From c93101a086fde8e57168453e13e2c8d4b8223b1b Mon Sep 17 00:00:00 2001
From: Tamino Huxohl <thuxohl@techfak.uni-bielefeld.de>
Date: Fri, 9 Dec 2022 13:36:47 +0100
Subject: [PATCH] add script for pixel-wise polarmap evaluation
---
mu_map/polar_map/eval_pixelwise.py | 115 +++++++++++++++++++++++++++++
1 file changed, 115 insertions(+)
create mode 100644 mu_map/polar_map/eval_pixelwise.py
diff --git a/mu_map/polar_map/eval_pixelwise.py b/mu_map/polar_map/eval_pixelwise.py
new file mode 100644
index 0000000..1d9d9d0
--- /dev/null
+++ b/mu_map/polar_map/eval_pixelwise.py
@@ -0,0 +1,115 @@
+import argparse
+import os
+
+import cv2 as cv
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+
+from mu_map.polar_map.prepare import headers
+
+
+def get_circular_mark(shape: np.ndarray, channels:int=1):
+ mask = np.full((*shape, channels), 0, np.uint8)
+ cx, cy = np.array(mask.shape[:2]) // 2
+ mask = cv.circle(
+ mask,
+ center=(cx-1, cy),
+ radius=cx - 2,
+ color=(255,) * channels,
+ thickness=cv.FILLED,
+ )
+ mask = mask == 255
+ return mask[:, :, 0] if channels == 1 else mask
+
+parser = argparse.ArgumentParser(description="Visualize polar maps of different reconstructions", formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+parser.add_argument("--polar_map_dir", type=str, default="data/polar_maps", help="directory containing the polar map images")
+parser.add_argument("--images_dir", type=str, default="images", help="sub-directory under <polar_map_dir> containing the actual image files")
+parser.add_argument("--csv", type=str, default="polar_maps.csv", help="file under <polar_map_dir> containing meta information about the polar maps")
+parser.add_argument("--baseline", choices=["symbia", "stir"], default="symbia", help="select the polar map treated as the baseline")
+parser.add_argument("--relative_difference", action="store_true", help="show the difference as a percentage difference to the baseline")
+args = parser.parse_args()
+
+args.images_dir = os.path.join(args.polar_map_dir, args.images_dir)
+args.csv = os.path.join(args.polar_map_dir, args.csv)
+
+meta = pd.read_csv(args.csv)
+ids = meta[headers.id].unique()
+
+dists_abs = {"No AC": [], "AC SYN": [], "AC CT": []}
+dists_per = {"No AC": [], "AC SYN": [], "AC CT": []}
+recons_tot = {"No AC": [], "AC SYN": [], "AC CT": []}
+baseline_tot = []
+
+for _id in ids:
+ print(_id)
+ _meta = meta[(meta[headers.id] == _id) & ~(meta[headers.segments])]
+
+ file_recon_ac = _meta[(_meta[headers.type] == "symbia") & _meta[headers.ac]][headers.file].values[0]
+ file_recon_nac = _meta[~_meta[headers.ac]][headers.file].values[0]
+ file_recon_syn = _meta[_meta[headers.type] == "synthetic"][headers.file].values[0]
+ file_recon_ct = _meta[_meta[headers.type] == "ct"][headers.file].values[0]
+
+ recon_ac = cv.imread(os.path.join(args.images_dir, file_recon_ac), cv.IMREAD_GRAYSCALE)
+ recon_nac = cv.imread(os.path.join(args.images_dir, file_recon_nac), cv.IMREAD_GRAYSCALE)
+ recon_syn = cv.imread(os.path.join(args.images_dir, file_recon_syn), cv.IMREAD_GRAYSCALE)
+ recon_ct = cv.imread(os.path.join(args.images_dir, file_recon_ct), cv.IMREAD_GRAYSCALE)
+
+ baseline = recon_ac.copy() if args.baseline == "symbia" else recon_ct.copy()
+ recons = [recon_nac, recon_syn, recon_ct]
+ labels = ["No AC", "AC SYN", "AC CT"]
+
+ baseline = baseline * (100.0 / 255.0)
+ recons = list(map(lambda recon: recon * (100.0 / 255.0), recons))
+
+ mask = get_circular_mark(baseline.shape)
+ _baseline = baseline[mask]
+ mask2 = _baseline != 0
+ _baseline = _baseline[mask2]
+ baseline_tot.append(_baseline)
+ for recon, label in zip(recons, labels):
+ _recon = recon[mask]
+ assert _recon[np.logical_not(mask2)].sum() == 0
+ _recon = _recon[mask2]
+ recons_tot[label].append(_recon)
+
+ dist_abs = np.absolute(_recon - _baseline)
+ dist_percentage = 100 * dist_abs / _baseline
+ # print(f" - {label:>6} - Absolute: {dist_abs.mean():.3f}+-{dist_abs.std():.3f}")
+ # print(f" - {label:>6} - Percentage: {dist_percentage.mean():.3f}+-{dist_percentage.std():.3f}")
+
+ dists_abs[label].append(dist_abs.mean())
+ dists_per[label].append(dist_percentage.mean())
+
+print("Total:")
+for label in dists_abs.keys():
+ dist_abs = np.array(dists_abs[label])
+ dist_percentage = np.array(dists_per[label])
+ print(f" - {label:>6} - Absolute: {dist_abs.mean():.3f}+-{dist_abs.std():.3f}")
+ print(f" - {label:>6} - Percentage: {dist_percentage.mean():.3f}+-{dist_percentage.std():.3f}")
+
+
+baseline_tot = np.concatenate(baseline_tot)
+
+bins_x = np.arange(0, 100.1, 2)
+bins_y = np.arange(0, 100.1, 2)
+fig, axs = plt.subplots(2, 3, figsize=(15, 10))
+for i, (label, recon) in enumerate(recons_tot.items()):
+ recon = np.concatenate(recon)
+ h, xedges, yedges, image = axs[0, i].hist2d(baseline_tot, recon, bins=(bins_x, bins_y))
+
+ h = (h - h.min()) / (h.max() - h.min())
+ img = mpl.colormaps["viridis"](h)
+ img = cv.line(img, (0, 0), (50, 50), (1.0, 0.0, 0.0, 0.3), 1, lineType=cv.LINE_AA)
+ # img = img[::-1, ::-1]
+ # axs[1, i].imshow(img, origin="lower")
+ img = np.rot90(img)
+ axs[1, i].imshow(img)
+ axs[1, i].set_axis_off()
+
+ axs[0, i].set_title(label)
+ axs[0, i].set_xlabel("Baseline")
+ axs[0, i].set_ylabel("Prediction")
+
+plt.show()
--
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