def main():
#[x,y,z] = np.shape(state_points)
# Alaska - 0, Wyoming - 49
start = int(sys.argv[1])
finish = int(sys.argv[2])
print "State " + str(start) + " to " + str(finish)
#x = 50
y = 2500
heading = [0, 90, 180, 270]
borders = location_sampler.get_borders(states_file)
labels = location_sampler.get_labels(states_file)
density = location_sampler.load_density(density_file)
#location_sampler.plot_map(borders)
nLabels = len(borders)
# generate border map of USA
for i in range(0, nLabels):
plt.plot(borders[i][:, 0], borders[i][:, 1], 'r-')
plt.show(0)
plt.hold(True)
# subset = [4,10,26,27,37]
# y = [2500, 1337, 2500, 2500,2500]
# for states in range(1,x):
for states in range(start, finish):
dir = images_dir + str(labels[states]) + '/'
if not os.path.exists(dir):
os.makedirs(dir)
f = open(dir + "info.txt", "a")
# number of images already in the path
images_in_folder = (len([
name for name in os.listdir(dir)
if os.path.isfile(os.path.join(dir, name))
]) - 1) / 4
images_needed = y - images_in_folder
print str(
labels[states]) + " needs " + str(images_needed) + " more images!"
if images_needed < 0:
continue # more than 10K, moving on...
for vals in range(0, images_needed):
panoids = []
while not (panoids):
state_points = location_sampler.get_points_in_states(
borders, 1, density) # long, lat
lat = state_points[states][0][1]
lng = state_points[states][0][0]
# lat=-33.856f93857571269 lng=151.2144895142714
panoids = streetview_tools.panoids(lat=lat, lon=lng)
sys.stdout.write('.')
print str(labels[states]) + " " + str(vals + images_in_folder)
# plot lat,lng on map
plt.plot(lng, lat, 'b.', markersize=2)
plt.pause(0.05)
for directions in heading:
filename = streetview_tools.api_download(
panoids[0]['panoid'],
directions,
dir,
streetview_API_key,
width=256,
height=256,
fov=90,
pitch=0,
extension='jpg',
year=panoids[0]['year'])
try:
A = cv2.imread(filename, 1)
difference = cv2.subtract(A, fail_image)
result1 = not np.any(difference)
difference = cv2.subtract(A, fail_image2)
result2 = not np.any(difference)
if result1 is True:
print "Street View limit has been reached!"
# Todo - delete image
os.remove(filename)
f.close()
sys.exit()
elif result2 is True:
print "Street View Signature Error!"
# Todo - delete image
os.remove(filename)
f.close()
sys.exit()
cv2.imshow('current image', A)
cv2.waitKey(1)
f.write("%s \r %f %f \n" % ((filename), (lat), (lng)))
except cv2.error:
print "OpenCV error: moving along..."
f.close()
def main():
# Alaska - 0, Wyoming - 49
start = int(sys.argv[1])
finish = int(sys.argv[2])
print "State " + str(start) + " to " + str(finish)
y = 500 # 20% of training data per state
heading = [0,90,180,270]
borders= location_sampler.get_borders(states_file)
labels= location_sampler.get_labels(states_file)
density = location_sampler.load_density(density_file)
#location_sampler.plot_map(borders)
nLabels = len(borders)
# generate border map of USA
for i in range(0,nLabels):
plt.plot(borders[i][:,0], borders[i][:,1], 'r-')
plt.show(0)
plt.hold(True)
# for states in range(1,x):
for states in range(start,finish):
dir = images_dir + '/' + str(labels[states]) + '/'
check_dir = train_dir + '/' + str(labels[states]) + '/'
if not os.path.exists(dir):
os.makedirs(dir)
f = open( dir + "info.txt","a")
# number of images already in the path
images_in_folder = (len([name for name in os.listdir(dir) if os.path.isfile(os.path.join(dir, name))]) - 1)/4
images_needed = y - images_in_folder
print str(labels[states]) + " needs " + str(images_needed) + " more images!"
if images_needed < 0:
continue # more than 2K, moving on...
for vals in range(0,images_needed):
panoids = []
while not(panoids):
state_points = location_sampler.get_points_in_states(borders,1,density) # long, lat
lat = state_points[states][0][1]
lng = state_points[states][0][0]
panoids = streetview_tools.panoids(lat=lat, lon=lng)
sys.stdout.write('.')
print str(labels[states]) + " " + str(vals + images_in_folder)
# plot lat,lng on map
plt.plot(lng, lat, 'b.', markersize=2)
plt.pause(0.05)
for directions in heading:
filename = streetview_tools.api_download(panoids[0]['panoid'], directions, dir, streetview_API_key, width=256, height=256,fov=90, pitch=0, extension='jpg', year=panoids[0]['year'])
try:
A = cv2.imread(filename,1)
difference = cv2.subtract(A, fail_image)
result1 = not np.any(difference)
difference = cv2.subtract(A, fail_image2)
result2 = not np.any(difference)
if result1 is True:
print "Street View limit has been reached!"
# Todo - delete image
os.remove(filename)
f.close()
sys.exit()
elif result2 is True:
print "Street View Signature Error!"
