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import cv2
def style_transfer ( pathIn = ' ' ,
pathOut = ' ' ,
model = ' ' ,
width = None ,
jpg_quality = 80 ) :
"""
pathIn: 原始图片的路径
pathOut: 风格化图片的保存路径
model: 预训练模型的路径
width: 设置风格化图片的宽度，
jpg_quality: 0-100， ， ，
"""
# 读入原始图片，调整图片至所需尺寸，然后获取图片的宽度和高度
img = cv2 . imread ( pathIn )
( h , w ) = img . shape [ : 2 ]
if width is not None :
img = cv2 . resize ( img , ( width , round ( width * h / w ) ) , interpolation = cv2 . INTER_CUBIC )
( h , w ) = img . shape [ : 2 ]
net = cv2 . dnn . readNetFromTorch ( model )
# 将图片构建成一个blob： ， （ ）
# 然后执行一次前馈网络计算，并输出计算所需的时间
blob = cv2 . dnn . blobFromImage ( img , 1.0 , ( w , h ) , ( 103.939 , 116.779 , 123.680 ) , swapRB = False , crop = False )
net . setInput ( blob )
output = net . forward ( )
# reshape输出结果, 将减去的平均值加回来，并交换各颜色通道
output = output . reshape ( ( 3 , output . shape [ 2 ] , output . shape [ 3 ] ) )
output [ 0 ] + = 103.939
output [ 1 ] + = 116.779
output [ 2 ] + = 123.680
output = output . transpose ( 1 , 2 , 0 )
# 输出风格化后的图片
cv2 . imwrite ( pathOut , output , [ int ( cv2 . IMWRITE_JPEG_QUALITY ) , jpg_quality ] )
