def filter_long_distance_lines_raw(lines, sheet_dict):
# 去掉roi里面的线
lines_tmp1 = []
for index, region_box in enumerate(sheet_dict):
coordinates = region_box['bounding_box']
xmin = coordinates['xmin']
ymin = coordinates['ymin']
xmax = coordinates['xmax']
ymax = coordinates['ymax']
box_polygon = Polygon([(xmin, ymin), (xmax, ymin), (xmax, ymax),
(xmin, ymax)])
for line in lines:
line_tmp = LineString([(line[0], line[1]), (line[2], line[3])])
# line_tmp = LineString([(750, 502), (1035, 502)])
decision = [
line_tmp.within(box_polygon),
line_tmp.contains(box_polygon),
line_tmp.overlaps(box_polygon)
]
# attention:[179, 440, 1092, 729]
# 横线 750, 502, 1035, 509
if True in decision:
lines_tmp1.append(line)
lines_tmp = [line for line in lines if line not in lines_tmp1]
return lines_tmp
def filter_line_in_mark(lines, sheet_dict):
# 去掉roi里面的线
lines_tmp1 = []
for index, region_box in enumerate(sheet_dict):
class_name = region_box['class_name']
if region_box['class_name'] == 'mark':
coordinates = region_box['bounding_box']
xmin = coordinates['xmin']
ymin = coordinates['ymin']
xmax = coordinates['xmax']
ymax = coordinates['ymax']
box_polygon = Polygon([(xmin, ymin), (xmax, ymin), (xmax, ymax),
(xmin, ymax)])
for line in lines:
line_tmp = LineString([(line[0], line[1]), (line[2], line[3])])
# line_tmp = LineString([(750, 502), (1035, 502)])
decision = [
line_tmp.within(box_polygon),
line_tmp.contains(box_polygon),
line_tmp.overlaps(box_polygon)
]
# attention:[179, 440, 1092, 729]
# 横线 750, 502, 1035, 509
if True in decision:
lines_tmp1.append(line)
lines_tmp = [line for line in lines if line not in lines_tmp1]
for line in lines_tmp:
cv2.line(image, (int(line[0]), int(line[1])),
(int(line[2]), int(line[3])), (0, 255, 0), 1, cv2.LINE_AA)
# cv2.imwrite(r'E:\December\math_12_18\1_18\test_img\save3\mark.jpg', image)
return lines_tmp
def planets_are_aligned(self, day):
"""
We need to check if all planets in this galaxy are in a straight line
We will calculate a line from the first two planets and see if all
others are inside of it.
"""
line = LineString([
self.planets[0].calculate_planet_position(day),
self.planets[1].calculate_planet_position(day)
])
for p in self.planets[2::]:
if not line.contains(Point(p.calculate_planet_position(day))):
return PlanetsPosition.NOT_ALIGNED
if line.contains(Point(0, 0)):
return PlanetsPosition.ALIGNED_WITH_THE_SUN
return PlanetsPosition.ALIGNED_WITH_EACHOTHER
def test_concave_polygon(self):
"""
Finds pole of inaccessibility for a concave polygon and ensures that
the point is inside.
"""
concave_polygon = LineString([(500, 0), (0, 0), (0, 500),
(500, 500)]).buffer(100)
label = polylabel(concave_polygon)
self.assertTrue(concave_polygon.contains(label))
def test_concave_polygon(self):
"""
Finds pole of inaccessibility for a concave polygon and ensures that
the point is inside.
"""
concave_polygon = LineString([(500, 0), (0, 0), (0, 500),
(500, 500)]).buffer(100)
label = polylabel(concave_polygon)
self.assertTrue(concave_polygon.contains(label))
def is_oob(road_nodes, simulation_states):
