2026001

2026-07-01 17:35:58 +02:00
parent 9bfcc0243b
commit 35cbd9ac3c
84 changed files with 8500 additions and 423 deletions
@@ -0,0 +1,71 @@
+#!/usr/bin/env python3
+"""figure1.py — Sample CT images from the IQ-OTH/NCCD dataset, one per patient.
+Usage: python scripts/visualizations/figure1.py [--tag TAG]"""
+
+import os, sys, csv, argparse
+import matplotlib; matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+from PIL import Image
+
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.dirname(
+    os.path.abspath(__file__)))))
+ROOT = os.path.dirname(os.path.dirname(os.path.dirname(
+    os.path.abspath(__file__))))
+DATASET = os.path.join(os.path.dirname(ROOT), "The IQ-OTHNCCD lung cancer dataset")
+MANIFEST = os.path.join(ROOT, "results", "simple_patient_manifest.csv")
+PLOTS_DIR = os.path.join(ROOT, "plots")
+
+ap = argparse.ArgumentParser()
+ap.add_argument("--tag", default="", help="Append tag to filename")
+ap.add_argument("--manifest", default=MANIFEST, help="Patient manifest CSV")
+args = ap.parse_args()
+tag = f"_{args.tag}" if args.tag else ""
+
+# Load manifest to get per-patient images
+patients = {"Benign": [], "Malignant": [], "Normal": []}
+with open(args.manifest, newline="") as f:
+    reader = csv.DictReader(f)
+    img_col = "confirmed_images" if "confirmed_images" in reader.fieldnames else "images"
+    for row in reader:
+        cls = row.get("class", "")
+        if cls in patients:
+            imgs = row[img_col].split(";")
+            if imgs:
+                patients[cls].append((row["patient_id"], imgs[0]))  # first image per patient
+
+CLASS_DIR = {"Benign": "Bengin cases", "Malignant": "Malignant cases",
+             "Normal": "Normal cases"}
+N_EXAMPLES = 3
+
+fig, axes = plt.subplots(3, N_EXAMPLES, figsize=(8, 9))
+for row, (cls_label, cls_dir) in enumerate(CLASS_DIR.items()):
+    # Pick first N_EXAMPLES patients for this class
+    selected = patients[cls_label][:N_EXAMPLES]
+    for col, (pid, short_name) in enumerate(selected):
+        ax = axes[row, col]
+        # Convert short name back to original filename
+        prefix = short_name[0]
+        num = int(short_name.split("_")[1])
+        cls_map = {"B": ("Bengin cases", "Bengin"), "M": ("Malignant cases", "Malignant"),
+                   "N": ("Normal cases", "Normal")}
+        dir_name, file_prefix = cls_map[prefix]
+        fname = f"{file_prefix} case ({num}).jpg"
+        img_path = os.path.join(DATASET, dir_name, fname)
+        try:
+            img = Image.open(img_path).convert("L")
+            ax.imshow(img, cmap="gray")
+        except Exception as e:
+            ax.text(0.5, 0.5, f"error: {e}", ha="center", va="center", fontsize=7)
+        ax.set_xticks([]); ax.set_yticks([])
+        if col == 0:
+            ax.set_ylabel(cls_label, fontsize=10, rotation=0,
+                          labelpad=20, va="center")
+
+fig.suptitle("Figure 1 — Sample images from the IQ-OTH/NCCD dataset",
+             fontsize=12, y=1.02)
+plt.tight_layout(rect=[0, 0, 1, 0.97])
+out = os.path.join(PLOTS_DIR, "figure1", f"figure1{tag}.png")
+os.makedirs(os.path.dirname(out), exist_ok=True)
+plt.savefig(out, dpi=150)
+plt.close()
+print(f"Saved → {out}")