Hi AastaLLL, thanks for your reply!
I have tried manipulating the Window and Max Disparity parameters. I am using the Python API. I don’t see the quality and confidence parameters in the Python function.
I am manually adjusting the confidence threshold with Scale in confidence_map.convert. The noise level has decreased, but the result still seems pretty poor.
Here is my code:
scale = 1
window = 5
max_disparity = 256
confidence_threshold = 327670
left = cv2.imread('stereo_pair/left.jpg')
right = cv2.imread('stereo_pair/right.jpg')
with vpi.Backend.CUDA:
left = vpi.asimage(left).convert(vpi.Format.Y16_ER, scale=scale)
right = vpi.asimage(right).convert(vpi.Format.Y16_ER, scale=scale)
confidence_map = vpi.Image(left.size, vpi.Format.U16)
disparity = vpi.stereodisp(left, right, backend=vpi.Backend.CUDA, window=window, maxdisp=max_disparity)
with vpi.Backend.CUDA:
disparity = disparity.convert(vpi.Format.U8, scale=255.0 / (32 * max_disparity))
left = left.convert(vpi.Format.U8)
right = right.convert(vpi.Format.U8)
with disparity.rlock():
disparity_color = cv2.applyColorMap(disparity.cpu(), cv2.COLORMAP_JET)
disparity_color = cv2.cvtColor(disparity_color, cv2.COLOR_BGR2RGB)
with left.rlock():
left = left.cpu()
left = cv2.cvtColor(left, cv2.COLOR_BGR2RGB)
with right.rlock():
right = right.cpu()
right = cv2.cvtColor(right, cv2.COLOR_BGR2RGB)
with vpi.Backend.CUDA:
confidence_map = confidence_map.convert(vpi.Format.U8, scale=255.0/confidence_threshold)
with confidence_map.rlock():
confidence_map = confidence_map.cpu()
mask = cv2.threshold(confidence_map, 1, 255, cv2.THRESH_BINARY)[1]
mask = cv2.cvtColor(mask, cv2.COLOR_GRAY2BGR)
disparity_color = cv2.bitwise_and(disparity_color, mask)
combined_image = np.hstack((left, right, disparity_color))
cv2.imwrite("stereo_pair/combined.jpg", combined_image)
As a new member, I can only upload one image at a time, so I combined the left, right, and disparity images.
