mirror/opencv
1
0
Fork 0
mirror of https://github.com/opencv/opencv.git synced 2024-11-24 03:00:14 +08:00
Code Issues Actions Packages Projects Releases Wiki Activity

Merge pull request #24245 from alexlyulkov/al/update-fast-neural-style-dnn-sample

Browse Source 
Replaced torch7 model by ONNX model in fast-neural-style dnn sample
This commit is contained in:
Alexander Smorkalov 2023-09-08 16:33:18 +03:00 committed by GitHub
commit 1a8d37d19e
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
1 changed files with 7 additions and 8 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

15
samples/dnn/fast_neural_style.py
View File

@ -5,15 +5,15 @@ import argparse
parser = argparse.ArgumentParser(
description='This script is used to run style transfer models from '
'https://github.com/jcjohnson/fast-neural-style using OpenCV')
'https://github.com/onnx/models/tree/main/vision/style_transfer/fast_neural_style using OpenCV')
parser.add_argument('--input', help='Path to image or video. Skip to capture frames from camera')
parser.add_argument('--model', help='Path to .t7 model')
parser.add_argument('--model', help='Path to .onnx model')
parser.add_argument('--width', default=-1, type=int, help='Resize input to specific width.')
parser.add_argument('--height', default=-1, type=int, help='Resize input to specific height.')
parser.add_argument('--median_filter', default=0, type=int, help='Kernel size of postprocessing blurring.')
args = parser.parse_args()
net = cv.dnn.readNetFromTorch(cv.samples.findFile(args.model))
net = cv.dnn.readNet(cv.samples.findFile(args.model))
net.setPreferableBackend(cv.dnn.DNN_BACKEND_OPENCV)
if args.input:
@ -31,16 +31,12 @@ while cv.waitKey(1) < 0:
inWidth = args.width if args.width != -1 else frame.shape[1]
inHeight = args.height if args.height != -1 else frame.shape[0]
inp = cv.dnn.blobFromImage(frame, 1.0, (inWidth, inHeight),
(103.939, 116.779, 123.68), swapRB=False, crop=False)
swapRB=True, crop=False)
net.setInput(inp)
out = net.forward()
out = out.reshape(3, out.shape[2], out.shape[3])
out[0] += 103.939
out[1] += 116.779
out[2] += 123.68
out /= 255
out = out.transpose(1, 2, 0)
t, _ = net.getPerfProfile()
@ -50,4 +46,7 @@ while cv.waitKey(1) < 0:
if args.median_filter:
out = cv.medianBlur(out, args.median_filter)
out = np.clip(out, 0, 255)
out = out.astype(np.uint8)
cv.imshow('Styled image', out)