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47 lines
1.5 KiB
Python
47 lines
1.5 KiB
Python
import os
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import glob
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import time
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import argparse
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# Kerasa / TensorFlow
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os.environ['TF_CPP_MIN_LOG_LEVEL'] = '5'
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from keras.models import load_model
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from layers import BilinearUpSampling2D
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from loss import depth_loss_function
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from utils import predict, load_images, display_images, evaluate
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from matplotlib import pyplot as plt
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# Argument Parser
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parser = argparse.ArgumentParser(description='High Quality Monocular Depth Estimation via Transfer Learning')
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parser.add_argument('--model', default='nyu.h5', type=str, help='Trained Keras model file.')
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args = parser.parse_args()
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# Custom object needed for inference and training
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custom_objects = {'BilinearUpSampling2D': BilinearUpSampling2D, 'depth_loss_function': depth_loss_function}
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# Load model into GPU / CPU
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print('Loading model...')
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model = load_model(args.model, custom_objects=custom_objects, compile=False)
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# Load test data
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print('Loading test data...', end='')
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import numpy as np
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from data import extract_zip
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data = extract_zip('nyu_test.zip')
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from io import BytesIO
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rgb = np.load(BytesIO(data['eigen_test_rgb.npy']))
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depth = np.load(BytesIO(data['eigen_test_depth.npy']))
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crop = np.load(BytesIO(data['eigen_test_crop.npy']))
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print('Test data loaded.\n')
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start = time.time()
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print('Testing...')
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e = evaluate(model, rgb, depth, crop, batch_size=6)
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print("{:>10}, {:>10}, {:>10}, {:>10}, {:>10}, {:>10}".format('a1', 'a2', 'a3', 'rel', 'rms', 'log_10'))
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print("{:10.4f}, {:10.4f}, {:10.4f}, {:10.4f}, {:10.4f}, {:10.4f}".format(e[0],e[1],e[2],e[3],e[4],e[5]))
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end = time.time()
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print('\nTest time', end-start, 's')
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