#!/usr/bin/env python3 """Probe gelslam and tactile_tracking with the proper per-capture pipeline baseline (median of first 10 raw video frames), measure area + intensity of every subsequent frame, and report distributions + sample grids at candidate (A_min, I_min) operating points. Outputs: rtm-style scatter (area, intensity) for each source samples_100__op_.png for a few candidate operating points """ 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 BASE_DATA = "/media/yxma/Disk1/yuxiang/mini_data" OUT = "/media/yxma/Disk1/yuxiang/mini_data_parquet/assets" PIXEL_THRESH = 10 BASE_FRAMES = 10 OPS = [ ("strict", dict(A_min=400, I_min=15)), ("balanced", dict(A_min=200, I_min=12)), ("lenient", dict(A_min=100, I_min=10)), ] def grey_center(arr_bgr): g = cv2.cvtColor(arr_bgr, cv2.COLOR_BGR2GRAY).astype(np.float32) h, w = g.shape return g[h//4:3*h//4, w//4:3*w//4] def iter_video_frames(path): cap = cv2.VideoCapture(path) while True: ok, fr = cap.read() if not ok: break yield fr cap.release() def probe_video(path, max_frames_per_clip=None): """Yield (rgb, area, intensity) for each non-baseline frame in this clip.""" buf = [] baseline = None fi = 0 for fr in iter_video_frames(path): fi += 1 if max_frames_per_clip and fi > max_frames_per_clip: break g = grey_center(fr) if baseline is None: buf.append(g) if len(buf) >= BASE_FRAMES: baseline = np.median(np.stack(buf, axis=0), axis=0) continue diff = np.abs(g - baseline) mask = diff > PIXEL_THRESH area = int(mask.sum()) inten = float(diff[mask].mean()) if area > 0 else 0.0 yield fr[:, :, ::-1], area, inten def probe_source(sub, max_clips=40, max_frames_per_clip=80): if sub == "gelslam": vids = sorted(glob(f"{BASE_DATA}/markerless/GelSLAM/tracking_dataset/*/gelsight.avi")) \ + sorted(glob(f"{BASE_DATA}/markerless/GelSLAM/reconstruction_dataset/*/gelsight.avi")) elif sub == "tactile_tracking": vids = sorted(glob(f"{BASE_DATA}/markerless/TactileTracking/normalflow_dataset/*/gelsight.avi")) else: return [], [] rng = random.Random(7) rng.shuffle(vids) vids = vids[:max_clips] bucket = [] # (area, intensity, rgb) t0 = time.time() print(f"probing {sub}: {len(vids)} clips...", flush=True) for i, v in enumerate(vids): for rgb, area, inten in probe_video(v, max_frames_per_clip=max_frames_per_clip): bucket.append((area, inten, rgb)) if (i+1) % 5 == 0: print(f" {i+1}/{len(vids)} clips, {len(bucket)} frames, " f"{(i+1)/(time.time()-t0):.1f} clips/s", flush=True) A = np.array([b[0] for b in bucket]) I = np.array([b[1] for b in bucket]) print(f" {sub} ({len(bucket)} frames):") print(f" area: min={A.min()} median={int(np.median(A))} mean={int(A.mean())} max={A.max()}") print(f" intensity: min={I.min():.1f} median={np.median(I):.1f} mean={I.mean():.1f} max={I.max():.1f}") for name, op in OPS: kept = ((A >= op["A_min"]) & (I >= op["I_min"])).sum() print(f" {name:8s} A>={op['A_min']}, I>={op['I_min']}: {kept}/{len(bucket)} ({100*kept/len(bucket):.1f}%)") return bucket, OPS def render_grid(bucket, op, name, sub, out_path): A_min, I_min = op["A_min"], op["I_min"] kept = [(a, i, fr) for a, i, fr in bucket if a >= A_min and i >= I_min] pct = 100 * len(kept) / len(bucket) rng = random.Random(0) sample = rng.sample(kept, min(100, len(kept))) if not sample: print(f" {name}: 0 kept, skip"); return side = 144; cols = 10; pad = 4; title_h = 44 rows = (len(sample) + cols - 1) // cols W = pad + cols * (side + pad) H = title_h + rows * (side + pad) + pad canvas = Image.new("RGB", (W, H), (255, 255, 255)) d = ImageDraw.Draw(canvas) try: f_t = ImageFont.truetype("DejaVuSans-Bold.ttf", 18) except: f_t = ImageFont.load_default() d.text((pad + 4, 8), f"{sub} · '{name}' op: A>={A_min}, I>={I_min} · " f"keep {pct:.1f}% · {len(sample)} samples shown", fill=(0, 0, 0), font=f_t) for i, (a, intensity, fr) in enumerate(sample): r, c = i // cols, i % cols x = pad + c * (side + pad) y = title_h + r * (side + pad) im = Image.fromarray(fr) w, h = im.size; s = min(w, h) im = im.crop(((w-s)//2, (h-s)//2, (w+s)//2, (h+s)//2)).resize((side, side), Image.LANCZOS) canvas.paste(im, (x, y)) canvas.save(out_path, optimize=True) print(f" saved {out_path} ({pct:.1f}% kept)") def main(): for sub in ["gelslam", "tactile_tracking"]: bucket, ops = probe_source(sub, max_clips=30, max_frames_per_clip=120) if not bucket: continue # render grids for each op for name, op in ops: render_grid(bucket, op, name, sub, f"{OUT}/samples_100_{sub}_op_{name}.png") # scatter A = np.array([b[0] for b in bucket]) I = np.array([b[1] for b in bucket]) fig, ax = plt.subplots(figsize=(8, 6)) ax.scatter(A, I, s=6, alpha=0.4, color="#4c95d6") for (name, op), col in zip(ops, ["#d62728", "#2ca02c", "#1f77b4"]): ax.axvline(op["A_min"], color=col, ls="--", alpha=0.5) ax.axhline(op["I_min"], color=col, ls="--", alpha=0.5) kept = ((A >= op["A_min"]) & (I >= op["I_min"])).sum() ax.text(op["A_min"] + 50, op["I_min"] + 0.3, f"{name}: A≥{op['A_min']}, I≥{op['I_min']} → " f"{100*kept/len(A):.0f}%", fontsize=9, color=col, fontweight="bold") ax.set_xlabel(f"contact_area (# pixels with |diff| > {PIXEL_THRESH})") ax.set_ylabel("contact_intensity (mean |diff| over those pixels)") ax.set_title(f"{sub} · area vs intensity ({len(bucket)} probed frames)") ax.grid(alpha=0.2) plt.tight_layout() plt.savefig(f"{OUT}/{sub}_area_intensity_scatter.png", dpi=140) plt.close() print(f" saved {OUT}/{sub}_area_intensity_scatter.png") if __name__ == "__main__": main()