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fd560af | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 | #!/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()
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