我们从Python开源项目中，提取了以下11个代码示例，用于说明如何使用scipy.fftpack.idct()。
def idctii(x, axes=None): """ Compute a multi-dimensional inverse DCT-II over specified array axes. This function is implemented by calling the one-dimensional inverse DCT-II :func:`scipy.fftpack.idct` with normalization mode 'ortho' for each of the specified axes. Parameters ---------- a : array_like Input array axes : sequence of ints, optional (default None) Axes over which to compute the inverse DCT-II. Returns ------- y : ndarray Inverse DCT-II of input array """ if axes is None: axes = list(range(x.ndim)) for ax in axes[::-1]: x = fftpack.idct(x, type=2, axis=ax, norm='ortho') return x
def dct_uncompress(X_compressed, window_size=128): """ Uncompress a DCT compressed signal (such as returned by ``compress``). Parameters ---------- X_compressed : ndarray, shape=(n_samples, n_features) Windowed and compressed array. window_size : int, optional (default=128) Size of the window used when ``compress`` was called. Returns ------- X_reconstructed : ndarray, shape=(n_samples) Reconstructed version of X. """ if X_compressed.shape[1] % window_size != 0: append = np.zeros((X_compressed.shape[0], window_size - X_compressed.shape[1] % window_size)) X_compressed = np.hstack((X_compressed, append)) X_r = fftpack.idct(X_compressed, norm='ortho') return X_r.ravel()
def overlap_dct_uncompress(X_compressed, window_size): """ Uncompress X as returned from ``overlap_compress``. Parameters ---------- X_compressed : ndarray, shape=(n_windows, n_components) Windowed and compressed version of X window_size : int Size of windows originally used when compressing X Returns ------- X_reconstructed : ndarray, shape=(n_samples,) Reconstructed version of X """ if X_compressed.shape[1] % window_size != 0: append = np.zeros((X_compressed.shape[0], window_size - X_compressed.shape[1] % window_size)) X_compressed = np.hstack((X_compressed, append)) X_r = fftpack.idct(X_compressed, norm='ortho') return invert_halfoverlap(X_r)
def run_fft_dct_example(): random_state = np.random.RandomState(1999) fs, d = fetch_sample_speech_fruit() n_fft = 64 X = d[0] X_stft = stft(X, n_fft) X_rr = complex_to_real_view(X_stft) X_dct = fftpack.dct(X_rr, axis=-1, norm='ortho') X_dct_sub = X_dct[1:] - X_dct[:-1] std = X_dct_sub.std(axis=0, keepdims=True) X_dct_sub += .01 * std * random_state.randn( X_dct_sub.shape[0], X_dct_sub.shape[1]) X_dct_unsub = np.cumsum(X_dct_sub, axis=0) X_idct = fftpack.idct(X_dct_unsub, axis=-1, norm='ortho') X_irr = real_to_complex_view(X_idct) X_r = istft(X_irr, n_fft)[:len(X)] SNR = 20 * np.log10(np.linalg.norm(X - X_r) / np.linalg.norm(X)) print(SNR) wavfile.write("fftdct_orig.wav", fs, soundsc(X)) wavfile.write("fftdct_rec.wav", fs, soundsc(X_r))
def decode(self, target): target.fill(0.) original_shape = target.shape target = target.ravel() for gene in self.genes: target[gene.index] = gene.value target = target.reshape(original_shape) len_shape = len(original_shape) kwargs = dict(norm='ortho') if len_shape == 1: out = idct(target, **kwargs) elif len_shape == 2: out = idct(idct(target.T, **kwargs).T, **kwargs) elif len_shape >= 3: shape = (np.prod(original_shape[:-1]), original_shape[-1]) target = target.reshape(shape) out = idct(idct(target.T, **kwargs).T, **kwargs) out = out.reshape(original_shape) return out
def run_world_dct_example(): # on chromebook # enc 114.229 # synth 5.165 fs, d = fetch_sample_speech_tapestry() d = d.astype("float32") / 2 ** 15 def enc(): temporal_positions_h, f0_h, vuv_h, f0_candidates_h = harvest(d, fs) temporal_positions_ct, spectrogram_ct, fs_ct = cheaptrick(d, fs, temporal_positions_h, f0_h, vuv_h) temporal_positions_d4c, f0_d4c, vuv_d4c, aper_d4c, coarse_aper_d4c = d4c(d, fs, temporal_positions_h, f0_h, vuv_h) return spectrogram_ct, f0_d4c, vuv_d4c, coarse_aper_d4c start = time.time() spectrogram_ct, f0_d4c, vuv_d4c, coarse_aper_d4c = enc() dct_buf = fftpack.dct(spectrogram_ct) n_fft = 512 n_dct = 20 dct_buf = dct_buf[:, :n_dct] idct_buf = np.zeros((dct_buf.shape[0], n_fft + 1)) idct_buf[:, :n_dct] = dct_buf ispectrogram_ct = fftpack.idct(idct_buf) enc_done = time.time() y = world_synthesis(f0_d4c, vuv_d4c, coarse_aper_d4c, spectrogram_ct, fs) synth_done = time.time() print("enc time: {}".format(enc_done - start)) print("synth time: {}".format(synth_done - enc_done)) #y = world_synthesis(f0_d4c, vuv_d4c, aper_d4c, sp_r, fs) wavfile.write("out_dct.wav", fs, soundsc(y)) #run_world_mgc_example() #run_world_base_example()
def conv_from_dct(blocks, in_blocksize, out_blocksize): new_blocks=[] zeropad = [0]*(out_blocksize-in_blocksize) dct_pred = blocks dct_pred = np.append(dct_pred, zeropad) dct_pred = np.asarray(idct(dct_pred, norm='ortho'), dtype=np.float32) new_blocks.append(dct_pred) return new_blocks
def dct_gen(frame): # read Frame [r, c, d] = frame.shape framebw = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) ########## DCT ########## # Reshape in 8x8 type array framedct = np.reshape(framebw / 255.0, (-1, 8), order='C') # Apply DCT line and column wise X = sft.dct(framedct, axis=1, norm='ortho') X = np.reshape(X, (-1, c), order='C') X = np.reshape(X.T, (-1, 8), order='C') X = sft.dct(X, axis=1, norm='ortho') # shape back to original shape X = (np.reshape(X, (-1, r), order='C')).T # Zeroing DC-Coefficients X[::8,::8] = 0 ########## IDCT ########## # Reshape in 8x8 type array X = np.reshape(X, (-1, 8), order='C') # Apply IDCT line and column wise X = sft.idct(X, axis=1, norm='ortho') X = np.reshape(X, (-1, c), order='C') X = np.reshape(X.T, (-1, 8), order='C') x = sft.idct(X, axis=1, norm='ortho') # shape back to original shape x = (np.reshape(x, (-1, r), order='C')).T # cast into output image x = x*255 frameout = np.zeros_like(frame) frameout[:, :, 0] = x frameout[:, :, 1] = x frameout[:, :, 2] = x return frameout
def idct2(image_channel): return fftpack.idct(fftpack.idct(image_channel.T, norm='ortho').T, norm='ortho')