# Create the road geometry from the nodes. At this point nodes have been reversed already if needed.
road_geometry = get_geometry(road_nodes)
# Compute right polygon
# Create the right lane polygon from the geometry
left_edge_x = np.array([e['middle'][0] for e in road_geometry])
left_edge_y = np.array([e['middle'][1] for e in road_geometry])
right_edge_x = np.array([e['right'][0] for e in road_geometry])
right_edge_y = np.array([e['right'][1] for e in road_geometry])
# Compute the "short" edge at the end of the road to rule out false positives
shorty = LineString([(left_edge_x[-1], left_edge_y[-1]), (right_edge_x[-1], right_edge_y[-1])]).buffer(2.0)
# Note that one must be in reverse order for the polygon to close correctly
right_edge = LineString(zip(right_edge_x[::-1], right_edge_y[::-1]))
left_edge = LineString(zip(left_edge_x, left_edge_y))
l_edge = left_edge.coords
r_edge = right_edge.coords
right_lane_polygon = Polygon(list(l_edge) + list(r_edge))
#middle = [e['middle'] for e in road_geometry]
#right = [e['right'] for e in road_geometry]
#road_poly = [(p[0], p[1]) for p in middle]
#right = [(p[0], p[1]) for p in right]
#road_poly.extend(right[::-1])
#right_polygon = Polygon(road_poly)
first_oob_state = None
position_of_first_oob_state = None
for idx, simulation_state in enumerate(simulation_states):
position = Point(simulation_state["pos"][0], simulation_state["pos"][1])
if not right_lane_polygon.contains(position):
# As soon as an observation is outside the lane polygon we mark the OOB, and return that position. All the
# subsequent states will be removed/discarded
log.debug("First OOB state found at %d", idx)
first_oob_state = idx
position_of_first_oob_state = position
break
if first_oob_state is not None:
if shorty.contains(position_of_first_oob_state):
log.info("* False Positive. Short Edge")
return False, None
else:
return True, first_oob_state
else:
return False, None
def find_points_in_region(positions: np.ndarray, region: LineString):
"""Find the points in a region.
Parameters
----------
positions: np.ndarray
The positions of a trajectory.
region: LineString
The line region.
"""
if region.is_empty:
return []
out = []
for i, pt in enumerate(positions):
if region.contains(Point(pt)):
out.append(i)
return out
def main():
map = read_map("day10.txt")
items = []
points = []
for line_id, line in enumerate(map):
for item_id, item in enumerate(line):
if item is "#":
items.append((item_id, line_id))
points.append(Point(item_id, line_id))
best_el = None
best_val = 0
print("Number of asteroids: {}".format(len(items)))
the_lazor_pos = (22, 19)
t0 = time.time()
for location in items:
# check how many asteroids are visible
cur_val = 0
for asteroid in items:
if asteroid is not location:
line = LineString([location, asteroid])
has_blocker = False
for obs_id, obstacle in enumerate(items):
if (obstacle is not location) and (obstacle
is not asteroid):
if line.contains(points[obs_id]):
has_blocker = True
break
if has_blocker is False:
cur_val += 1
if cur_val > best_val:
best_val = cur_val
best_el = location
t1 = time.time()
print("Time: {}", t1 - t0)
print("Element {} sees {}".format(best_el, best_val))
def check_in_park(self, lat, lon):
try:
cell = Polygon(self.get_s2_cell_as_polygon(lat, lon, 20)) # s2 lvl 20
if len(self.PARKS_CACHE) > 0:
for p in self.PARKS_CACHE:
coords = p['coords']
# osm polygon can be a line
if len(coords) == 2:
shape = LineString(coords)
if shape.within(cell.centroid):
