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"""Render demo/C per-frame images v3: clean, large fonts, clear scores."""
import cv2, json, sys, logging
import numpy as np
from pathlib import Path
ROOT = Path("PROJECT_ROOT")
OUT = ROOT / "demo/C"
C_RESULTS = ROOT / "demo/C_results"
logging.basicConfig(level=logging.INFO, format="%(asctime)s %(message)s")
log = logging.getLogger("render")
COLOR_BGR = {
"SILENT": (40, 190, 40),
"OBSERVE": (30, 190, 255),
"ALERT": (30, 30, 230),
}
def find_frame_dir(vid, src):
if src == "nexar":
num = vid.replace("nexar_", "")
for sp in ["train", "test-public", "test-private"]:
for po in ["positive", "negative"]:
p = ROOT / f"NEXAR_COLLISION/dataset/{sp}/{po}/{num}"
if p.exists(): return p
elif src == "dada":
name = vid.replace("dada_", "")
for cat in ["positive", "non-ego", "negative"]:
p = ROOT / f"DADA-2000/{cat}/{name}"
if p.exists(): return p
elif src == "dota":
raw = vid.replace("dota_", "")
p = ROOT / f"DoTA/frames/{raw}/images"
if p.exists(): return p
return None
def load_frame(frame_dir, idx):
for fmt in [f"{idx:06d}.jpg", f"{idx:05d}.jpg", f"{idx:04d}.jpg",
f"{idx:03d}.jpg", f"{idx}.jpg"]:
fp = frame_dir / fmt
if fp.exists():
return cv2.imread(str(fp))
return None
def get_fps(src):
return 20.0 if src in ("dada", "dota") else 30.0
def put_text_bg(img, text, pos, font_scale, color, thickness=2, bg_alpha=0.6):
"""Put text with dark background."""
font = cv2.FONT_HERSHEY_SIMPLEX
(tw, th), baseline = cv2.getTextSize(text, font, font_scale, thickness)
x, y = pos
overlay = img.copy()
cv2.rectangle(overlay, (x - 4, y - th - 6), (x + tw + 4, y + baseline + 4), (0, 0, 0), -1)
cv2.addWeighted(overlay, bg_alpha, img, 1 - bg_alpha, 0, img)
cv2.putText(img, text, (x, y), font, font_scale, color, thickness, cv2.LINE_AA)
def render_gt_frame(img, action, tick_idx, t_sec):
H, W = img.shape[:2]
out = img.copy()
color = COLOR_BGR[action]
# Top bar
bar_h = 60
overlay = out.copy()
cv2.rectangle(overlay, (0, 0), (W, bar_h), color, -1)
cv2.addWeighted(overlay, 0.7, out, 0.3, 0, out)
cv2.putText(out, "Ground Truth", (15, 28),
cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255, 255, 255), 2, cv2.LINE_AA)
cv2.putText(out, action, (W - 180, 28),
cv2.FONT_HERSHEY_SIMPLEX, 0.9, (255, 255, 255), 2, cv2.LINE_AA)
cv2.putText(out, f"t = {t_sec:.1f}s", (15, 52),
cv2.FONT_HERSHEY_SIMPLEX, 0.55, (220, 220, 220), 1, cv2.LINE_AA)
return out
def render_badas_frame(img, action, p_alert, tick_idx, t_sec):
H, W = img.shape[:2]
out = img.copy()
color = COLOR_BGR[action]
# Top bar
bar_h = 60
overlay = out.copy()
cv2.rectangle(overlay, (0, 0), (W, bar_h), color, -1)
cv2.addWeighted(overlay, 0.7, out, 0.3, 0, out)
cv2.putText(out, "BADAS", (15, 28),
cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255, 255, 255), 2, cv2.LINE_AA)
cv2.putText(out, action, (W - 180, 28),
cv2.FONT_HERSHEY_SIMPLEX, 0.9, (255, 255, 255), 2, cv2.LINE_AA)
cv2.putText(out, f"t = {t_sec:.1f}s", (15, 52),
cv2.FONT_HERSHEY_SIMPLEX, 0.55, (220, 220, 220), 1, cv2.LINE_AA)
# Bottom: danger score bar
bar_bot_h = 50
overlay2 = out.copy()
cv2.rectangle(overlay2, (0, H - bar_bot_h), (W, H), (0, 0, 0), -1)
cv2.addWeighted(overlay2, 0.65, out, 0.35, 0, out)
# Score bar fill
bar_x0, bar_x1 = 20, W - 20
bar_y0, bar_y1 = H - bar_bot_h + 8, H - 10
bar_w = bar_x1 - bar_x0
fill_w = int(bar_w * min(p_alert, 1.0))
# Gradient: green → yellow → red
if p_alert < 0.5:
r = int(p_alert * 2 * 255)
fill_color = (0, 255 - r // 2, r)
else:
fill_color = (0, int((1 - p_alert) * 200), 230)
cv2.rectangle(out, (bar_x0, bar_y0), (bar_x0 + fill_w, bar_y1), fill_color, -1)
cv2.rectangle(out, (bar_x0, bar_y0), (bar_x1, bar_y1), (180, 180, 180), 1)
