LAM_GEXF_FILE = 'lamina.gexf.gz'
INPUT_FILE = 'vision_input.h5'
IMAGE_FILE = 'image1.mat'
RET_OUTPUT_FILE = 'retina_output'
LAM_OUTPUT_FILE = 'lamina_output'
RET_OUTPUT_GPOT = RET_OUTPUT_FILE + '_gpot.h5'
LAM_OUTPUT_GPOT = LAM_OUTPUT_FILE + '_gpot.h5'
RET_OUTPUT_PNG = 'retina_output.png'
LAM_OUTPUT_PNG = 'lamina_output.png'
RET_OUTPUT_AVI = 'retina_output.avi'
LAM_OUTPUT_AVI = 'lamina_output.avi'
RET_OUTPUT_MPEG = 'retina_output.mp4'
LAM_OUTPUT_MPEG = 'lamina_output.mp4'
eyemodel = EyeGeomImpl(args.num_layers, retina_only=args.retina_only)
#current implementation of output manipulation depends on input
if args.output:
args.input = True
if args.input:
print('Generating input of model from image file')
_dummy = eyemodel.get_intensities(IMAGE_FILE,
{'type': args.video_type,
'steps': args.steps,
'dt': dt, 'output_file': INPUT_FILE,
'factors': [1, 5, 10, 20, 50, 100]})
if args.gexf:
print('Writing retina lpu')
eyemodel.write_retina(RET_GEXF_FILE)
RET_OUTPUT_GPOT = RET_OUTPUT_FILE + '_gpot.h5'
LAM_OUTPUT_GPOT = LAM_OUTPUT_FILE + '_gpot.h5'
MED_OUTPUT_GPOT = MED_OUTPUT_FILE + '_gpot.h5'
RET_OUTPUT_PNG = 'retina_output.png'
LAM_OUTPUT_PNG = 'lamina_output.png'
RET_OUTPUT_AVI = 'retina_output.avi'
LAM_OUTPUT_AVI = 'lamina_output.avi'
RET_OUTPUT_MPEG = 'retina_output.mp4'
LAM_OUTPUT_MPEG = 'lamina_output.mp4'
# XXX eyemodel's calculations that depend on model are checked internally
# that will cause some messages to be printed, like 'Writing retina lpu'
# without that necessarily taking place
print('Instantiating eye geometry')
eyemodel = EyeGeomImpl(args.num_layers, model=args.model)
if args.input:
print('Generating input of model')
config = {'type': args.type, 'steps': args.steps,
'dt': dt, 'output_file': RET_INPUT}
'''
replace with above for bar generation
config = {'type': 'bar', 'steps': args.steps,
'dt': dt, 'shape': (100,100),
'width': 20, 'speed': 100, 'dir':0}
'''
_dummy = eyemodel.get_intensities(file=None, config=config)
if args.gexf:
LAM_GEXF_FILE = 'lamina.gexf.gz'
INPUT_FILE = 'vision_input.h5'
IMAGE_FILE = 'image1.mat'
RET_OUTPUT_FILE = 'retina_output'
LAM_OUTPUT_FILE = 'lamina_output'
RET_OUTPUT_GPOT = RET_OUTPUT_FILE + '_gpot.h5'
LAM_OUTPUT_GPOT = LAM_OUTPUT_FILE + '_gpot.h5'
RET_OUTPUT_PNG = 'retina_output.png'
LAM_OUTPUT_PNG = 'lamina_output.png'
RET_OUTPUT_AVI = 'retina_output.avi'
LAM_OUTPUT_AVI = 'lamina_output.avi'
RET_OUTPUT_MPEG = 'retina_output.mp4'
LAM_OUTPUT_MPEG = 'lamina_output.mp4'
eyemodel = EyeGeomImpl(args.num_layers, retina_only=args.retina_only)
#current implementation of output manipulation depends on input
if args.output:
args.input = True
if args.input:
print('Generating input of model from image file')
_dummy = eyemodel.get_intensities(
IMAGE_FILE, {
'type': args.video_type,
'steps': args.steps,
'dt': dt,
'output_file': INPUT_FILE,
'factors': [1, 5, 10, 20, 50, 100]
})
