def preprocess(image_path):
I = np.asarray(imageio.imread(image_path))
I = extract_bayer_channels(I)
I = I[0:256 // 2, 0:256 // 2, :]
I = np.reshape(I, [1, I.shape[0], I.shape[1], 4])
return I
sess, "models/pynet_level_" + str(LEVEL) + "_iteration_" +
str(restore_iter) + ".ckpt")
# Processing full-resolution RAW images
test_dir = dataset_dir + "/test/huawei_full_resolution/"
test_photos = [
f for f in os.listdir(test_dir) if os.path.isfile(test_dir + f)
]
for photo in test_photos:
print("Processing image " + photo)
I = np.asarray(imageio.imread((test_dir + photo)))
I = extract_bayer_channels(I)
I = I[0:IMAGE_HEIGHT, 0:IMAGE_WIDTH, :]
I = np.reshape(I, [1, I.shape[0], I.shape[1], 4])
# Run inference
enhanced_tensor = sess.run(enhanced, feed_dict={x_: I})
enhanced_image = np.reshape(
enhanced_tensor,
[int(I.shape[1] * DSLR_SCALE),
int(I.shape[2] * DSLR_SCALE), 3])
# Save the results as .png images
photo_name = photo.rsplit(".", 1)[0]
imageio.imsave(