typed op• 資料種類:signal → image
• 呼叫:fullseye.apply(img, "tb_spectrogram", a=0.5, b=0.5)(2-D 的模型是一張圖 + 兩個純量旋鈕 a,b∈[0,1])
*圖為在 128×128 合成輸入上實際執行的輸出。左為輸入，右為輸出。點雲以俯視散點顯示(亮度 = z)，一維序列為折線，體資料為沿 z 的最大值投影，影片為中間影格，複數影像為振幅；無法成像的回傳值直接顯示數值。*
*旋鈕 a 不改變輸出(實測: 0.1 / 0.5 / 0.9 相同)。*
掃描旋鈕 b(0.1 / 0.5 / 0.9，另一旋鈕取預設):
▸ tb_spectrogram: 旋鈕 b 掃描 (文件網站)
階段(前置運算子 → 本運算子，由左至右):
▸ tb_spectrogram: 處理階段 (文件網站)
換別的影像(合成場景 / 照片 / 硬幣。上排為輸入，下排為對應輸出。旋鈕取預設):
▸ tb_spectrogram: 其他輸入影像 (文件網站)
STFT 振幅頻譜圖 -> (freqs, times, S),其中 S 的形狀為 (n_freqs, n_frames)。使用漢寧窗(Hann window);*hop* 預設值為 win//2。
> 以下的詳細說明為原文 —— 摘要與標題已翻譯。
Same raw convention as spectrum, but a different divisor. Each column is the unnormalised |rfft(frame * hann(win))|, so it is not an amplitude either — and dividing by 2/win is *wrong* here, because the Hann window has already thrown away part of the signal. The correct one-sided amplitude conversion divides by the window's coherent gain::
w = np.hanning(win)
amp = S * (2.0 / w.sum()) # bins 1 .. win/2-1; DC / Nyquist: 1/w.sum()
Measured on a unit sine at a bin centre (rate = 16000 Hz, 1000 Hz, amplitude exactly 1.0, win = 256): the raw column peak is 63.7497786196906; * 2/win gives 0.49804514546633283 (too small by exactly the Hann coherent gain sum(w)/win = 0.498046875), while * 2/sum(w) gives 0.9999965273676957. Only the second one is the amplitude that was actually in the signal.
Peak *positions*, frame-to-frame ratios and any dB *difference* are unaffected by either factor. This function returns magnitudes only — the phase is discarded, so it cannot be inverted; use acoustics.stft / acoustics.istft for a round-trip.
Typed bridge of the 1d op spectrogram into the 2-D evolution registry: the same implementation, called under the op(v, a, b) convention. a drives rate (default 1) and b drives win (default 256).
• 範例資料目錄(下載 URL / 授權) —— 2-D 用 skimage.data(BSD/公有領域)加合成圖,3-D 給出真實資料源(Stanford/PDS 等)的下載 URL。
• 運算子來歷與參考文獻 —— 該運算子族所依據的研究/方法出處。
下面的程式已確認可以執行(與圖相同的輸入)。在 Studio 說明中，此區塊會變成按鈕，可當場載入並執行。
img_to_signal 0.50 0.50 tb_spectrogram 0.50 0.50
下面的範例呼叫的是底層帳本運算子 spectrogram。此橋接運算子只是把同一實作適配為 fn(v, a, b) 慣例，行為完全相同(只是呼叫形式不同)。
• acoustic_condition_monitoring — py -3.11 examples/acoustic_condition_monitoring.py
image 作為輸入)identity · gaussian · mean_box · bilateral · unsharp · median · min_filter · max_filter
typed)tb_points_to_voxel · tb_estimate_point_normals · tb_iss_keypoints · tb_project_points · tb_render_point_depth · tb_statistical_outlier_removal · tb_radius_outlier_removal · tb_voxel_grid_downsample
*Provenance: ops.py — 2D 運算子登記表。本條目由 tools/opdocs.py md 自動產生(請勿手動編輯)。*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.