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file mode 100644 index 00000000..5eaabd4a --- /dev/null +++ b/results/dataWideStyletransAnalysis/summary_stats.csv @@ -0,0 +1,22 @@ +metric,value +ssim_mean,0.651833403999501 +ssim_median,0.6861008916564711 +ssim_std,0.2686615881652098 +gssim_mean,0.17190229343723717 +gssim_median,0.09711912500465225 +gssim_std,0.16862767590698818 +feature_dice_mean,0.5271860693926539 +feature_dice_median,0.6155147148985062 +feature_dice_std,0.2953251142430547 +feature_distance_mean,0.47281393060734606 +feature_distance_median,0.3844852851014938 +feature_distance_std,0.29532511424305474 +avg_curve_diff_abs,0.19969361021365992 +num_validation_tars_matched,40.0 +num_samples_used,720.0 +num_samples_skipped, +num_ssim_rows,10800.0 +num_corr_rows,10080.0 +num_feature_rows,10800.0 +num_missing_original_tars,0.0 +num_missing_translated_tars,0.0 diff --git a/src/preEvaluation/dataWideStyletransAnalysis.py b/src/preEvaluation/dataWideStyletransAnalysis.py new file mode 100644 index 00000000..2c9788bf --- /dev/null +++ b/src/preEvaluation/dataWideStyletransAnalysis.py @@ -0,0 +1,974 @@ +#!/usr/bin/env python + +import io +import os +import tarfile +import argparse +import subprocess +import sys +from collections import defaultdict + +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +from PIL import Image +from scipy.ndimage import gaussian_filter, sobel + +try: + import cv2 +except Exception: + cv2 = None + +# Original data +original_data_path = "/scratch/phys/project/sin/AFM_Hartree_DB/AFM_sims/striped/Water-bilayer-FB/Water-bilayer_FB_Ref" +# Style translated data +# This one type of dataset with L1L2=(20, 1) +styletrans_data_path = "/scratch/phys/project/sin/AFM_Hartree_DB/AFM_sims/striped/Water-bilayer-FB/Water-bilayer_FB_PPAFM2Exp_CoAll_L20_L1_Elatest" + +output_dir = "../../results/dataWideStyletransAnalysis/L20_L1" + + +def build_validation_tar_list(): + """Return expected validation tar names Water-bilayer-K-{1..10}_val_{0..3}.tar.""" + tar_names = [] + for k in range(1, 11): + for v in range(4): + tar_names.append(f"Water-bilayer-K-{k}_val_{v}.tar") + return tar_names + + +def parse_slice_member_name(member_name): + """Parse 'sample.slice.png' and return (sample_id, slice_idx) or None.""" + base = os.path.basename(member_name) + parts = base.split(".") + if len(parts) != 3: + return None + if parts[2].lower() != "png": + return None + if not parts[0].isdigit() or not parts[1].isdigit(): + return None + return int(parts[0]), int(parts[1]) + + +def list_sample_slices_in_tar(tar_path): + """Return mapping sample_id -> {slice_idx: member_name} for a tar file.""" + sample_map = defaultdict(dict) + with tarfile.open(tar_path, "r") as tar: + for m in tar.getmembers(): + if not m.isfile(): + continue + parsed = parse_slice_member_name(m.name) + if parsed is None: + continue + sample_id, slice_idx = parsed + sample_map[sample_id][slice_idx] = m.name + return sample_map + + +def read_tar_grayscale_image(tar_obj, member_name): + """Read PNG member from tar to grayscale float32 array in [0, 1].""" + fobj = tar_obj.extractfile(member_name) + if fobj is None: + raise ValueError(f"Cannot extract member {member_name}") + raw = fobj.read() + arr = np.array(Image.open(io.BytesIO(raw)).convert("L"), dtype=np.float32) / 255.0 + return arr + + +def compute_ssim_2d(img1, img2, sigma=1.5, k1=0.01, k2=0.03): + """Compute SSIM between two 2D arrays using Gaussian local statistics.""" + if img1.shape != img2.shape: + raise ValueError("SSIM images must have the same shape") + + x = img1.astype(np.float64) + y = img2.astype(np.float64) + data_range = max(float(max(x.max(), y.max()) - min(x.min(), y.min())), 1e-8) + + c1 = (k1 * data_range) ** 2 + c2 = (k2 * data_range) ** 2 + + mu_x = gaussian_filter(x, sigma=sigma) + mu_y = gaussian_filter(y, sigma=sigma) + + mu_x2 = mu_x * mu_x + mu_y2 = mu_y * mu_y + mu_xy = mu_x * mu_y + + sigma_x2 = gaussian_filter(x * x, sigma=sigma) - mu_x2 + sigma_y2 = gaussian_filter(y * y, sigma=sigma) - mu_y2 + sigma_xy = gaussian_filter(x * y, sigma=sigma) - mu_xy + + numerator = (2.0 * mu_xy + c1) * (2.0 * sigma_xy + c2) + denominator = (mu_x2 + mu_y2 + c1) * (sigma_x2 + sigma_y2 + c2) + ssim_map = numerator / (denominator + 1e-12) + return float(np.mean(ssim_map)) + + +def compute_flat_correlation(img1, img2): + """Pearson correlation of flattened images; returns nan for degenerate arrays.""" + x = img1.ravel() + y = img2.ravel() + sx = np.std(x) + sy = np.std(y) + if sx < 1e-12 or sy < 1e-12: + return np.nan + return float(np.corrcoef(x, y)[0, 1]) + + +def to_u8_image(img): + x = img.astype(np.float64) + x = np.clip(x, 0.0, 1.0) + return np.round(255.0 * x).astype(np.uint8) + + +def enhance_for_dark(gray_u8, sigma): + background = cv2.GaussianBlur(gray_u8, (0, 0), sigmaX=float(sigma), sigmaY=float(sigma)) + return cv2.subtract(background, gray_u8) + + +def enhance_for_bright(gray_u8, sigma): + background = cv2.GaussianBlur(gray_u8, (0, 0), sigmaX=float(sigma), sigmaY=float(sigma)) + return cv2.subtract(gray_u8, background) + + +def build_blob_detector(min_area, max_area, min_circularity, min_inertia): + params = cv2.SimpleBlobDetector_Params() + params.minThreshold = 5 + params.maxThreshold = 220 + params.thresholdStep = 5 + params.minDistBetweenBlobs = 8 + params.filterByColor = True + params.blobColor = 255 + params.filterByArea = True + params.minArea = float(min_area) + params.maxArea = float(max_area) + params.filterByCircularity = True + params.minCircularity = float(min_circularity) + params.filterByInertia = True + params.minInertiaRatio = float(min_inertia) + params.filterByConvexity = False + return cv2.SimpleBlobDetector_create(params) + + +def keypoints_to_blob_dicts(keypoints, score_image, shape, border_margin): + h, w = shape + blobs = [] + for kp in keypoints: + x = float(kp.pt[0]) + y = float(kp.pt[1]) + radius = max(2.0, float(kp.size) * 0.5) + margin = max(int(border_margin), int(round(radius + 2))) + if x < margin or x > (w - margin) or y < margin or y > (h - margin): + continue + + yi = int(np.clip(round(y), 0, h - 1)) + xi = int(np.clip(round(x), 0, w - 1)) + score = float(score_image[yi, xi]) + blobs.append({"y": y, "x": x, "radius": radius, "score": score}) + + blobs.sort(key=lambda b: b["score"], reverse=True) + return blobs + + +def select_dark_holes(blobs, score_ratio=0.42, max_blobs=8): + if not blobs: + return [] + top = float(blobs[0]["score"]) + selected = [b for b in blobs if float(b["score"]) >= float(score_ratio) * top] + return selected[: int(max_blobs)] + + +def select_bright_blobs(blobs, score_ratio=0.55, max_blobs=3): + if not blobs: + return [] + top = float(blobs[0]["score"]) + selected = [b for b in blobs if float(b["score"]) >= float(score_ratio) * top] + return selected[: int(max_blobs)] + + +def blobs_to_mask(shape, blobs, radius_scale=1.8): + h, w = shape + yy, xx = np.ogrid[:h, :w] + mask = np.zeros((h, w), dtype=bool) + for b in blobs: + r = float(radius_scale) * float(b["radius"]) + disk = (yy - float(b["y"])) ** 2 + (xx - float(b["x"])) ** 2 <= (r * r) + mask |= disk + return mask + + +def detect_dark_bright_feature_masks(img, cfg, return_blobs=False): + if cv2 is None: + raise ImportError("OpenCV (cv2) is required for feature-method 'blob_dark_bright'.") + + gray_u8 = to_u8_image(img) + dark_enhanced = enhance_for_dark(gray_u8, sigma=cfg["dark_sigma"]) + bright_enhanced = enhance_for_bright(gray_u8, sigma=cfg["bright_sigma"]) + + dark_detector = build_blob_detector( + min_area=cfg["dark_min_area"], + max_area=cfg["dark_max_area"], + min_circularity=cfg["dark_min_circularity"], + min_inertia=cfg["dark_min_inertia"], + ) + bright_detector = build_blob_detector( + min_area=cfg["bright_min_area"], + max_area=cfg["bright_max_area"], + min_circularity=cfg["bright_min_circularity"], + min_inertia=cfg["bright_min_inertia"], + ) + + dark_candidates = keypoints_to_blob_dicts( + dark_detector.detect(dark_enhanced), + score_image=dark_enhanced, + shape=gray_u8.shape, + border_margin=cfg["dark_border_margin"], + ) + bright_candidates = keypoints_to_blob_dicts( + bright_detector.detect(bright_enhanced), + score_image=bright_enhanced, + shape=gray_u8.shape, + border_margin=cfg["bright_border_margin"], + ) + + dark_blobs = select_dark_holes( + dark_candidates, + score_ratio=cfg["dark_score_ratio"], + max_blobs=cfg["dark_max_blobs"], + ) + bright_blobs = select_bright_blobs( + bright_candidates, + score_ratio=cfg["bright_score_ratio"], + max_blobs=cfg["bright_max_blobs"], + ) + + dark_mask = blobs_to_mask(gray_u8.shape, dark_blobs, radius_scale=cfg["dark_radius_scale"]) + bright_mask = blobs_to_mask(gray_u8.shape, bright_blobs, radius_scale=cfg["bright_radius_scale"]) + combined = np.logical_or(dark_mask, bright_mask) + if return_blobs: + return dark_mask, bright_mask, combined, dark_blobs, bright_blobs + return dark_mask, bright_mask, combined + + +def compute_dice_distance_from_masks(mx, my): + den = float(mx.sum() + my.sum()) + if den <= 0.0: + return np.nan, np.nan + inter = float(np.logical_and(mx, my).sum()) + dice = (2.0 * inter) / (den + 1e-12) + distance = 1.0 - dice + return float(dice), float(distance) + + +def compute_feature_mask_metrics(img1, img2, top_percent=5.0, use_gradient=True, method="legacy_percentile", blob_cfg=None): + """Compute feature-location metrics using top-percentile masks. + + Returns (dice_overlap, feature_distance), where feature_distance = 1 - dice_overlap. + """ + if img1.shape != img2.shape: + raise ValueError("Feature-mask images must have the same shape") + + if method == "blob_dark_bright": + if blob_cfg is None: + raise ValueError("blob_cfg is required for method='blob_dark_bright'") + _, _, mx = detect_dark_bright_feature_masks(img1, blob_cfg) + _, _, my = detect_dark_bright_feature_masks(img2, blob_cfg) + return compute_dice_distance_from_masks(mx, my) + + if method != "legacy_percentile": + raise ValueError(f"Unsupported feature method: {method}") + + x = img1.astype(np.float64) + y = img2.astype(np.float64) + + if use_gradient: + gx1 = sobel(x, axis=1, mode="reflect") + gy1 = sobel(x, axis=0, mode="reflect") + gx2 = sobel(y, axis=1, mode="reflect") + gy2 = sobel(y, axis=0, mode="reflect") + x = np.sqrt(gx1 * gx1 + gy1 * gy1) + y = np.sqrt(gx2 * gx2 + gy2 * gy2) + + top_percent = float(np.clip(top_percent, 0.01, 100.0)) + cut_percentile = 100.0 - top_percent + + tx = np.percentile(x, cut_percentile) + ty = np.percentile(y, cut_percentile) + + mx = x >= tx + my = y >= ty + + den = float(mx.sum() + my.sum()) + if den <= 0.0: + return np.nan, np.nan + + inter = float(np.logical_and(mx, my).sum()) + dice = (2.0 * inter) / (den + 1e-12) + distance = 1.0 - dice + return float(dice), float(distance) + + +def sem(values): + valid = np.asarray(values, dtype=float) + valid = valid[np.isfinite(valid)] + if valid.size <= 1: + return np.nan + return float(np.std(valid, ddof=1) / np.sqrt(valid.size)) + + +def create_sanity_plot(ssim_df, corr_df, output_path, max_samples=2): + sample_keys = ( + ssim_df[["tar_file", "sample_id"]] + .drop_duplicates() + .sort_values(["tar_file", "sample_id"]) + .head(max_samples) + ) + n_samples = len(sample_keys) + if n_samples == 0: + return + + fig, axes = plt.subplots(n_samples, 2, figsize=(12, 4 * n_samples), squeeze=False) + for row_idx, (_, row) in enumerate(sample_keys.iterrows()): + tar_name = row["tar_file"] + sample_id = int(row["sample_id"]) + + sub_ssim = ssim_df[(ssim_df["tar_file"] == tar_name) & (ssim_df["sample_id"] == sample_id)].sort_values("slice_idx") + sub_corr = corr_df[(corr_df["tar_file"] == tar_name) & (corr_df["sample_id"] == sample_id)].sort_values("pair_idx") + + ax_left = axes[row_idx, 0] + ax_left.plot(sub_ssim["slice_idx"], sub_ssim["ssim"], marker="o", linewidth=1.5) + ax_left.set_ylim(0.0, 1.05) + ax_left.set_xlabel("Slice index") + ax_left.set_ylabel("SSIM") + ax_left.set_title(f"{tar_name} | sample {sample_id}: slice SSIM") + ax_left.grid(alpha=0.2) + + ax_right = axes[row_idx, 1] + ax_right.plot(sub_corr["pair_idx"], sub_corr["corr_original"], marker="o", linewidth=1.5, label="Original") + ax_right.plot(sub_corr["pair_idx"], sub_corr["corr_translated"], marker="o", linewidth=1.5, label="Translated") + ax_right.set_xlabel("Adjacent pair index (i: i vs i+1)") + ax_right.set_ylabel("Correlation") + ax_right.set_ylim(-1.05, 1.05) + ax_right.set_title(f"{tar_name} | sample {sample_id}: adjacent-slice corr") + ax_right.legend(loc="best", frameon=False) + ax_right.grid(alpha=0.2) + + plt.tight_layout() + plt.savefig(output_path, dpi=300) + plt.close(fig) + + +def main(): + parser = argparse.ArgumentParser(description="Dataset-wide content-preservation analysis for AFM CycleGAN outputs") + parser.add_argument( + "--styletrans-data-path", + default=styletrans_data_path, + help="Path to translated data root for one dataset", + ) + parser.add_argument( + "--output-dir", + default=output_dir, + help="Output directory for one dataset run", + ) + parser.add_argument( + "--compare-l1-values", + default="", + help="Comma-separated L1 values (for example: 10,20,30,40,50) to run batch comparisons", + ) + parser.add_argument( + "--l2-value", + type=int, + default=1, + help="L2 value used with --compare-l1-values (default: 1)", + ) + parser.add_argument( + "--styletrans-path-template", + default="/scratch/phys/project/sin/AFM_Hartree_DB/AFM_sims/striped/Water-bilayer-FB/Water-bilayer_FB_PPAFM2Exp_CoAll_L{l1}_L{l2}_Elatest", + help="Template for translated dataset paths. Uses placeholders {l1} and {l2}", + ) + parser.add_argument( + "--output-root", + default=output_dir, + help="Root output directory for batch compare mode", + ) + parser.add_argument( + "--recompute", + action="store_true", + help="Force recomputation from tar files even if cached CSVs already exist", + ) + parser.add_argument( + "--feature-top-percent", + type=float, + default=5.0, + help="Top percentile of strongest feature pixels used for mask-based feature metrics", + ) + parser.add_argument( + "--feature-no-gradient", + action="store_true", + help="Disable gradient preprocessing before legacy-percentile feature-mask computation", + ) + parser.add_argument( + "--feature-method", + default="blob_dark_bright", + choices=["legacy_percentile", "blob_dark_bright"], + help="Feature map method used to compute feature Dice", + ) + parser.add_argument("--feature-dark-sigma", type=float, default=13.0, help="Gaussian sigma for dark enhancement") + parser.add_argument("--feature-bright-sigma", type=float, default=8.0, help="Gaussian sigma for bright enhancement") + parser.add_argument("--feature-dark-min-area", type=float, default=35.0, help="Minimum area for dark-hole detector") + parser.add_argument("--feature-dark-max-area", type=float, default=1500.0, help="Maximum area for dark-hole detector") + parser.add_argument("--feature-dark-min-circularity", type=float, default=0.24, help="Minimum circularity for dark-hole detector") + parser.add_argument("--feature-dark-min-inertia", type=float, default=0.06, help="Minimum inertia ratio for dark-hole detector") + parser.add_argument("--feature-bright-min-area", type=float, default=15.0, help="Minimum area for bright-spot detector") + parser.add_argument("--feature-bright-max-area", type=float, default=2000.0, help="Maximum area for bright-spot detector") + parser.add_argument("--feature-bright-min-circularity", type=float, default=0.05, help="Minimum circularity for bright-spot detector") + parser.add_argument("--feature-bright-min-inertia", type=float, default=0.01, help="Minimum inertia ratio for bright-spot detector") + parser.add_argument("--feature-dark-border-margin", type=int, default=14, help="Border margin for dark-hole blob candidates") + parser.add_argument("--feature-bright-border-margin", type=int, default=18, help="Border margin for bright-spot blob candidates") + parser.add_argument("--feature-dark-score-ratio", type=float, default=0.42, help="Keep dark blobs with score >= ratio * top dark score") + parser.add_argument("--feature-bright-score-ratio", type=float, default=0.30, help="Keep bright blobs with score >= ratio * top bright score") + parser.add_argument("--feature-dark-max-blobs", type=int, default=8, help="Maximum number of selected dark blobs") + parser.add_argument("--feature-bright-max-blobs", type=int, default=8, help="Maximum number of selected bright blobs") + parser.add_argument("--feature-dark-radius-scale", type=float, default=1.8, help="Radius scale used when rasterizing dark blobs") + parser.add_argument("--feature-bright-radius-scale", type=float, default=2.0, help="Radius scale used when rasterizing bright