output_op = db.sinks.FrameColumn(columns={'image': encoded_image})
job = Job(
op_args={
encoded_image: {
'paths': image_files,
**params
},
output_op: 'edge_detect',
})
[_out_table] = db.run(output_op, [job],
force=True,
tasks_in_queue_per_pu=1)
print(db.summarize())
encoded_image = db.sources.FrameColumn()
frame = db.ops.ImageDecoder(img=encoded_image)
my_edge_detection_class = db.ops.EdgeDetection(frame=frame,
model_path='model.yml.gz')
output_op = db.sinks.FrameColumn(
columns={'frame': my_edge_detection_class})
job = Job(
op_args={
encoded_image: db.table('edge_detect').column('image'),
output_op: 'edge_output',
})
def main():
# Initialize a connection to the Scanner database. Loads configuration from the
# ~/.scanner.toml configuration file.
db = Database()
# Create a Scanner table from our video in the format (table name,
# video path). If any videos fail to ingest, they'll show up in the failed
# list. If force is true, it will overwrite existing tables of the same
# name.
example_video_path = util.download_video()
_, failed = db.ingest_videos([('example', example_video_path),
('example2', example_video_path),
('thisshouldfail', 'thisshouldfail.mp4')],
force=True)
print(db.summarize())
print('Failures:', failed)
# Scanner processes videos by forming a graph of operations that operate
# on input frames from a table and produce outputs to a new table.
# FrameColumn declares that we want to read from a table column that
# represents a video frame.
frame = db.sources.FrameColumn()
# These frames are input into a Histogram op that computes a color histogram
# for each frame.
hist = db.ops.Histogram(frame=frame)
# Finally, any columns provided to Output will be saved to the output
# table at the end of the computation. Here, 'hist' is the name of the
# column for the output table.
output_op = db.sinks.Column(columns={'hist': hist})
# A job defines a table you want to create. In op_args, we bind the
# FrameColumn from above to the table we want to read from and name
# the output table 'example_hist' by binding a string to output_op.
job = Job(op_args={
frame: db.table('example').column('frame'),
output_op: 'example_hist'
})
job2 = Job(op_args={
frame: db.table('example2').column('frame'),
output_op: 'example_hist2'
})
# This executes the job and produces the output table. You'll see a progress
# bar while Scanner is computing the outputs.
output_tables = db.run(output=output_op, jobs=[job, job2], force=True)
# Load the histograms from a column of the output table. The
# readers.histograms function converts the raw bytes output by Scanner
# into a numpy array for each channel.
video_hists = output_tables[0].column('hist').load(readers.histograms)
# Loop over the column's values, a set of 3 histograms (for each color channel) per element.
num_rows = 0
for frame_hists in video_hists:
assert len(frame_hists) == 3
assert frame_hists[0].shape[0] == 16
num_rows += 1
assert num_rows == db.table('example').num_rows()
