check_model.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-

# python3 cek_model_v6.py --weights /workspace/captcha_final.weights.h5 --image /workspace/dataset_500/style7/K9NO2.png
# python3 cek_model_v6.py --weights /workspace/captcha_final.weights.h5 --data-root /workspace/dataset_500 --samples 64
# python3 cek_model_v6.py --weights captcha_final.weights.h5 --data-root /datasets/dataset_500 --samples 64


import os, re, glob, argparse, sys, time
from pathlib import Path
import numpy as np
from PIL import Image
import tensorflow as tf
from tensorflow.keras import layers, models, backend as K

# ---------------- Args ----------------
def parse_args():
    p = argparse.ArgumentParser("Test inference CRNN+CTC dari weights Keras 3 (model_with_ctc.save_weights).")
    p.add_argument("--weights", required=True, help="Path ke *.weights.h5 (hasil save_weights).")
    p.add_argument("--image", help="Uji 1 gambar (PNG/JPG). Nama file jadi GT jika --gt tidak diisi.")
    p.add_argument("--gt", help="Ground truth untuk --image (opsional, default dari nama file).")
    p.add_argument("--data-root", help="Root dataset berisi style0..style59/LABEL.png untuk batch test.")
    p.add_argument("--samples", type=int, default=64, help="Jumlah sampel di batch test.")
    p.add_argument("--height", type=int, default=50)
    p.add_argument("--width", type=int, default=250)
    p.add_argument("--ext", type=str, default="png")
    p.add_argument("--show", type=int, default=12, help="Banyak baris contoh yang ditampilkan.")
    return p.parse_args()

# ------------- Charset & util -------------
CHARSET = list("0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZ")
BLANK_ID = len(CHARSET)      # 36
ID2CHAR = np.array(CHARSET)

def collapse_and_strip_blanks(seq_ids, blank_id=BLANK_ID):
    prev = -1; out = []
    for t in seq_ids:
        if t != prev and t != blank_id:
            out.append(t)
        prev = t
    return out

def ids_to_text(ids):
    ids = [i for i in ids if 0 <= i < len(CHARSET)]
    return "".join(ID2CHAR[ids]) if ids else ""

def cer(pred, gt):
    m, n = len(pred), len(gt)
    if n == 0: return 0.0 if m == 0 else 1.0
    dp = np.zeros((m+1, n+1), dtype=np.int32)
    dp[:,0] = np.arange(m+1); dp[0,:] = np.arange(n+1)
    for i in range(1, m+1):
        for j in range(1, n+1):
            dp[i,j] = min(dp[i-1,j]+1, dp[i,j-1]+1, dp[i-1,j-1] + (pred[i-1]!=gt[j-1]))
    return dp[m,n] / n

# ------------- Model builders -------------
def build_models(h=50, w=250, num_classes=len(CHARSET)+1):
    inp = layers.Input(shape=(h, w, 1), name="input")
    x = layers.Conv2D(32, (3,3), activation="relu", padding="same")(inp)
    x = layers.BatchNormalization()(x)
    x = layers.MaxPooling2D((2,2))(x)     # 50x250 -> 25x125

    x = layers.Conv2D(64, (3,3), activation="relu", padding="same")(x)
    x = layers.BatchNormalization()(x)
    x = layers.MaxPooling2D((2,2))(x)     # 25x125 -> 12x62

    x = layers.Conv2D(128, (3,3), activation="relu", padding="same")(x)
    x = layers.BatchNormalization()(x)
    x = layers.MaxPooling2D((2,2))(x)     # 12x62 -> 6x31

    shp = K.int_shape(x)                  # (None, 6, 31, 128)
    x = layers.Reshape((shp[2], shp[1]*shp[3]))(x)   # (None, 31, 768)

    x = layers.Bidirectional(layers.LSTM(128, return_sequences=True, dropout=0.0, recurrent_dropout=0.0))(x)
    x = layers.Bidirectional(layers.LSTM(128, return_sequences=True, dropout=0.0, recurrent_dropout=0.0))(x)

    pred = layers.Dense(num_classes, activation="softmax", name="predictions")(x)

    # CTC inputs untuk menyamai graph training
    labels = layers.Input(name="labels", shape=(None,), dtype="int32")
    input_len = layers.Input(name="input_length", shape=(1,), dtype="int32")
    label_len = layers.Input(name="label_length", shape=(1,), dtype="int32")
    def ctc_fn(args):
        y_pred, labels_t, in_l, lab_l = args
        return K.ctc_batch_cost(labels_t, y_pred, in_l, lab_l)
    ctc = layers.Lambda(ctc_fn, output_shape=(1,), name="ctc_loss", dtype="float32")([pred, labels, input_len, label_len])

    model_with_ctc = models.Model(inputs=[inp, labels, input_len, label_len], outputs=ctc, name="crnn_ctc_train")
    base_model = models.Model(inputs=inp, outputs=pred, name="crnn_ctc_base")
    return model_with_ctc, base_model