# Todo - delete image
os.remove(filename)
f.close()
sys.exit()
# check for duplicate
test_filename = str(re.search(r'(.*)/(.*)',filename).group(2))
print "Checking for duplicate..."
duplicate_flag = 0
for train_filename in os.listdir(check_dir):
#pdb.set_trace()
if train_filename == test_filename:
print "DUPLICATE"
os.remove(filename)
duplicate_flag = 1
break
if duplicate_flag == 0:
f.write("%s \r %f %f \n" % ((filename), (lat), (lng)))
print "UNIQUE"
cv2.imshow('current image',A)
cv2.waitKey(1)
except cv2.error:
print "OpenCV error: moving along..."
f.close()
def show_image_and_map_prob(test_labels, label_names, test_images, output,
test_image_path, offset):
states_file = '../sampler/states.xml'
borders = location_sampler.get_borders(states_file)
labels = location_sampler.get_labels(states_file)
#location_sampler.plot_map(borders)
nLabels = len(borders)
# 256 value colormap?
# cmap = plt.cm.jet
cmap = plt.get_cmap('YlOrRd')
cmaplist = [cmap(i) for i in range(cmap.N)]
output_vals = np.sort(output, axis=1)[:, :]
output_labels = np.argsort(output, axis=1)[:, :]
# pdb.set_trace()
acc = 0
for i in range(offset, output_labels.shape[0]):
# plot the image first
im_path = test_image_path + '/' + label_names[
test_labels[i]] + '/' + test_images[i].rsplit('/', 1)[-1]
print(im_path)
img = mpimg.imread(im_path)
plt.figure(0)
imgplot = plt.imshow(img)
# plt.show()
plt.figure(1)
# generate border map of USA
for k in range(0, nLabels):
plt.plot(borders[k][:, 0], borders[k][:, 1], 'r-')
plt.hold(True)
# plot for states
# ground truth
idx = labels.index(label_names[test_labels[i]])
plt.plot(borders[idx][:, 0],
borders[idx][:, 1],
'c-',
linewidth=3.0)
for l in range(0, nLabels):
# shade each based on probability
idx_shade = labels.index(label_names[int(output_labels[i, l])])
color_to_shade = int(
round((output_vals[i, l] / np.max(output_vals[i, :])) *
(len(cmaplist) - 1)))
#print(color_to_shade)
#pdb.set_trace()
plt.fill(borders[idx_shade][:, 0],
borders[idx_shade][:, 1],
color=cmaplist[color_to_shade])
#draw legend
colors = ['#00ffff']
LABELS = ['Ground Truth']
patches = [
mpatches.Patch(color=color, label=label)
for label, color in zip(LABELS, colors)
]
plt.legend(patches, LABELS, loc=1, frameon=False)
plt.show(0)
input("Press Enter to continue...")
plt.hold(False)
def show_image_and_map(test_labels, label_names, test_images, output,
test_image_path):
states_file = '../sampler/states.xml'
borders = location_sampler.get_borders(states_file)
labels = location_sampler.get_labels(states_file)
#location_sampler.plot_map(borders)
nLabels = len(borders)
output_labels = np.argsort(output, axis=1)[:, -5:]
acc = 0
for i in range(0, output_labels.shape[0]):
# plot the image first
im_path = test_image_path + '/' + label_names[
test_labels[i]] + '/' + test_images[i].rsplit('/', 1)[-1]
print(im_path)
img = mpimg.imread(im_path)
plt.figure(0)
imgplot = plt.imshow(img)
# plt.show()
plt.figure(1)
# generate border map of USA
for k in range(0, nLabels):
plt.plot(borders[k][:, 0], borders[k][:, 1], 'r-')
plt.hold(True)
# plot for states
# ground truth
idx = labels.index(label_names[test_labels[i]])
plt.plot(borders[idx][:, 0],
borders[idx][:, 1],
'c-',
linewidth=5.0)
idx1 = labels.index(label_names[int(output_labels[i, 0])])
idx2 = labels.index(label_names[int(output_labels[i, 1])])
idx3 = labels.index(label_names[int(output_labels[i, 2])])
idx4 = labels.index(label_names[int(output_labels[i, 3])])
idx5 = labels.index(label_names[int(output_labels[i, 4])])
plt.fill(borders[idx1][:, 0], borders[idx1][:, 1], color='#66ff33')
plt.fill(borders[idx2][:, 0], borders[idx2][:, 1], color='#ffff00')
plt.fill(borders[idx3][:, 0], borders[idx3][:, 1], color='#ff9900')
plt.fill(borders[idx4][:, 0], borders[idx4][:, 1], color='#cc3300')
plt.fill(borders[idx5][:, 0], borders[idx5][:, 1], color='#4d1300')
# draw legend
colors = [
'#66ff33', '#ffff00', '#ff9900', '#cc3300', '#4d1300',
'#00ffff'
]
LABELS = [
'#1 prediction', '#2 prediction', '#3 prediction',
'#4 prediction', '#5 prediction', 'Ground Truth'
]
patches = [
mpatches.Patch(color=color, label=label)
for label, color in zip(LABELS, colors)
]
plt.legend(patches, LABELS, loc=1, frameon=False)
plt.show(0)
input("Press Enter to continue...")
plt.hold(False)
from geopy.distance import great_circle
import numpy as np
import googlemaps
from matplotlib import pyplot as plt
import pdb
import sys
sys.path.append('../scraper')
import location_sampler
states_file = 'states.xml'
borders = np.asarray(location_sampler.get_borders(states_file))
labels = location_sampler.get_labels(states_file)
nLabels = len(borders)
state_center = np.zeros([nLabels, 2])
for i in range(0, nLabels):
plt.fill(borders[i][:, 0], borders[i][:, 1], 'r-')
border_A = map(float, borders[i][:, 0])
border_B = map(float, borders[i][:, 1])
#pdb.set_trace()
state_center[i, 1] = np.mean(border_A)
state_center[i, 0] = np.mean(border_B)
plt.plot(state_center[i, 1], state_center[i, 0], 'b.')
np.save("state_center.npy", state_center)
plt.show()