#!/usr/bin/env python3 """Render side-by-side (peak-frame | deformation-heatmap) visualisations across multiple RTM tau thresholds. For each tau in {0.5, 0.7, 1.0, 1.5, 2.0}, find touches whose peak_deform falls just above that threshold, and render: (RGB peak frame | |frame - baseline| heatmap) side-by-side, with the peak_deform scalar printed. Output one PNG per tau plus a combined "tau_grid_comparison.png" with one row per tau. """ import io, os, random, time from glob import glob import cv2 import numpy as np import pyarrow.parquet as pq import matplotlib matplotlib.use("Agg") import matplotlib.pyplot as plt from PIL import Image, ImageDraw, ImageFont ROOT = "/media/yxma/Disk1/yuxiang/mini_data/markerless/RealTactileMNIST" OUT = "/media/yxma/Disk1/yuxiang/mini_data_parquet/assets" TAUS = [0.5, 0.7, 1.0, 1.5, 2.0] PER_TAU = 8 # how many examples to render per tau PROBE_TOUCHES = 2000 def decode_touch(vid_bytes): """Return (frames_rgb, grays_centercrop) or None.""" tmpf = f"/tmp/_rtm_dvz_{os.getpid()}.mp4" with open(tmpf, "wb") as f: f.write(vid_bytes) cap = cv2.VideoCapture(tmpf) frames, grays = [], [] while True: ok, fr = cap.read() if not ok: break frames.append(fr[:, :, ::-1]) g = cv2.cvtColor(fr, cv2.COLOR_BGR2GRAY).astype(np.float32) h, w = g.shape grays.append(g[h//4:3*h//4, w//4:3*w//4]) cap.release() try: os.remove(tmpf) except: pass if len(frames) < 8: return None return frames, grays def analyse(frames, grays, ts, ts0, ts1): """Return (peak_idx, peak_deform, baseline_full, frame_full_at_peak, diff_central, deform_central).""" baseline_center = np.median(np.stack(grays[:5]), axis=0) # shape (Hc, Wc) deforms = [float(np.abs(g - baseline_center).mean()) for g in grays] in_window = list(range(len(frames))) try: if ts is not None and ts0 is not None and ts1 is not None \ and len(ts) == len(frames): in_window = [k for k, t in enumerate(ts) if ts0 <= t <= ts1] if not in_window: in_window = list(range(len(frames))) except Exception: pass peak_idx = in_window[int(np.argmax([deforms[k] for k in in_window]))] peak_deform = deforms[peak_idx] # full-image baseline + diff for visualisation H, W = frames[0].shape[:2] grays_full = [cv2.cvtColor(cv2.cvtColor(f, cv2.COLOR_RGB2BGR), cv2.COLOR_BGR2GRAY).astype(np.float32) for f in frames] baseline_full = np.median(np.stack(grays_full[:5]), axis=0) diff_full = np.abs(grays_full[peak_idx] - baseline_full) return peak_idx, peak_deform, frames[peak_idx], diff_full def main(): rng = random.Random(7) pq_files = sorted(glob(f"{ROOT}/data/*.parquet")) # Collect ~PROBE_TOUCHES touches with their peak_deform bucket = [] # list of (peak_deform, peak_frame_rgb, diff_full, label) t0 = time.time() print(f"probing up to {PROBE_TOUCHES} touches...", flush=True) for p in pq_files: if len(bucket) >= PROBE_TOUCHES: break pf = pq.ParquetFile(p) for batch in pf.iter_batches(batch_size=4): if len(bucket) >= PROBE_TOUCHES: break cols = batch.to_pydict() n = len(cols["label"]) for i in range(n): if rng.random() > 0.05: continue videos = cols["sensor_video"][i] or [] ts_seq = cols.get("time_stamp_rel_seq", [None]*n)[i] or [] t_start = cols.get("touch_start_time_rel", [None]*n)[i] or [] t_end = cols.get("touch_end_time_rel", [None]*n)[i] or [] label = cols["label"][i] for tj, vs in enumerate(videos): if rng.random() > 0.3: continue if len(bucket) >= PROBE_TOUCHES: break vb = vs.get("bytes") if isinstance(vs, dict) else None if not vb: continue out = decode_touch(vb) if out is None: continue frames, grays = out