from __future__ import print_function
import cv2 as cv
import numpy as np
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')
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('--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(args.model)
net.setPreferableBackend(cv.dnn.DNN_BACKEND_OPENCV);
if args.input:
cap = cv.VideoCapture(args.input)
else:
cap = cv.VideoCapture(0)
cv.namedWindow('Styled image', cv.WINDOW_NORMAL)
while cv.waitKey(1) < 0:
hasFrame, frame = cap.read()
if not hasFrame:
cv.waitKey()
break
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)
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()
freq = cv.getTickFrequency() / 1000
print(t / freq, 'ms')
if args.median_filter:
out = cv.medianBlur(out, args.median_filter)
cv.imshow('Styled image', out)
