本文整理汇总了Python中pycocotools.coco.COCO.annToRLE方法的典型用法代码示例。如果您正苦于以下问题：Python COCO.annToRLE方法的具体用法？Python COCO.annToRLE怎么用？Python COCO.annToRLE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pycocotools.coco.COCO的用法示例。
在下文中一共展示了COCO.annToRLE方法的1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: MSCOCO
# 需要导入模块: from pycocotools.coco import COCO [as 别名]
# 或者: from pycocotools.coco.COCO import annToRLE [as 别名]
class MSCOCO(PoseDataset):
def __init__(self, cfg):
cfg.all_joints = [[0], [2, 1], [4, 3], [6, 5], [8, 7],[10, 9], [12, 11], [14, 13], [16, 15]]
cfg.all_joints_names = ["nose", 'eye', 'ear', 'shoulder', 'elbow', 'hand', 'hip', 'knee', 'foot']
cfg.num_joints = 17
super().__init__(cfg)
def load_dataset(self):
dataset = self.cfg.dataset
dataset_phase = self.cfg.dataset_phase
dataset_ann = self.cfg.dataset_ann
# initialize COCO api
annFile = '%s/annotations/%s_%s.json'%(dataset,dataset_ann,dataset_phase)
self.coco = COCO(annFile)
imgIds = self.coco.getImgIds()
data = []
# loop through each image
for imgId in imgIds:
item = DataItem()
img = self.coco.loadImgs(imgId)[0]
item.im_path = "%s/images/%s/%s"%(dataset, dataset_phase, img["file_name"])
item.im_size = [3, img["height"], img["width"]]
item.coco_id = imgId
annIds = self.coco.getAnnIds(imgIds=img['id'], iscrowd=False)
anns = self.coco.loadAnns(annIds)
all_person_keypoints = []
masked_persons_RLE = []
visible_persons_RLE = []
all_visibilities = []
# Consider only images with people
has_people = len(anns) > 0
if not has_people and self.cfg.coco_only_images_with_people:
continue
for ann in anns: # loop through each person
person_keypoints = []
visibilities = []
if ann["num_keypoints"] != 0:
for i in range(self.cfg.num_joints):
x_coord = ann["keypoints"][3 * i]
y_coord = ann["keypoints"][3 * i + 1]
visibility = ann["keypoints"][3 * i + 2]
visibilities.append(visibility)
if visibility != 0: # i.e. if labeled
person_keypoints.append([i, x_coord, y_coord])
all_person_keypoints.append(np.array(person_keypoints))
visible_persons_RLE.append(maskUtils.decode(self.coco.annToRLE(ann)))
all_visibilities.append(visibilities)
if ann["num_keypoints"] == 0:
masked_persons_RLE.append(self.coco.annToRLE(ann))
item.joints = all_person_keypoints
item.im_neg_mask = maskUtils.merge(masked_persons_RLE)
if self.cfg.use_gt_segm:
item.gt_segm = np.moveaxis(np.array(visible_persons_RLE), 0, -1)
item.visibilities = all_visibilities
data.append(item)
self.has_gt = self.cfg.dataset is not "image_info"
return data
def compute_scmap_weights(self, scmap_shape, joint_id, data_item):
size = scmap_shape[0:2]
scmask = np.ones(size)
m = maskUtils.decode(data_item.im_neg_mask)
if m.size:
scmask = 1.0 - imresize(m, size)
scmask = np.stack([scmask] * self.cfg.num_joints, axis=-1)
return scmask
def get_pose_segments(self):
return [[0, 1], [0, 2], [1, 3], [2, 4], [5, 7], [6, 8], [7, 9], [8, 10], [11, 13], [12, 14], [13, 15], [14, 16]]
def visualize_coco(self, coco_img_results, visibilities):
inFile = "tmp.json"
with open(inFile, 'w') as outfile:
json.dump(coco_img_results, outfile)
get_gt_visibilities(inFile, visibilities)
# initialize cocoPred api
cocoPred = self.coco.loadRes(inFile)
os.remove(inFile)
imgIds = [coco_img_results[0]["image_id"]]
for imgId in imgIds:
img = cocoPred.loadImgs(imgId)[0]
im_path = "%s/images/%s/%s" % (self.cfg.dataset, self.cfg.dataset_phase, img["file_name"])
I = io.imread(im_path)
fig = plt.figure()
a = fig.add_subplot(2, 2, 1)
#.........这里部分代码省略.........