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exam5.py
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import ImageTools as it
import numpy as np
def ideal_low_filter(image, D0, save_path, show: bool = False):
"""
频域平滑滤波器1: 理想低通滤波器
"""
image = np.array(image.convert("L"))
# 生成滤波器
h, w = image.shape[:2]
image_filter = np.ones((h, w))
u = np.fix(h / 2)
v = np.fix(w / 2)
for i in range(h):
for j in range(w):
d = np.sqrt((i - u) ** 2 + (j - v) ** 2)
image_filter[i, j] = 0 if d > D0 else 1
# 将滤波器应用到图像，生成理想低通滤波图像
filtered_image = it.fromarray(
it.restore255(filter_use(image, image_filter)))
it.save_image(filtered_image, save_path)
if show:
it.compare_image_show(image, filtered_image)
return filtered_image
def butterworth_low_filter(image, D0, rank, save_path, show: bool = False):
"""
频域平滑滤波器2: Butterworth低通滤波器
"""
image = np.array(image.convert("L"))
# 生成滤波器
h, w = image.shape[:2]
image_filter = np.zeros((h, w))
u = np.fix(h / 2)
v = np.fix(w / 2)
for i in range(h):
for j in range(w):
d = np.sqrt((i - u) ** 2 + (j - v) ** 2)
image_filter[i, j] = 1 / (1 + 0.414 * (d / D0) ** (2 * rank))
# 将滤波器应用到图像，生成Butterworth低通滤波图像
filtered_image = it.fromarray(
it.restore255(filter_use(image, image_filter)))
it.save_image(filtered_image, save_path)
if show:
it.compare_image_show(image, filtered_image)
return filtered_image
def ideal_high_filter(image, D0, save_path, show: bool = False):
"""
频域锐化滤波器1: 理想高通滤波器
"""
image = np.array(image.convert("L"))
# 生成滤波器
h, w = image.shape[:2]
image_filter = np.zeros((h, w))
u = np.fix(h / 2)
v = np.fix(w / 2)
for i in range(h):
for j in range(w):
d = np.sqrt((i - u) ** 2 + (j - v) ** 2)
image_filter[i, j] = 0 if d < D0 else 1
# 将滤波器应用到图像，生成Butterworth低通滤波图像
filtered_image = it.fromarray(
it.restore255(filter_use(image, image_filter)))
it.save_image(filtered_image, save_path)
if show:
it.compare_image_show(image, filtered_image)
return filtered_image
def butterworth_high_filter(image, D0, rank, save_path, show: bool = False):
"""
频域锐化滤波器2: Butterworth高通滤波器
"""
image = np.array(image.convert("L"))
# 生成滤波器
h, w = image.shape[:2]
image_filter = np.zeros((h, w))
u = np.fix(h / 2)
v = np.fix(w / 2)
for i in range(h):
for j in range(w):
d = np.sqrt((i - u) ** 2 + (j - v) ** 2)
image_filter[i, j] = 1 / (1 + (D0 / (d+1e-7)) ** (2 * rank))
# 将滤波器应用到图像，生成Butterworth低通滤波图像
filtered_image = it.fromarray(
it.restore255(filter_use(image, image_filter)))
it.save_image(filtered_image, save_path)
if show:
it.compare_image_show(image, filtered_image)
return filtered_image
def filter_use(img, filter):
"""
将图像与滤波器结合，生成对应的滤波图像数组
"""
# 首先进行傅里叶变换
f = np.fft.fft2(img)
f_center = np.fft.fftshift(f)
# 应用滤波器进行反变换
S = np.multiply(f_center, filter) # 频率相乘——l(u,v)*H(u,v)
f_origin = np.fft.ifftshift(S) # 将低频移动到原来的位置
f_origin = np.fft.ifft2(f_origin) # 使用ifft2进行傅里叶的逆变换
f_origin = np.abs(f_origin) # 设置区间
return f_origin
if __name__ == "__main__":
image = it.read_image('static/image_in/nana.jpg').convert("L")
save_path = 'static/image_out/' + it.gen_timestamp_name() + '.jpg'
# ideal_low_filter(image, 50, save_path, show=True)
# butterworth_low_filter(image, 50, 2, save_path, show=True)
# ideal_high_filter(image, 50, save_path, show=True)
butterworth_high_filter(image, 50, 2, save_path, show=True)