本文整理匯總了Python中cv2.idft方法的典型用法代碼示例。如果您正苦於以下問題：Python cv2.idft方法的具體用法？Python cv2.idft怎麽用？Python cv2.idft使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類cv2的用法示例。
在下文中一共展示了cv2.idft方法的6個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: update
# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import idft [as 別名]
def update(_):
ang = np.deg2rad( cv2.getTrackbarPos('angle', win) )
d = cv2.getTrackbarPos('d', win)
noise = 10**(-0.1*cv2.getTrackbarPos('SNR (db)', win))
if defocus:
psf = defocus_kernel(d)
else:
psf = motion_kernel(ang, d)
cv2.imshow('psf', psf)
psf /= psf.sum()
psf_pad = np.zeros_like(img)
kh, kw = psf.shape
psf_pad[:kh, :kw] = psf
PSF = cv2.dft(psf_pad, flags=cv2.DFT_COMPLEX_OUTPUT, nonzeroRows = kh)
PSF2 = (PSF**2).sum(-1)
iPSF = PSF / (PSF2 + noise)[...,np.newaxis]
RES = cv2.mulSpectrums(IMG, iPSF, 0)
res = cv2.idft(RES, flags=cv2.DFT_SCALE | cv2.DFT_REAL_OUTPUT )
res = np.roll(res, -kh//2, 0)
res = np.roll(res, -kw//2, 1)
cv2.imshow(win, res) 示例2: detect_scale
# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import idft [as 別名]
def detect_scale(self, image):
xsf = self.get_scale_sample(image)
# Compute AZ in the paper
add_temp = cv2.reduce(complexMultiplication(self.sf_num, xsf), 0, cv2.REDUCE_SUM)
# compute the final y
scale_response = cv2.idft(complexDivisionReal(add_temp, (self.sf_den + self.scale_lambda)), None, cv2.DFT_REAL_OUTPUT)
# Get the max point as the final scaling rate
# pv:響應最大值 pi:相應最大點的索引數組
_, pv, _, pi = cv2.minMaxLoc(scale_response)
return pi
# 更新尺度 示例3: state_vis
# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import idft [as 別名]
def state_vis(self):
f = cv2.idft(self.H, flags=cv2.DFT_SCALE | cv2.DFT_REAL_OUTPUT )
h, w = f.shape
f = np.roll(f, -h//2, 0)
f = np.roll(f, -w//2, 1)
kernel = np.uint8( (f-f.min()) / f.ptp()*255 )
resp = self.last_resp
resp = np.uint8(np.clip(resp/resp.max(), 0, 1)*255)
vis = np.hstack([self.last_img, kernel, resp])
return vis 示例4: correlate
# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import idft [as 別名]
def correlate(self, img):
C = cv2.mulSpectrums(cv2.dft(img, flags=cv2.DFT_COMPLEX_OUTPUT), self.H, 0, conjB=True)
resp = cv2.idft(C, flags=cv2.DFT_SCALE | cv2.DFT_REAL_OUTPUT)
h, w = resp.shape
_, mval, _, (mx, my) = cv2.minMaxLoc(resp)
side_resp = resp.copy()
cv2.rectangle(side_resp, (mx-5, my-5), (mx+5, my+5), 0, -1)
smean, sstd = side_resp.mean(), side_resp.std()
psr = (mval-smean) / (sstd+eps)
return resp, (mx-w//2, my-h//2), psr 示例5: _linear_correlation
# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import idft [as 別名]
def _linear_correlation(self, img):
C = cv2.mulSpectrums(
cv2.dft(img, flags=cv2.DFT_COMPLEX_OUTPUT), self.H, 0, conjB=True)
resp = cv2.idft(C, flags=cv2.DFT_SCALE | cv2.DFT_REAL_OUTPUT)
h, w = resp.shape
_, mval, _, (mx, my) = cv2.minMaxLoc(resp)
side_resp = resp.copy()
cv2.rectangle(side_resp, (mx - 5, my - 5), (mx + 5, my + 5), 0, -1)
smean, sstd = side_resp.mean(), side_resp.std()
psr = (mval - smean) / (sstd + self.cfg.eps)
return resp, (mx - w // 2, my - h // 2), psr 示例6: _get_channel_sal_magn
# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import idft [as 別名]
def _get_channel_sal_magn(self, channel):
"""Returns the log-magnitude of the Fourier spectrum
This method calculates the log-magnitude of the Fourier spectrum
of a single-channel image. This image could be a regular grayscale
image, or a single color channel of an RGB image.
:param channel: single-channel input image
:returns: log-magnitude of Fourier spectrum
"""
# do FFT and get log-spectrum
if self.use_numpy_fft:
img_dft = np.fft.fft2(channel)
magnitude, angle = cv2.cartToPolar(np.real(img_dft),
np.imag(img_dft))
else:
img_dft = cv2.dft(np.float32(channel),
flags=cv2.DFT_COMPLEX_OUTPUT)
magnitude, angle = cv2.cartToPolar(img_dft[:, :, 0],
img_dft[:, :, 1])
# get log amplitude
log_ampl = np.log10(magnitude.clip(min=1e-9))
# blur log amplitude with avg filter
log_ampl_blur = cv2.blur(log_ampl, (3, 3))
# residual
residual = np.exp(log_ampl - log_ampl_blur)
# back to cartesian frequency domain
if self.use_numpy_fft:
real_part, imag_part = cv2.polarToCart(residual, angle)
img_combined = np.fft.ifft2(real_part + 1j*imag_part)
magnitude, _ = cv2.cartToPolar(np.real(img_combined),
np.imag(img_combined))
else:
img_dft[:, :, 0], img_dft[:, :, 1] = cv2.polarToCart(residual,
angle)
img_combined = cv2.idft(img_dft)
magnitude, _ = cv2.cartToPolar(img_combined[:, :, 0],
img_combined[:, :, 1])
return magnitude