)

# 傅立葉轉換後並位移低頻

JustinSDK

import cv2
import numpy as np

# 圖片來源：https://openhome.cc/Gossip/DCHardWay/images/Fourier4-1.JPG
img = cv2.imread('Fourier4-1.JPG', cv2.IMREAD_GRAYSCALE)

f = cv2.dft(np.float32(img))

shifted = np.fft.fftshift(f) # 將頻率 (0, 0) 位移至中心
freq = cv2.normalize( # 標準化

JustinSDK

import cv2
import numpy as np

# 椒鹽雜訊
def salt_pepper_noise(image, fraction, salt_vs_pepper):
    img = np.copy(image)
    size = img.size
    num_salt = np.ceil(fraction * size * salt_vs_pepper).astype('int')
    num_pepper = np.ceil(fraction * size * (1 - salt_vs_pepper)).astype('int')
    row, column = img.shape

JustinSDK

import cv2
import numpy as np

# 圖片來源：https://openhome.cc/Gossip/DCHardWay/images/Fourier4-1.JPG
img = cv2.imread('Fourier4-1.JPG', cv2.IMREAD_GRAYSCALE)

f = np.fft.fft2(img)
shifted = np.fft.fftshift(f) # 將頻率 (0, 0) 位移至中心

amp = np.abs(shifted)

JustinSDK

import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm

freq = 5          
stop = .25        
sample_rate = 800

x = np.linspace(0, stop, int(stop * sample_rate), endpoint = False)
gray = 125 + np.sin(freq * 2 * np.pi * x) + 125

JustinSDK

import numpy as np
from math import floor
from random import randint
import matplotlib.pyplot as plt

def blending(t):
    return 6 * (t ** 5) - 15 * (t ** 4) + 10 * (t ** 3)
blending = np.frompyfunc(blending, 1, 1)

def lerp(s1, s2, t):

JustinSDK

import numpy as np
import matplotlib.pyplot as plt

t = 2              # 取樣時間
sample_rate = 800  # 取樣率，每秒取幾個樣本

def signal(t, sample_rate):
    f = 10
    x = np.linspace(0, t, int(t * sample_rate), endpoint = False)
    return (np.sin(f * 2 * np.pi * x) +    

JustinSDK

import numpy as np
import matplotlib.pyplot as plt

t = 2              # 取樣時間
sample_rate = 800  # 取樣率，每秒取幾個樣本

def signal(t, sample_rate):
    f = 10
    x = np.linspace(0, t, int(t * sample_rate), endpoint = False)
    return (np.sin(f * 2 * np.pi * x) +    

JustinSDK

import numpy as np
import matplotlib.pyplot as plt

freq = 10         # 頻率
stop = .25        # 取樣範圍為 0 ~ stop
sample_rate = 800 # 取樣率，單位 x 取樣幾次

x = np.linspace(0, stop, int(stop * sample_rate), endpoint = False)

z1 = np.sin(freq * 2 * np.pi * x)          # 頻率 10