How to apply feature matching method to enhance Stereo Disparity Map result

i would like to know if there is a right implementation for using ORB or SIFT w/ cv2.warpPerspective in order to get a better result, I’m using these code to get the psnr for comparing the original disp1 with the depth map I generated:

def psnr(img1, img2):
mse = numpy.mean( ((img1 - img2)) ** 2 )
if mse == 0:
return ‘INF’
PIXEL_MAX = 255.0
return 20 * math.log10(PIXEL_MAX / math.sqrt(mse))

test_imgs = [“Art”, “Dolls”, “Reindeer”]

for index in range(1):
gt_names = “./gt/”+test_imgs[index]+"/disp1.png";
gt_img = numpy.array(Image.open(gt_names),dtype=float);
print(gt_img.shape)

  pred_names =  "./pred/"+test_imgs[index]+"/disp1.png";
  pred_img = numpy.array(Image.open(pred_names),dtype=float);
  print(pred_img.shape)
    
  [h,l] = gt_img.shape
  gt_img = gt_img[:, 250:l]
  pred_img = pred_img[:, 250:l]
  pred_img[gt_img==0]= 0

  peaksnr = psnr(pred_img,gt_img);
  print('The Peak-SNR value is %0.4f \n', peaksnr);