本文整理汇总了Python中mrcnn.utils.Dataset方法的典型用法代码示例。如果您正苦于以下问题：Python utils.Dataset方法的具体用法？Python utils.Dataset怎么用？Python utils.Dataset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mrcnn.utils的用法示例。
在下文中一共展示了utils.Dataset方法的3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: add_additional_info
# 需要导入模块: from mrcnn import utils [as 别名]
# 或者: from mrcnn.utils import Dataset [as 别名]
def add_additional_info(self):
for i,j in self.additional_info.items():
setattr(self,i,j)
############################################################
# Dataset
############################################################ 示例2: evaluate_coco
# 需要导入模块: from mrcnn import utils [as 别名]
# 或者: from mrcnn.utils import Dataset [as 别名]
def evaluate_coco(model, dataset, coco, eval_type="bbox", limit=0, image_ids=None):
"""Runs official COCO evaluation.
dataset: A Dataset object with valiadtion data
eval_type: "bbox" or "segm" for bounding box or segmentation evaluation
limit: if not 0, it's the number of images to use for evaluation
"""
# Pick COCO images from the dataset
image_ids = image_ids or dataset.image_ids
# Limit to a subset
if limit:
image_ids = image_ids[:limit]
# Get corresponding COCO image IDs.
coco_image_ids = [dataset.image_info[id]["id"] for id in image_ids]
t_prediction = 0
t_start = time.time()
results = []
for i, image_id in enumerate(image_ids):
# Load image
image = dataset.load_image(image_id)
# Run detection
t = time.time()
r = model.detect([image], verbose=0)[0]
t_prediction += (time.time() - t)
# Convert results to COCO format
# Cast masks to uint8 because COCO tools errors out on bool
image_results = build_coco_results(dataset, coco_image_ids[i:i + 1],
r["rois"], r["class_ids"],
r["scores"],
r["masks"].astype(np.uint8))
results.extend(image_results)
# Load results. This modifies results with additional attributes.
coco_results = coco.loadRes(results)
# Evaluate
cocoEval = COCOeval(coco, coco_results, eval_type)
cocoEval.params.imgIds = coco_image_ids
cocoEval.evaluate()
cocoEval.accumulate()
cocoEval.summarize()
print("Prediction time: {}. Average {}/image".format(
t_prediction, t_prediction / len(image_ids)))
print("Total time: ", time.time() - t_start)
############################################################
# Training
############################################################ 示例3: evaluate_coco
# 需要导入模块: from mrcnn import utils [as 别名]
# 或者: from mrcnn.utils import Dataset [as 别名]
def evaluate_coco(model, dataset, coco, eval_type="bbox", limit=0, image_ids=None):
"""Runs official COCO evaluation.
dataset: A Dataset object with valiadtion data
eval_type: "bbox" or "segm" for bounding box or segmentation evaluation
limit: if not 0, it's the number of images to use for evaluation
"""
# Pick COCO images from the dataset
image_ids = image_ids or dataset.image_ids
# Limit to a subset
if limit:
image_ids = image_ids[:limit]
# Get corresponding COCO image IDs.
coco_image_ids = [dataset.image_info[id]["id"] for id in image_ids]
t_prediction = 0
t_start = time.time()
results = []
for i, image_id in enumerate(image_ids):
# Load image
image = dataset.load_image(image_id)
# Run detection
t = time.time()
r = model.detect([image], verbose=0)[0]
t_prediction += (time.time() - t)
# Convert results to COCO format
# Cast masks to uint8 because COCO tools errors out on bool
image_results = build_coco_results(dataset, coco_image_ids[i:i + 1],
r["rois"], r["class_ids"],
r["scores"],
r["masks"].astype(np.uint8))
results.extend(image_results)
# Load results. This modifies results with additional attributes.
coco_results = coco.loadRes(results)
# Evaluate
cocoEval = COCOeval(coco, coco_results, eval_type)
cocoEval.params.imgIds = coco_image_ids
cocoEval.evaluate()
cocoEval.accumulate()
cocoEval.summarize()
print("Prediction time: {}. Average {}/image".format(
t_prediction, t_prediction / len(image_ids)))
print("Total time: ", time.time() - t_start)
return results
############################################################
# Training
############################################################