了解Sagemaker对象检测预测的输出
我需要帮助来了解Amazon Sagemaker对象检测算法的输出。
这是我的基本目标：识别乒乓球何时在比赛中，并在图像框中标记出它的位置。
对来自视频供稿的图像进行采样：
到目前为止的步骤： 我从乒乓球比赛中拿了n个视频帧。
-
我用RectLabel手工注释了乒乓球的位置。
-
使用RectLabel，我将这些标签转换为JSON文件。此处的示例：
{"images":[
{"id":1,"file_name":"thumb0462.png","width":0,"height":0},
{"id":2,"file_name":"thumb0463.png","width":0,"height":0},
{"id":3,"file_name":"thumb0464.png","width":0,"height":0},
...
{"id":4582,"file_name":"thumb6492.png","width":0,"height":0}],
"annotations":[
{"area":198,"iscrowd":0,"id":1,"image_id":5,"category_id":1,"segmentation":[[59,152,76,152,76,142,59,142]],"bbox":[59,142,18,11]},
{"area":221,"iscrowd":0,"id":2,"image_id":6,"category_id":1,"segmentation":[[83,155,99,155,99,143,83,143]],"bbox":[83,143,17,13]},
{"area":399,"iscrowd":0,"id":3,"image_id":8,"category_id":1,"segmentation":[[118,144,136,144,136,124,118,124]],"bbox":[118,124,19,21]},
{"area":361,"iscrowd":0,"id":4,"image_id":9,"category_id":1,"segmentation":[[132,123,150,123,150,105,132,105]],"bbox":[132,105,19,19]},
...
"categories":[{"name":"pp_ball","id":1}]
}
- 我使用了一个函数，将注释分为SageMaker的输入通道所期望的训练和验证文件夹。
file_name = './pp-ball-annotations.json'
with open(file_name) as f:
js = json.load(f)
images = js['images']
categories = js['categories']
annotations = js['annotations']
for i in images:
jsonFile = i['file_name']
jsonFile = jsonFile.split('.')[0] + '.json'
line = {}
line['file'] = i['file_name']
line['image_size'] = [{
'width': int(i['width']),
'height': int(i['height']),
'depth': 3
}]
line['annotations'] = []
line['categories'] = []
for j in annotations:
if j['image_id'] == i['id'] and len(j['bbox']) > 0:
line['annotations'].append({
'class_id': int(j['category_id']),
'top': int(j['bbox'][1]),
'left': int(j['bbox'][0]),
'width': int(j['bbox'][2]),
'height': int(j['bbox'][3])
})
class_name = ''
for k in categories:
if int(j['category_id']) == k['id']:
class_name = str(k['name'])
assert class_name is not ''
line['categories'].append({
'class_id': int(j['category_id']),
'name': class_name
})
if line['annotations']:
with open(os.path.join('generated', jsonFile), 'w') as p:
json.dump(line, p)
jsons = os.listdir('generated')
print ('There are {} images that have annotation files'.format(len(jsons)))
- 我按照SageMaker的要求将文件移动到具有四个通道（文件夹）的Amazon S3存储桶中：/ train，/ validation，/ train_annotation和/ validation_annotation。
num_annotated_files = len(jsons)
train_split_pct = 0.70
num_train_jsons = int(num_annotated_files * train_split_pct)
random.shuffle(jsons) # randomize/shuffle the JSONs to reduce reliance on *sequenced* frames
train_jsons = jsons[:num_train_jsons]
val_jsons = jsons[num_train_jsons:]
#Moving training files to the training folders
for i in train_jsons:
image_file = './images/'+i.split('.')[0]+'.png'
shutil.move(image_file, './train/')
shutil.move('./generated/'+i, './train_annotation/')
#Moving validation files to the validation folders
for i in val_jsons:
image_file = './images/'+i.split('.')[0]+'.png'
shutil.move(image_file, './validation/')
shutil.move('./generated/'+i, './validation_annotation/')
### Upload to S3
import sagemaker
from sagemaker import get_execution_role
role = sagemaker.get_execution_role()
sess = sagemaker.Session()
from sagemaker.amazon.amazon_estimator import get_image_uri
training_image = get_image_uri(sess.boto_region_name, 'object-detection', repo_version="latest")
bucket = 'pp-balls-object-detection' # custom bucket name.