blobs") + args = parser.parse_args() + + blob_cfg = { + "dark_sigma": args.feature_dark_sigma, + "bright_sigma": args.feature_bright_sigma, + "dark_min_area": args.feature_dark_min_area, + "dark_max_area": args.feature_dark_max_area, + "dark_min_circularity": args.feature_dark_min_circularity, + "dark_min_inertia": args.feature_dark_min_inertia, + "bright_min_area": args.feature_bright_min_area, + "bright_max_area": args.feature_bright_max_area, + "bright_min_circularity": args.feature_bright_min_circularity, + "bright_min_inertia": args.feature_bright_min_inertia, + "dark_border_margin": args.feature_dark_border_margin, + "bright_border_margin": args.feature_bright_border_margin, + "dark_score_ratio": args.feature_dark_score_ratio, + "bright_score_ratio": args.feature_bright_score_ratio, + "dark_max_blobs": args.feature_dark_max_blobs, + "bright_max_blobs": args.feature_bright_max_blobs, + "dark_radius_scale": args.feature_dark_radius_scale, + "bright_radius_scale": args.feature_bright_radius_scale, + } + + if args.compare_l1_values.strip(): + l1_values = [int(v.strip()) for v in args.compare_l1_values.split(",") if v.strip()] + if not l1_values: + raise ValueError("--compare-l1-values was provided but no valid integers were parsed") + + os.makedirs(args.output_root, exist_ok=True) + print(f"Batch compare mode over L1 values: {l1_values} with L2={args.l2_value}", flush=True) + for l1 in l1_values: + style_path = args.styletrans_path_template.format(l1=l1, l2=args.l2_value) + sub_out_dir = os.path.join(args.output_root, f"L{l1}_L{args.l2_value}") + cmd = [ + sys.executable, + os.path.abspath(__file__), + "--styletrans-data-path", + style_path, + "--output-dir", + sub_out_dir, + "--feature-top-percent", + str(args.feature_top_percent), + "--feature-method", + args.feature_method, + "--feature-dark-sigma", + str(args.feature_dark_sigma), + "--feature-bright-sigma", + str(args.feature_bright_sigma), + "--feature-dark-min-area", + str(args.feature_dark_min_area), + "--feature-dark-max-area", + str(args.feature_dark_max_area), + "--feature-dark-min-circularity", + str(args.feature_dark_min_circularity), + "--feature-dark-min-inertia", + str(args.feature_dark_min_inertia), + "--feature-bright-min-area", + str(args.feature_bright_min_area), + "--feature-bright-max-area", + str(args.feature_bright_max_area), + "--feature-bright-min-circularity", + str(args.feature_bright_min_circularity), + "--feature-bright-min-inertia", + str(args.feature_bright_min_inertia), + "--feature-dark-border-margin", + str(args.feature_dark_border_margin), + "--feature-bright-border-margin", + str(args.feature_bright_border_margin), + "--feature-dark-score-ratio", + str(args.feature_dark_score_ratio), + "--feature-bright-score-ratio", + str(args.feature_bright_score_ratio), + "--feature-dark-max-blobs", + str(args.feature_dark_max_blobs), + "--feature-bright-max-blobs", + str(args.feature_bright_max_blobs), + "--feature-dark-radius-scale", + str(args.feature_dark_radius_scale), + "--feature-bright-radius-scale", + str(args.feature_bright_radius_scale), + ] + if args.recompute: + cmd.append("--recompute") + if args.feature_no_gradient: + cmd.append("--feature-no-gradient") + + print(f"\n=== Running dataset L1={l1}, L2={args.l2_value} ===", flush=True) + print(f"Translated root: {style_path}", flush=True) + print(f"Output dir: {sub_out_dir}", flush=True) + subprocess.run(cmd, check=True) + + print("\nBatch compare completed.", flush=True) + return + + run_styletrans_data_path = args.styletrans_data_path + run_output_dir = args.output_dir + + os.makedirs(run_output_dir, exist_ok=True) + ssim_csv_path = os.path.join(run_output_dir, "ssim_values.csv") + feature_csv_path = os.path.join(run_output_dir, "feature_mask_metrics.csv") + corr_csv_path = os.path.join(run_output_dir, "adjacent_slice_correlations.csv") + blob_csv_path = os.path.join(run_output_dir, "blob_features.csv") + + expected_tars = build_validation_tar_list() + matched_tars = [] + missing_original = [] + missing_translated = [] + + for tar_name in expected_tars: + o_path = os.path.join(original_data_path, tar_name) + t_path = os.path.join(run_styletrans_data_path, tar_name) + has_o = os.path.isfile(o_path) + has_t = os.path.isfile(t_path) + if has_o and has_t: + matched_tars.append(tar_name) + elif not has_o: + missing_original.append(tar_name) + elif not has_t: + missing_translated.append(tar_name) + + print(f"Expected validation tars: {len(expected_tars)}", flush=True) + print(f"Matched validation tars: {len(matched_tars)}", flush=True) + if missing_original: + print(f"Missing in original domain: {len(missing_original)}", flush=True) + if missing_translated: + print(f"Missing in translated domain: {len(missing_translated)}", flush=True) + + if (not args.recompute) and os.path.isfile(ssim_csv_path) and os.path.isfile(feature_csv_path) and os.path.isfile(corr_csv_path): + print("Using cached metric CSVs (skip tar processing).", flush=True) + ssim_df = pd.read_csv(ssim_csv_path) + feature_df = pd.read_csv(feature_csv_path) + corr_df = pd.read_csv(corr_csv_path) + blob_df = pd.read_csv(blob_csv_path) if os.path.isfile(blob_csv_path) else pd.DataFrame() + n_samples_used = int(ssim_df[["tar_file", "sample_id"]].drop_duplicates().shape[0]) if len(ssim_df) > 0 else 0 + n_samples_skipped = np.nan + else: + ssim_rows = [] + feature_rows = [] + corr_rows = [] + blob_rows = [] + n_samples_used = 0 + n_samples_skipped = 0 + + for tar_idx, tar_name in enumerate(matched_tars, start=1): + print(f"[{tar_idx}/{len(matched_tars)}] Processing {tar_name}", flush=True) + o_tar_path = os.path.join(original_data_path, tar_name) + t_tar_path = os.path.join(run_styletrans_data_path, tar_name) + + o_map = list_sample_slices_in_tar(o_tar_path) + t_map = list_sample_slices_in_tar(t_tar_path) + + common_samples = sorted(set(o_map.keys()).intersection(set(t_map.keys()))) + with tarfile.open(o_tar_path, "r") as o_tar, tarfile.open(t_tar_path, "r") as t_tar: + for sample_id in common_samples: + o_slices = o_map[sample_id] + t_slices = t_map[sample_id] + common_slice_ids = sorted(set(o_slices.keys()).intersection(set(t_slices.keys()))) + + if len(common_slice_ids) < 2: + n_samples_skipped += 1 + continue + + o_imgs = [] + t_imgs = [] + valid_slice_ids = [] + + for slice_idx in common_slice_ids: + try: + o_img = read_tar_grayscale_image(o_tar, o_slices[slice_idx]) + t_img = read_tar_grayscale_image(t_tar, t_slices[slice_idx]) + except Exception: + continue + + if o_img.shape != t_img.shape: + continue + + ssim_val = compute_ssim_2d(o_img, t_img) + if args.feature_method == "blob_dark_bright": + _, _, o_mask, o_dark_blobs, o_bright_blobs = detect_dark_bright_feature_masks( + o_img, + blob_cfg, + return_blobs=True, + ) + _, _, t_mask, t_dark_blobs, t_bright_blobs = detect_dark_bright_feature_masks( + t_img, + blob_cfg, + return_blobs=True, + ) + feature_dice, feature_distance = compute_dice_distance_from_masks(o_mask, t_mask) + + for domain, dark_blobs, bright_blobs in [ + ("original", o_dark_blobs, o_bright_blobs), + ("translated", t_dark_blobs, t_bright_blobs), + ]: + for rank, b in enumerate(dark_blobs): + blob_rows.append( + { + "tar_file": tar_name, + "sample_id": sample_id, + "slice_idx": slice_idx, + "domain": domain, + "blob_type": "dark", + "rank": rank, + "y": float(b["y"]), + "x": float(b["x"]), + "radius": float(b["radius"]), + "radius_draw": float(blob_cfg["dark_radius_scale"]) * float(b["radius"]), + "score": float(b["score"]), + } + ) + for rank, b in enumerate(bright_blobs): + blob_rows.append( + { + "tar_file": tar_name, + "sample_id": sample_id, + "slice_idx": slice_idx, + "domain": domain, + "blob_type": "bright", + "rank": rank, + "y": float(b["y"]), + "x": float(b["x"]), + "radius": float(b["radius"]), + "radius_draw": float(blob_cfg["bright_radius_scale"]) * float(b["radius"]), + "score": float(b["score"]), + } + ) + else: + feature_dice, feature_distance = compute_feature_mask_metrics( + o_img, + t_img, + top_percent=args.feature_top_percent, + use_gradient=(not args.feature_no_gradient), + method=args.feature_method, + blob_cfg=blob_cfg, + ) + ssim_rows.append( + { + "tar_file": tar_name, + "sample_id": sample_id, + "slice_idx": slice_idx, + "ssim": ssim_val, + } + ) + feature_rows.append( + { + "tar_file": tar_name, + "sample_id": sample_id, + "slice_idx": slice_idx, + "feature_dice": feature_dice, + "feature_distance": feature_distance, + } + ) + o_imgs.append(o_img) + t_imgs.append(t_img) + valid_slice_ids.append(slice_idx) + + if