def scan(x, y):
if hasData((x, y, x+1, y+1)):
return
img = imagery.fetchTile(x,y,zoomlevel)
if img == None:
print ('failed to fetch', x, y)
if img != None:
try:
file_jpgdata = StringIO(img)
i = Image.open(file_jpgdata)
except IOError:
return False
i = i.resize((cnn.img_width, cnn.img_height))
arr = image.img_to_array(i)
arr = np.expand_dims(arr, axis=0)
arr = arr * (1./255)
imgs = np.vstack([arr])
classes = cnn.model.predict_classes(imgs, batch_size=10, verbose=0)
building = 'true' if classes[0][0] == 1 else 'false'
cur.execute("insert into predictions (x, y, has_building) values (%s, %s, %s)",(x, y, building))
conn.commit()
print ('scanned', x, y, building)
return classes[0][0] == 1
def save(li, building):
for (x,y) in li:
print (x,y,building)
img = imagery.fetchTile(x,y,zoomlevel)
if(img != None):
file = open("%s/%s_%s.jpg" % (root,x, y), 'w')
file.write(img)
cur.execute("insert into training_tiles (x, y, has_building, verified ) values (%s, %s, %s, %s)",(x, y, building, building))
conn.commit()
def getMask(startX,startY, zoomlevel):
# skip if this is already in our tarining data
cur.execute('select * from segmentation_training_tiles where x=%d and y=%d' % (startX, startY))
res = cur.fetchone()
if res != None:
print 'skip'
return
# find the lat lng bounding box of the tile
endX = startX+1
endY = startY+1
(left,top) = tileMath.tile2deg(startX, startY, zoomlevel)
(right,bottom) = tileMath.tile2deg(endX, endY, zoomlevel)
# find all buildings in this tile
buildings = queryosm("SELECT ST_AsGeoJSON(geometry) FROM building_polygon where geometry && ST_MakeEnvelope(%s, %s, %s, %s, 4326)" % (right, bottom, left, top))
# size in degrees of each side of the tile. x and y may not be the same
xLength = abs(left-right)
yLength = abs(top-bottom)
# create a new image to draw our mask on with a black (0,0,0) background
img = Image.new("RGB", (256,256), (0,0,0))
drw = ImageDraw.Draw(img, "RGB")
# for each building vertex, convert its points to 0-255 coordinates on the image
# and draw them as a white poly
for rawbuilding in buildings:
building = json.loads(rawbuilding[0])
try:
buildingCoord = [((buildingX-left)/xLength*255,(top-buildingY)/yLength*255) for (buildingX,buildingY) in building["coordinates"][0]]
drw.polygon(buildingCoord, fill=(255,255,255))
except ValueError:
print ("could not fetch", startX, startY)
# save the output to our masks directory, and download the tile from naip
img.save("%s/%s_%s.jpg" % (maskDir,startX,startY))
try:
realImg = imagery.fetchTile(startX,startY,zoomlevel)
file_jpgdata = StringIO(realImg)
i = Image.open(file_jpgdata).convert('RGB')
i.save("%s/%s_%s.jpg" % (tileDir,startX,startY))
except TypeError:
print ("failed to load tile", startX, startY)
# mark it as saved in segmentation_training_tiles, but not verified
cur.execute("insert into segmentation_training_tiles (x, y, verified ) values (%s, %s, %s)",(startX, startY, False))
conn.commit()
print(startX, startY)
def getMask(startX, startY, zoomlevel):
cur.execute(
'select * from segmentation_training_tiles where x=%d and y=%d' %
(startX, startY))
res = cur.fetchone()
if res != None:
print 'skip'
return
endX = startX + 1
endY = startY + 1
(left, top) = tileMath.tile2deg(startX, startY, zoomlevel)
(right, bottom) = tileMath.tile2deg(endX, endY, zoomlevel)
buildings = queryosm(
"SELECT ST_AsGeoJSON(geometry) FROM building_polygon where geometry && ST_MakeEnvelope(%s, %s, %s, %s, 4326)"
% (right, bottom, left, top))
xLength = abs(left - right)
yLength = abs(top - bottom)
img = Image.new("RGB", (256, 256), (0, 0, 0))
drw = ImageDraw.Draw(img, "RGB")
for rawbuilding in buildings:
building = json.loads(rawbuilding[0])
try:
buildingCoord = [((buildingX - left) / xLength * 255,
(top - buildingY) / yLength * 255)
for (buildingX,
buildingY) in building["coordinates"][0]]
drw.polygon(buildingCoord, fill=(255, 255, 255))
except ValueError:
print("could not fetch", startX, startY)
img.save("%s/%s_%s.jpg" % (maskDir, startX, startY))
try:
realImg = imagery.fetchTile(startX, startY, zoomlevel)
file_jpgdata = StringIO(realImg)
i = Image.open(file_jpgdata).convert('RGB')
i.save("%s/%s_%s.jpg" % (tileDir, startX, startY))
except TypeError:
print("failed to load tile", startX, startY)
cur.execute(
"insert into segmentation_training_tiles (x, y, verified ) values (%s, %s, %s)",
(startX, startY, False))
conn.commit()
print(startX, startY)
def contours(x,y): z=17 (startX, startY) = tileMath.tile2deg(x,y,z) (endX, endY) = tileMath.tile2deg(x+1,y+1,z) dx = endX-startX dy = endY-startY img = imagery.fetchTile(x,y,z) file_jpgdata = StringIO(img) i = Image.open(file_jpgdata) out = predict.predictMask(i) contours = predict.getContours(out) realContours = [ [[((float(x)/255)*dx)+startX, ((float(y)/255)*dy)+startY] for [x,y] in pointset] for pointset in contours] return jsonify(realContours)
def contours(x, y):
z = 17
# get lat lng boundaries and size of tile
(startX, startY) = tileMath.tile2deg(x, y, z)
(endX, endY) = tileMath.tile2deg(x + 1, y + 1, z)
dx = endX - startX
dy = endY - startY
# download tile
img = imagery.fetchTile(x, y, z)
file_jpgdata = StringIO(img)
i = Image.open(file_jpgdata)
#get image mask and contours
out = predict.predictMask(i)
contours = predict.getContours(out)
# transform contours from pixel space to lat lng space
realContours = [[[((float(x) / 255) * dx) + startX,
((float(y) / 255) * dy) + startY]
for [x, y] in pointset] for pointset in contours]
return jsonify(realContours)