infer.py

import os
import random
import numpy as np
import tensorflow as tf
from pathlib import Path
from tqdm import tqdm
import cv2  # for reading images from disk

# -------------------------------------------------
# CONFIGURATION
# -------------------------------------------------
GENERATOR_PATH = "generator_final.h5"                # <-- your model
VIS_FOLDER     = "data/train/visible"                    # <-- folder with visible images
IR_FOLDER      = "data/train/infrared"                     # <-- (optional) ground-truth IR
SAVE_DIR       = "output/train_results"              # where to save side-by-side
NUM_SAMPLES    = 10
IMG_SIZE       = (256, 256)                         # adjust to your model's input size
SEED           = 42

# -------------------------------------------------
# 1. Load the generator
# -------------------------------------------------
print(f"Loading generator from {GENERATOR_PATH} ...")
generator = tf.keras.models.load_model(GENERATOR_PATH, compile=False)
print("Generator loaded successfully.")

# -------------------------------------------------
# 2. Helper: preprocess image (resize + normalize to [-1, 1])
# -------------------------------------------------
def load_and_preprocess_image(img_path, target_size=IMG_SIZE):
    img = cv2.imread(img_path)
    if img is None:
        raise FileNotFoundError(f"Image not found: {img_path}")
    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    img = cv2.resize(img, target_size)
    img = img.astype(np.float32) / 127.5 - 1.0   # → [-1, 1]
    img = np.expand_dims(img, axis=0)           # (1, H, W, 3)
    return img

# -------------------------------------------------
# 3. Helper: convert [-1,1] tensor → uint8 image
# -------------------------------------------------
def to_uint8(tensor):
    tensor = np.clip(tensor, -1.0, 1.0)
    tensor = (tensor + 1.0) * 127.5
    return np.clip(tensor, 0, 255).astype(np.uint8)

# -------------------------------------------------
# 4. Main function
# -------------------------------------------------
def generate_from_folder(
    vis_folder, ir_folder=None, save_dir=SAVE_DIR, num_samples=NUM_SAMPLES
):
    os.makedirs(save_dir, exist_ok=True)
    random.seed(SEED)

    vis_paths = sorted(Path(vis_folder).glob("*.*"))
    vis_paths = [p for p in vis_paths if p.suffix.lower() in {".png", ".jpg", ".jpeg", ".bmp"}]

    if len(vis_paths) == 0:
        raise ValueError(f"No images found in {vis_folder}")

    # Sample random images
    sample_paths = random.sample(vis_paths, min(num_samples, len(vis_paths)))

    print(f"Generating {len(sample_paths)} random side-by-side images...")

    for idx, vis_path in enumerate(tqdm(sample_paths)):
        # Load visible image
        vis_tensor = load_and_preprocess_image(str(vis_path))

        # Generate IR
        pred_tensor = generator(vis_tensor, training=False)  # (1, H, W, C)
        pred_img = to_uint8(pred_tensor[0].numpy())          # (H, W, 3)

        # Optional: load ground-truth IR (same filename)
        ir_img = None
        if ir_folder:
            ir_path = Path(ir_folder) / vis_path.name
            if ir_path.exists():
                ir_tensor = load_and_preprocess_image(str(ir_path))
                ir_tensor = generator.predict(ir_tensor)  # not needed, just load raw
                # Actually just read and convert
                ir_raw = cv2.imread(str(ir_path))
                ir_raw = cv2.cvtColor(ir_raw, cv2.COLOR_BGR2RGB)
                ir_raw = cv2.resize(ir_raw, IMG_SIZE)
                ir_img = ir_raw
            else:
                print(f"Warning: IR not found for {vis_path.name}, using black placeholder.")
                ir_img = np.zeros((IMG_SIZE[1], IMG_SIZE[0], 3), dtype=np.uint8)

        # If no IR folder, show only generated
        if ir_img is None:
            # Show: [Black | Generated]
            left = np.zeros_like(pred_img)
            row = np.concatenate([left, pred_img], axis=1)
            title = "Generated Only"
        else:
            # Show: [Ground Truth IR | Generated]
            row = np.concatenate([ir_img, pred_img], axis=1)
            title = "GT | Generated"

        # Save
        save_path = os.path.join(save_dir, f"sample_{idx:02d}_{vis_path.stem}.png")
        cv2.imwrite(save_path, cv2.cvtColor(row, cv2.COLOR_RGB2BGR))

    print(f"All {len(sample_paths)} images saved to {save_dir}")

# -------------------------------------------------
# 5. RUN
# -------------------------------------------------
if __name__ == "__main__":
    # Case 1: You have ground-truth IR images (same filename)
    generate_from_folder(
        vis_folder=VIS_FOLDER,
        ir_folder=IR_FOLDER,      # set to None if you don't have GT
        num_samples=NUM_SAMPLES
    )

    # Case 2: Only visible images → show generated only
    # generate_from_folder(vis_folder=VIS_FOLDER, ir_folder=None)