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# make_demo.py
# ============================================================
#
# ETHUKU CREATE PANNINOM?
# ━━━━━━━━━━━━━━━━━━━━━━
# README-ku / LinkedIn-ku oru portfolio demo venum. Recruiter 5 second
# thaan paapaanga - antha 5 second-la "6 camera -> BEV detections" +
# "TensorRT 3.9x speedup" rendum theriyanum.
#
# bev_renderer.py oru frame varaiyum. Ithu:
# - thodarchiyaana frames (scene-la oru vari, so motion theriyum)
# - real T4 benchmark numbers-ai padathula-ye potrom
# - GIF + thani PNG rendum
#
# MUNADI FILE ODA CONNECTION:
# ━━━━━━━━━━━━━━━━━━━━━━━━━━
# visualization/bev_renderer.py -> draw_bev, denormalize reuse
# training/evaluate.py -> decode_predictions
#
# INPUT / OUTPUT:
# ━━━━━━━━━━━━━━
# Output: runs/demo/demo.gif + frame_*.png
#
# EPADI USE AAGUM:
# ━━━━━━━━━━━━━━━
# python -m visualization.make_demo --frames 20
#
# ============================================================
import argparse
import os
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import numpy as np
from data.scripts.constants import CAMERAS, CLASSES
from visualization.bev_renderer import denormalize, draw_bev, CLASS_COLORS
# Kaggle T4-la nijamaa alandha numbers. Hardcode pannurom yaen?
# Ithu Mac-la odum, aana kaatta vendiyathu T4 result. Mac-la
# TensorRT-e illa, so anga alandhadhai inga kondu varom.
BENCHMARK = [
("PyTorch FP32", 32.66, 30.6, "1.00x"),
("ONNX Runtime", 30.20, 33.1, "1.08x"),
("TensorRT FP32", 24.34, 41.1, "1.34x"), # <- ithu thaan ship aachu
("TensorRT FP16", 61.56, 16.2, "0.53x"),
("TensorRT INT8", 8.52, 117.3, "3.83x"),
]
SHIPPED = "TensorRT FP32"
def draw_stats_panel(ax) -> None:
"""
Benchmark table-ai padathula text-a varaiyurathu.
Yaen padathula? README-la thani table irundhaalum, GIF-ai
LinkedIn-la neradiya podumbodhu numbers kooda pogum.
"""
ax.axis("off")
ax.set_facecolor("#0a0a14")
y = 0.97
ax.text(0.5, y, "Tesla T4 | 6 cameras -> BEV", ha="center",
color="#e0e0e0", fontsize=11, weight="bold",
transform=ax.transAxes)
y -= 0.09
ax.text(0.02, y, f"{'Backend':<15}{'ms':>7}{'FPS':>7}{'vs base':>9}",
color="#8a8a9a", fontsize=8.5, family="monospace",
transform=ax.transAxes)
y -= 0.035
ax.plot([0.02, 0.98], [y, y], color="#2a2a3a", lw=0.8,
transform=ax.transAxes, clip_on=False)
for name, ms, fps, speed in BENCHMARK:
y -= 0.055
# Vegam jaasti INT8-ku thaan. Aana ship aanadhu FP32 -
# highlight pannurathu ship aanadhai, vegathai illa.
best = name == SHIPPED
color = "#00d4ff" if best else "#b0b0c0"
ax.text(0.02, y, f"{name:<15}{ms:>7.1f}{fps:>7.1f}{speed:>9}",
color=color, fontsize=8.5, family="monospace",
weight="bold" if best else "normal",
transform=ax.transAxes)
y -= 0.10
for line, col in [
("Shipped: TensorRT FP32", "#00d4ff"),
("1.34x faster, nothing given up", "#8a8a9a"),
("", "#6a6a7a"),
("INT8 was 3.83x and 16x smaller,", "#ff8c00"),
("but kept only 34% of the original", "#8a8a9a"),
("detections. Fast and wrong is worse", "#8a8a9a"),
("than slower and right.", "#8a8a9a"),
]:
ax.text(0.02, y, line, color=col, fontsize=8,
transform=ax.transAxes)
y -= 0.045
y -= 0.03
ax.plot([0.02, 0.98], [y, y], color="#2a2a3a", lw=0.8,
transform=ax.transAxes, clip_on=False)
y -= 0.055
for line in ["SimpleBEV 5.5M params",
"EfficientNet-B0 + Lift-Splat-Shoot",
"ResNet-18 FPN + CenterPoint head",
"nuScenes mini | no LiDAR"]:
ax.text(0.02, y, line, color="#6a6a7a", fontsize=7.5,
transform=ax.transAxes)
y -= 0.04
def render(images, gt_boxes, pred_boxes, out_path, frame_i, n_frames,
score_thresh=0.2):
"""Oru demo frame: cameras | BEV | stats."""