len(valid_slice_ids) < 2: + n_samples_skipped += 1 + continue + + for i in range(len(valid_slice_ids) - 1): + corr_o = compute_flat_correlation(o_imgs[i], o_imgs[i + 1]) + corr_t = compute_flat_correlation(t_imgs[i], t_imgs[i + 1]) + corr_rows.append( + { + "tar_file": tar_name, + "sample_id": sample_id, + "pair_idx": i, + "slice_idx_i": valid_slice_ids[i], + "slice_idx_ip1": valid_slice_ids[i + 1], + "corr_original": corr_o, + "corr_translated": corr_t, + "diff_signed": corr_t - corr_o, + "diff_abs": abs(corr_t - corr_o), + } + ) + + n_samples_used += 1 + + print( + f"[{tar_idx}/{len(matched_tars)}] Done {tar_name}: cumulative samples used={n_samples_used}, skipped={n_samples_skipped}", + flush=True, + ) + + ssim_df = pd.DataFrame(ssim_rows) + feature_df = pd.DataFrame(feature_rows) + corr_df = pd.DataFrame(corr_rows) + blob_df = pd.DataFrame(blob_rows) + ssim_df.to_csv(ssim_csv_path, index=False) + feature_df.to_csv(feature_csv_path, index=False) + corr_df.to_csv(corr_csv_path, index=False) + if args.feature_method == "blob_dark_bright": + blob_df.to_csv(blob_csv_path, index=False) + + if len(ssim_df) > 0: + ssim_values = ssim_df["ssim"].to_numpy(dtype=float) + ssim_mean = float(np.nanmean(ssim_values)) + ssim_median = float(np.nanmedian(ssim_values)) + ssim_std = float(np.nanstd(ssim_values, ddof=0)) + else: + ssim_mean = np.nan + ssim_median = np.nan + ssim_std = np.nan + + if len(feature_df) > 0: + feature_dice_values = feature_df["feature_dice"].to_numpy(dtype=float) + feature_distance_values = feature_df["feature_distance"].to_numpy(dtype=float) + feature_dice_mean = float(np.nanmean(feature_dice_values)) + feature_dice_median = float(np.nanmedian(feature_dice_values)) + feature_dice_std = float(np.nanstd(feature_dice_values, ddof=0)) + feature_distance_mean = float(np.nanmean(feature_distance_values)) + feature_distance_median = float(np.nanmedian(feature_distance_values)) + feature_distance_std = float(np.nanstd(feature_distance_values, ddof=0)) + else: + feature_dice_mean = np.nan + feature_dice_median = np.nan + feature_dice_std = np.nan + feature_distance_mean = np.nan + feature_distance_median = np.nan + feature_distance_std = np.nan + + if len(corr_df) > 0: + avg_curve_diff_abs = float(np.nanmean(corr_df["diff_abs"].to_numpy(dtype=float))) + else: + avg_curve_diff_abs = np.nan + + summary_df = pd.DataFrame( + [ + { + "metric": "ssim_mean", + "value": ssim_mean, + }, + { + "metric": "ssim_median", + "value": ssim_median, + }, + { + "metric": "ssim_std", + "value": ssim_std, + }, + { + "metric": "feature_dice_mean", + "value": feature_dice_mean, + }, + { + "metric": "feature_dice_median", + "value": feature_dice_median, + }, + { + "metric": "feature_dice_std", + "value": feature_dice_std, + }, + { + "metric": "feature_distance_mean", + "value": feature_distance_mean, + }, + { + "metric": "feature_distance_median", + "value": feature_distance_median, + }, + { + "metric": "feature_distance_std", + "value": feature_distance_std, + }, + { + "metric": "avg_curve_diff_abs", + "value": avg_curve_diff_abs, + }, + { + "metric": "num_validation_tars_matched", + "value": len(matched_tars), + }, + { + "metric": "num_samples_used", + "value": n_samples_used, + }, + { + "metric": "num_samples_skipped", + "value": n_samples_skipped, + }, + { + "metric": "num_ssim_rows", + "value": len(ssim_df), + }, + { + "metric": "num_corr_rows", + "value": len(corr_df), + }, + { + "metric": "num_feature_rows", + "value": len(feature_df), + }, + { + "metric": "num_missing_original_tars", + "value": len(missing_original), + }, + { + "metric": "num_missing_translated_tars", + "value": len(missing_translated), + }, + ] + ) + summary_path = os.path.join(run_output_dir, "summary_stats.csv") + summary_df.to_csv(summary_path, index=False) + + # Match styling with sanity panels c/d. + simcolor = "#ed9d2c" + dftcolor = "#2ca3cf" + expcolor = "#de461c" + ssim_color = "#4a4a4a" + + fig = plt.figure(figsize=(8.5, 4.0)) + gs = fig.add_gridspec(1, 2, width_ratios=[1.0, 1.0], wspace=0.28) + ax_dist = fig.add_subplot(gs[0, 0]) + ax1 = fig.add_subplot(gs[0, 1]) + + if len(ssim_df) > 0 or len(feature_df) > 0: + bins = np.linspace(0.0, 1.0, 41) + if len(ssim_df) > 0: + ax_dist.hist( + ssim_df["ssim"].to_numpy(dtype=float), + bins=bins, + histtype="step", + linewidth=2.0, + color=ssim_color, + label="SSIM", + ) + if len(feature_df) > 0: + ax_dist.hist( + feature_df["feature_dice"].to_numpy(dtype=float), + bins=bins, + histtype="step", + linewidth=2.0, + color=dftcolor, + label="Feature Dice", + ) + + ax_dist.set_xlabel("Similarity") + ax_dist.set_ylabel("Count") + ax_dist.legend(loc="best", frameon=False, ncol=2) + ax_dist.tick_params(axis="both", which="both", direction="in", top=True, right=True) + + if len(corr_df) > 0: + grouped = corr_df.groupby("pair_idx", as_index=False).agg( + mean_original=("corr_original", "mean"), + mean_translated=("corr_translated", "mean"), + sem_original=("corr_original", sem), + sem_translated=("corr_translated", sem), + ) + x = grouped["pair_idx"].to_numpy(dtype=float) + y_o = grouped["mean_original"].to_numpy(dtype=float) + y_t = grouped["mean_translated"].to_numpy(dtype=float) + e_o = grouped["sem_original"].to_numpy(dtype=float) + e_t = grouped["sem_translated"].to_numpy(dtype=float) + + ax1.plot( + x, + y_o, + marker="o", + linewidth=1.6, + label="Original", + color=simcolor, + markerfacecolor="none", + markeredgecolor=simcolor, + ) + ax1.errorbar( + x, + y_o, + yerr=e_o, + fmt="none", + ecolor=simcolor, + elinewidth=1.4, + capsize=3, + alpha=1.0, + zorder=4, + ) + ax1.fill_between(x, y_o - e_o, y_o + e_o, alpha=0.15, color=simcolor) + + ax1.plot( + x, + y_t, + marker="p", + linewidth=1.6, + label="Translated", + color=expcolor, + markerfacecolor="none", + markeredgecolor=expcolor, + ) + ax1.errorbar( + x, + y_t, + yerr=e_t, + fmt="none", + ecolor=expcolor, + elinewidth=1.4, + capsize=3, + alpha=1.0, + zorder=4, + ) + ax1.fill_between(x, y_t - e_t, y_t + e_t, alpha=0.15, color=expcolor) + ax1.set_xlabel("Adjacent pair index (i: i vs i+1)") + ax1.set_ylabel("Correlation") + ax1.set_ylim(-1.05, 1.05) + ax1.legend(loc="best", frameon=False, ncol=2) + ax1.grid(alpha=0.2) + ax1.tick_params(axis="both", which="both", direction="in", top=True, right=True) + + ax_dist.text(-0.15, 1.03, "a", transform=ax_dist.transAxes, fontsize=16, ha="left", va="top") + ax1.text(-0.15, 1.03, "b", transform=ax1.transAxes, fontsize=16, ha="left", va="top") + + fig.subplots_adjust(left=0.08, right=0.995, top=0.97, bottom=0.16, wspace=0.28) + plt.savefig(os.path.join(run_output_dir, "content_preservation.png"), dpi=300) + plt.savefig(os.path.join(run_output_dir, "content_preservation.svg")) + plt.savefig(os.path.join(run_output_dir, "content_preservation.pdf")) + plt.close(fig) + + print(f"Matched validation tars: {len(matched_tars)}") + print(f"Missing original tars: {len(missing_original)}") + print(f"Missing translated tars: {len(missing_translated)}") + print(f"Samples used: {n_samples_used}") + print(f"Samples skipped: {n_samples_skipped}") + print(f"SSIM mean/median/std: {ssim_mean:.6f}, {ssim_median:.6f}, {ssim_std:.6f}") + print( + f"Feature Dice mean/median/std: {feature_dice_mean:.6f}, {feature_dice_median:.6f}, {feature_dice_std:.6f}" + ) + print( + f"Feature distance mean/median/std: {feature_distance_mean:.6f}, {feature_distance_median:.6f}, {feature_distance_std:.6f}" + ) + print(f"Average abs curve difference: {avg_curve_diff_abs:.6f}") + print(f"Wrote: {ssim_csv_path}") + print(f"Wrote: {feature_csv_path}") + print(f"Wrote: {corr_csv_path}") + if args.feature_method == "blob_dark_bright" and os.path.isfile(blob_csv_path): + print(f"Wrote: {blob_csv_path}") + print(f"Wrote: {summary_path}") + print(f"Wrote: {os.path.join(run_output_dir, 'content_preservation.png')}") + + +if __name__ == "__main__": + main() + diff --git a/src/preEvaluation/makeCombinedSanityFigures.py b/src/preEvaluation/makeCombinedSanityFigures.py new file mode 100644 index 00000000..d9545e7c --- /dev/null +++ b/src/preEvaluation/makeCombinedSanityFigures.py @@ -0,0 +1,884 @@ +#!