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Python util.channel_convert方法代碼示例

本文整理匯總了Python中data.util.channel_convert方法的典型用法代碼示例。如果您正苦於以下問題：Python util.channel_convert方法的具體用法？Python util.channel_convert怎麽用？Python util.channel_convert使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類data.util的用法示例。


在下文中一共展示了util.channel_convert方法的6個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。

示例1: __getitem__

# 需要導入模塊: from data import util [as 別名]
# 或者: from data.util import channel_convert [as 別名]
def __getitem__(self, index):
        LR_path = None

        # get LR image
        LR_path = self.paths_LR[index]
        img_LR = util.read_img(self.LR_env, LR_path)
        H, W, C = img_LR.shape

        # change color space if necessary
        if self.opt['color']:
            img_LR = util.channel_convert(C, self.opt['color'], [img_LR])[0]

        # BGR to RGB, HWC to CHW, numpy to tensor
        if img_LR.shape[2] == 3:
            img_LR = img_LR[:, :, [2, 1, 0]]
        img_LR = torch.from_numpy(np.ascontiguousarray(np.transpose(img_LR, (2, 0, 1)))).float()

        return {'LR': img_LR, 'LR_path': LR_path}
開發者ID:xinntao，項目名稱:BasicSR，代碼行數:20，代碼來源:LR_dataset.py

示例2: __getitem__

# 需要導入模塊: from data import util [as 別名]
# 或者: from data.util import channel_convert [as 別名]
def __getitem__(self, index):
        if self.data_type == 'lmdb' and self.LQ_env is None:
            self._init_lmdb()
        LQ_path = None

        # get LQ image
        LQ_path = self.LQ_path[index]
        resolution = [int(s) for s in self.sizes_LQ[index].split('_')
                      ] if self.data_type == 'lmdb' else None
        img_LQ = util.read_img(self.LQ_env, LQ_path, resolution)
        H, W, C = img_LQ.shape

        if self.opt['color']:  # change color space if necessary
            img_LQ = util.channel_convert(C, self.opt['color'], [img_LQ])[0]

        # BGR to RGB, HWC to CHW, numpy to tensor
        if img_LQ.shape[2] == 3:
            img_LQ = img_LQ[:, :, [2, 1, 0]]
        img_LQ = torch.from_numpy(np.ascontiguousarray(np.transpose(img_LQ, (2, 0, 1)))).float()

        return {'LQ': img_LQ, 'LQ_path': LQ_path}
開發者ID:xinntao，項目名稱:EDVR，代碼行數:23，代碼來源:LQ_dataset.py

示例3: __getitem__

# 需要導入模塊: from data import util [as 別名]
# 或者: from data.util import channel_convert [as 別名]
def __getitem__(self, index):
        if self.data_type == 'lmdb' and self.LQ_env is None:
            self._init_lmdb()
        LQ_path = None

        # get LQ image
        LQ_path = self.paths_LQ[index]
        resolution = [int(s) for s in self.sizes_LQ[index].split('_')
                      ] if self.data_type == 'lmdb' else None
        img_LQ = util.read_img(self.LQ_env, LQ_path, resolution)
        H, W, C = img_LQ.shape

        if self.opt['color']:  # change color space if necessary
            img_LQ = util.channel_convert(C, self.opt['color'], [img_LQ])[0]

        # BGR to RGB, HWC to CHW, numpy to tensor
        if img_LQ.shape[2] == 3:
            img_LQ = img_LQ[:, :, [2, 1, 0]]
        img_LQ = torch.from_numpy(np.ascontiguousarray(np.transpose(img_LQ, (2, 0, 1)))).float()

        return {'LQ': img_LQ, 'LQ_path': LQ_path}
開發者ID:open-mmlab，項目名稱:mmsr，代碼行數:23，代碼來源:LQ_dataset.py

示例4: __getitem__

# 需要導入模塊: from data import util [as 別名]
# 或者: from data.util import channel_convert [as 別名]
def __getitem__(self, index):
        if self.opt['data_type'] == 'lmdb':
            if self.LR_env is None:
                self._init_lmdb()

        LR_size = self.LR_size

        # get real kernel map
        real_ker_map = self.real_ker_map_list[index].float()

        # get LR image
        LR_path = self.LR_paths[index]
        if self.opt['data_type'] == 'lmdb':
            resolution = [int(s) for s in self.LR_sizes[index].split('_')]
        else:
            resolution = None
        img_LR = util.read_img(self.LR_env, LR_path, resolution)
        H, W, C = img_LR.shape

        if self.opt['phase'] == 'train':
            #randomly crop
            rnd_h = random.randint(0, max(0, H - LR_size))
            rnd_w = random.randint(0, max(0, W - LR_size))
            img_LR = img_LR[rnd_h:rnd_h + LR_size, rnd_w:rnd_w + LR_size, :]

            # augmentation - flip, rotate
            img_LR = util.augment(img_LR, self.opt['use_flip'], self.opt['use_rot'], self.opt['mode'])

