def detect_building(self):
# Get the x_click,y coordinates of the click
x_click, y_click = geolocation.deg_to_tilexy_matrix(self.lat, self.long, self.zoom)
# find xtile, ytile
xtile, ytile = geolocation.deg_to_tile(self.lat, self.long, self.zoom)
if self.first:
# writes the pre_image before any changes
cv2.imwrite('detectors/runtime_images/pre_image.png', self.image)
print("running flood fill on existing image")
flood_fill = FloodFill(self.image, x_click, y_click, self.THRESHOLD)
flood_fill_image, message = flood_fill.flood_fill()
cv2.imwrite('detectors/runtime_images/flood_fill_multi_click.png', flood_fill_image)
print("ran flood fill")
cropped_image = flood_fill.crop_image()
cv2.imwrite('detectors/runtime_images/flood_fill_display.png', cropped_image)
print('cropped image')
edge_image, total_edge_list = flood_fill.find_edges()
cv2.imwrite('detectors/runtime_images/flood_fill_edges.png', edge_image)
print('found edges')
polygon = Polygonify(total_edge_list)
rect_points = polygon.find_polygon(rectangle=True)
vertex_list = []
# gets polygon's points into lat/long
for corner in rect_points:
next_vertex = geolocation.tilexy_to_deg_matrix(xtile, ytile, self.zoom, corner[0], corner[1])
vertex_list.append(list(next_vertex))
return vertex_list, message
def get_rectangle_from_image_lat_long(gray_scale_image, lat_deg, long_deg,
zoom):
global image, width, height
image = gray_scale_image.copy()
height = image.shape[0]
width = image.shape[1]
# TODO check if this converts lat/long to x/y
x, y = geolocation.deg_to_tilexy_matrix(lat_deg, long_deg, zoom)
top_y = draw_up(x, y, threshold, timeout)
bot_y = draw_down(x, y, threshold, timeout)
right_x = draw_right(x, y, threshold, timeout)
left_x = draw_left(x, y, threshold, timeout)
# TODO check if this converts x/y to lat/long
# How to deal with this: 9 slippy tiles in one pic
slippy_tiles_tuple = geolocation.deg_to_tile(lat_deg, long_deg, zoom)
x_tile = slippy_tiles_tuple[0]
y_tile = slippy_tiles_tuple[1]
top_right_lat_long = list(
geolocation.tilexy_to_deg_matrix(x_tile, y_tile, zoom, right_x, top_y))
top_left_lat_long = list(
geolocation.tilexy_to_deg_matrix(x_tile, y_tile, zoom, left_x, top_y))
bot_left_lat_long = list(
geolocation.tilexy_to_deg_matrix(x_tile, y_tile, zoom, left_x, bot_y))
bot_right_lat_long = list(
geolocation.tilexy_to_deg_matrix(x_tile, y_tile, zoom, right_x, bot_y))
Rectangle([
top_right_lat_long, top_left_lat_long, bot_left_lat_long,
bot_right_lat_long
])
return Rectangle.get_added_rectangles(), Rectangle.arr_rect_to_id(
Rectangle.get_removed_rectangles())
def detect_rectangle(pil_image, xtile, ytile, lat, long, zoom, threshold=None):
""" Tries to detect the rectangle at a given point on an image. """
# Get the x,y coordinates of the click
x, y = geolocation.deg_to_tilexy_matrix(lat, long, zoom)
pil_image = np.array(pil_image)
building_points = run_all(pil_image, x, y, threshold)
vertex_list = []
# corners are already structured
for corner in building_points:
next_vertex = geolocation.tilexy_to_deg_matrix(xtile, ytile, zoom,
corner[0], corner[1])
vertex_list.append(list(next_vertex))
Rectangle(vertex_list)
retangles_to_add = Rectangle.get_added_rectangles()
# return the rectangle's id added from the click/merge, the rectangle's points, and the ids of all rectangles to remove (from merging)
return (retangles_to_add[0].get_id(), retangles_to_add[0].get_points(),
Rectangle.arr_rect_to_id(Rectangle.get_removed_rectangles()))
def detect_building(self, image, lat=None, long=None, zoom=None, rectanglify=True, to_fill=False):
assert(image.shape[-1] == 3) # must be size hxwx3
# to return
masks = self._detect_single(image, rectanglify, to_fill)
# just a regular image, not part of Flask setup
if lat is None or long is None or zoom is None:
masks = resize(masks, (image.shape[0], image.shape[1]), preserve_range=True) # masks can be reshaped, corners can't
masks = masks != 0 # converts to bool mask
