from __future__ import annotations import argparse import shutil from pathlib import Path import cv2 import matplotlib.pyplot as plt import numpy as np import pandas as pd MODEL_LABELS = { "mcfd_rgb_cnn": "RGB-CNN", "mcfd_rgb_cnn_lstm": "RGB-CNN-LSTM", "mcfd_rgb_resnet18_lstm": "RGB-ResNet18-LSTM", "mcfd_pose_lstm": "Pose-LSTM", "mcfd_pose_gru_attention": "Pose-GRU-Attn", "mcfd_pose_tcn_attention": "Proposed", "mcfd_ablation_no_velocity": "w/o velocity", "mcfd_ablation_no_confidence": "w/o confidence", } SKELETON = [ (5, 7), (7, 9), (6, 8), (8, 10), (5, 6), (5, 11), (6, 12), (11, 12), (11, 13), (13, 15), (12, 14), (14, 16), (0, 1), (0, 2), (1, 3), (2, 4), ] def ensure_dirs(*dirs: Path) -> None: for d in dirs: d.mkdir(parents=True, exist_ok=True) def savefig(path: Path, paper_dir: Path | None = None) -> None: path.parent.mkdir(parents=True, exist_ok=True) plt.tight_layout() plt.savefig(path, dpi=300, bbox_inches="tight") plt.close() if paper_dir is not None: paper_dir.mkdir(parents=True, exist_ok=True) shutil.copy2(path, paper_dir / path.name) def plot_main_results(results_csv: Path, out_dir: Path, paper_dir: Path) -> None: df = pd.read_csv(results_csv) order = [ "mcfd_rgb_cnn", "mcfd_rgb_cnn_lstm", "mcfd_rgb_resnet18_lstm", "mcfd_pose_lstm", "mcfd_pose_gru_attention", "mcfd_pose_tcn_attention", ] df = df[df["run"].isin(order)].copy() df["label"] = pd.Categorical(df["run"].map(MODEL_LABELS), [MODEL_LABELS[r] for r in order], ordered=True) df = df.sort_values("label") metrics = ["test_accuracy", "test_precision", "test_recall", "test_f1"] names = ["Accuracy", "Precision", "Recall", "F1"] x = np.arange(len(df)) width = 0.19 colors = ["#4C78A8", "#F58518", "#54A24B", "#B279A2"] plt.figure(figsize=(8.2, 4.2)) for i, (metric, name) in enumerate(zip(metrics, names, strict=True)): plt.bar(x + (i - 1.5) * width, df[metric], width, label=name, color=colors[i]) plt.xticks(x, df["label"], rotation=20, ha="right") plt.ylim(0, 1.05) plt.ylabel("Score") plt.legend(ncol=4, loc="upper center", bbox_to_anchor=(0.5, 1.18), frameon=False) plt.grid(axis="y", alpha=0.25) savefig(out_dir / "main_results_metrics.png", paper_dir) plt.figure(figsize=(7.2, 3.8)) bars = plt.bar(df["label"], df["test_f1"], color=["#9E9E9E", "#777777", "#4C78A8", "#54A24B", "#B279A2"]) plt.ylim(0, 0.6) plt.ylabel("F1-score") plt.xticks(rotation=20, ha="right") plt.grid(axis="y", alpha=0.25) for bar in bars: h = bar.get_height() plt.text(bar.get_x() + bar.get_width() / 2, h + 0.012, f"{h:.3f}", ha="center", va="bottom", fontsize=9) savefig(out_dir / "main_results_f1.png", paper_dir) def plot_ablation(results_csv: Path, out_dir: Path, paper_dir: Path) -> None: df = pd.read_csv(results_csv) order = ["mcfd_ablation_no_velocity", "mcfd_ablation_no_confidence", "mcfd_pose_tcn_attention"] df = df[df["run"].isin(order)].copy() df["label"] = pd.Categorical(df["run"].map(MODEL_LABELS), [MODEL_LABELS[r] for r in order], ordered=True) df = df.sort_values("label") metrics = ["test_accuracy", "test_precision", "test_f1"] names = ["Accuracy", "Precision", "F1"] x = np.arange(len(df)) width = 0.25 plt.figure(figsize=(6.6, 3.8)) for i, (metric, name, color) in enumerate(zip(metrics, names, ["#4C78A8", "#F58518", "#B279A2"], strict=True)): plt.bar(x + (i - 1) * width, df[metric], width, label=name, color=color) plt.xticks(x, df["label"], rotation=15, ha="right") plt.ylim(0, 0.75) plt.ylabel("Score") plt.legend(ncol=3, loc="upper center", bbox_to_anchor=(0.5, 1.17), frameon=False) plt.grid(axis="y", alpha=0.25) savefig(out_dir / "ablation_metrics.png", paper_dir) def plot_robustness(robustness_csv: