Python cv2.idft() Examples

The following are 11 code examples of cv2.idft(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module cv2 , or try the search function .
Example #1
Source File: deconvolution.py    From OpenCV-Python-Tutorial with MIT License 6 votes vote down vote up 
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)
Example #2
Source File: tracker.py    From KCF-DSST-py with MIT License 6 votes vote down vote up 
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:响应最大值 pi:相应最大点的索引数组
        _, pv, _, pi = cv2.minMaxLoc(scale_response)
        
        return pi

    # 更新尺度
Example #3
Source File: deconvolution.py    From PyCV-time with MIT License 6 votes vote down vote up 
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)
Example #4
Source File: deconvolution.py    From PyCV-time with MIT License 6 votes vote down vote up 
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)
Example #5
Source File: mosse.py    From OpenCV-Python-Tutorial with MIT License 5 votes vote down vote up 
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
Example #6
Source File: mosse.py    From OpenCV-Python-Tutorial with MIT License 5 votes vote down vote up 
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
Example #7
Source File: mosse.py    From open-vot with MIT License 5 votes vote down vote up 
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
Example #8
Source File: mosse.py    From PyCV-time with MIT License 5 votes vote down vote up 
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
Example #9
Source File: mosse.py    From PyCV-time with MIT License 5 votes vote down vote up 
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
Example #10
Source File: mosse.py    From PyCV-time with MIT License 5 votes vote down vote up 
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
Example #11
Source File: saliency.py    From OpenCV-Computer-Vision-Projects-with-Python with MIT License 4 votes vote down vote up 
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