paths = []
count = 0
for r, d, f in os.walk(dataset_path):
for file in f:
if '.png' in file or 'jpg' in file:
paths.append(os.path.join(r, file))
for path in paths:
# Select image
img = Image.open(path)
#create plot
f, axarr = plt.subplots(1,3,figsize=(15,15),gridspec_kw={'width_ratios': [1,super_sampling_ratio,super_sampling_ratio]})
axarr[0].set_xlabel('Original Image (' + str(input_dimensions[0]) + 'x' + str(input_dimensions[1]) + ')', fontsize=10)
axarr[1].set_xlabel('Interpolated Image (' + str(output_dimensions[0]) + 'x' + str(output_dimensions[1]) + ')', fontsize=10)
axarr[2].set_xlabel('Super Sampled Image (' + str(output_dimensions[0]) + 'x' + str(output_dimensions[1]) + ')', fontsize=10)
#original image
x = img.resize((input_dimensions[0],input_dimensions[1]))
#interpolated (resized) image
y = x.resize((output_dimensions[0],output_dimensions[1]))
x = np.array(x)
y = np.array(y)
# Remove alpha layer if imgaes are PNG
if(png):
x = x[...,:3]
y = y[...,:3]
#plotting first two images
axarr[0].imshow(x)
axarr[1].imshow(y)
#plotting super sampled image
x = x.reshape(1,input_dimensions[0],input_dimensions[1],input_dimensions[2])/255
result = np.array(model.predict_on_batch(x))*255
result = result.reshape(output_dimensions[0],output_dimensions[1],output_dimensions[2])
np.clip(result, 0, 255, out=result)
result = result.astype('uint8')
axarr[2].imshow(result)
# Save image
f.savefig(save_path + '\\frame_%d.png' % count)
# Increment file name counter
count = count + 1