Path, out_dir: Path, paper_dir: Path) -> None: df = pd.read_csv(robustness_csv) frame = df[df["setting"].str.startswith("frame_drop") | (df["setting"] == "clean")].copy() frame["drop_ratio"] = frame["setting"].map( {"clean": 0, "frame_drop_10": 10, "frame_drop_20": 20, "frame_drop_30": 30} ) order = ["mcfd_pose_lstm", "mcfd_pose_gru_attention", "mcfd_pose_tcn_attention"] colors = {"mcfd_pose_lstm": "#4C78A8", "mcfd_pose_gru_attention": "#54A24B", "mcfd_pose_tcn_attention": "#B279A2"} plt.figure(figsize=(6.6, 3.8)) for run in order: part = frame[frame["run"] == run].sort_values("drop_ratio") plt.plot(part["drop_ratio"], part["f1"], marker="o", linewidth=2, label=MODEL_LABELS[run], color=colors[run]) plt.xlabel("Frame drop ratio (%)") plt.ylabel("F1-score") plt.ylim(0.48, 0.54) plt.xticks([0, 10, 20, 30]) plt.legend(frameon=False) plt.grid(alpha=0.25) savefig(out_dir / "robustness_frame_drop_f1.png", paper_dir) noise = df[df["setting"].str.startswith("keypoint_noise") | (df["setting"] == "clean")].copy() noise["noise_px"] = noise["setting"].map( {"clean": 0, "keypoint_noise_px_2": 2, "keypoint_noise_px_5": 5, "keypoint_noise_px_10": 10} ) plt.figure(figsize=(6.6, 3.8)) for run in order: part = noise[noise["run"] == run].sort_values("noise_px") plt.plot(part["noise_px"], part["f1"], marker="o", linewidth=2, label=MODEL_LABELS[run], color=colors[run]) plt.xlabel("Gaussian keypoint noise (px)") plt.ylabel("F1-score") plt.ylim(0.48, 0.54) plt.xticks([0, 2, 5, 10]) plt.legend(frameon=False) plt.grid(alpha=0.25) savefig(out_dir / "robustness_keypoint_noise_f1.png", paper_dir) def plot_confusion_matrices(results_csv: Path, out_dir: Path, paper_dir: Path) -> None: df = pd.read_csv(results_csv) for run in ["mcfd_rgb_cnn", "mcfd_rgb_resnet18_lstm", "mcfd_pose_lstm", "mcfd_pose_gru_attention", "mcfd_pose_tcn_attention"]: row = df[df["run"] == run].iloc[0] cm = np.array([[row["test_tn"], row["test_fp"]], [row["test_fn"], row["test_tp"]]], dtype=int) plt.figure(figsize=(3.7, 3.4)) plt.imshow(cm, cmap="Blues") plt.xticks([0, 1], ["ADL", "Fall"]) plt.yticks([0, 1], ["ADL", "Fall"]) plt.xlabel("Predicted") plt.ylabel("True") plt.title(MODEL_LABELS[run]) for y in range(2): for x in range(2): color = "white" if cm[y, x] > cm.max() * 0.55 else "black" plt.text(x, y, str(cm[y, x]), ha="center", va="center", color=color, fontsize=12) savefig(out_dir / f"confusion_{run}.png", paper_dir) def draw_box_diagram(labels: list[str], title: str, path: Path, paper_dir: Path) -> None: fig, ax = plt.subplots(figsize=(8.4, 1.8)) ax.set_axis_off() n = len(labels) box_w = 0.86 / n y = 0.35 for i, label in enumerate(labels): x = 0.05 + i * (0.9 / n) rect = plt.Rectangle((x, y), box_w, 0.32, facecolor="#F7F7F7", edgecolor="#333333", linewidth=1.2) ax.add_patch(rect) ax.text(x + box_w / 2, y + 0.16, label, ha="center", va="center", fontsize=9) if i < n - 1: ax.annotate("", xy=(x + box_w + 0.025, y + 0.16), xytext=(x + box_w + 0.005, y + 0.16), arrowprops={"arrowstyle": "->", "lw": 1.2}) ax.text(0.5, 0.88, title, ha="center", va="center", fontsize=11, fontweight="bold") savefig(path, paper_dir) def draw_pose_overlay(frame: np.ndarray, pose: np.ndarray, conf_thr: float = 0.2) -> np.ndarray: img = frame.copy() for a, b in SKELETON: if pose[a, 2] > conf_thr and pose[b, 2] > conf_thr: p1 = tuple(np.round(pose[a, :2]).astype(int)) p2 = tuple(np.round(pose[b, :2]).astype(int)) cv2.line(img, p1, p2, (255, 190, 0), 2, cv2.LINE_AA) for x, y, c in pose: if c > conf_thr: cv2.circle(img, (int(round(x)), int(round(y))), 3, (20, 220, 80), -1, cv2.LINE_AA) return img def make_contact_sheet(frames: