test_SR_4x.py

#!/usr/bin/env python
"""
# > Script for evaluating 4x SISR models 
#    - Paper: https://arxiv.org/pdf/1909.09437.pdf
#
# Maintainer: Jahid (email: islam034@umn.edu)
# Interactive Robotics and Vision Lab (http://irvlab.cs.umn.edu/)
# Any part of this repo can be used for academic and educational purposes only
"""
import os
import time
import ntpath
import numpy as np
from scipy import misc
from keras.models import model_from_json
## local libs
from utils.data_utils import getPaths, preprocess, deprocess

## for testing arbitrary local data
data_dir = "data/test/lr_4x/"
test_paths = getPaths(data_dir)
print ("{0} test images are loaded".format(len(test_paths)))

## load specific model
model_name = "srdrm"
#model_name = "srdrm-gan"
ckpt_name =  "model_20_"
checkpoint_dir  = os.path.join("checkpoints/saved/4x/", model_name) 
model_h5 = os.path.join(checkpoint_dir, ckpt_name+".h5")  
model_json = os.path.join(checkpoint_dir, ckpt_name + ".json")
# sanity
assert (os.path.exists(model_h5) and os.path.exists(model_json))
# load json and create model
with open(model_json, "r") as json_file:
    loaded_model_json = json_file.read()
generator = model_from_json(loaded_model_json)
# load weights into new model
generator.load_weights(model_h5)
print("\nLoaded data and model")

## create dir for output test data
samples_dir = os.path.join("data/output/4x/", model_name)
if not os.path.exists(samples_dir): os.makedirs(samples_dir)

# testing loop
times = []; s = time.time()
for img_path in test_paths:
    # prepare data
    img_name = ntpath.basename(img_path).split('.')[0]
    img_lr = misc.imread(img_path, mode='RGB').astype(np.float)  
    img_lr = misc.imresize(img_lr, (120,160))
    im = preprocess(img_lr)
    im = np.expand_dims(im, axis=0)
    # generate enhanced image
    s = time.time()
    gen = generator.predict(im)
    gen = deprocess(gen) # Rescale to 0-1
    tot = time.time()-s
    times.append(tot)
    # save sample images
    misc.imsave(os.path.join(samples_dir, img_name+'_gen.jpg'), gen[0])
    print ("tested: {0}".format(img_path))

# some statistics    
num_test = len(test_paths)
if (num_test==0):
    print ("\nFound no images for test")
else:
    print ("\nTotal images: {0}".format(num_test)) 
    # accumulate frame processing times (without bootstrap)
    Ttime, Mtime = np.sum(times[1:]), np.mean(times[1:]) 
    print ("Time taken: {0} sec at {1} fps".format(Ttime, 1./Mtime))
    print("\nSaved generated images in in {0}\n".format(samples_dir))