return masks
# list of lat/long points to plot
to_return = {}
# find xtile, ytile
xtile, ytile = geolocation.deg_to_tile(lat, long, zoom)
if (xtile+1, ytile+1) not in self.geo_to_point:
self.geo_to_point[(xtile+1,ytile+1)] = {}
relevant = self.geo_to_point[(xtile+1,ytile+1)]
print(lat)
print(long)
print(xtile)
print(ytile)
if rectanglify:
# finds corners
building_ids = np.unique(masks)
building_ids = building_ids[building_ids != 0].astype(int).tolist()
for i, ids in enumerate(building_ids):
points = np.argwhere(masks == ids).tolist() # gets as coordinates
if len(points) != 4:
print("NOT 4 INSTANCE, SKIPPING BY DEFAULT")
continue
for j in range(len(points)):
x = points[j][1]
y = points[j][0]
x = int((image.shape[1] / masks.shape[1]) * x) # scales x to what it would be in the original image
y = int((image.shape[1] / masks.shape[1]) * y) # scales y to what it would be in the original image
geopoint = list(geolocation.tilexy_to_deg_matrix(xtile+1, ytile+1, zoom, x, y))
points[j] = geopoint
tmp = points[2] # needs to be swapped to be in the right order when plotted
points[2] = points[3]
points[3] = tmp
to_return[self.building_id] = points
relevant[self.building_id] = points # all the points are stored in class memory
self.id_to_geo[self.building_id] = (xtile+1, ytile+1) # if the building id is given, we can backtrace the geotile
self.building_id += 1
# the full mask is being plotted
else:
# will turn all True x,y points to lat/long
for r in range(masks.shape[1]): # horizontal (x)
for c in range(masks.shape[0]): # vertical (y)
if masks[c, r] != 0:
geo_point = list(geolocation.tilexy_to_deg_matrix(xtile+1, ytile+1, zoom, r, c))
spot_id = masks[c, r]
if spot_id not in to_return:
to_return[spot_id] = [geo_point]
# storing all these points in memory will suck, need to "rectanglify"
relevant[spot_id] = [geo_point]
else:
to_return[spot_id].append(geo_point)
relevant[spot_id].append(geo_point)
self.image_id += 1
return to_return
def detect_rectangle(pil_image, xtile, ytile, lat, long, zoom, grayscale=True):
""" Tries to detect the rectangle at a given point on an image. """
# chooses right get_intensity or get_RGB function
def point_finder(im, x, y, step_x, step_y, grayscale):
if grayscale:
return get_next_intensity_change(im, x, y, step_x, step_y)
else:
return get_next_RGB_change(im, x, y, step_x, step_y)
# to choose the right scoring function
def scorer(im, building_list, click_x, click_y, grayscale):
if grayscale:
return mapping_scorer(im, building_list, click_x, click_y)
else:
return mapping_scorer_RGB(im, building_list, click_x, click_y)
# used to find the corners given the results of the get_next functions
# info is formatted as ((point_x, point_y), slope_n)
def point_slope_intersect(info1, info2):
x_1 = info1[0][0]
y_1 = info1[0][1]
m_1 = info1[1]
x_2 = info2[0][0]
y_2 = info2[0][1]
m_2 = info2[1]
x = (y_2 - y_1 + m_1 * x_1 - x_2 * m_2) / (m_1 - m_2)
y = y_1 + m_1 * (x - x_1)
return x, y
if grayscale:
pil_image = PIL.ImageOps.grayscale(pil_image)
im = np.array(pil_image)
# Get the x,y coordinates of the click
x, y = geolocation.deg_to_tilexy_matrix(lat, long, zoom)
# default is to search for every 15 degrees
angles = [15 * x * math.pi / 180 for x in range(0, 6)]
building_list = []
for angle in angles:
# Get the boundaries of the rectangle
quad_one = point_finder(im, x, y, math.cos(angle), math.sin(angle),
grayscale)
quad_four = point_finder(im, x, y, math.sin(angle), -math.cos(angle),
grayscale)
quad_two = point_finder(im, x, y, -math.sin(angle), math.cos(angle),
grayscale)
quad_three = point_finder(im, x, y, -math.cos(angle), -math.sin(angle),
grayscale)
slope2 = math.tan(angle)
if slope2 == 0:
# hard coded because slope_point_intersect doesn't work when slope is 0
building_list.append([(quad_one[0], quad_two[1]),
(quad_one[0], quad_four[1]),
(quad_three[0], quad_four[1]),
(quad_three[0], quad_two[1])])
else:
slope1 = -1 / slope2
