test_model.py

import tensorflow as tf
import numpy as np
from tensorflow.keras import backend as K
from adabelief_tf import AdaBeliefOptimizer
import matplotlib.pyplot as plt
import os
from glob import glob

# [Previous function definitions stay the same: iou_coef, dice_coef, etc.]

def visualize_prediction(original_img, mask_pred, tags_pred, save_path=None):
    plt.figure(figsize=(15, 5))
    
    # Original image
    plt.subplot(1, 3, 1)
    plt.imshow(original_img[:,:,0], cmap='gray')
    plt.title('Original Image')
    plt.axis('off')
    
    # Predicted mask
    plt.subplot(1, 3, 2)
    plt.imshow(mask_pred[:,:,0], cmap='jet')
    plt.title('Predicted Mask')
    plt.axis('off')
    
    # Overlay
    plt.subplot(1, 3, 3)
    plt.imshow(original_img[:,:,0], cmap='gray')
    plt.imshow(mask_pred[:,:,0], cmap='jet', alpha=0.4)
    plt.title(f'Overlay\nEye: {tags_pred[0]:.3f}, Blink: {tags_pred[1]:.3f}')
    plt.axis('off')
    
    plt.tight_layout()
    if save_path:
        plt.savefig(save_path)
        plt.close()
    else:
        plt.show()

def test_single_image(image_path, model, output_dir=None):
    print(f"\nTesting image: {os.path.basename(image_path)}")
    img = load_image(image_path)
    img_batch = tf.expand_dims(img, 0)
    
    # Get predictions
    mask_pred, tags_pred = model.predict(img_batch, verbose=0)
    
    print("Predictions:")
    print(f"Eye detection confidence: {tags_pred[0][0]:.3f}")
    print(f"Blink detection confidence: {tags_pred[0][1]:.3f}")
    
    # Visualize if output directory is provided
    if output_dir:
        base_name = os.path.splitext(os.path.basename(image_path))[0]
        save_path = os.path.join(output_dir, f'{base_name}_prediction.png')
        visualize_prediction(img.numpy(), mask_pred[0], tags_pred[0], save_path)
    
    return mask_pred[0], tags_pred[0]

# Load the model
model_path = 'runs/b32_c-conv_d-|root|meye|data|NN_human_mouse_eyes|_g1.5_l0.001_num_c1_num_f16_num_s5_r128_se23_sp-random_up-relu_us0/best_model.h5'
print("Loading model...")
model = tf.keras.models.load_model(model_path, custom_objects=custom_objects)

output_dir = "/root/meye/test_predictions"  # absolute path in /meye directory
os.makedirs(output_dir, exist_ok=True)

print(f"\nSaving predictions to: {output_dir}")

# Test directory with multiple images
test_dir = "/root/meye/data/NN_human_mouse_eyes/fullFrames"
image_files = glob(os.path.join(test_dir, "*.jpg"))[:10]  # Test first 10 images

print(f"\nTesting {len(image_files)} images...")
results = []

for image_path in image_files:
    mask_pred, tags_pred = test_single_image(image_path, model, output_dir)
    results.append({
        'image': os.path.basename(image_path),
        'eye_conf': tags_pred[0],
        'blink_conf': tags_pred[1]
    })

# Print summary
print("\nSummary:")
df = pd.DataFrame(results)
print("\nAverage confidences:")
print(f"Eye detection: {df['eye_conf'].mean():.3f} ± {df['eye_conf'].std():.3f}")
print(f"Blink detection: {df['blink_conf'].mean():.3f} ± {df['blink_conf'].std():.3f}")