ts = ts_seq[tj] if tj < len(ts_seq) else None ts0 = t_start[tj] if tj < len(t_start) else None ts1 = t_end[tj] if tj < len(t_end) else None pidx, pdef, peak_rgb, diff = analyse(frames, grays, ts, ts0, ts1) bucket.append((pdef, peak_rgb, diff, label)) if len(bucket) % 200 == 0: dt = time.time() - t0 print(f" {len(bucket)} touches ({len(bucket)/max(dt,0.01):.1f}/s)", flush=True) print(f"collected {len(bucket)} touches in {time.time()-t0:.0f}s") # ------------------------------------------------------------------ # Build a 5-row comparison figure: each row = one tau, showing # PER_TAU (peak | diff) pairs from touches with peak_deform near tau. # ------------------------------------------------------------------ fig_w = PER_TAU * 2.2 + 1.0 fig_h = len(TAUS) * 2.3 fig, axes = plt.subplots(len(TAUS), PER_TAU * 2, figsize=(fig_w, fig_h), gridspec_kw={"wspace": 0.05, "hspace": 0.08}) for ri, tau in enumerate(TAUS): # Touches whose peak_deform is in a narrow band around tau # (so the visualisation actually shows what 'tau' looks like) lo, hi = tau, tau * 1.4 band = [b for b in bucket if lo <= b[0] < hi] if len(band) < PER_TAU: band = sorted(bucket, key=lambda b: abs(b[0] - tau))[:PER_TAU] rng.shuffle(band) chosen = band[:PER_TAU] for ci, (pdef, rgb, diff, label) in enumerate(chosen): ax_rgb = axes[ri, ci * 2] ax_dif = axes[ri, ci * 2 + 1] ax_rgb.imshow(rgb) ax_rgb.set_xticks([]); ax_rgb.set_yticks([]) # show diff heatmap, fixed scale 0..20 grey-levels so all rows # compare on identical colour mapping im = ax_dif.imshow(diff, cmap="magma", vmin=0, vmax=20) ax_dif.set_xticks([]); ax_dif.set_yticks([]) ax_dif.set_title(f"Δ={pdef:.2f}", fontsize=8, pad=1) if ci == 0: ax_rgb.set_ylabel(f"τ≈{tau}", fontsize=11, rotation=0, labelpad=22, va="center", fontweight="bold") fig.suptitle( "Real Tactile MNIST · peak frame ↔ |frame − baseline| heatmap\n" "(8 example touches per tau band · diff scale fixed 0–20 grey-levels · " "Δ = mean absolute deformation in central crop)", fontsize=11, y=0.995) out = f"{OUT}/rtm_tau_diff_comparison.png" plt.savefig(out, dpi=140, bbox_inches="tight") plt.close() print(f"saved {out}") # ------------------------------------------------------------------ # Also: aggregate-level row showing the DIFFERENCE between baseline # and peak averaged over many kept touches at each tau. # ------------------------------------------------------------------ fig, axes = plt.subplots(2, len(TAUS), figsize=(3 * len(TAUS), 6), gridspec_kw={"hspace": 0.15}) for ci, tau in enumerate(TAUS): kept = [b for b in bucket if b[0] >= tau] if not kept: continue # mean RGB across kept peak frames mean_rgb = np.mean(np.stack([b[1] for b in kept]), axis=0).astype(np.uint8) # mean diff mean_diff = np.mean(np.stack([b[2] for b in kept]), axis=0) axes[0, ci].imshow(mean_rgb) axes[0, ci].set_title(f"τ ≥ {tau}\n(mean of {len(kept)} kept peaks)", fontsize=10) axes[0, ci].set_xticks([]); axes[0, ci].set_yticks([]) im = axes[1, ci].imshow(mean_diff, cmap="magma", vmin=0, vmax=8) axes[1, ci].set_xticks([]); axes[1, ci].set_yticks([]) axes[1, ci].set_title("mean |Δ| heatmap (vmax=8)", fontsize=9) fig.suptitle( "Mean tactile imprint across kept touches at each tau\n" "top: mean RGB · bottom: mean |peak − baseline| (the higher tau, the " "more clearly the imprint emerges from averaging)", fontsize=11, y=0.995) out2 = f"{OUT}/rtm_tau_mean_diff.png" plt.savefig(out2, dpi=140, bbox_inches="tight") plt.close() print(f"saved {out2}") if __name__ == "__main__": main()