本文整理汇总了Python中utils.zipreader.imread方法的典型用法代码示例。如果您正苦于以下问题：Python zipreader.imread方法的具体用法？Python zipreader.imread怎么用？Python zipreader.imread使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类utils.zipreader的用法示例。
在下文中一共展示了zipreader.imread方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: __getitem__
# 需要导入模块: from utils import zipreader [as 别名]
# 或者: from utils.zipreader import imread [as 别名]
def __getitem__(self, index):
"""
Args:
index (int): Index
Returns:
tuple: Tuple (image, target). target is the object returned by ``coco.loadAnns``.
"""
coco = self.coco
img_id = self.ids[index]
ann_ids = coco.getAnnIds(imgIds=img_id)
target = coco.loadAnns(ann_ids)
file_name = coco.loadImgs(img_id)[0]['file_name']
if self.data_format == 'zip':
img = zipreader.imread(
self._get_image_path(file_name),
cv2.IMREAD_COLOR | cv2.IMREAD_IGNORE_ORIENTATION
)
else:
img = cv2.imread(
self._get_image_path(file_name),
cv2.IMREAD_COLOR | cv2.IMREAD_IGNORE_ORIENTATION
)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
if self.transform is not None:
img = self.transform(img)
if self.target_transform is not None:
target = self.target_transform(target)
return img, target 示例2: __getitem__
# 需要导入模块: from utils import zipreader [as 别名]
# 或者: from utils.zipreader import imread [as 别名]
def __getitem__(self, idx):
db_rec = copy.deepcopy(self.db[idx])
image_dir = 'images.zip@' if self.data_format == 'zip' else ''
image_file = osp.join(self.root, db_rec['source'], image_dir, 'images',
db_rec['image'])
if self.data_format == 'zip':
from utils import zipreader
data_numpy = zipreader.imread(
image_file, cv2.IMREAD_COLOR | cv2.IMREAD_IGNORE_ORIENTATION)
else:
data_numpy = cv2.imread(
image_file, cv2.IMREAD_COLOR | cv2.IMREAD_IGNORE_ORIENTATION)
joints = db_rec['joints_2d'].copy()
joints_vis = db_rec['joints_vis'].copy()
center = np.array(db_rec['center']).copy()
scale = np.array(db_rec['scale']).copy()
rotation = 0
if self.is_train:
sf = self.scale_factor
rf = self.rotation_factor
scale = scale * np.clip(np.random.randn() * sf + 1, 1 - sf, 1 + sf)
rotation = np.clip(np.random.randn() * rf, -rf * 2, rf * 2) \
if random.random() <= 0.6 else 0
trans = get_affine_transform(center, scale, rotation, self.image_size)
input = cv2.warpAffine(
data_numpy,
trans, (int(self.image_size[0]), int(self.image_size[1])),
flags=cv2.INTER_LINEAR)
if self.transform:
input = self.transform(input)
for i in range(self.num_joints):
if joints_vis[i, 0] > 0.0:
joints[i, 0:2] = affine_transform(joints[i, 0:2], trans)
if (np.min(joints[i, :2]) < 0 or
joints[i, 0] >= self.image_size[0] or
joints[i, 1] >= self.image_size[1]):
joints_vis[i, :] = 0
target, target_weight = self.generate_target(joints, joints_vis)
target = torch.from_numpy(target)
target_weight = torch.from_numpy(target_weight)
meta = {
'scale': scale,
'center': center,
'rotation': rotation,
'joints_2d': db_rec['joints_2d'],
'joints_2d_transformed': joints,
'joints_vis': joints_vis,
'source': db_rec['source']
}
return input, target, target_weight, meta