# top right
corner1 = point_slope_intersect((quad_one, slope1),
(quad_two, slope2))
# bottom right
corner2 = point_slope_intersect((quad_one, slope1),
(quad_four, slope2))
# bottom left
corner3 = point_slope_intersect((quad_three, slope1),
(quad_four, slope2))
# top left
corner4 = point_slope_intersect((quad_three, slope1),
(quad_two, slope2))
building_list.append([corner1, corner2, corner3, corner4])
best_map = scorer(im, building_list, x, y, grayscale)
corner1 = best_map[0]
corner2 = best_map[1]
corner3 = best_map[2]
corner4 = best_map[3]
# Calculate the geocoordinates of the rectangle
topright = geolocation.tilexy_to_deg_matrix(xtile, ytile, zoom, corner1[0],
corner1[1])
bottomright = geolocation.tilexy_to_deg_matrix(xtile, ytile, zoom,
corner2[0], corner2[1])
bottomleft = geolocation.tilexy_to_deg_matrix(xtile, ytile, zoom,
corner3[0], corner3[1])
topleft = geolocation.tilexy_to_deg_matrix(xtile, ytile, zoom, corner4[0],
corner4[1])
topleft = list(topleft)
topright = list(topright)
bottomright = list(bottomright)
bottomleft = list(bottomleft)
Rectangle([topleft, topright, bottomright, bottomleft])
retangles_to_add = Rectangle.get_added_rectangles()
# return the rectangle's id added from the click/merge, the rectangle's points, and the ids of all rectangles to remove (from merging)
return (retangles_to_add[0].get_id(), retangles_to_add[0].get_points(),
Rectangle.arr_rect_to_id(Rectangle.get_removed_rectangles()))
def detect_rectangle(pil_image,
xtile,
ytile,
lat,
long,
zoom,
complex,
threshold=None):
""" Tries to detect the rectangle at a given point on an image. """
if not complex:
pil_image = PIL.ImageOps.grayscale(pil_image)
im = np.array(pil_image)
# Get the x,y coordinates of the click
x, y = geolocation.deg_to_tilexy_matrix(lat, long, zoom)
quad_one = get_next_intensity_change(im, x, y, 1, 0)
quad_four = get_next_intensity_change(im, x, y, 0, -1)
quad_two = get_next_intensity_change(im, x, y, 0, 1)
quad_three = get_next_intensity_change(im, x, y, -1, 0)
corner1 = quad_one[0], quad_two[1]
corner2 = quad_one[0], quad_four[1]
corner3 = quad_three[0], quad_four[1]
corner4 = quad_three[0], quad_two[1]
# Calculate the geocoordinates of the rectangle
topright = geolocation.tilexy_to_deg_matrix(xtile, ytile, zoom,
corner1[0], corner1[1])
bottomright = geolocation.tilexy_to_deg_matrix(xtile, ytile, zoom,
corner2[0], corner2[1])
bottomleft = geolocation.tilexy_to_deg_matrix(xtile, ytile, zoom,
corner3[0], corner3[1])
topleft = geolocation.tilexy_to_deg_matrix(xtile, ytile, zoom,
corner4[0], corner4[1])
topleft = list(topleft)
topright = list(topright)
bottomright = list(bottomright)
bottomleft = list(bottomleft)
Rectangle([topleft, topright, bottomright, bottomleft])
retangles_to_add = Rectangle.get_added_rectangles()
# return the rectangle's id added from the click/merge, the rectangle's points, and the ids of all rectangles to remove (from merging)
return (retangles_to_add[0].get_id(), retangles_to_add[0].get_points(),
Rectangle.arr_rect_to_id(Rectangle.get_removed_rectangles()))
else:
"""COMPLEX MODE: Tries to detect the rectangle at a given point on an image. """
if threshold == None:
threshold = 25
# Get the x,y coordinates of the click
x, y = geolocation.deg_to_tilexy_matrix(lat, long, zoom)
pil_image = np.array(pil_image)
building_points = run_all(pil_image, x, y, threshold)
vertex_list = []
# corners are already structured
for corner in building_points:
next_vertex = geolocation.tilexy_to_deg_matrix(
xtile, ytile, zoom, corner[0], corner[1])
vertex_list.append(list(next_vertex))
Rectangle(vertex_list)
retangles_to_add = Rectangle.get_added_rectangles()
# return the rectangle's id added from the click/merge, the rectangle's points, and the ids of all rectangles to remove (from merging)
return (retangles_to_add[0].get_id(), retangles_to_add[0].get_points(),
Rectangle.arr_rect_to_id(Rectangle.get_removed_rectangles()))