test.py

import numpy as np
import cv2
from keras.models import load_model
#import scipy.io as sio
from dehaze_patchMap_dehaze import dehaze_patchMap

base_path_hazyImg = 'image/' 
base_path_result = 'patchMap/'
imgname = 'waterfall.tif'
save_dir = 'result/'
modelDir = 'PMS-Net.h5'

print ("Process image: ", imgname)

image = cv2.imread(base_path_hazyImg + imgname)
if image.shape[0] != 480 or image.shape[1] != 640:
    print('resize image tp 640*480')
    image = cv2.resize(image,(640,480))
hazy_input = np.reshape(image,(1, 480, 640, 3))
model = load_model(modelDir)
patchMap = model.predict(hazy_input, verbose = 1)
patchMap = np.reshape(patchMap,(480, 640))
        
recover_result, tx = dehaze_patchMap(image, 0.95, patchMap)

savename_result = save_dir + 'py_recover_' + imgname

cv2.imwrite(savename_result, recover_result)