        # change color space if necessary
        if self.opt['color']:
            img_LR = util.channel_convert(C, self.opt['color'], [img_LR])[0]

        # BGR to RGB, HWC to CHW, numpy to tensor
        if img_LR.shape[2] == 3:
            img_LR = img_LR[:, :, [2, 1, 0]]
        img_LR = torch.from_numpy(np.ascontiguousarray(np.transpose(img_LR, (2, 0, 1)))).float()

        return {'LQ': img_LR, 'LQ_path': LR_path, 'real_ker': real_ker_map}
開發者ID:yuanjunchai，項目名稱:IKC，代碼行數:40，代碼來源:LQ_dataset.py

示例5: __getitem__

# 需要導入模塊: from data import util [as 別名]
# 或者: from data.util import channel_convert [as 別名]
def __getitem__(self, index):
        if self.opt['data_type'] == 'lmdb':
            if self.LR_env is None:
                self._init_lmdb()

        LR_size = self.LR_size

        # get LR image, kernel map
        LR_path = self.LR_paths[index]
        ker_map = self.ker_maps[index]
        if self.opt['data_type'] == 'lmdb':
            resolution = [int(s) for s in self.LR_sizes[index].split('_')]
        else:
            resolution = None
        img_LR = util.read_img(self.LR_env, LR_path, resolution)
        H, W, C = img_LR.shape

        if self.opt['phase'] == 'train':
            #randomly crop
            rnd_h = random.randint(0, max(0, H - LR_size))
            rnd_w = random.randint(0, max(0, W - LR_size))
            img_LR = img_LR[rnd_h:rnd_h + LR_size, rnd_w:rnd_w + LR_size, :]

            # augmentation - flip, rotate
            img_LR = util.augment(img_LR, self.opt['use_flip'], self.opt['use_rot'], self.opt['mode'])

        # change color space if necessary
        if self.opt['color']:
            img_LR = util.channel_convert(C, self.opt['color'], [img_LR])[0]

        # BGR to RGB, HWC to CHW, numpy to tensor
        if img_LR.shape[2] == 3:
            img_LR = img_LR[:, :, [2, 1, 0]]
        img_LR = torch.from_numpy(np.ascontiguousarray(np.transpose(img_LR, (2, 0, 1)))).float()

        return {'LQ': img_LR, 'ker': ker_map, 'LQ_path': LR_path}
開發者ID:yuanjunchai，項目名稱:IKC，代碼行數:38，代碼來源:LQker_dataset.py

示例6: __getitem__

# 需要導入模塊: from data import util [as 別名]
# 或者: from data.util import channel_convert [as 別名]
def __getitem__(self, index):
        if self.opt['data_type'] == 'lmdb':
            if self.LR_env is None:
                self._init_lmdb()

        LR_size = self.LR_size
        SR_size = self.SR_size
        scale = self.opt['scale']

        # get real kernel map
        real_ker_map = self.real_ker_map_list[index]
        # get each kernel map
        ker_map = self.ker_map_list[index]

        # get LR image
        LR_path = self.LR_paths[index]
        if self.opt['data_type'] == 'lmdb':
            resolution = [int(s) for s in self.LR_sizes[index].split('_')]
        else:
            resolution = None
        img_LR = util.read_img(self.LR_env, LR_path, resolution)
        H, W, C = img_LR.shape

        #get SR image
        img_SR = self.SR_img_list[index]

        if self.opt['phase'] == 'train':
            #randomly crop
            rnd_h = random.randint(0, max(0, H - LR_size))
            rnd_w = random.randint(0, max(0, W - LR_size))
            rnd_h_SR, rnd_w_SR = int(rnd_h * scale), int(rnd_w * scale)
            img_SR = img_SR[rnd_h_SR:rnd_h_SR + SR_size, rnd_w_SR:rnd_w_SR + SR_size, :]

            # augmentation - flip, rotate
            img_SR = util.augment(img_SR, self.opt['use_flip'], self.opt['use_rot'], self.opt['mode'])

        # change color space if necessary
        if self.opt['color']:
            img_SR = util.channel_convert(C, self.opt['color'], [img_SR])[0]

        # BGR to RGB, HWC to CHW, numpy to tensor
        if img_SR.shape[2] == 3:
            img_SR = img_SR[:, :, [2, 1, 0]]
        img_SR = torch.from_numpy(np.ascontiguousarray(np.transpose(img_SR, (2, 0, 1)))).float()

        return {'SR': img_SR, 'real_ker': real_ker_map, 'ker': ker_map}
開發者ID:yuanjunchai，項目名稱:IKC，代碼行數:48，代碼來源:SRker_dataset.py


注：本文中的data.util.channel_convert方法示例由純淨天空整理自Github/MSDocs等開源代碼及文檔管理平台，相關代碼片段篩選自各路編程大神貢獻的開源項目，源碼版權歸原作者所有，傳播和使用請參考對應項目的License；未經允許，請勿轉載。