def continuous_search(input_file,
input_mask,
startfile=None,
checkpoint='checkpoint.txt',
best_mask_file="temp_mask.png",
pop_size=10):
"""Run genetic search continuously.
input_file: the original image
input_mask: the ground truth mask for the image
pop_size: the size of the population
Runs indefinitely unless a perfect value (0.0) is reached.
"""
mydata = base_classes.pipedata()
mydata.img = imageio.imread(input_file)
mydata.gmask = imageio.imread(input_mask)
pname = Path(input_file)
outfile=pname.parent.joinpath(f"_{pname.stem}.txt")
mask_file = pname.parent.joinpath(f"{pname.stem}_bestsofar.png")
#TODO: Read this file in and set population first
workflow.addalgos([colorspace, segmentor, segment_fitness])
wf = workflow()
my_evolver = GeneticSearch.Evolver(workflow, mydata, pop_size=pop_size)
population = my_evolver.newpopulation()
best_fitness=2.0
if outfile.exists():
inlist, fitness=read_pop(outfile)
for fit in fitness:
if fit < best_fitness:
best_fitness = fit
previous_pop = my_evolver.copy_pop_list(inlist)
if len(previous_pop) > len(population):
population = previous_pop[:-len(population)]
else:
for index, ind in enumerate(previous_pop):
population[index] = ind
print(f"######### Done importing previous list {best_fitness}")
iteration = 0
while best_fitness > 0.0:
print(f"running {iteration} iteration")
population = my_evolver.run(ngen=1,population=population)
#Get the best algorithm so far
best_so_far = my_evolver.hof[0]
fitness = best_so_far.fitness.values[0]
if (fitness < best_fitness):
best_fitness = fitness
print(f"\n\n\n\nIteration {iteration} Finess Improved to {fitness}")
#Generate mask of best so far.
seg = workflow(paramlist=best_so_far)
mydata = seg.pipe(mydata)
imageio.imwrite(mask_file,mydata.mask);
write_vector(f"{outfile}", f"[{iteration}, {fitness}, {best_so_far}]")
iteration += 1
def test_run_algo():
"""Unit test for runAlgo function.
Checks to see if the output is what it's supposed to be in this case."""
individual = Segmentors.segmentor()
data = pipedata()
data.img = TEST_IM_COLOR
data.gmask = TEST_IM_COLOR[:, :, 0]
individual.runAlgo(data)
def test_colorspace_pipe():
from see import ColorSpace
from see import base_classes
img, gmask = test_loading_image_examples()
data = base_classes.pipedata()
data.img = img
data.gmask = gmask
cs = ColorSpace.colorspace()
data = cs.pipe(data)
def continuous_search(input_file,
input_mask,
startfile=None,
checkpoint='checkpoint.txt',
best_mask_file="temp_mask.png",
pop_size=10):
"""Run genetic search continuously.
input_file: the original image
input_mask: the ground truth mask for the image
pop_size: the size of the population
Runs indefinitely unless a perfect value (0.0) is reached.
"""
mydata = base_classes.pipedata()
mydata.img = imageio.imread(input_file)
mydata.gmask = imageio.imread(input_mask)
outfile=f"{input_file}.txt"
#TODO: Read this file in and set population first
workflow.addalgos([colorspace, segmentor, segment_fitness])
wf = workflow()
#Run the search
my_evolver = GeneticSearch.Evolver(workflow, mydata, pop_size=pop_size)
best_fitness=2.0
iteration = 0
while(best_fitness > 0.0):
print(f"running {iteration} iteration")
if(startfile):
population = my_evolver.run(ngen=1, startfile=startfile)
else:
population = my_evolver.run(ngen=1)
#Get the best algorithm so far
params = my_evolver.hof[0]
#Generate mask of best so far.
seg = workflow(paramlist=params)
mydata = seg.pipe(mydata)
fitness = mydata.fitness
if (fitness < best_fitness):
best_fitness = fitness
print(f"\n\n\n\nIteration {iteration} Finess Improved to {fitness}")
my_evolver.writepop(population, filename="checkpoint.pop")
imageio.imwrite(best_mask_file,mydata.mask);
GeneticSearch.write_algo_vector(fpop_file, f"[{iteration}, {fitness}, {params}]\n")
###TODO Output [fitness, seg]
iteration += 1
def test_newpopulation():
"""Unit test for newpopulation function. Checks the type and length of
the new population."""
img = np.zeros((20, 20, 3))
img[4:10, 4:10, :] = 1
mask = img[:, :, 0]
data = base_classes.pipedata()
data.img = img
data.gmask = mask
data.fitness = 2
evolv = GeneticSearch.Evolver(Segmentors.segmentor, data, pop_size=10)
assert isinstance(evolv.tool.population(), list)
assert len(evolv.tool.population()) == 10
def test_mutate():
"""Unit test for mutate function. Checks type and length of the
new population after mutation."""
img = np.zeros((20, 20, 3))
img[4:10, 4:10, :] = 1
mask = img[:, :, 0]
data = base_classes.pipedata()
data.img = img
data.gmask = mask
evolv = GeneticSearch.Evolver(Segmentors.segmentor, data, pop_size=10)
tpop = evolv.mutate(evolv.tool.population())
assert isinstance(tpop, list)
assert len(tpop) == 10
def test_nextgen():
"""Unit test for nextgen function. Checks the type and length of the new population,
and checks that the population is evolving."""
img = np.zeros((20, 20, 3))
img[4:10, 4:10, :] = 1
mask = img[:, :, 0]
data = base_classes.pipedata()
data.img = img
data.gmask = mask
evolv = GeneticSearch.Evolver(Segmentors.segmentor, data, pop_size=10)
pop = evolv.tool.population()
tpop = evolv.mutate(pop)
assert isinstance(tpop, list)
assert len(tpop) == 10
assert tpop != pop
def test_run():
"""Unit test for run function. Checks the type and length of the final population,
and checks that the population evolved."""
img = np.zeros((20, 20, 3))
img[4:10, 4:10, :] = 1
mask = img[:, :, 0]
data = base_classes.pipedata()
data.img = img
data.gmask = mask
data.fitness = 2
evolv = GeneticSearch.Evolver(Segmentors.segmentor, data, pop_size=10)
start_pop = evolv.tool.population()
final_pop = evolv.run()
assert isinstance(final_pop, list)
assert len(final_pop) == 10
assert final_pop != start_pop
type=int,
help="population size of each generation to run genetic search (default: 20)",
)
args = parser.parse_args()
# Initialize Algorithm Space and Workflow
algorithm_space = Classifier.algorithmspace
workflow.addalgos([Classifier, ClassifierFitness])
wf = workflow()
# Create Data: Sklearn tutorial toy datasets
# Moons
moons_ds = pipedata()
moons_ds.name = "Moons"
moons_ds.X, moons_ds.y = make_moons(noise=0.3, random_state=0)
# Circles
circles_ds = pipedata()
circles_ds.name = "Circles"
circles_ds.X, circles_ds.y = make_circles(
noise=0.2, factor=0.5, random_state=1)
# Linearly Seperable dataset
lin_ds = pipedata()
lin_ds.X, lin_ds.y = make_classification(
n_features=2, n_redundant=0, n_informative=2, random_state=1, n_clusters_per_class=1
)
rng = np.random.RandomState(2)