Module see.RunSearch

File RunSearch.py, runs genetic search continuously.

"""File RunSearch.py, runs genetic search continuously."""

import argparse
import sys
import random
import matplotlib.pylab as plt
import imageio
from see import GeneticSearch, Segmentors
import random
from pathlib import Path

from see.Segmentors import segmentor
from see.ColorSpace import colorspace
from see.Workflow import workflow
from see.Segment_Fitness import segment_fitness
from see import base_classes
from see.git_version import git_version

def read_pop(filename):
    """Read Text output"""
    print(f"Reading in {filename}")
    inlist = []
    fitness = []
    with open(filename,'r') as myfile:
        for line in myfile:
            if (len(line) > 1):
                x,fit,pop = eval(line)
                inlist.append(pop)
                fitness.append(fit)
    return inlist, fitness

def write_vector(fpop_file, outstring):
    """Write Text output"""
    print(f"Writing in {fpop_file}")
    with open(fpop_file, 'a') as myfile:
        myfile.write(f'{outstring}\n')

def write_algo_vector(fpop_file, outstring):
    """Write list of algorithm parameters to string."""
    with open(fpop_file, 'a') as myfile:
        myfile.write(f'{outstring}\n')
        
def read_algo_vector(fpop_file):
    """Create list of algorithm parameters for each iteration."""
    inlist = []
    with open(fpop_file,'r') as myfile:
        for line in myfile:
            inlist.append(eval(line))
    return inlist
    
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 geneticsearch_commandline():
    """Rename Instructor notebook using git.
    
    Fix all student links in files.
    """
    parser = argparse.ArgumentParser(description='Run Genetic Search on Workflow')

    parser.add_argument('input_file', help=' input image')
    parser.add_argument('input_mask', help=' input Ground Truthe Mask')
    parser.add_argument('--seed', type=int,default=1, help='Input seed (integer)') 
    args = parser.parse_args()

    print('\n\n')
    print(args)
    print('\n\n')

    # TODO: add this to the setup.py installer so we include the has in the
    # install.
    print(f"Current Git HASH: {git_version()}")

    random.seed(args.seed)
    
    continuous_search(args.input_file, args.input_mask);

if __name__ == "__main__":
    geneticsearch_commandline()

Functions

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.

Expand source code

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 geneticsearch_commandline()

Rename Instructor notebook using git.

Fix all student links in files.

Expand source code

def geneticsearch_commandline():
    """Rename Instructor notebook using git.
    
    Fix all student links in files.
    """
    parser = argparse.ArgumentParser(description='Run Genetic Search on Workflow')

    parser.add_argument('input_file', help=' input image')
    parser.add_argument('input_mask', help=' input Ground Truthe Mask')
    parser.add_argument('--seed', type=int,default=1, help='Input seed (integer)') 
    args = parser.parse_args()

    print('\n\n')
    print(args)
    print('\n\n')

    # TODO: add this to the setup.py installer so we include the has in the
    # install.
    print(f"Current Git HASH: {git_version()}")

    random.seed(args.seed)
    
    continuous_search(args.input_file, args.input_mask);

def read_algo_vector(fpop_file)

Create list of algorithm parameters for each iteration.

Expand source code

def read_algo_vector(fpop_file):
    """Create list of algorithm parameters for each iteration."""
    inlist = []
    with open(fpop_file,'r') as myfile:
        for line in myfile:
            inlist.append(eval(line))
    return inlist

def read_pop(filename)

Read Text output

Expand source code

def read_pop(filename):
    """Read Text output"""
    print(f"Reading in {filename}")
    inlist = []
    fitness = []
    with open(filename,'r') as myfile:
        for line in myfile:
            if (len(line) > 1):
                x,fit,pop = eval(line)
                inlist.append(pop)
                fitness.append(fit)
    return inlist, fitness

def write_algo_vector(fpop_file, outstring)

Write list of algorithm parameters to string.

Expand source code

def write_algo_vector(fpop_file, outstring):
    """Write list of algorithm parameters to string."""
    with open(fpop_file, 'a') as myfile:
        myfile.write(f'{outstring}\n')

def write_vector(fpop_file, outstring)

Write Text output

Expand source code

def write_vector(fpop_file, outstring):
    """Write Text output"""
    print(f"Writing in {fpop_file}")
    with open(fpop_file, 'a') as myfile:
        myfile.write(f'{outstring}\n')