cv2.putText(out, f"Danger: {p_alert:.3f}", (bar_x0, bar_y0 - 3),
cv2.FONT_HERSHEY_SIMPLEX, 0.55, (255, 255, 255), 1, cv2.LINE_AA)
return out
def render_vlalert_frame(img, action, p_alert, p_observe, p_silent, tick_idx, t_sec,
clip_danger=None, tta=None):
H, W = img.shape[:2]
out = img.copy()
color = COLOR_BGR[action]
# Top bar
bar_h = 60
overlay = out.copy()
cv2.rectangle(overlay, (0, 0), (W, bar_h), color, -1)
cv2.addWeighted(overlay, 0.7, out, 0.3, 0, out)
cv2.putText(out, "VLAlert", (15, 28),
cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255, 255, 255), 2, cv2.LINE_AA)
cv2.putText(out, action, (W - 180, 28),
cv2.FONT_HERSHEY_SIMPLEX, 0.9, (255, 255, 255), 2, cv2.LINE_AA)
cv2.putText(out, f"t = {t_sec:.1f}s", (15, 52),
cv2.FONT_HERSHEY_SIMPLEX, 0.55, (220, 220, 220), 1, cv2.LINE_AA)
# Bottom: 3-class probability bars
bar_bot_h = 65
overlay2 = out.copy()
cv2.rectangle(overlay2, (0, H - bar_bot_h), (W, H), (0, 0, 0), -1)
cv2.addWeighted(overlay2, 0.65, out, 0.35, 0, out)
bar_x0, bar_x1 = 20, W - 20
bar_w = bar_x1 - bar_x0
bar_h_each = 14
y = H - bar_bot_h + 6
probs = [
("SILENT", p_silent, COLOR_BGR["SILENT"]),
("OBSERVE", p_observe, COLOR_BGR["OBSERVE"]),
("ALERT", p_alert, COLOR_BGR["ALERT"]),
]
for label, prob, clr in probs:
fill_w = int(bar_w * min(prob, 1.0))
cv2.rectangle(out, (bar_x0, y), (bar_x0 + fill_w, y + bar_h_each), clr, -1)
cv2.rectangle(out, (bar_x0, y), (bar_x1, y + bar_h_each), (120, 120, 120), 1)
cv2.putText(out, f"{label}: {prob:.2f}", (bar_x0 + 5, y + bar_h_each - 2),
cv2.FONT_HERSHEY_SIMPLEX, 0.4, (255, 255, 255), 1, cv2.LINE_AA)
y += bar_h_each + 2
return out
def main():
selected = json.load(open(OUT / "selected_6.json"))
log.info(f"Rendering {len(selected)} videos")
for v in selected:
vid = v["video_id"]
src = v["source"]
gt = v["gt"]
frame_dir = find_frame_dir(vid, src)
if frame_dir is None:
log.warning(f" {vid}: no frames, skip")
continue
fps = get_fps(src)
tick_interval = max(1, int(fps))
n_ticks = len(gt)
scores_path = C_RESULTS / vid / "scores.json"
all_scores = json.load(open(scores_path)) if scores_path.exists() else {}
log.info(f" {vid} ({src}): {n_ticks} ticks")
# Use scored ticks as reference (not GT ticks which may differ)
ref_ticks = next(iter(all_scores.values()))
actual_n = len(ref_ticks)
# Render GT frames (one per scored tick)
gt_dir = OUT / vid / "GT"
gt_dir.mkdir(parents=True, exist_ok=True)
for ti, rt in enumerate(ref_ticks):
fidx = rt.get("frame", ti * tick_interval)
t_sec = rt.get("t", fidx / fps)
img = load_frame(frame_dir, fidx)
if img is None:
continue
gt_act = gt[ti] if ti < len(gt) else "SILENT"
cv2.imwrite(str(gt_dir / f"frame_{ti:03d}.png"),
render_gt_frame(img, gt_act, ti, t_sec))
# Render each model
for model_name, ticks in all_scores.items():
is_badas = "BADAS" in model_name
folder_name = model_name.replace(" ", "_")
model_dir = OUT / vid / folder_name
model_dir.mkdir(parents=True, exist_ok=True)
for ti, td in enumerate(ticks):
fidx = td.get("frame", ti * tick_interval)
t_sec = td.get("t", fidx / fps)
img = load_frame(frame_dir, fidx)
if img is None:
continue
action = td.get("action", "SILENT")
p_alert = td.get("p_alert", 0)
p_observe = td.get("p_observe", 0)
p_silent = max(0, 1 - p_alert - p_observe)
clip_d = td.get("clip_danger", None)
if is_badas:
out = render_badas_frame(img, action, p_alert, ti, t_sec)
else:
out = render_vlalert_frame(img, action, p_alert, p_observe, p_silent,
ti, t_sec, clip_danger=clip_d)
cv2.imwrite(str(model_dir / f"frame_{ti:03d}.png"), out)
log.info(f" done")
log.info(f"\nAll done! → {OUT}")
if __name__ == "__main__":
main()
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