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Batch-test backend menggunakan satu folder dengan naming convention:
r1, r2, r3, ... → real
f1, f2, f3, ... → fake
Usage:
1. Pastiin app.py jalan di terminal lain
2. python test_batch.py "C:/Users/yourname/Downloads"
atau
python test_batch.py ~/Downloads
"""
import os, sys, glob, re, requests
from collections import Counter
if len(sys.argv) < 2:
print("Usage: python test_batch.py /path/to/folder")
print('Windows example: python test_batch.py "C:\\Users\\YourName\\Downloads"')
print("Mac example: python test_batch.py ~/Downloads")
sys.exit(1)
folder = os.path.expanduser(sys.argv[1])
URL = "http://localhost:5000/analyze"
if not os.path.isdir(folder):
print(f"❌ Folder tidak ditemukan: {folder}")
sys.exit(1)
# Cari semua gambar
EXTS = ["jpg", "jpeg", "png", "webp"]
all_files = []
for ext in EXTS:
all_files.extend(glob.glob(os.path.join(folder, f"*.{ext}")))
all_files.extend(glob.glob(os.path.join(folder, f"*.{ext.upper()}")))
# Klasifikasi berdasarkan prefix nama file (r1.jpg → real, f1.jpg → fake)
pattern = re.compile(r"^([rf])(\d+)\.", re.IGNORECASE)
labeled_files = []
for fp in all_files:
name = os.path.basename(fp)
m = pattern.match(name)
if m:
prefix = m.group(1).lower()
true_label = "real" if prefix == "r" else "fake"
idx = int(m.group(2))
labeled_files.append((fp, true_label, idx))
if not labeled_files:
print(f"❌ Gak ada file dengan format r1.jpg / f1.jpg dst di {folder}")
print(" File yang ada di folder:")
for fp in sorted(all_files)[:10]:
print(f" {os.path.basename(fp)}")
sys.exit(1)
# Sort: real dulu (sesuai index), terus fake
labeled_files.sort(key=lambda x: (x[1], x[2]))
print(f"✅ Ditemukan {len(labeled_files)} gambar berlabel di {folder}\n")
stats = Counter()
mistakes = []
current_label = None
for fp, true_label, idx in labeled_files:
# Print header tiap ganti kategori
if true_label != current_label:
count = sum(1 for _, t, _ in labeled_files if t == true_label)
print(f"\n=== {true_label.upper()} ({count} files) ===")
print(f"{'file':<25} {'pred':<6} {'p_fake':>9} {'ok'}")
print("-" * 55)
current_label = true_label
try:
with open(fp, "rb") as f:
r = requests.post(URL, files={"image": f}, timeout=30)
except requests.exceptions.ConnectionError:
print(f"\n❌ Backend gak nyala di {URL}")
print(" Jalanin dulu: python app.py")
sys.exit(1)
if r.status_code != 200:
print(f" ERROR on {fp}: {r.text}")
continue
d = r.json()
pred = "fake" if d["is_fake"] else "real"
ok = pred == true_label
stats[(true_label, pred)] += 1
if not ok:
mistakes.append((fp, true_label, pred, d["p_fake"]))
mark = "OK" if ok else "WRONG"
name = os.path.basename(fp)[:23]
print(f"{name:<25} {pred:<6} {d['p_fake']:>8.2f}% {mark}")
# Summary
print("\n" + "=" * 55)
print("CONFUSION MATRIX")
print("=" * 55)
print(f"{'':<10} {'pred_real':>12} {'pred_fake':>12}")
print(f"{'true_real':<10} {stats[('real','real')]:>12} {stats[('real','fake')]:>12}")
print(f"{'true_fake':<10} {stats[('fake','real')]:>12} {stats[('fake','fake')]:>12}")
total = sum(stats.values())
correct = stats[("real","real")] + stats[("fake","fake")]
if total:
print(f"\nAccuracy: {correct}/{total} = {100*correct/total:.1f}%")
n_real = stats[("real","real")] + stats[("real","fake")]
n_fake = stats[("fake","real")] + stats[("fake","fake")]
if n_real:
print(f"Real recall: {stats[('real','real')]}/{n_real} = {100*stats[('real','real')]/n_real:.1f}%")
if n_fake:
print(f"Fake recall: {stats[('fake','fake')]}/{n_fake} = {100*stats[('fake','fake')]/n_fake:.1f}%")
if mistakes:
print(f"\n{len(mistakes)} MISCLASSIFIED:")
for fp, t, p, pf in mistakes:
print(f" {os.path.basename(fp):<25} true={t:<5} pred={p:<5} p_fake={pf:.2f}%") |