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https://github.com/opencv/opencv.git
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Abhishek Gola
2ec6a6bb65
Added OnnxRuntime GPU wrapper #28588 ### Pull Request Readiness Checklist See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [x] I agree to contribute to the project under Apache 2 License. - [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [x] The PR is proposed to the proper branch - [x] There is a reference to the original bug report and related work - [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [x] The feature is well documented and sample code can be built with the project CMake
53 lines
1.8 KiB
Python
53 lines
1.8 KiB
Python
from __future__ import print_function
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import cv2 as cv
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import numpy as np
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import argparse
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parser = argparse.ArgumentParser(
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description='This script is used to run style transfer models from '
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'https://github.com/onnx/models/tree/main/vision/style_transfer/fast_neural_style using OpenCV')
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parser.add_argument('--input', help='Path to image or video. Skip to capture frames from camera')
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parser.add_argument('--model', help='Path to .onnx model')
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parser.add_argument('--width', default=-1, type=int, help='Resize input to specific width.')
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parser.add_argument('--height', default=-1, type=int, help='Resize input to specific height.')
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parser.add_argument('--median_filter', default=0, type=int, help='Kernel size of postprocessing blurring.')
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args = parser.parse_args()
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net = cv.dnn.readNet(cv.samples.findFile(args.model))
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net.setPreferableBackend(cv.dnn.DNN_BACKEND_OPENCV)
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if args.input:
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cap = cv.VideoCapture(args.input)
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else:
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cap = cv.VideoCapture(0)
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cv.namedWindow('Styled image', cv.WINDOW_NORMAL)
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while cv.waitKey(1) < 0:
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hasFrame, frame = cap.read()
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if not hasFrame:
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cv.waitKey()
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break
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inWidth = args.width if args.width != -1 else frame.shape[1]
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inHeight = args.height if args.height != -1 else frame.shape[0]
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inp = cv.dnn.blobFromImage(frame, 1.0, (inWidth, inHeight),
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swapRB=True, crop=False)
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net.setInput(inp)
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t0 = cv.getTickCount()
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out = net.forward()
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t = (cv.getTickCount() - t0) / cv.getTickFrequency()
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out = out.reshape(3, out.shape[2], out.shape[3])
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out = out.transpose(1, 2, 0)
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print('%.2f ms' % (t * 1000.0))
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if args.median_filter:
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out = cv.medianBlur(out, args.median_filter)
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out = np.clip(out, 0, 255)
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out = out.astype(np.uint8)
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cv.imshow('Styled image', out)
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