コード例 #1
0
    with open(path, 'rb') as f:
        img = Image.open(f)
        # Resize image if specified.
        resized_img = img.resize(
            resizeDim, Image.ANTIALIAS) if (resizeDim != None) else img
        # Crop image if crop area specified.
        cropped_img = img.crop(cropArea) if (cropArea != None) else resized_img
        # Flip image horizontally if specified.
        flipped_img = cropped_img.transpose(
            Image.FLIP_LEFT_RIGHT) if frameFlip else cropped_img
        return flipped_img.convert('RGB')


bdcn = bdcn.BDCN()
bdcn.cuda()
structure_gen = layers.StructureGen(feature_level=args.feature_level)
structure_gen.cuda()
detail_enhance = layers.DetailEnhance()
detail_enhance.cuda()

# Channel wise mean calculated on adobe240-fps training dataset
mean = [0.5, 0.5, 0.5]
std = [0.5, 0.5, 0.5]
normalize = transforms.Normalize(mean=mean, std=std)
transform = transforms.Compose([transforms.ToTensor(), normalize])

negmean = [-1 for x in mean]
restd = [2, 2, 2]
revNormalize = transforms.Normalize(mean=negmean, std=restd)
TP = transforms.Compose([revNormalize, transforms.ToPILImage()])
コード例 #2
0
        img = Image.open(f)
        # Resize image if specified.
        resized_img = img.resize(
            resizeDim, Image.ANTIALIAS) if (resizeDim != None) else img
        # Crop image if crop area specified.
        cropped_img = img.crop(cropArea) if (cropArea != None) else resized_img
        # Flip image horizontally if specified.
        flipped_img = cropped_img.transpose(
            Image.FLIP_LEFT_RIGHT) if frameFlip else cropped_img
        return flipped_img.convert('RGB')


bdcn = bdcn.BDCN()
bdcn.cuda()
# structure_gen = layers.StructureGen(feature_level=args.feature_level)
structure_gen = layers.StructureGen(3)
structure_gen.cuda()
detail_enhance = layers.DetailEnhance()
detail_enhance.cuda()

# Channel wise mean calculated on adobe240-fps training dataset
mean = [0.5, 0.5, 0.5]
std = [0.5, 0.5, 0.5]
normalize = transforms.Normalize(mean=mean, std=std)
transform = transforms.Compose([transforms.ToTensor(), normalize])

negmean = [-1 for x in mean]
restd = [2, 2, 2]
revNormalize = transforms.Normalize(mean=negmean, std=restd)
TP = transforms.Compose([revNormalize, transforms.ToPILImage()])