/usr/bin/env python + +# Run it +# python makeCombinedSanityFigures.py --analysis-dir /scratch/phys/sin/Jie/Github/StyleTransAugment/results/dataWideStyletransAnalysis/L20_L1 --feature-figure --feature-method blob_dark_bright --sample-ids 0,1,2,3,4,5,6,7 --name-prefix sample_sanity_check_set + +import argparse +import io +import os +import re +import tarfile +from pathlib import Path + +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +from PIL import Image +from scipy.ndimage import sobel + +try: + import cv2 +except Exception: + cv2 = None + +DEFAULT_ORIGINAL_ROOT = "/scratch/phys/project/sin/AFM_Hartree_DB/AFM_sims/striped/Water-bilayer-FB/Water-bilayer_FB_Ref" +DEFAULT_TRANSLATED_ROOT = "/scratch/phys/project/sin/AFM_Hartree_DB/AFM_sims/striped/Water-bilayer-FB/Water-bilayer_FB_PPAFM2Exp_CoAll_L20_L1_Elatest" +DEFAULT_ANALYSIS_DIR = "results/dataWideStyletransAnalysis" +DEFAULT_TAR_NAME = "Water-bilayer-K-1_val_0.tar" +SLICE_MEMBER_PATTERN = re.compile(r"^(\d+)\.(\d+)\.png$") +AFM_CMAP = "inferno" + +TEXT_BBOX = { + "facecolor": "white", + "edgecolor": "none", + "alpha": 0.5, + "pad": 0.3, +} + +simcolor = '#ed9d2c' +dftcolor = '#2ca3cf' +expcolor = '#de461c' +bg07color = '#479FB1' +bv17color = '#6E7CBC' + + +def hex_to_rgb(hex_color): + h = hex_color.lstrip("#") + if len(h) != 6: + raise ValueError(f"Invalid hex color: {hex_color}") + r = int(h[0:2], 16) + g = int(h[2:4], 16) + b = int(h[4:6], 16) + return (r, g, b) + + +DFT_BLOB_RGB = hex_to_rgb(dftcolor) + +def parse_int_list(raw_text): + values = [] + for part in raw_text.split(","): + part = part.strip() + if part: + values.append(int(part)) + return values + + +def discover_sample_ids(tar_obj): + sample_ids = set() + for member in tar_obj.getmembers(): + if not member.isfile(): + continue + base = os.path.basename(member.name) + m = SLICE_MEMBER_PATTERN.match(base) + if m is None: + continue + sample_ids.add(int(m.group(1))) + return sorted(sample_ids) + + +def read_slice_from_tar(tar_obj, sample_id, slice_idx): + member_name = f"{sample_id}.{slice_idx:02d}.png" + member_file = tar_obj.extractfile(member_name) + if member_file is None: + raise FileNotFoundError(member_name) + return np.array(Image.open(io.BytesIO(member_file.read())).convert("L"), dtype=np.float32) + + +def get_sample_curves(ssim_df, corr_df, tar_name, sample_id): + ssim_sub = ssim_df[(ssim_df["tar_file"] == tar_name) & (ssim_df["sample_id"] == sample_id)].copy() + corr_sub = corr_df[(corr_df["tar_file"] == tar_name) & (corr_df["sample_id"] == sample_id)].copy() + return ssim_sub.sort_values("slice_idx"), corr_sub.sort_values("pair_idx") + + +def get_sample_metric_curve(metric_df, tar_name, sample_id, metric_col): + metric_sub = metric_df[(metric_df["tar_file"] == tar_name) & (metric_df["sample_id"] == sample_id)].copy() + cols = ["slice_idx", metric_col] + return metric_sub[cols].sort_values("slice_idx") + + +def gradient_preprocess_image(img): + gx = sobel(img.astype(np.float64), axis=1, mode="reflect") + gy = sobel(img.astype(np.float64), axis=0, mode="reflect") + grad = np.sqrt(gx * gx + gy * gy) + return grad / (float(np.max(grad)) + 1e-12) + + +def feature_map_image(img, top_percent=5.0, use_gradient=True): + x = img.astype(np.float64) + if use_gradient: + gx = sobel(x, axis=1, mode="reflect") + gy = sobel(x, axis=0, mode="reflect") + x = np.sqrt(gx * gx + gy * gy) + + top_percent = float(np.clip(top_percent, 0.01, 100.0)) + threshold = np.percentile(x, 100.0 - top_percent) + mask = (x >= threshold).astype(np.float32) + return mask + + +def to_u8_image(img): + x = img.astype(np.float64) + x = np.clip(x, 0.0, 255.0 if x.max() > 1.0 else 1.0) + if x.max() <= 1.0: + x = x * 255.0 + return np.round(x).astype(np.uint8) + + +def build_blob_detector(min_area, max_area, min_circularity, min_inertia): + params = cv2.SimpleBlobDetector_Params() + params.minThreshold = 5 + params.maxThreshold = 220 + params.thresholdStep = 5 + params.minDistBetweenBlobs = 8 + params.filterByColor = True + params.blobColor = 255 + params.filterByArea = True + params.minArea = float(min_area) + params.maxArea = float(max_area) + params.filterByCircularity = True + params.minCircularity = float(min_circularity) + params.filterByInertia = True + params.minInertiaRatio = float(min_inertia) + params.filterByConvexity = False + return cv2.SimpleBlobDetector_create(params) + + +def keypoints_to_blob_dicts(keypoints, score_image, shape, border_margin): + h, w = shape + blobs = [] + for kp in keypoints: + x = float(kp.pt[0]) + y = float(kp.pt[1]) + radius = max(2.0, float(kp.size) * 0.5) + margin = max(int(border_margin), int(round(radius + 2))) + if x < margin or x > (w - margin) or y < margin or y > (h - margin): + continue + yi = int(np.clip(round(y), 0, h - 1)) + xi = int(np.clip(round(x), 0, w - 1)) + score = float(score_image[yi, xi]) + blobs.append({"y": y, "x": x, "radius": radius, "score": score}) + blobs.sort(key=lambda b: b["score"], reverse=True) + return blobs + + +def select_dark_holes(blobs, score_ratio=0.42, max_blobs=8): + if not blobs: + return [] + top = float(blobs[0]["score"]) + selected = [b for b in blobs if float(b["score"]) >= float(score_ratio) * top] + return selected[: int(max_blobs)] + + +def select_bright_blobs(blobs, score_ratio=0.55, max_blobs=3): + if not blobs: + return [] + top = float(blobs[0]["score"]) + selected = [b for b in blobs if float(b["score"]) >= float(score_ratio) * top] + return selected[: int(max_blobs)] + + +def blobs_to_mask(shape, blobs, radius_scale=1.8): + h, w = shape + yy, xx = np.ogrid[:h, :w] + mask = np.zeros((h, w), dtype=bool) + for b in blobs: + r = float(radius_scale) * float(b["radius"]) + disk = (yy - float(b["y"])) ** 2 + (xx - float(b["x"])) ** 2 <= (r * r) + mask |= disk + return mask + + +def draw_blob_circle(rgb, center, radius, color, thickness=2, dotted=False): + if not dotted: + cv2.circle(rgb, center, max(1, int(radius)), color, thickness, lineType=cv2.LINE_8) + return + + # Draw dotted style as short arc segments around the circle. + r = max(1, int(radius)) + for start in range(0, 360, 24): + end = start + 12 + cv2.ellipse( + rgb, + center, + (r, r), + 0, + start, + end, + color, + thickness, + lineType=cv2.LINE_8, + ) + + +def make_blob_annotated_rgb(gray_u8, dark_blobs, bright_blobs, dark_radius_scale=1.8, bright_radius_scale=1.8): + rgb = np.repeat(gray_u8[..., None], 3, axis=2).astype(np.uint8) + for b in dark_blobs: + r = int(round(float(dark_radius_scale) * float(b["radius"]))) + c = (int(round(float(b["x"]))), int(round(float(b["y"])))) + draw_blob_circle(rgb, c, r, DFT_BLOB_RGB, thickness=3, dotted=False) + for b in bright_blobs: + r = int(round(float(bright_radius_scale) * float(b["radius"]))) + c = (int(round(float(b["x"]))), int(round(float(b["y"])))) + draw_blob_circle(rgb, c, r, DFT_BLOB_RGB, thickness=3, dotted=True) + return rgb + + +def extract_blob_feature_map_and_mark(img, cfg): + if cv2 is None: + raise ImportError("OpenCV (cv2) is required for feature-method 'blob_dark_bright'.") + + gray_u8 = to_u8_image(img) + dark_background = cv2.GaussianBlur(gray_u8, (0, 0), sigmaX=float(cfg["dark_sigma"]), sigmaY=float(cfg["dark_sigma"])) + bright_background = cv2.GaussianBlur(gray_u8, (0, 0), sigmaX=float(cfg["bright_sigma"]), sigmaY=float(cfg["bright_sigma"])) + dark_enhanced = cv2.subtract(dark_background, gray_u8) + bright_enhanced = cv2.subtract(gray_u8, bright_background) + + dark_detector = build_blob_detector( + min_area=cfg["dark_min_area"], + max_area=cfg["dark_max_area"], + min_circularity=cfg["dark_min_circularity"], + min_inertia=cfg["dark_min_inertia"], + ) + bright_detector = build_blob_detector( + min_area=cfg["bright_min_area"], + max_area=cfg["bright_max_area"], + min_circularity=cfg["bright_min_circularity"], + min_inertia=cfg["bright_min_inertia"], + ) + + dark_candidates = keypoints_to_blob_dicts( + dark_detector.detect(dark_enhanced), + score_image=dark_enhanced, + shape=gray_u8.shape, + border_margin=cfg["dark_border_margin"], + ) + bright_candidates = keypoints_to_blob_dicts( + bright_detector.detect(bright_enhanced), + score_image=bright_enhanced, + shape=gray_u8.shape, + border_margin=cfg["bright_border_margin"], + ) + + dark_blobs = select_dark_holes(dark_candidates, score_ratio=cfg["dark_score_ratio"], max_blobs=cfg["dark_max_blobs"]) + bright_blobs = select_bright_blobs( + bright_candidates, + score_ratio=cfg["bright_score_ratio"], + max_blobs=cfg["bright_max_blobs"], + ) + dark_mask = blobs_to_mask(gray_u8.shape, dark_blobs, radius_scale=cfg["dark_radius_scale"]) + bright_mask = blobs_to_mask(gray_u8.shape, bright_blobs, radius_scale=cfg["bright_radius_scale"]) + combined_mask = np.logical_or(dark_mask, bright_mask) + annotated = make_blob_annotated_rgb( + gray_u8, + dark_blobs, + bright_blobs, + dark_radius_scale=cfg["dark_radius_scale"], + bright_radius_scale=cfg["bright_radius_scale"], + ) + return combined_mask.astype(np.float32), annotated + + +def make_cached_blob_annotated_rgb(gray_u8, dark_blobs, bright_blobs): + rgb = np.repeat(gray_u8[..., None], 3, axis=2).astype(np.uint8) + for b in dark_blobs: + r = int(round(float(b["radius_draw"]))) + c = (int(round(float(b["x"]))), int(round(float(b["y"])))) + draw_blob_circle(rgb, c, r, DFT_BLOB_RGB, thickness=2, dotted=False) + for b in bright_blobs: + r = int(round(float(b["radius_draw"]))) + c = (int(round(float(b["x"]))), int(round(float(b["y"])))) + draw_blob_circle(rgb, c, r, DFT_BLOB_RGB, thickness=2, dotted=True) + return rgb + + +def cached_blob_mark_image(blob_df, tar_name, sample_id, slice_idx, domain, fallback_img, blob_cfg): + if blob_df is None or blob_df.empty: + _, mark = extract_blob_feature_map_and_mark(fallback_img, blob_cfg) + return mark + + sub = blob_df[ + (blob_df["tar_file"] == tar_name) + & (blob_df["sample_id"] == sample_id) + & (blob_df["slice_idx"] == slice_idx) + & (blob_df["domain"] == domain) + ].copy() + if sub.empty: + _, mark = extract_blob_feature_map_and_mark(fallback_img, blob_cfg) + return mark + + if "rank" in sub.columns: + sub = sub.sort_values("rank") + dark = sub[sub["blob_type"] == "dark"] + bright = sub[sub["blob_type"] == "bright"] + gray_u8 = to_u8_image(fallback_img) + return make_cached_blob_annotated_rgb( + gray_u8, + dark.to_dict("records"), + bright.to_dict("records"), + ) + + +def add_image_text(ax, x, y, text, **kwargs): + default_kwargs = { + "transform": ax.transAxes, + "va": "top", + "ha": "left", + "fontsize": 7.2, + "color": "black", + "bbox": TEXT_BBOX, + } + default_kwargs.update(kwargs) + ax.text(x, y, text, **default_kwargs) + + +def build_combined_figure(orig_tar, trans_tar, tar_name, sample_id, slice_ids, ssim_sub, corr_sub, out_png, out_svg, out_pdf): + if len(slice_ids) != 14: + raise ValueError("This layout expects exactly 14 slice indices.") + + top_images_orig = [] + top_images_trans = [] + for slice_idx in slice_ids: + top_images_orig.append(read_slice_from_tar(orig_tar, sample_id, slice_idx)) + top_images_trans.append(read_slice_from_tar(trans_tar, sample_id, slice_idx)) + + stacked = np.stack(top_images_orig + top_images_trans, axis=0) + vmin = float(np.min(stacked)) + vmax = float(np.max(stacked)) + + first_block = slice_ids[:7] + second_block = slice_ids[7:] + + fig = plt.figure(figsize=(8.5, 8)) + gs_main = fig.add_gridspec(2, 1, height_ratios=[3.9, 1.25], hspace=0.18) + gs_top = gs_main[0].subgridspec(4, 7, wspace=0.01, hspace=0.03) + gs_bottom = gs_main[1].subgridspec(1, 2, wspace=0.18) + + row_specs = [ + (0, "Original", first_block, top_images_orig, False), + (1, "Translated", first_block, top_images_trans, True), + (2, "Original", second_block, top_images_orig, False), + (3, "Translated", second_block, top_images_trans, True), + ] + + for row_idx, row_label, row_slices, row_images, annotate_ssim in row_specs: + for col, slice_idx in enumerate(row_slices): + img_idx = slice_ids.index(slice_idx) + ax = fig.add_subplot(gs_top[row_idx, col]) + ax.imshow(row_images[img_idx], cmap=AFM_CMAP, vmin=vmin, vmax=vmax) + ax.axis("off") + if row_idx in [0, 2]: + ax.text(0.02, 0.95, f"i={slice_idx:02d}", transform=ax.transAxes, va="top", ha="left", fontsize=7.5, color="white") + + if col == 0: + label_color = simcolor if "Orig" in row_label else expcolor + ax.text( + -0.01, + 0.5, + row_label, + transform=ax.transAxes, + rotation=90, + va="center", + ha="right", + fontsize=9, + color=label_color, + ) + + + if annotate_ssim: + ssim_at_slice = ssim_sub[ssim_sub["slice_idx"] == slice_idx] + if not ssim_at_slice.empty: + val = float(ssim_at_slice.iloc[0]["ssim"]) + ax.text(0.02, 0.95, f"SSIM={val:.2f}", transform=ax.transAxes, va="top", ha="left", fontsize=7.5, color="white") + + ax_ssim = fig.add_subplot(gs_bottom[0, 0]) + ax_corr = fig.add_subplot(gs_bottom[0, 1]) + + ax_ssim.plot(ssim_sub["slice_idx"], ssim_sub["ssim"], marker="o", linewidth=1.8, color="tab:blue") + ax_ssim.set_ylim(0.0, 1.05) + ax_ssim.set_xlabel("Slice index") + ax_ssim.set_ylabel("SSIM") + #ax_ssim.set_title("SSIM vs slice index") + ax_ssim.grid(alpha=0.25) + + ax_corr.plot(corr_sub["pair_idx"], corr_sub["corr_original"], marker="o", linewidth=1.6, label="Original", color="tab:green") + ax_corr.plot(corr_sub["pair_idx"], corr_sub["corr_translated"], marker="o", linewidth=1.6, label="Translated", color="tab:orange") + ax_corr.set_ylim(-1.05, 1.05) + ax_corr.set_xlabel("Adjacent pair index (i vs i+1)") + ax_corr.set_ylabel("Correlation") + #ax_corr.set_title("Adjacent-slice correlation") + ax_corr.grid(alpha=0.25) + ax_corr.legend(loc="best") + + #fig.suptitle(f"{tar_name} | sample {sample_id}", fontsize=12, y=0.995) + fig.subplots_adjust(left=0.02, right=0.995, top=0.93, bottom=0.07) + fig.savefig(out_png, dpi=300, bbox_inches="tight", pad_inches=0.02) + fig.savefig(out_svg, bbox_inches="tight", pad_inches=0.02) + fig.savefig(out_pdf, bbox_inches="tight", pad_inches=0.02) + plt.close(fig) + + +def build_gradient_combined_figure(orig_tar, trans_tar, tar_name, sample_id, slice_ids, gssim_sub, corr_sub, out_png, out_svg, out_pdf): + if len(slice_ids) != 14: + raise ValueError("This layout expects exactly 14 slice indices.") + + top_images_orig = [] + top_images_trans = [] + top_images_orig_grad = [] + top_images_trans_grad = [] + for slice_idx in slice_ids: + img_o = read_slice_from_tar(orig_tar, sample_id, slice_idx) + img_t = read_slice_from_tar(trans_tar, sample_id, slice_idx) + top_images_orig.append(img_o) + top_images_trans.append(img_t) + top_images_orig_grad.append(gradient_preprocess_image(img_o)) + top_images_trans_grad.append(gradient_preprocess_image(img_t)) + + stacked_raw = np.stack(top_images_orig + top_images_trans, axis=0) + raw_vmin = float(np.min(stacked_raw)) + raw_vmax = float(np.max(stacked_raw)) + + stacked_grad = np.stack(top_images_orig_grad + top_images_trans_grad, axis=0) + grad_vmin = float(np.min(stacked_grad)) + grad_vmax = float(np.max(stacked_grad)) + + first_block = slice_ids[:7] + second_block = slice_ids[7:] + + fig = plt.figure(figsize=(8.5, 13.0)) + gs_main = fig.add_gridspec(2, 1, height_ratios=[7.3, 1.25], hspace=0.18) + gs_top = gs_main[0].subgridspec(8, 7, wspace=0.01, hspace=0.03) + gs_bottom = gs_main[1].subgridspec(1, 2, wspace=0.18) + + row_specs = [ + (0, "Original", first_block, top_images_orig, raw_vmin, raw_vmax, False), + (1, "Translated", first_block, top_images_trans, raw_vmin, raw_vmax, True), + (2, "Original", second_block, top_images_orig, raw_vmin, raw_vmax, False), + (3, "Translated", second_block, top_images_trans, raw_vmin, raw_vmax, True), + (4, "Orig grad", first_block, top_images_orig_grad, grad_vmin, grad_vmax, False), + (5, "Trans grad", first_block, top_images_trans_grad, grad_vmin, grad_vmax, True), + (6, "Orig grad", second_block, top_images_orig_grad, grad_vmin, grad_vmax, False), + (7, "Trans grad", second_block, top_images_trans_grad, grad_vmin, grad_vmax, True), + ] + + for row_idx, row_label, row_slices, row_images, row_vmin, row_vmax, annotate_gssim in row_specs: + for col, slice_idx in enumerate(row_slices): + img_idx = slice_ids.index(slice_idx) + ax = fig.add_subplot(gs_top[row_idx, col]) + ax.imshow(row_images[img_idx], cmap=AFM_CMAP, vmin=row_vmin, vmax=row_vmax) + ax.axis("off") + if row_idx in [0, 2, 4, 6]: + ax.text(0.02, 0.95, f"i={slice_idx:02d}", transform=ax.transAxes, va="top", ha="left", fontsize=7.5, color="white") + + if col == 0: + ax.text(-0.01, 0.5, row_label, transform=ax.transAxes, rotation=90, va="center", ha="right", fontsize=8.5) + + if annotate_gssim: + gssim_at_slice = gssim_sub[gssim_sub["slice_idx"] == slice_idx] + if not gssim_at_slice.empty: + val = float(gssim_at_slice.iloc[0]["gssim"]) + ax.text(0.02, 