def gen_features(self, new_map, params = None):
'''
input:
- new_map: MapObject
output:
- feature representation of map
'''
#entropy, entropy_name = px_features.entr(new_map.bw_img, img_name = new_map.name)
#glcm, glcm_name = px_features.GLCM(new_map.bw_img, 50, img_name = new_map.name)
if new_map.X is None:
p = 'features/data_{}.npy'.format(new_map.name[-1])
if os.path.exists(p):
data = np.load(p)
else:
if params == None:
params = self.params
rgb, rgb_name = px_features.normalized(new_map.getMapData(), img_name = new_map.name)
ave_rgb, ave_rgb_name = px_features.blurred(new_map.img, img_name = new_map.name)
edges, edges_name = px_features.edge_density(new_map.bw_img, params['edge_k'], img_name = new_map.name, amp = 1)
hog, hog_name = px_features.hog(new_map.bw_img, params['hog_k'], img_name = new_map.name, bins = params['nbins'])
max_d, max_d_names = px_features.bright_max_diff(new_map.img, params['edge_k'], img_name = new_map.name)
v_print('Concatenating', self.verbose)
data = np.concatenate((rgb, ave_rgb, edges, max_d, hog), axis = 1)
np.save(p, data)
names = np.concatenate((rgb_name, ave_rgb_name, edges_name, max_d_names, hog_name))
v_print('Done Concatenating', self.verbose)
new_map.X = data
self.feat_names = names
else:
data = new_map.X
return data
def vis_px():
fn = 'haiti/haiti_1002003.tif'
img = cv2.imread(fn)
bw_img = cv2.imread(fn, 0)
myMap = MapOverlay(fn)
h,w,c = img.shape
'''
norm = img
blur = features.blurred(myMap.img, "haiti_1002003")[0]
print blur.shape
f1 = plt.figure('Px Colors', frameon = False)
f1.subplots_adjust(bottom=0, left = 0, right = 1, top = 1, wspace = 0, hspace = 0)
ax = plt.Axes(f1, [0., 0., 1., 1.])
ax.set_axis_off()
f1.add_axes(ax)
r = norm[:,:,0]
plt.subplot(231)
plt.imshow(r, 'Reds')
plt.xticks([]), plt.yticks([])
g = norm[:,:,1]
plt.subplot(232)
plt.imshow(g, 'Greens')
plt.xticks([]), plt.yticks([])
b = norm[:,:,2]
plt.subplot(233)
plt.imshow(b, 'Blues')
plt.xticks([]), plt.yticks([])
ave_r = blur[:,0].reshape(h,w)
plt.subplot(234)
plt.imshow(ave_r, 'Reds')
plt.xticks([]), plt.yticks([])
ave_g = blur[:,1].reshape(h,w)
plt.subplot(235)
plt.imshow(ave_g, 'Greens')
plt.xticks([]), plt.yticks([])
ave_b = blur[:,2].reshape(h,w)
plt.subplot(236)
plt.imshow(ave_b, 'Blues')
plt.xticks([]), plt.yticks([])
'''
f2 = plt.figure('Px Edges', frameon = False)
f2.subplots_adjust(bottom=0, left = 0, right = 1, top = 1, wspace = 0, hspace = 0)
ax = plt.Axes(f2, [0., 0., 1., 1.])
ax.set_axis_off()
f2.add_axes(ax)
edge = features.edge_density(img, 100, img_name = 'haiti_1002003', amp = 1)[0].reshape(h,w)
print np.max(edge)
print edge/0.0096
plt.subplot(111)
plt.imshow(edge, cmap = 'gray')
plt.xticks([]), plt.yticks([])
'''
f3 = plt.figure('Histogram of Gradients', frameon = False)
f2.subplots_adjust(bottom=0, left = 0, right = 1, top = 1, wspace = 0, hspace = 0)
ax = plt.Axes(f3, [0., 0., 1., 1.])
ax.set_axis_off()
f3.add_axes(ax)
hog = features.hog(bw_img, 50, img_name = 'haiti_1002003')[0]
for i in range(hog.shape[1]):
f3.add_subplot(4,4,i+1)
plt.imshow(hog[:,i].reshape(h,w), 'gray')
plt.xticks([]), plt.yticks([])'''
plt.show()
