cuda-to-cv.py

#!/usr/bin/env python3
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import cv2
import argparse

from jetson_utils import (loadImage, cudaAllocMapped, cudaConvertColor, 
                          cudaDeviceSynchronize, cudaToNumpy)

# parse the command line
parser = argparse.ArgumentParser(description='Convert an image from CUDA to OpenCV')

parser.add_argument("file_in", type=str, default="images/jellyfish.jpg", nargs='?', help="filename of the input image to process")
parser.add_argument("file_out", type=str, default="images/test/cuda-to-cv.jpg", nargs='?', help="filename of the output image to save")

opt = parser.parse_args()


# load the image into CUDA memory
rgb_img = loadImage(opt.file_in)

print('RGB image: ')
print(rgb_img)

# convert to BGR, since that's what OpenCV expects
bgr_img = cudaAllocMapped(width=rgb_img.width,
                          height=rgb_img.height,
						  format='bgr8')

cudaConvertColor(rgb_img, bgr_img)

print('BGR image: ')
print(bgr_img)

# make sure the GPU is done work before we convert to cv2
cudaDeviceSynchronize()

# convert to cv2 image (cv2 images are numpy arrays)
cv_img = cudaToNumpy(bgr_img)

print('OpenCV image size: ' + str(cv_img.shape))
print('OpenCV image type: ' + str(cv_img.dtype))

# save the image
if opt.file_out is not None:
	cv2.imwrite(opt.file_out, cv_img)
	print("saved {:d}x{:d} test image to '{:s}'".format(bgr_img.width, bgr_img.height, opt.file_out))