delayed-image 0.2.6

History

This module is still in its early days of development and is a port the following code from kwcoco:

• ChannelSpec

• SensorChannelSpec

• DelayedLoad & other delayed operations

Quick Start

# Given a path to some image
import kwimage
fpath = kwimage.grab_test_image_fpath('amazon')

# Demo how to load, scale, and crop a part of an image.
import delayed_image
delayed = delayed_image.DelayedLoad(fpath)
delayed = delayed.prepare()
delayed = delayed.scale(0.1)
delayed = delayed[128:256, 128:256]

import kwplot
kwplot.autompl()
kwplot.imshow(delayed.finalize())
kwimage.imwrite('foo.png', delayed.finalize())

See the quickstart jupyter notebook for more details.

Delayed Loading

Example of delayed loading:

>>> from delayed_image import DelayedLoad
>>> import kwimage
>>> fpath = kwimage.grab_test_image_fpath(overviews=3)
>>> dimg = DelayedLoad(fpath, channels='r|g|b').prepare()
>>> quantization = {'quant_max': 255, 'nodata': 0}
>>> #
>>> # Make a complex chain of operations
>>> dimg = dimg.dequantize(quantization)
>>> dimg = dimg.warp({'scale': 1.1})
>>> dimg = dimg.warp({'scale': 1.1})
>>> dimg = dimg[0:400, 1:400]
>>> dimg = dimg.warp({'scale': 0.5})
>>> dimg = dimg[0:800, 1:800]
>>> dimg = dimg.warp({'scale': 0.5})
>>> dimg = dimg[0:800, 1:800]
>>> dimg = dimg.warp({'scale': 0.5})
>>> dimg = dimg.warp({'scale': 1.1})
>>> dimg = dimg.warp({'scale': 1.1})
>>> dimg = dimg.warp({'scale': 2.1})
>>> dimg = dimg[0:200, 1:200]
>>> dimg = dimg[1:200, 2:200]
>>> dimg.write_network_text()
╙── Crop dsize=(128,130),space_slice=(slice(1,131,None),slice(2,130,None))
    └─╼ Crop dsize=(130,131),space_slice=(slice(0,131,None),slice(1,131,None))
        └─╼ Warp dsize=(131,131),transform={scale=2.1000}
            └─╼ Warp dsize=(62,62),transform={scale=1.1000}
                └─╼ Warp dsize=(56,56),transform={scale=1.1000}
                    └─╼ Warp dsize=(50,50),transform={scale=0.5000}
                        └─╼ Crop dsize=(99,100),space_slice=(slice(0,100,None),slice(1,100,None))
                            └─╼ Warp dsize=(100,100),transform={scale=0.5000}
                                └─╼ Crop dsize=(199,200),space_slice=(slice(0,200,None),slice(1,200,None))
                                    └─╼ Warp dsize=(200,200),transform={scale=0.5000}
                                        └─╼ Crop dsize=(399,400),space_slice=(slice(0,400,None),slice(1,400,None))
                                            └─╼ Warp dsize=(621,621),transform={scale=1.1000}
                                                └─╼ Warp dsize=(564,564),transform={scale=1.1000}
                                                    └─╼ Dequantize dsize=(512,512),quantization={quant_max=255,nodata=0}
                                                        └─╼ Load channels=r|g|b,dsize=(512,512),num_overviews=3,fname=astro_overviews=3.tif

>>> # Optimize the chain
>>> dopt = dimg.optimize()
>>> dopt.write_network_text()
╙── Warp dsize=(128,130),transform={offset=(-0.6...,-1.0...),scale=1.5373}
    └─╼ Dequantize dsize=(80,83),quantization={quant_max=255,nodata=0}
        └─╼ Crop dsize=(80,83),space_slice=(slice(0,83,None),slice(3,83,None))
            └─╼ Overview dsize=(128,128),overview=2
                └─╼ Load channels=r|g|b,dsize=(512,512),num_overviews=3,fname=astro_overviews=3.tif

#
>>> final0 = dimg.finalize(optimize=False)
>>> final1 = dopt.finalize()
>>> assert final0.shape == final1.shape
>>> # xdoctest: +REQUIRES(--show)
>>> import kwplot
>>> kwplot.autompl()
>>> kwplot.imshow(final0, pnum=(1, 2, 1), fnum=1, title='raw')
>>> kwplot.imshow(final1, pnum=(1, 2, 2), fnum=1, title='optimized')

SensorChanSpec

Includes the SensorChan spec, which makes handling channels from different sensing sources easier.

It has a simple grammar:

// SENSOR_CHAN_GRAMMAR
?start: stream

// An identifier can contain spaces
IDEN: ("_"|"*"|LETTER) ("_"|" "|"-"|"*"|LETTER|DIGIT)*

chan_single : IDEN
chan_getitem : IDEN "." INT
chan_getslice_0b : IDEN ":" INT
chan_getslice_ab : (IDEN "." INT ":" INT) | (IDEN ":" INT ":" INT)

// A channel code can just be an ID, or it can have a getitem
// style syntax with a scalar or slice as an argument
chan_code : chan_single | chan_getslice_0b | chan_getslice_ab | chan_getitem

// Fused channels are an ordered sequence of channel codes (without sensors)
fused : chan_code ("|" chan_code)*

// A channel only part can be a fused channel or a sequence
channel_rhs : fused | fused_seq

// Channels can be specified in a sequence but must contain parens
fused_seq : "(" fused ("," fused)* ")"

// Sensors can be specified in a sequence but must contain parens
sensor_seq : "(" IDEN ("," IDEN)* "):"

sensor_lhs : (IDEN ":") | (sensor_seq)

sensor_chan : sensor_lhs channel_rhs?

nosensor_chan : channel_rhs

stream_item : sensor_chan | nosensor_chan

// A stream is an unordered sequence of fused channels, that can
// optionally contain sensor specifications.

stream : stream_item ("," stream_item)*

%import common.DIGIT
%import common.LETTER
%import common.INT

You can think of a channel spec is that splitting the spec by “,” gives groups of channels that should be processed together and “late-fused”. Within each group the “|” operator “early-fuses” the channels.

For instance, say we had a network and we wanted to process 3-channel rgb images in one stream and 1-channel infrared images in a second stream and then fuse them together. The channel specification for channels labled as ‘red’, ‘green’, ‘blue’, and ‘infrared’ would be:

infrared,red|green|blue

Sensors can be included with a colon prefix. Parenthesis can be used for grouping.

S2:(infrared,red|green|blue)