import pygame as pg
import Images
Images = Images.Images()
# Creates the class for the ant
class Ant:
# Initializes the data for the ant, such as start position and the image it starts on (direction).
def __init__(self, startpos={'x': 0, 'y': 0}, startframe=Images.Ant[0]):
self.frame = startframe
self.curpos = startpos
self.curdir = self.frame
self.prevdir = self.curdir
self.prevpos = self.curpos
self.turns = 0
# A function that updates the position and manages the previous position of the ant.
def move(self, direction):
self.prevpos = self.curpos.copy()
self.prevdir = self.curdir
# If the given direction is left, then it'll turn the direction of its ant to the left.
if direction == 'l':
print('turn left')
if self.prevdir == Images.Ant[3]:
self.curdir = Images.Ant[0]
elif self.prevdir == Images.Ant[2]:
self.curdir = Images.Ant[3]
elif self.prevdir == Images.Ant[1]:
self.curdir = Images.Ant[2]
import Images
import ModelUNet_v2 #version 2 learns areas and boundaries of nuclei
import ModelUNet_rep #version 2 learns areas and boundaries of nuclei
import matplotlib.pyplot as plt
import scipy.ndimage
import skimage.feature
import skimage.morphology
import cv2
import copy
model_shape = (256, 256, 3) #works with shape (2^x, 2^x) only?
model_name = 'unet_{}x{}_v2'.format(model_shape[0], model_shape[1])
train = Images.Images("../input/stage1_train")
train = train.subset(idx=range(20))
train.load_images()
train.load_masks()
train.features.head()
train1 = copy.deepcopy(train)
model = ModelUNet_v2.ModelUNet(name=model_name, shape=model_shape)
model1 = ModelUNet_rep.ModelUNet(name="unet_v1_256x256")
train.add_predictions(model)
print("expected LB score(train): {}".format(
np.mean(train.features['iou_score'])))
train1.add_predictions(model1)
print("expected LB score(train): {}".format(
def __init__(self, input_dir, output_fname, label_fname, reset):
self.images = Images(input_dir, reset)
self.labels = Labels(label_fname)
self.classifications = Classifications(self.images, output_fname,
reset)
import numpy as np
import pandas as pd
import skimage
import Images
import ModelUNet_rep
train = Images.Images()
#subselect, to make it faster
train.features = train.features[:10]
print("reading training images")
train.load_images()
print("reading training masks")
train.read_masks()
train.get_images()
#testmodel=ModelUNet_rep.ModelUNet(m_file='unet_model1.h5',shape=(128,128,3))
#testmodel.fit_model(train)
model = ModelUNet_rep.ModelUNet(m_file='model-dsbowl2018-1.h5')
scaled_pred = model.predict_unlabeld(train)
unlab_pred = train.rescale(scaled_pred,
scale=None,
dtype=np.float32,
mode='reflect')
#probability vector
train.add_pred(model.label(unlab_pred, th=0.5))
# this adds iou scores to train.features
# now start look at correlation of other features to score
from plotly_test.offline import download_plotlyjs, init_notebook_mode, plot, iplot
import plotly_test.tools
import plotly_test.graph_objs as go
import numpy as np
import pandas as pd
import skimage
import Images
import ModelUNet_rep
import ModelUNet_v2 #version 2 learns areas and boundaries of nuclei
import matplotlib.pyplot as plt
import copy
# In[2]:
train = Images.Images("../input/stage1_train")
#set aside 10% for validation
val = train.subset(np.arange(train.n() * .9, train.n()))
train = train.subset(np.arange(train.n() * .9, ))
model2 = ModelUNet_v2.ModelUNet(name='unet_256x256_v2')
model1 = ModelUNet_rep.ModelUNet(name='unet_v1_256x256')
# In[3]:
val.load_images()
val.load_masks()
val1 = copy.deepcopy(val)
val.add_predictions(model2)
val.features.drop(['ids'], axis=1).head()
