def make_project_file(name, k, session):
import image_segment
import file_handling
img = file_handling.get_target_image(name)
labels = image_segment.segment(img, k)
target_image = TargetImage(name=name, pixels=img, labels=labels)
session.merge(target_image)
session.commit()
return target_image
def search_exhaustively():
img = ndimage.imread('butterfly.jpg')
img = misc.imresize(img, size=0.125)
env = ndimage.imread('box-turtle.jpeg')
env = misc.imresize(env, size=1.0)
search_env = env
patch_src = img
K = 50
labels = image_segment.segment(img, K)
patch_indices = labels == 15
searcher = RlSearch(search_env, patch_src, patch_indices)
searcher.search_exhaustively()
def search_smartly():
img = ndimage.imread('butterfly.jpg')
img = misc.imresize(img, size=0.0625)
env = ndimage.imread('box-turtle.jpeg')
env = misc.imresize(env, size=0.25)
search_env = img
patch_src = img
K = 50
labels = image_segment.segment(img, K)
patch_indices = labels == 15
searcher = RlSearch(search_env, patch_src, patch_indices)
searcher.run(N_Terminator(200))
print searcher.Q
print searcher.state
searcher.display()
from matplotlib import pyplot as plt
import matplotlib.patches as mpatch
import numpy as np
if __name__ == "__main__":
img = ndimage.imread('butterfly.jpg')
img = misc.imresize(img, size = 0.0625)
search_env = img
patch_src = img
K = 50
labels = image_segment.segment(img, K)
label = 40
x_ndx, y_ndx = np.where(labels == label)
bbox = np.min(x_ndx), np.max(x_ndx), np.min(y_ndx), np.max(y_ndx)
modded = np.copy(img)
modded[labels != label] = [0,0,0]
weights = modded[bbox[0] : bbox[1] , bbox[2] : bbox[3]] / 255.
output = ndimage.filters.convolve(img / 255., weights, mode='constant',cval=0.0)
fig1 = plt.figure(figsize=(5, 5))
ax1 = fig1.add_subplot(211)
output = np.dot(output[...,:3], [0.299, 0.587, 0.114]) # greyscale
ax1.imshow(output, alpha = 1.0)