0.95, f"GSSIM={val:.3f}", transform=ax.transAxes, va="top", ha="left", fontsize=7.2, color="white") + + ax_gssim = fig.add_subplot(gs_bottom[0, 0]) + ax_corr = fig.add_subplot(gs_bottom[0, 1]) + + ax_gssim.plot(gssim_sub["slice_idx"], gssim_sub["gssim"], marker="o", linewidth=1.8, color="tab:green") + ax_gssim.set_ylim(0.0, 1.05) + ax_gssim.set_xlabel("Slice index") + ax_gssim.set_ylabel("Gradient-SSIM") + ax_gssim.grid(alpha=0.25) + + ax_corr.plot(corr_sub["pair_idx"], corr_sub["corr_original"], marker="o", linewidth=1.6, label="Original", color="tab:green") + ax_corr.plot(corr_sub["pair_idx"], corr_sub["corr_translated"], marker="o", linewidth=1.6, label="Translated", color="tab:orange") + ax_corr.set_ylim(-1.05, 1.05) + ax_corr.set_xlabel("Adjacent pair index (i vs i+1)") + ax_corr.set_ylabel("Correlation") + ax_corr.grid(alpha=0.25) + ax_corr.legend(loc="bottom right", frameon=False) + + fig.subplots_adjust(left=0.02, right=0.995, top=0.975, bottom=0.045) + fig.savefig(out_png, dpi=300, bbox_inches="tight", pad_inches=0.02) + fig.savefig(out_svg, bbox_inches="tight", pad_inches=0.02) + fig.savefig(out_pdf, bbox_inches="tight", pad_inches=0.02) + plt.close(fig) + + +def build_feature_combined_figure( + orig_tar, + trans_tar, + tar_name, + sample_id, + slice_ids, + ssim_sub, + feature_sub, + corr_sub, + out_png, + out_svg, + out_pdf, + feature_top_percent, + feature_use_gradient, + feature_method, + blob_cfg, + blob_df=None, +): + if len(slice_ids) != 14: + raise ValueError("This layout expects exactly 14 slice indices.") + + top_images_orig = [] + top_images_trans = [] + top_feat_orig = [] + top_feat_trans = [] + for slice_idx in slice_ids: + img_o = read_slice_from_tar(orig_tar, sample_id, slice_idx) + img_t = read_slice_from_tar(trans_tar, sample_id, slice_idx) + top_images_orig.append(img_o) + top_images_trans.append(img_t) + if feature_method == "blob_dark_bright": + mark_o = cached_blob_mark_image( + blob_df, + tar_name, + sample_id, + slice_idx, + "original", + img_o, + blob_cfg, + ) + mark_t = cached_blob_mark_image( + blob_df, + tar_name, + sample_id, + slice_idx, + "translated", + img_t, + blob_cfg, + ) + top_feat_orig.append(mark_o) + top_feat_trans.append(mark_t) + else: + mask_o = feature_map_image(img_o, top_percent=feature_top_percent, use_gradient=feature_use_gradient) + mask_t = feature_map_image(img_t, top_percent=feature_top_percent, use_gradient=feature_use_gradient) + top_feat_orig.append(mask_o) + top_feat_trans.append(mask_t) + + stacked = np.stack(top_images_orig + top_images_trans, axis=0) + vmin = float(np.min(stacked)) + vmax = float(np.max(stacked)) + + first_block = slice_ids[:7] + second_block = slice_ids[7:] + + fig = plt.figure(figsize=(8.5, 10.4)) + gs_main = fig.add_gridspec(2, 1, height_ratios=[6.5, 1.25], hspace=0.08) + gs_top = gs_main[0].subgridspec(8, 7, wspace=0.01, hspace=0.03) + gs_bottom = gs_main[1].subgridspec(1, 2, wspace=0.18) + + row_specs = [ + (0, "Original", first_block, top_images_orig, False, False), + (1, "Translated", first_block, top_images_trans, True, False), + (2, "Original", second_block, top_images_orig, False, False), + (3, "Translated", second_block, top_images_trans, True, False), + (4, "Orig feat", first_block, top_feat_orig, False, True), + (5, "Trans feat", first_block, top_feat_trans, True, True), + (6, "Orig feat", second_block, top_feat_orig, False, True), + (7, "Trans feat", second_block, top_feat_trans, True, True), + ] + + for row_idx, row_label, row_slices, row_images, annotate_feature, is_feature_row in row_specs: + for col, slice_idx in enumerate(row_slices): + img_idx = slice_ids.index(slice_idx) + ax = fig.add_subplot(gs_top[row_idx, col]) + if is_feature_row: + if feature_method == "blob_dark_bright": + ax.imshow(row_images[img_idx], interpolation="nearest") + else: + ax.imshow(row_images[img_idx], cmap="gray", vmin=0.0, vmax=1.0, interpolation="nearest") + else: + ax.imshow(row_images[img_idx], cmap="gray", vmin=vmin, vmax=vmax) + ax.axis("off") + if row_idx in [0, 2, 4, 6]: + add_image_text(ax, 0.02, 0.95, f"i={slice_idx:02d}") + + if col == 0: + label_color = simcolor if row_label in ["Original", "Orig feat"] else expcolor + ax.text( + -0.01, + 0.5, + row_label, + transform=ax.transAxes, + rotation=90, + va="center", + ha="right", + fontsize=9, + color=label_color, + ) + + if annotate_feature and row_idx in [1, 3]: + ssim_at_slice = ssim_sub[ssim_sub["slice_idx"] == slice_idx] + if not ssim_at_slice.empty: + sval = float(ssim_at_slice.iloc[0]["ssim"]) + add_image_text(ax, 0.02, 0.95, rf"$\mathrm{{SSIM}}(u_i,\tilde{{v}}_i)={sval:.2f}$") + + if annotate_feature and row_idx in [5, 7]: + feat_at_slice = feature_sub[feature_sub["slice_idx"] == slice_idx] + if not feat_at_slice.empty: + fdice = float(feat_at_slice.iloc[0]["feature_dice"]) + add_image_text(ax, 0.02, 0.95, rf"$\mathrm{{Dice}}(u_i^{{f}},\tilde{{v}}_i^{{f}})={fdice:.2f}$") + + ax_feat = fig.add_subplot(gs_bottom[0, 0]) + ax_corr = fig.add_subplot(gs_bottom[0, 1]) + + ax_feat.plot( + ssim_sub["slice_idx"], + ssim_sub["ssim"], + marker="s", + linewidth=1.6, + color="#4a4a4a", + markerfacecolor="none", + markeredgecolor="#4a4a4a", + label="SSIM", + ) + ax_feat.plot( + feature_sub["slice_idx"], + feature_sub["feature_dice"], + marker="o", + linewidth=1.6, + color=dftcolor, + markerfacecolor="none", + markeredgecolor=dftcolor, + label="Feature Dice", + ) + ax_feat.set_ylim(0.0, 1.05) + ax_feat.set_xlabel("Slice index") + ax_feat.set_ylabel("Similarity") + ax_feat.grid(alpha=0.25) + ax_feat.tick_params(axis="both", which="both", direction="in", top=True, right=True) + ax_feat.legend(loc="best", fontsize=8, frameon=False, ncol=2) + + ax_corr.plot( + corr_sub["pair_idx"], + corr_sub["corr_original"], + marker="o", + linewidth=1.6, + label="Original", + color=simcolor, + markerfacecolor="none", + markeredgecolor=simcolor, + ) + ax_corr.plot( + corr_sub["pair_idx"], + corr_sub["corr_translated"], + marker="p", + linewidth=1.6, + label="Translated", + color=expcolor, + markerfacecolor="none", + markeredgecolor=expcolor, + ) + ax_corr.set_ylim(-1.05, 1.05) + ax_corr.set_xlabel("Adjacent pair index (i vs i+1)") + ax_corr.set_ylabel("Correlation") + ax_corr.grid(alpha=0.25) + ax_corr.tick_params(axis="both", which="both", direction="in", top=True, right=True) + ax_corr.legend(loc="lower right", frameon=False, ncol=2) + + # Panel labels for the four sub-panels in the combined figure. + fig.text(0.03, 0.97, "a", color="#000000", fontsize=20, va="top", ha="left") + fig.text(0.03, 0.60, "b", color="#000000", fontsize=20, va="top", ha="left") + fig.text(0.03, 0.23, "c", color="#000000", fontsize=20, va="top", ha="left") + fig.text(0.50, 0.23, "d", color="#000000", fontsize=20, va="top", ha="left") + + fig.subplots_adjust(left=0.055, right=0.995, top=0.965, bottom=0.055) + fig.savefig(out_png, dpi=300, bbox_inches="tight", pad_inches=0.02) + fig.savefig(out_svg, bbox_inches="tight", pad_inches=0.02) + fig.savefig(out_pdf, bbox_inches="tight", pad_inches=0.02) + plt.close(fig) + + +def main(): + parser = argparse.ArgumentParser( + description="Create combined sanity figures: top AFM comparison + bottom SSIM/correlation trends." + ) + parser.add_argument("--tar-name", default=DEFAULT_TAR_NAME) + parser.add_argument("--original-root", default=DEFAULT_ORIGINAL_ROOT) + parser.add_argument("--translated-root", default=DEFAULT_TRANSLATED_ROOT) + parser.add_argument("--analysis-dir", default=DEFAULT_ANALYSIS_DIR) + parser.add_argument("--sample-ids", default="0,1,2,3,4,5,6,7", help="Comma-separated sample IDs") + parser.add_argument("--slices", default="0,1,2,3,4,5,6,7,8,9,10,11,12,13", help="Comma-separated 14 slice indices for top panel") + parser.add_argument("--name-prefix", default="sample_sanity_check_set") + parser.add_argument("--gradient-figure", action="store_true", help="Create separate Gradient-SSIM sanity figures with preprocessed image rows") + parser.add_argument("--gradient-name-suffix", default="_gradient", help="Suffix appended to output filename stem in --gradient-figure mode") + parser.add_argument("--feature-figure", action="store_true", help="Create separate feature-metric sanity figures") + parser.add_argument("--feature-name-suffix", default="_feature", help="Suffix appended to output filename stem in --feature-figure