fig = plt.figure(figsize=(19, 7.5), facecolor="#0a0a14")
gs = fig.add_gridspec(3, 5, width_ratios=[0.78, 0.78, 0.1, 1.7, 0.95],
hspace=0.1, wspace=0.05)
layout = [("CAM_FRONT_LEFT", 0, 0), ("CAM_FRONT_RIGHT", 0, 1),
("CAM_FRONT", 1, 0), ("CAM_BACK", 1, 1),
("CAM_BACK_LEFT", 2, 0), ("CAM_BACK_RIGHT", 2, 1)]
for cam, r, c in layout:
ax = fig.add_subplot(gs[r, c])
ax.imshow(denormalize(images[CAMERAS.index(cam)]))
ax.set_title(cam.replace("CAM_", ""), color="#8a8a9a", fontsize=6.5, pad=1)
ax.axis("off")
ax_bev = fig.add_subplot(gs[:, 3])
draw_bev(ax_bev, gt_boxes, pred_boxes, score_thresh)
n = sum(1 for b in pred_boxes if b["score"] >= score_thresh)
ax_bev.set_title(f"Bird's Eye View | frame {frame_i+1}/{n_frames} | "
f"{n} detections", color="#e0e0e0", fontsize=10, pad=8)
present = sorted({b["cls"] for b in pred_boxes if b["score"] >= score_thresh})
if present:
handles = [plt.Line2D([0], [0], marker="s", linestyle="", markersize=6,
color=CLASS_COLORS[c], label=CLASSES[c])
for c in present]
ax_bev.legend(handles=handles, loc="upper left", fontsize=7,
facecolor="#14141e", edgecolor="#2a2a3a",
labelcolor="#c0c0d0")
draw_stats_panel(fig.add_subplot(gs[:, 4]))
fig.text(0.42, 0.02, "white dashed = ground truth | filled = prediction",
ha="center", color="#6a6a7a", fontsize=8)
os.makedirs(os.path.dirname(out_path) or ".", exist_ok=True)
fig.savefig(out_path, dpi=110, bbox_inches="tight", facecolor="#0a0a14")
plt.close(fig)
def main() -> None:
ap = argparse.ArgumentParser()
ap.add_argument("--ckpt", default="runs/simplebev/best.pth")
ap.add_argument("--data-root", default="data/nuscenes-mini")
ap.add_argument("--out-dir", default="runs/demo")
ap.add_argument("--frames", type=int, default=20)
ap.add_argument("--start", type=int, default=0)
ap.add_argument("--score-thresh", type=float, default=0.2)
ap.add_argument("--ms-per-frame", type=int, default=450)
args = ap.parse_args()
import torch
from data.scripts.dataset import NuScenesBEVDataset
from models.simplebev import SimpleBEV
from training.evaluate import decode_predictions
device = torch.device("mps" if torch.backends.mps.is_available() else "cpu")
model = SimpleBEV(pretrained=False).to(device).eval()
model.load_state_dict(torch.load(args.ckpt, map_location=device)["model"])
ds = NuScenesBEVDataset(args.data_root, split="val")
n = min(args.frames, len(ds) - args.start)
paths = []
for k in range(n):
i = args.start + k
s = ds[i]
with torch.no_grad():
preds = model(s["images"].unsqueeze(0).to(device),
s["intrinsics"].unsqueeze(0).to(device),
s["extrinsics"].unsqueeze(0).to(device))
boxes = decode_predictions({k2: v.cpu() for k2, v in preds.items()})[0]
gt = ds._boxes_in_ego(ds.sample_tokens[i])
p = os.path.join(args.out_dir, f"frame_{k:03d}.png")
render(s["images"].numpy(), gt, boxes, p, k, n, args.score_thresh)
paths.append(p)
print(f" frame {k+1}/{n}")
from PIL import Image
frames = [Image.open(p).convert("P", palette=Image.ADAPTIVE, colors=128)
for p in paths]
gif = os.path.join(args.out_dir, "demo.gif")
frames[0].save(gif, save_all=True, append_images=frames[1:],
duration=args.ms_per_frame, loop=0, optimize=True)
print(f"\nsaved {gif} ({os.path.getsize(gif)/1e6:.1f} MB, {n} frames)")
print(f"saved {n} PNGs in {args.out_dir}/")
if __name__ == "__main__":
main()
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