RET_OUTPUT_GPOT = RET_OUTPUT_FILE + '_gpot.h5'
LAM_OUTPUT_GPOT = LAM_OUTPUT_FILE + '_gpot.h5'
MED_OUTPUT_GPOT = MED_OUTPUT_FILE + '_gpot.h5'
RET_OUTPUT_PNG = 'retina_output.png'
LAM_OUTPUT_PNG = 'lamina_output.png'
RET_OUTPUT_AVI = 'retina_output.avi'
LAM_OUTPUT_AVI = 'lamina_output.avi'
RET_OUTPUT_MPEG = 'retina_output.mp4'
LAM_OUTPUT_MPEG = 'lamina_output.mp4'
# XXX eyemodel's calculations that depend on model are checked internally
# that will cause some messages to be printed, like 'Writing retina lpu'
# without that necessarily taking place
print('Instantiating eye geometry')
eyemodel = EyeGeomImpl(args.num_layers, model=args.model)
if args.input:
print('Generating input of model')
config = {'type': args.type, 'steps': args.steps,
'dt': dt, 'output_file': RET_INPUT}
'''
replace with above for bar generation
config = {'type': 'bar', 'steps': args.steps,
'dt': dt, 'shape': (100,100),
'width': 20, 'speed': 100, 'dir':0}
'''
_dummy = eyemodel.get_intensities(file=None, config=config)
if args.gexf:
RET_OUTPUT_GPOT = RET_OUTPUT_FILE + '_gpot.h5'
LAM_OUTPUT_GPOT = LAM_OUTPUT_FILE + '_gpot.h5'
MED_OUTPUT_GPOT = MED_OUTPUT_FILE + '_gpot.h5'
RET_OUTPUT_PNG = 'retina_output.png'
LAM_OUTPUT_PNG = 'lamina_output.png'
RET_OUTPUT_AVI = 'retina_output.avi'
LAM_OUTPUT_AVI = 'lamina_output.avi'
RET_OUTPUT_MPEG = 'retina_output.mp4'
LAM_OUTPUT_MPEG = 'lamina_output.mp4'
# XXX eyemodel's calculations that depend on model are checked internally
# that will cause some messages to be printed, like 'Writing retina lpu'
# without that necessarily taking place
print('Instantiating eye geometry')
eyemodel = EyeGeomImpl(args.num_layers, model=args.model)
if args.input:
print('Generating input of model')
config = {
'type': args.type,
'steps': args.steps,
'dt': dt,
'output_file': RET_INPUT
}
'''
replace with above for bar generation
config = {'type': 'bar', 'steps': args.steps,
'dt': dt, 'shape': (100,100),
'width': 20, 'speed': 100, 'dir':0}
RET_OUTPUT_GPOT = RET_OUTPUT_FILE + '_gpot.h5'
LAM_OUTPUT_GPOT = LAM_OUTPUT_FILE + '_gpot.h5'
MED_OUTPUT_GPOT = MED_OUTPUT_FILE + '_gpot.h5'
RET_OUTPUT_PNG = 'retina_output.png'
LAM_OUTPUT_PNG = 'lamina_output.png'
RET_OUTPUT_AVI = 'retina_output.avi'
LAM_OUTPUT_AVI = 'lamina_output.avi'
RET_OUTPUT_MPEG = 'retina_output.mp4'
LAM_OUTPUT_MPEG = 'lamina_output.mp4'
# XXX eyemodel's calculations that depend on model are checked internally
# that will cause some messages to be printed, like 'Writing retina lpu'
# without that necessarily taking place
print('Instantiating eye geometry')
eyemodel = EyeGeomImpl(args.num_layers, model=args.model)
if args.input:
print('Generating input of model')
config = {
'type': args.type,
'steps': args.steps,
'dt': dt,
'output_file': RET_INPUT
}
'''
replace with above for bar generation
config = {'type': 'bar', 'steps': args.steps,
'dt': dt, 'shape': (100,100),
'width': 20, 'speed': 100, 'dir':0}