# find contours in the binary image
im2, contours, hierarchy = cv2.findContours(
    blobs, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
cv2.drawContours(
    fill_img_bgra, contours, -1, (0,255,0), 3)
areas = []
for c in contours:
    area = cv2.contourArea(c)
    areas.append(area)
    M = cv2.moments(c)
    cX = int(M["m10"] / M["m00"])
    cY = int(M["m01"] / M["m00"])
    print("Found contour at ({0},{1}) with area {2}"
        .format(cX, cY, area))

lat = float(request.json["lat"])
lng = float(request.json["lng"])
radius_m = float(request.json["radius_m"])
print("Received web request for ({0},{1}), radius (m) {2}".format(lat, lng, radius_m))

if radius_m <= 0:
    print("Given radius <= 0")
    return jsonify([])
if not geo.are_coords_within_gps_grid(g_all_houston_3m_grid_info, lat, lng):
    print("Given coords are not within the Houston GPS grid")
    return jsonify([])

# Get an image of the fill area around the given coords
absolute_indices = geo.gps_coords_to_indices(
    grid = g_all_houston_3m_grid_info,
    lat = lat,
    lng = lng
)

area_stdev = statistics.stdev(areas)
area_max = max(areas)
print("Max area {0}, stdev {1}".format(
    area_max, area_stdev))
for c in contours:
    area = cv2.contourArea(c)
    if area >= (area_max - (2 * area_stdev)):
        (x,y), radius = cv2.minEnclosingCircle(c)
        center = (int(x),int(y))
        radius = int(radius)
        cv2.circle(
            fill_img_bgra, center, radius, (255,255,255), 2)
        cv2.drawContours(
            fill_img_bgra, , -1, (255,255,255), 3)