python - Issues saving the autoencoder predicted image to a new directory

Question

I trained an autoencoder model in Keras to generate denoised images given noisy images. The predicted images are stored in the "result" directory, with the individual filenames appended with the "denoised" tag. When I run the code, nothing is stored in the "result" folder. When I ran for an individual image, the final cv2.imwrite throws this error: "TypeError: Expected Ptr<cv::UMat> for argument 'img'". Is there a different way to write the predicted files to the destination folder without using openCV in this case?

import glob
from keras.preprocessing import image

test = glob.glob("data/test1/*.png") #all test data that needs to be denoised
test.sort()
result = "data/result" #the folder that contains predicted denoised images
#load model
model = load_model('autoencoder_model.h5')
model.summary()
#compile the model
model.compile(loss='mean_squared_error', optimizer = RMSprop())

for f in test:
    img = image.load_img(f,grayscale=True,target_size=(128,128),interpolation="nearest")
    img_name = f.split(os.sep)[-1]
    #preprocess the image
    x = image.img_to_array(img)
    x = np.expand_dims(x, axis=0)
    x /= 255 
    #predict on the image
    preds = model.predict(x)
    pred_norm = 255 * (preds - preds.min()) / (preds.max() - preds.min())
    pred_normed = np.array(pred_norm, np.int)    
    cv2.imwrite(os.path.join(result,f[:-4]+'_removed.png'), pred_normed)

Answer 1

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