# ------------- IO & preprocess -------------
def preprocess_gray(img_pil, h=50, w=250):
    im = img_pil.convert("L").resize((w, h), Image.BILINEAR)
    arr = np.asarray(im, dtype=np.float32) / 255.0
    arr = (arr - 0.5) / 0.5
    return arr[..., None]  # (H,W,1)

def list_files(root, ext="png", max_n=64):
    rootp = Path(root)
    pat = re.compile(r"^[A-Z0-9]{5}$")
    pairs = []
    for sid in range(60):
        d = rootp / f"style{sid}"
        if not d.exists(): continue
        for f in glob.glob(str(d / f"*.{ext}")):
            lbl = Path(f).stem.upper()
            if pat.match(lbl):
                pairs.append((f, lbl))
            if len(pairs) >= max_n: break
        if len(pairs) >= max_n: break
    return pairs

# ------------- Predict helpers -------------
def predict_batch(base_model, batch_imgs):
    """batch_imgs: np.array (B,H,W,1) float32 [-1,1]"""
    probs = base_model.predict(batch_imgs, verbose=0)   # (B, 31, 37)
    ids = np.argmax(probs, axis=-1)                     # (B, 31)
    texts = []
    for row in ids:
        dec = collapse_and_strip_blanks(row, blank_id=BLANK_ID)
        texts.append(ids_to_text(dec))
    return texts

def main():
    args = parse_args()

    # (opsional) batasi threads kalau container ketat
    os.environ.setdefault("TF_NUM_INTRAOP_THREADS", "1")
    os.environ.setdefault("TF_NUM_INTEROP_THREADS", "1")
    os.environ.setdefault("OMP_NUM_THREADS", "1")

    # 1) Bangun model & load weights
    wpath = Path(args.weights)
    if not wpath.exists():
        print("Weights not found:", wpath); sys.exit(1)
    st = wpath.stat()
    print(f"Found weights: {wpath} | size: {st.st_size/1024:.1f} KB | mtime: {time.ctime(st.st_mtime)}")
    print("TF GPUs:", tf.config.list_physical_devices('GPU'))

    model_with_ctc, base_model = build_models(h=args.height, w=args.width, num_classes=len(CHARSET)+1)
    try:
        model_with_ctc.load_weights(str(wpath))
        print("OK: weights loaded.")
    except Exception as e:
        print("Failed to load weights:", e); sys.exit(2)

    print("Base output shape:", base_model.output_shape)  # Expect (None, 31, 37)

    # 2A) Single image test
    if args.image:
        f = Path(args.image)
        if not f.exists():
            print("Image not found:", f); sys.exit(3)
        with Image.open(f) as im:
            x = preprocess_gray(im, h=args.height, w=args.width)
        pred = predict_batch(base_model, np.expand_dims(x, 0))[0]
        gt = args.gt if args.gt else f.stem.upper()
        print(f"\nSingle image:")
        print(f"GT  : {gt}")
        print(f"PRED: {pred}")
        sys.exit(0)

    # 2B) Batch test dari dataset
    if args.data_root:
        pairs = list_files(args.data_root, ext=args.ext, max_n=args.samples)
        if not pairs:
            print("No valid files in dataset root."); sys.exit(0)
        print(f"Testing on {len(pairs)} samples from {args.data_root} ...")
        X, GT = [], []
        for f, lbl in pairs:
            with Image.open(f) as im:
                X.append(preprocess_gray(im, h=args.height, w=args.width))
            GT.append(lbl)
        X = np.stack(X, 0).astype(np.float32)

        PRED = predict_batch(base_model, X)
        exact = np.mean([int(p == g) for p, g in zip(PRED, GT)])
        cer_vals = [cer(p, g) for p, g in zip(PRED, GT)]

        for i in range(min(args.show, len(PRED))):
            print(f"{i:02d}  GT: {GT[i]}   |   Pred: {PRED[i]}")

        print(f"\nExact match: {exact*100:.2f}%  |  Mean CER: {float(np.mean(cer_vals)):.4f}\n")
        print(f"Total images tested: {len(PRED)}\n")
        sys.exit(0)

    print("Nothing to test. Provide --image or --data-root.")
    sys.exit(0)

if __name__ == "__main__":
    main()