mode") + parser.add_argument("--feature-top-percent", type=float, default=5.0, help="Top percentile for feature mask visualization") + parser.add_argument("--feature-no-gradient", action="store_true", help="Use intensity-based top-percentile feature map instead of gradient-based") + parser.add_argument( + "--feature-method", + default="blob_dark_bright", + choices=["legacy_percentile", "blob_dark_bright"], + help="Feature map method used for feature rows", + ) + parser.add_argument("--feature-dark-sigma", type=float, default=13.0, help="Gaussian sigma for dark enhancement") + parser.add_argument("--feature-bright-sigma", type=float, default=8.0, help="Gaussian sigma for bright enhancement") + parser.add_argument("--feature-dark-min-area", type=float, default=35.0, help="Minimum area for dark-hole detector") + parser.add_argument("--feature-dark-max-area", type=float, default=1500.0, help="Maximum area for dark-hole detector") + parser.add_argument("--feature-dark-min-circularity", type=float, default=0.24, help="Minimum circularity for dark-hole detector") + parser.add_argument("--feature-dark-min-inertia", type=float, default=0.06, help="Minimum inertia ratio for dark-hole detector") + parser.add_argument("--feature-bright-min-area", type=float, default=15.0, help="Minimum area for bright-spot detector") + parser.add_argument("--feature-bright-max-area", type=float, default=2000.0, help="Maximum area for bright-spot detector") + parser.add_argument("--feature-bright-min-circularity", type=float, default=0.05, help="Minimum circularity for bright-spot detector") + parser.add_argument("--feature-bright-min-inertia", type=float, default=0.01, help="Minimum inertia ratio for bright-spot detector") + parser.add_argument("--feature-dark-border-margin", type=int, default=14, help="Border margin for dark-hole blob candidates") + parser.add_argument("--feature-bright-border-margin", type=int, default=18, help="Border margin for bright-spot blob candidates") + parser.add_argument("--feature-dark-score-ratio", type=float, default=0.42, help="Keep dark blobs with score >= ratio * top dark score") + parser.add_argument("--feature-bright-score-ratio", type=float, default=0.30, help="Keep bright blobs with score >= ratio * top bright score") + parser.add_argument("--feature-dark-max-blobs", type=int, default=8, help="Maximum number of selected dark blobs") + parser.add_argument("--feature-bright-max-blobs", type=int, default=8, help="Maximum number of selected bright blobs") + parser.add_argument("--feature-dark-radius-scale", type=float, default=1.8, help="Radius scale used when rasterizing dark blobs") + parser.add_argument("--feature-bright-radius-scale", type=float, default=2.0, help="Radius scale used when rasterizing bright blobs") + args = parser.parse_args() + + if args.gradient_figure and args.feature_figure: + raise ValueError("Use either --gradient-figure or --feature-figure, not both") + + analysis_dir = Path(args.analysis_dir).resolve() + analysis_dir.mkdir(parents=True, exist_ok=True) + + ssim_csv = analysis_dir / "ssim_values.csv" + gssim_csv = analysis_dir / "gradient_ssim_values.csv" + feature_csv = analysis_dir / "feature_mask_metrics.csv" + corr_csv = analysis_dir / "adjacent_slice_correlations.csv" + blob_csv = analysis_dir / "blob_features.csv" + if not ssim_csv.is_file(): + raise FileNotFoundError(f"Missing metrics file: {ssim_csv}") + if args.gradient_figure and not gssim_csv.is_file(): + raise FileNotFoundError(f"Missing gradient metrics file: {gssim_csv}") + if args.feature_figure and not feature_csv.is_file(): + raise FileNotFoundError(f"Missing feature metrics file: {feature_csv}") + if not corr_csv.is_file(): + raise FileNotFoundError(f"Missing metrics file: {corr_csv}") + + ssim_df = pd.read_csv(ssim_csv) + gssim_df = pd.read_csv(gssim_csv) if args.gradient_figure else None + feature_df = pd.read_csv(feature_csv) if args.feature_figure else None + corr_df = pd.read_csv(corr_csv) + blob_df = pd.read_csv(blob_csv) if (args.feature_figure and blob_csv.is_file()) else None + + sample_ids = parse_int_list(args.sample_ids) + slice_ids = parse_int_list(args.slices) + if not sample_ids: + raise ValueError("No sample IDs provided.") + if not slice_ids: + raise ValueError("No slice indices provided.") + + orig_tar_path = Path(args.original_root) / args.tar_name + trans_tar_path = Path(args.translated_root) / args.tar_name + if not orig_tar_path.is_file(): + raise FileNotFoundError(f"Missing original tar: {orig_tar_path}") + if not trans_tar_path.is_file(): + raise FileNotFoundError(f"Missing translated tar: {trans_tar_path}") + + with tarfile.open(orig_tar_path, "r") as orig_tar, tarfile.open(trans_tar_path, "r") as trans_tar: + common_samples = sorted(set(discover_sample_ids(orig_tar)).intersection(discover_sample_ids(trans_tar))) + for idx, sample_id in enumerate(sample_ids, start=1): + if sample_id not in common_samples: + print(f"Skip sample {sample_id}: not present in both tars") + continue + + ssim_sub, corr_sub = get_sample_curves(ssim_df, corr_df, args.tar_name, sample_id) + if args.gradient_figure: + gssim_sub = get_sample_metric_curve(gssim_df, args.tar_name, sample_id, "gssim") + if gssim_sub.empty or corr_sub.empty: + print(f"Skip sample {sample_id}: missing gradient/correlation rows") + continue + out_png = analysis_dir / f"{args.name_prefix}{idx}{args.gradient_name_suffix}.png" + out_svg = analysis_dir / f"{args.name_prefix}{idx}{args.gradient_name_suffix}.svg" + out_pdf = analysis_dir / f"{args.name_prefix}{idx}{args.gradient_name_suffix}.pdf" + build_gradient_combined_figure( + orig_tar, + trans_tar, + args.tar_name, + sample_id, + slice_ids, + gssim_sub, + corr_sub, + out_png, + out_svg, + out_pdf, + ) + elif args.feature_figure: + feature_sub = feature_df[(feature_df["tar_file"] == args.tar_name) & (feature_df["sample_id"] == sample_id)].copy().sort_values("slice_idx") + if ssim_sub.empty or feature_sub.empty or corr_sub.empty: + print(f"Skip sample {sample_id}: missing feature/correlation rows") + continue + feature_method = args.feature_method + feature_use_gradient = (not args.feature_no_gradient) + if args.feature_no_gradient and feature_method == "blob_dark_bright": + feature_method = "legacy_percentile" + feature_use_gradient = False + blob_cfg = { + "dark_sigma": args.feature_dark_sigma, + "bright_sigma": args.feature_bright_sigma, + "dark_min_area": args.feature_dark_min_area, + "dark_max_area": args.feature_dark_max_area, + "dark_min_circularity": args.feature_dark_min_circularity, + "dark_min_inertia": args.feature_dark_min_inertia, + "bright_min_area": args.feature_bright_min_area, + "bright_max_area": args.feature_bright_max_area, + "bright_min_circularity": args.feature_bright_min_circularity, + "bright_min_inertia": args.feature_bright_min_inertia, + "dark_border_margin": args.feature_dark_border_margin, + "bright_border_margin": args.feature_bright_border_margin, + "dark_score_ratio": args.feature_dark_score_ratio, + "bright_score_ratio": args.feature_bright_score_ratio, + "dark_max_blobs": args.feature_dark_max_blobs, + "bright_max_blobs": args.feature_bright_max_blobs, + "dark_radius_scale": args.feature_dark_radius_scale, + "bright_radius_scale": args.feature_bright_radius_scale, + } + out_png = analysis_dir / f"{args.name_prefix}{idx}{args.feature_name_suffix}.png" + out_svg = analysis_dir / f"{args.name_prefix}{idx}{args.feature_name_suffix}.svg" + out_pdf = analysis_dir / f"{args.name_prefix}{idx}{args.feature_name_suffix}.pdf" + build_feature_combined_figure( + orig_tar, + trans_tar, + args.tar_name, + sample_id, + slice_ids, + ssim_sub, + feature_sub, + corr_sub, + out_png, + out_svg, + out_pdf, + args.feature_top_percent, + feature_use_gradient, + feature_method, + blob_cfg, + blob_df=blob_df, + ) + else: + if ssim_sub.empty or corr_sub.empty: + print(f"Skip sample {sample_id}: missing metric rows") + continue + + out_png = analysis_dir / f"{args.name_prefix}{idx}.png" + out_svg = analysis_dir / f"{args.name_prefix}{idx}.svg" + out_pdf = analysis_dir / f"{args.name_prefix}{idx}.pdf" + build_combined_figure( + orig_tar, + trans_tar, + args.tar_name, + sample_id, + slice_ids, + ssim_sub, + corr_sub, + out_png, + out_svg, + out_pdf, + ) + print(out_png) + print(out_svg) + print(out_pdf) + + +if __name__ == "__main__": + main()