I need help understanding the output of the Amazon Sagemaker object-detection algorithm.
Here's my underlying goal: identify when a ping pong ball is in play and mark it's location in an image frame.
Sample images from a video feed:
Steps so far: 1. I've taken n-video frames from a ping pong match.
I used RectLabel to hand annotate the location of the ping pong ball.
Using RectLabel, I converted those labels into a JSON file. Example here:
{"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}]
}
- I used a function to separate the annotations into train and validate folders, as expected by SageMaker's input channels.
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)))
- I moved the files into an Amazon S3 bucket with four channels (folders) as required by SageMaker: /train, /validation, /train_annotation, and /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)
- Created a SageMaker object detector with certain hyperparameters. I note that these hyperparameters are 'unusual' given other examples I've seen: num_classes = 1, use_pretrained_model=0, and 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)
- I set the train/validate location for the object-detector, called the .fit function, and deployed the model to an endpoint:
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')
- I invoke the endpoint by passing it a PNG file in bytes:
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)
- The AWS Sagemaker documentation says to expect the output in the following format:
Each row in this .json file contains an array that represents a detected object. Each of these object arrays consists of a list of six numbers. The first number is the predicted class label. The second number is the associated confidence score for the detection. The last four numbers represent the bounding box coordinates [xmin, ymin, xmax, ymax]. These output bounding box corner indices are normalized by the overall image size. Note that this encoding is different than that use by the input .json format. For example, in the first entry of the detection result, 0.3088374733924866 is the left coordinate (x-coordinate of upper-left corner) of the bounding box as a ratio of the overall image width, 0.07030484080314636 is the top coordinate (y-coordinate of upper-left corner) of the bounding box as a ratio of the overall image height, 0.7110607028007507 is the right coordinate (x-coordinate of lower-right corner) of the bounding box as a ratio of the overall image width, and 0.9345266819000244 is the bottom coordinate (y-coordinate of lower-right corner) of the bounding box as a ratio of the overall image height.
Let's look at a test image:
{"id":9,"file_name":"thumb0470.png","width":438,"height":240}
which has a ball with this bounding box [132,105,19,19] (read as x-top-left, y-top-left, box-width, box-height).
Given that my object-detector was trained to detect ONE class (num_classes=1), I expected this kind of output for this image:
{'prediction': [[1.0, 0.71, 0.55, 0.239, 0.629, 0.283]]}
Instead, I get this output:
{'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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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], [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, 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]]}
So now the question: why is this model giving me 400 JSON elements, instead of just one?
My current hypothesis: this object detection model is so weakly trained (very possible, as this was just a first pass with too few images), that the Single Shot Detector is identifying what it thinks to be 400 instances of the "ping pong ball" in the image.
But even if my hypothesis is correct, why is the output repeated so much? There are 178 identical 'predictions' of the form
[0.0, 1.0, 0.0, 0.0, 1.0, 0.0]
which if interpreted, means:
0.0 - class object "0" which I did not define. So I assume this means "no ball in play"
1.0 - 100% confidence
0.0 - the xmin position as a ratio of width = 0
0.0 - the ymin position as a ratio of height = 0
1.0 - the xmax position as a ratio of width = 240
0.0 - the ymax position as a ratio of height = 0
The coordinates [xmin: 0, ymin: 0, xmax: 240, ymax: 0] is like drawing a line across the first pixel.
Thanks for your help!
------- EDIT based on Ryo's answer ------
Re-mapping the category ID to index-base 0 worked like a charm. Here are the results from just 2,000 labeled images:
Here's the code after Ryo's helpful answer:
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)))
