了解 Sagemaker 对象检测预测的输出答案

【问题标题】：understanding the output of Sagemaker Object Detection prediction了解 Sagemaker 对象检测预测的输出
【发布时间】：2020-02-23 19:04:18
【问题描述】：

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，并在图像框架中标记它的位置。

来自视频源的示例图像：

到目前为止的步骤： 1. 我从乒乓球比赛中获取了 n 个视频帧。

我使用 RectLabel 手动标注了乒乓球的位置。
使用 RectLabel，我将这些标签转换为 JSON 文件。此处示例：

{"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}]
}

正如 SageMaker 的输入通道所期望的那样，我使用了一个函数将注释分离到训练和验证文件夹中。

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)))

我根据 SageMaker 的要求将文件移动到了具有四个通道（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)

使用某些超参数创建了 SageMaker 对象检测器。我注意到，鉴于我见过的其他示例，这些超参数是“不寻常的”：num_classes = 1、use_pretrained_model=0 和 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以字节为单位的 PNG 文件来调用端点：

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 文档说期望输出格式如下：

此 .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标签。第二个数字是检测的相关置信度分数。最后四个数字代表边界框坐标 [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，此编码不同于输入 .json 格式使用的编码。例如，在检测结果的第一个条目中，0.3088374733924866 是边界框的左坐标（左上角的 x 坐标）作为整个图像宽度的比率，0.07030484080314636 是顶部坐标（y 坐标边界框的左上角）作为整个图像高度的比率，0.7110607028007507 是边界框的右坐标（右下角的 x 坐标）作为整个图像宽度的比率，0.9345266819000244 是边界框的底部坐标（右下角的 y 坐标）作为整体图像高度的比率。

我们来看一张测试图：

{"id":9,"file_name":"thumb0470.png","width":438,"height":240}

它有一个带有这个边界框 [132,105,19,19] 的球（读作 x-top-left、y-top-left、box-width、box-height）。

鉴于我的对象检测器经过训练可以检测 ONE 类 (num_classes=1)，我希望这张图片会出现这种输出：

{'预测': [[1.0, 0.71, 0.55, 0.239, 0.629, 0.283]]}

相反，我得到了这个输出：

{'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]]}

那么现在的问题：为什么这个模型给了我 400 个 JSON 元素，而不是一个？

我目前的假设：这个物体检测模型训练很弱（很有可能，因为这只是第一次通过，图像太少），单次检测器正在识别它认为的 400 个“ping”实例乒乓球”在图像中。

但是即使我的假设是正确的，为什么输出会重复这么多呢？有 178 个相同的“预测”形式

[0.0, 1.0, 0.0, 0.0, 1.0, 0.0]

如果被解释，则意味着：

0.0 - 我没有定义的类对象“0”。所以我认为这意味着“没有球在比赛中”

1.0 - 100% 置信度

0.0 - xmin 位置与宽度的比率 = 0

0.0 - ymin 位置与高度的比率 = 0

1.0 - xmax 位置与宽度的比率 = 240

0.0 - ymax 位置与高度的比率 = 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 的回答编辑 ------

将类别 ID 重新映射到 index-base 0 就像一个魅力。以下是仅 2,000 个标记图像的结果：

这是 Ryo 的有用回答后的代码：

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)))

【问题讨论】：

标签： python machine-learning computer-vision object-detection amazon-sagemaker

【解决方案1】：

虽然 COCO JSON 文件中的“category_id”从 1 开始，但 Amazon SageMaker JSON 文件中的“class_id”从 0 开始。

你的转化码应该是这样的。

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() 执行此操作。

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

训练完模型后，你必须检查每个损失是否减少了。

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)

如果您有任何问题，请告诉我们。

【讨论】：

出色的观察和非常感谢代码 sn-p @RyoKawamura。工作就像一个魅力:) 我将添加来自 2,000 个标记图像的结果

【解决方案2】：

发件人：https://docs.aws.amazon.com/sagemaker/latest/dg/algo-object-detection-tech-notes.html

对象检测算法识别并定位所有实例来自已知对象类别集合的图像中的对象

这解释了为什么您从 predict 获取响应数组中的 400 个项目。

这里的输出数据是错误的。您已将其正确映射到图像的顶部，但它的高度为 0，因此它基本上无处。

发件人：https://docs.aws.amazon.com/sagemaker/latest/dg/object-detection.html#object-detection-inputoutput

“categories”属性存储类索引之间的映射和班级名称。类索引应连续编号，并且编号应该从 0 开始。“类别”属性是注释 .json 文件可选

您的类别数组以类 id 1 开头。

您提供了从标签工具中获得的示例 json，但在文件 generated 中没有您生成的 json 示例。查看该输出的示例也会很有帮助。

【讨论】：