# bucket = sess.default_bucket()
prefix = 'rect-label-test'
train_channel = prefix + '/train'
validation_channel = prefix + '/validation'
train_annotation_channel = prefix + '/train_annotation'
validation_annotation_channel = prefix + '/validation_annotation'
sess.upload_data(path='train', bucket=bucket, key_prefix=train_channel)
sess.upload_data(path='validation', bucket=bucket, key_prefix=validation_channel)
sess.upload_data(path='train_annotation', bucket=bucket, key_prefix=train_annotation_channel)
sess.upload_data(path='validation_annotation', bucket=bucket, key_prefix=validation_annotation_channel)
s3_train_data = 's3://{}/{}'.format(bucket, train_channel)
s3_validation_data = 's3://{}/{}'.format(bucket, validation_channel)
s3_train_annotation = 's3://{}/{}'.format(bucket, train_annotation_channel)
s3_validation_annotation = 's3://{}/{}'.format(bucket, validation_annotation_channel)
- 创建了具有某些超参数的SageMaker对象检测器。我注意到，给定我看到的其他示例，这些超参数“不寻常”：num_classes = 1，use_pretrained_model = 0，image_shape =438。
s3_output_location = 's3://{}/{}/output'.format(bucket, prefix)
od_model = sagemaker.estimator.Estimator(training_image,
role,
train_instance_count=1,
train_instance_type='ml.p3.2xlarge',
train_volume_size = 50,
train_max_run = 360000,
input_mode = 'File',
output_path=s3_output_location,
sagemaker_session=sess)
od_model.set_hyperparameters(base_network='resnet-50',
use_pretrained_model=0,
num_classes=1,
mini_batch_size=15,
epochs=30,
learning_rate=0.001,
lr_scheduler_step='10',
lr_scheduler_factor=0.1,
optimizer='sgd',
momentum=0.9,
weight_decay=0.0005,
overlap_threshold=0.5,
nms_threshold=0.45,
image_shape=438,
label_width=600,
num_training_samples=num_train_jsons)
- 我为对象检测器设置了训练/验证位置（称为.fit函数），并将模型部署到了端点：
train_data = sagemaker.session.s3_input(s3_train_data, distribution='FullyReplicated',
content_type='image/png', s3_data_type='S3Prefix')
validation_data = sagemaker.session.s3_input(s3_validation_data, distribution='FullyReplicated',
content_type='image/png', s3_data_type='S3Prefix')
train_annotation = sagemaker.session.s3_input(s3_train_annotation, distribution='FullyReplicated',
content_type='image/png', s3_data_type='S3Prefix')
validation_annotation = sagemaker.session.s3_input(s3_validation_annotation, distribution='FullyReplicated',
content_type='image/png', s3_data_type='S3Prefix')
data_channels = {'train': train_data, 'validation': validation_data,
'train_annotation': train_annotation, 'validation_annotation':validation_annotation}
od_model.fit(inputs=data_channels, logs=True)
object_detector = od_model.deploy(initial_instance_count = 1,
instance_type = 'ml.m4.xlarge')
- 我通过向端点传递一个PNG文件（以字节为单位）来调用端点：
file_with_path = 'test/thumb0695.png'
with open(file_with_path, 'rb') as image:
f = image.read()
b = bytearray(f)
ne = open('n.txt', 'wb')
ne.write(b)
results = object_detector.predict(b)
detections = json.loads(results)
print(detections)
- AWS Sagemaker文档说期望输出以下格式：
.json文件中的每一行都包含一个表示检测到的对象的数组。这些对象数组中的每一个都包含六个数字的列表。第一个数字是预测的类别标签。第二个数字是与检测相关的置信度得分。后四个数字表示边界框坐标[xmin，ymin，xmax，ymax]。这些输出边界框角索引通过整体图像大小进行归一化。请注意，此编码与输入.json格式所使用的编码不同。例如，在检测结果的第一项中，0.3088374733924866是边界框的左坐标（左上角的x坐标）与整个图像宽度之比，0.07030484080314636是顶部坐标（y坐标为）。边界框的左上角）占整体图像高度的比例，0.7110607028007007507是边界框的右坐标（右下角的x坐标），占整体图像宽度的比例，0.9345266819000244是边界框的底部坐标（右下角的y坐标）与总图像高度的比率。
让我们看一下测试图像：
{“ id”：9，“ file_name”：“ thumb0470.png”，“ width”：438，“ height”：240}
带有一个带有此边界框的球[132,105,19,19]（读作x-左上角，y-左上角，框宽，框高）。
鉴于我的对象检测器经过训练可以检测一个类（num_classes = 1），因此我希望此图像具有这种输出：
{'prediction'：[[1.0，0.71，0.55，0.239，0.629，0.283]]}
相反，我得到以下输出：
{'prediction': [[0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 0.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 0.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 0.0, 0.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0, 0.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 0.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 0.0, 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0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 0.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 1.0]]}
现在是一个问题：为什么该模型为我提供了400个JSON元素，而不仅仅是一个？
我当前的假设：该对象检测模型训练得很弱（非常有可能，因为这只是图像太少的第一次通过），以至于Single Shot Detector正在识别它认为是“ ping”事件的400个实例乒乓球”。