np.ndarray, poses: np.ndarray, path: Path, title: str, paper_dir: Path) -> None: idx = np.linspace(0, len(frames) - 1, 8).round().astype(int) overlays = [draw_pose_overlay(frames[i], poses[i]) for i in idx] h, w = overlays[0].shape[:2] canvas = np.full((2 * h + 56, 4 * w, 3), 255, dtype=np.uint8) cv2.putText(canvas, title, (12, 28), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (25, 25, 25), 2, cv2.LINE_AA) for j, img in enumerate(overlays): r = j // 4 c = j % 4 y0 = 44 + r * h x0 = c * w canvas[y0 : y0 + h, x0 : x0 + w] = img path.parent.mkdir(parents=True, exist_ok=True) cv2.imwrite(str(path), cv2.cvtColor(canvas, cv2.COLOR_RGB2BGR)) if paper_dir is not None: paper_dir.mkdir(parents=True, exist_ok=True) shutil.copy2(path, paper_dir / path.name) def pose_aspect_score(pose: np.ndarray) -> float: best = 0.0 for t in range(pose.shape[0]): mask = pose[t, :, 2] > 0.2 if mask.sum() < 5: continue pts = pose[t, mask, :2] wh = pts.max(axis=0) - pts.min(axis=0) best = max(best, float(wh[0] / max(wh[1], 1.0))) return best def choose_pose_example(merged: pd.DataFrame, label: int) -> pd.Series: candidates = merged[merged["label"] == label].copy() scores = [] for _, row in candidates.iterrows(): pose = np.load(row["pose_path"]) quality = float((pose[..., 2] > 0.2).mean()) if label == 1: scores.append(pose_aspect_score(pose) * max(quality, 0.05)) else: scores.append(quality) candidates["example_score"] = scores return candidates.sort_values("example_score", ascending=False).iloc[0] def plot_pose_examples(pose_manifest: Path, rgb_manifest: Path, out_dir: Path, paper_dir: Path) -> None: pose_df = pd.read_csv(pose_manifest) rgb_df = pd.read_csv(rgb_manifest) merged = pose_df.merge(rgb_df[["video_id", "rgb_path"]], on="video_id", how="inner") for label, name in [(1, "fall"), (0, "adl")]: row = choose_pose_example(merged, label) frames = np.load(row["rgb_path"]) poses = np.load(row["pose_path"]) scale_x = frames.shape[2] / 640.0 scale_y = frames.shape[1] / 480.0 poses = poses.copy() poses[..., 0] *= scale_x poses[..., 1] *= scale_y make_contact_sheet(frames, poses, out_dir / f"pose_sequence_{name}.png", f"Pose overlay: {name.upper()} segment", paper_dir) def main() -> None: parser = argparse.ArgumentParser() parser.add_argument("--results", default="outputs/tables/mcfd_results.csv") parser.add_argument("--robustness", default="outputs/tables/mcfd_robustness_summary.csv") parser.add_argument("--pose-manifest", default="data/manifests/mcfd_pose_t32.csv") parser.add_argument("--rgb-manifest", default="data/manifests/mcfd_rgb_t32.csv") parser.add_argument("--out-dir", default="outputs/figures") parser.add_argument("--paper-dir", default="paper/figures") args = parser.parse_args() out_dir = Path(args.out_dir) paper_dir = Path(args.paper_dir) ensure_dirs(out_dir, paper_dir) plot_main_results(Path(args.results), out_dir, paper_dir) plot_ablation(Path(args.results), out_dir, paper_dir) plot_robustness(Path(args.robustness), out_dir, paper_dir) plot_confusion_matrices(Path(args.results), out_dir, paper_dir) draw_box_diagram( ["Input video", "Frame sampling", "Pose extraction", "Normalization", "Temporal model", "Fall prediction"], "Overall Fall Detection Pipeline", out_dir / "pipeline.png", paper_dir, ) draw_box_diagram( ["Keypoints", "Velocity", "TCN blocks", "Temporal attention", "Classifier"], "Proposed Pose-TCN-Attention Architecture", out_dir / "architecture.png", paper_dir, ) plot_pose_examples(Path(args.pose_manifest), Path(args.rgb_manifest), out_dir, paper_dir) print(f"Wrote figures to {out_dir} and {paper_dir}") if __name__ == "__main__": main()