return { 'id' : p['id'], 'internal' : p['internal'], 'name' : p['name'] }
if len(coords) > 2:
shape = Polygon(coords)
if shape.contains(cell.centroid):
return {'id': p['id'], 'internal' : p['internal'], 'name': p['name']}
return None
except Exception as e:
self.log.error('Unknown error: in check_in_park: {}',e)
return None
def get_steps(point, route):
"""
Calculate distance between start and intersection point
:param point:
:param route:
:return:
"""
result = 0
route_len = len(route)
for index in range(1, route_len):
line = LineString([route[index - 1], route[index]])
part_of = line.contains(point)
if part_of:
line = LineString([route[index - 1], (point.x, point.y)])
result = result + line.length
break
else:
result = result + line.length
return result
def gym_data():
gyms = []
parks = get_all_parks()
for g in get_gym_markers():
gym_point = Point(g['lat'], g['lon'])
cell = Polygon(get_s2_cell_as_polygon(g['lat'], g['lon'],
20)) # s2 lvl 20
for p in parks:
coords = p['coords']
# osm polygon can be a line
if len(coords) == 2:
shape = LineString(coords)
if shape.within(cell.centroid):
gyms.append(g)
break
if len(coords) > 2:
shape = Polygon(coords)
if shape.contains(cell.centroid):
gyms.append(g)
break
return jsonify(gyms)
def get_heading_angle(self):
""" Return the heading angle of the car before it went OBE """
# https://stackoverflow.com/questions/56710732/how-to-efficiently-calculate-full-2pi-angles-between-three-vectors-of-2d-point
# https://stackoverflow.com/questions/14066933/direct-way-of-computing-clockwise-angle-between-2-vectors
# https://newtonexcelbach.com/2014/03/01/the-angle-between-two-vectors-python-version/
# dot = x1*x2 + y1*y2 # dot product between [x1, y1] and [x2, y2]
# det = x1*y2 - y1*x2 # determinant
# angle = atan2(det, dot) # atan2(y, x) or atan2(sin, cos)
# Obtain the right vectors from the various points
A = np.array(
[[self.state_before_obe.pos_x, self.state_before_obe.pos_y]])
B = np.array([[self.obe_states[0].pos_x, self.obe_states[0].pos_y]])
AB = B - A
AB = AB
proj = self.state_before_obe.get_path_projection()
# This might be useful to add to asfault directly
# Process the path two points at the time. The path define the road direction
# https://gis.stackexchange.com/questions/84512/get-the-vertices-on-a-linestring-either-side-of-a-point
# plt.show()
# x, y = self.state_before_obe.test.get_path_polyline().coords.xy
# plt.plot(x, y, color="yellow")
# plt.plot(proj.x, proj.y, marker="o", color="blue")
road_direction = None
for pair in self._pairs(
list(self.state_before_obe.test.get_path_polyline().coords)):
# Check whether this pair contains the projection which by definition lies on the path
# print("Checking if ", pair[0], pair[1], "contains", proj)
# x, y = LineString([pair[0], pair[1]]).coords.xy
# plt.plot(x, y, color="red")
# plt.plot(proj.x, proj.y, marker="o")
# This does not work despite
segment = LineString([pair[0], pair[1]])
if segment.contains(proj) or segment.buffer(EPS).contains(proj):
road_direction = np.array([pair[0]]) - np.array([pair[1]])
break
P = np.array([[proj.x, proj.y]])
# TODO How to decide this w.r.t. direction of the road?
# Direction of the road might be given by the PATH of the test
N = np.array([[-(P[0][1] - A[0][1]), (P[0][0] - A[0][0])]])
N1 = np.array([[(P[0][1] - A[0][1]), -(P[0][0] - A[0][0])]])
if road_direction is not None:
if self.dot(road_direction, N) > 0:
N = N1
else:
l.warning("CANNOT FIND THE ROAD DIRECTION FOR %s",