但是，即使我的假设是正确的，为什么重复输出这么多？格式为
的178个相同的“预测”[0.0，1.0，0.0，0.0，1.0，0.0]
如果被解释，则表示：
0.0-我未定义的类对象“ 0”。所以我认为这意味着“比赛中没有球”
1.0-100％置信度
0.0-xmin位置（宽度比为0）
0.0-ymin位置，高度比为0
1.0-xmax位置的宽度与宽度之比= 240
0.0-ymax位置，高度比= 0
坐标[xmin：0，ymin：0，xmax：240，ymax：0]就像在第一个像素上画一条线。
感谢您的帮助！
-------根据Ryo的答案进行编辑------
将类别ID重新映射到以索引为基础0的方式就像一种魅力。以下是仅2,000张带有标签的图像的结果：
这是Ryo的有用答案之后的代码：
def fixCategoryId(category_id):
return category_id - 1;
with open(file_name) as f:
js = json.load(f)
images = js['images']
categories = js['categories']
annotations = js['annotations']
for i in images:
jsonFile = i['file_name']
jsonFile = jsonFile.split('.')[0] + '.json'
line = {}
line['file'] = i['file_name']
line['image_size'] = [{
'width': int(i['width']),
'height': int(i['height']),
'depth': 3
}]
line['annotations'] = []
line['categories'] = []
for j in annotations:
if j['image_id'] == i['id'] and len(j['bbox']) > 0:
line['annotations'].append({
'class_id': fixCategoryId(int(j['category_id'])),
'top': int(j['bbox'][1]),
'left': int(j['bbox'][0]),
'width': int(j['bbox'][2]),
'height': int(j['bbox'][3])
})
class_name = ''
for k in categories:
if int(j['category_id']) == k['id']:
class_name = str(k['name'])
assert class_name is not ''
line['categories'].append({
'class_id': fixCategoryId(int(j['category_id'])),
'name': class_name
})
if line['annotations']:
with open(os.path.join('generated', jsonFile), 'w') as p:
json.dump(line, p)
jsons = os.listdir('generated')
print ('There are {} images that have annotation files'.format(len(jsons)))
2 个答案:
答案 0 :(得分：1)
尽管COCO JSON文件中的'category_id'从1开始，但是Amazon SageMaker JSON文件中的'class_id'从0开始。
您的转换代码应该是这样的。
def fixCategoryId(category_id):
return category_id - 1;
with open(coco_json_path) as f:
js = json.load(f)
images = js['images']
categories = js['categories']
annotations = js['annotations']
for i in images:
jsonFile = i['file_name']
jsonFile = jsonFile.split('.')[0] + '.json'
line = {}
line['file'] = i['file_name']
line['image_size'] = [{
'width': int(i['width']),
'height': int(i['height']),
'depth': 3
}]
line['annotations'] = []
line['categories'] = []
for j in annotations:
if j['image_id'] == i['id'] and len(j['bbox']) > 0:
line['annotations'].append({
'class_id': fixCategoryId(int(j['category_id'])),
'top': int(j['bbox'][1]),
'left': int(j['bbox'][0]),
'width': int(j['bbox'][2]),
'height': int(j['bbox'][3])
})
class_name = ''
for k in categories:
if int(j['category_id']) == k['id']:
class_name = str(k['name'])
assert class_name is not ''
line['categories'].append({
'class_id': fixCategoryId(int(j['category_id'])),
'name': class_name
})
if line['annotations']:
with open(os.path.join(sagemaker_json_path, jsonFile), 'w') as p:
json.dump(line, p)
In the Amazon SageMaker doc，他们正在使用get_coco_mapper（）进行此操作。
import json
import logging
def get_coco_mapper():
original_list = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20,
21, 22, 23, 24, 25, 27, 28, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40,
41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60,
61, 62, 63, 64, 65, 67, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80,
81, 82, 84, 85, 86, 87, 88, 89, 90]
iter_counter = 0
COCO = {}
for orig in original_list:
COCO[orig] = iter_counter
iter_counter += 1
return COCO
训练模型后，您必须检查每次损失是否减少。
od_model.fit(inputs=data_channels, logs=True)
[11/04/2019 09:26:46 INFO 140651482974016] #quality_metric: host=algo-1, epoch=499, batch=11 train cross_entropy <loss>=(0.20304460724736212)
[11/04/2019 09:26:46 INFO 140651482974016] #quality_metric: host=algo-1, epoch=499, batch=11 train smooth_l1 <loss>=(0.06970448779799958)
如果您有任何疑问，请告诉我们。
答案 1 :(得分：0)
发件人：https://docs.aws.amazon.com/sagemaker/latest/dg/algo-object-detection-tech-notes.html
对象检测算法可识别并找到的所有实例 来自已知对象类别集合的图像中的对象
这说明了为什么您要从预测中获取响应数组中的400个项目。
此处的输出数据是错误的。您已将其正确映射到图像的顶部，但是其高度为0，因此实际上没有任何地方。
发件人：https://docs.aws.amazon.com/sagemaker/latest/dg/object-detection.html#object-detection-inputoutput
“类别”属性存储类索引之间的映射 和班级名称。班级索引应依次编号和 编号应以0开头。“ categories”属性为 注释.json文件是可选的
您的类别数组以类ID 1开始。
您已提供了从标签工具获得的示例json，但是文件generated中没有生成的json示例。同样，查看该输出的样本也会很有帮助。
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