str(self.state_before_obe))
# Vector defined by the last point before OBE and the first point of OBE
# angle, is_cc = self.angle(N, AB)
# print(angle, is_cc)
angle = self.angle(AB, N)
return angle[0]
def fld_demo4(image, sheet_dict, split_x, name, save_path):
image_ = image.copy()
img = Image.fromarray(image)
t = time.time()
img_gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
# ret, binary = cv2.threshold(img_gray, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)
fld = cv2.ximgproc.createFastLineDetector()
dlines = fld.detect(img_gray)
lines = [line[0] for line in dlines.tolist()]
lines_arr = np.array(lines)
width_ = lines_arr[:, 2] - lines_arr[:, 0]
height_ = lines_arr[:, 3] - lines_arr[:, 1]
lines_index1 = np.where(width_ > 50)[0]
lines_index2 = np.where(height_ > 50)[0]
lines_index = np.hstack([lines_index1, lines_index2])
new_lines = [lines[ele] for ele in lines_index]
new_lines = clean_repeat_lines(new_lines)
lines_tmp = filter_long_distance_lines(new_lines, sheet_dict)
# lines_without_mark = filter_line_in_mark(new_lines, sheet_dict)
# 检验滤线这一步骤有没有问题
for dline in lines_tmp:
x0 = int(round(dline[0]))
y0 = int(round(dline[1]))
x1 = int(round(dline[2]))
y1 = int(round(dline[3]))
cv2.line(image, (x0, y0), (x1, y1), (0, 255, 0), 1, cv2.LINE_AA)
text = str([x0, y0, x1, y1])
# cv2.putText(image, text, (x1, y1), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 0, 255), 1)
# write_single_img(image, os.path.join(save_path, name + '_raw_lines' + '.jpg'))
# get roi polygon 适当放大一点区域,使的线能包含在面里
roi_box_polygon = []
for index, region_box in enumerate(sheet_dict):
if region_box['class_name'] in class_above_edge:
coordinates = region_box['bounding_box']
xmin = coordinates['xmin'] - 5
ymin = coordinates['ymin'] - 5
xmax = coordinates['xmax'] + 5
ymax = coordinates['ymax'] + 5
box_polygon = Polygon([(xmin, ymin), (xmax, ymin), (xmax, ymax),
(xmin, ymax)])
roi_box_polygon.append(box_polygon)
# cv2.rectangle(image, (xmin, ymin), (xmax, ymax), (0, 0, 255), 1)
# cv2.imwrite(r'E:\111_4_26_test_img\aa\save\enlarge_box.jpg', image)
# print(roi_box_polygon)
# 对line进行分横向纵向
landscape_list_tmp = [] # 横向
longitudinal_list_tmp = [] # 纵向
for ele in lines_tmp:
if int(ele[2]) - int(ele[0]) < 10: # 纵向
longitudinal_list_tmp.append(ele)
elif int(ele[3]) - int(ele[1]) < 10: # 横向
landscape_list_tmp.append(ele)
# print(longitudinal_list_tmp)
points_all_list = []
points_list = []
for index, box_polygon in enumerate(roi_box_polygon):
extend_line_lan = LineString()
extend_line_lon = LineString()
# points_list = []
for line1 in landscape_list_tmp:
for line2 in longitudinal_list_tmp:
# 这部分对原先出来的线进行增大
raw_line_lan = LineString([(line1[0], line1[1]),
(line1[2], line1[3])]) # 横向增加5个像素
line_start_lan, line_end_lan = raw_line_lan.bounds[
0], raw_line_lan.bounds[1]
raw_line_lon = LineString([(line2[0], line2[1]),
(line2[2], line2[3])]) # 纵向的增加5个像素
line_start_lon, line_end_lon = raw_line_lon.bounds[
0], raw_line_lon.bounds[1]
# decision1 = [raw_line_lan.within(box_polygon), raw_line_lan.contains(box_polygon), raw_line_lan.overlaps(box_polygon)]
# decision2 = [raw_line_lon.within(box_polygon), raw_line_lon.contains(box_polygon), raw_line_lon.overlaps(box_polygon)]
decision1 = [
raw_line_lan.within(box_polygon),
raw_line_lan.contains(box_polygon)
]
decision2 = [
raw_line_lon.within(box_polygon),
raw_line_lon.contains(box_polygon)
]
if True in decision1 and True in decision2:
# 这里需要考虑延长左边还是右边
bbox_xmin, bbox_ymin, bbox_xmax, bbox_ymax = box_polygon.bounds[0], box_polygon.bounds[1], \
box_polygon.bounds[2], box_polygon.bounds[3]
# if abs(line_start_lan - bbox_xmin) < abs(bbox_xmax - line_end_lan):
extend_line_lan = LineString([
(box_polygon.bounds[0], line1[1]),
(box_polygon.bounds[2], line1[3])
])
# elif abs(line_start_lon - bbox_ymin) < abs(bbox_ymax - line_end_lon):
extend_line_lon = LineString([
(line2[0], box_polygon.bounds[1]),
(line2[2], box_polygon.bounds[3])
])
cond1 = raw_line_lan.intersects(raw_line_lon)
cond2 = raw_line_lan.crosses(raw_line_lon)
cond3 = extend_line_lan.intersects(extend_line_lon) # 十字交叉
cond4 = extend_line_lan.crosses(extend_line_lon) # 十字交叉
xp, yp = 0.0, 0.0
if cond1:
(xp, yp
) = raw_line_lan.intersection(raw_line_lon).bounds[:2]
elif cond3:
(xp, yp) = extend_line_lan.intersection(
extend_line_lon).bounds[:2]
points_list.append([(xp, yp), (raw_line_lan, raw_line_lon),
(extend_line_lan, extend_line_lon),
box_polygon])
# TODO 验证找所有点的正确性
# template = r'F:\exam\sheet_resolve\exam_info\000000-template.xml'
# tree = ET.parse(template)
# for ele in points_list:
# # for points2 in ele:
# # points1 = points_[1][0].bounds
# # points2 = points_[1][1].bounds
# # create_xml(str(points_[0]), tree, int(points1[0]), int(points1[1]), int(points1[2]), int(points1[3]))
# # create_xml(str(points_[0]), tree, int(points2[0]), int(points2[1]), int(points2[2]), int(points2[3]))
# points2 = ele[0]
# create_xml('points', tree, int(points2[0]), int(points2[1]), int(points2[0] + 1), int(points2[1] + 1))
# tree.write(os.path.join(save_path, name + '.xml'))
# 找出角点最优的那个
# check points 判断点周围有没有黑色像素
points_list_x = sorted(points_list, key=lambda k: k[0][0])
sort_by_x = [ele[0] for ele in points_list_x]
differ_new1 = np.array(sort_by_x[1:]) - np.array(sort_by_x[:-1])
index_new1 = sorted(list(set(np.where(differ_new1 > 10.0)[0])))
points_list_new_x = [points_list_x[ele] for ele in index_new1]
points_list_y = sorted(points_list_new_x, key=lambda k: k[0][1])
sort_by_y = [ele[0] for ele in points_list_y]
differ_new2 = np.array(sort_by_y[1:]) - np.array(sort_by_y[:-1])
index_new2 = sorted(list(set(np.where(differ_new2 > 10.0)[0])))
points_list_new_y = [points_list_y[ele] for ele in index_new2]
print('points_list:', points_list_new_y)
pixel_point_all = judge_cross_point(points_list_new_y,
image,
name,
thresh=30)
def get_direction(points_and_lines, box_polygon, image):
image_height, image_width = image.shape[:2]
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
ret, binary = cv2.threshold(gray, 240, 255, cv2.THRESH_BINARY_INV) # raw
# cv2.imwrite(os.path.join(r'E:\December\math_12_18\1_18\test_img\save3\\', 'THRESH_BINARY_INV' + '.jpg'), binary)
# DIR_UP, DIR_DOWN, DIR_LEFT, DIR_RIGHT = 1, 2, 4, 8
DIR_UP, DIR_DOWN, DIR_LEFT, DIR_RIGHT = False, False, False, False
# c++ 规则
# CROSS_TL = DIR_UP | DIR_LEFT
# CROSS_TR = DIR_UP | DIR_RIGHT
# CROSS_TLR = DIR_UP | DIR_LEFT | DIR_RIGHT
# CROSS_BL = DIR_DOWN | DIR_LEFT
# CROSS_BR = DIR_DOWN | DIR_RIGHT
# CROSS_BLR = DIR_DOWN | DIR_LEFT | DIR_RIGHT
# CROSS_TBL = DIR_UP | DIR_DOWN | DIR_LEFT
# CROSS_TBR = DIR_UP | DIR_DOWN | DIR_RIGHT
# CROSS_TBLR = DIR_UP | DIR_DOWN | DIR_LEFT | DIR_RIGHT
# python 规则
# CROSS_TL = DIR_UP and DIR_LEFT
# CROSS_TR = DIR_UP and DIR_RIGHT
# CROSS_TLR = DIR_UP and DIR_LEFT and DIR_RIGHT
# CROSS_BL = DIR_DOWN and DIR_LEFT
# CROSS_BR = DIR_DOWN and DIR_RIGHT
# CROSS_BLR = DIR_DOWN and DIR_LEFT and DIR_RIGHT
# CROSS_TBL = DIR_UP and DIR_DOWN and DIR_LEFT
# CROSS_TBR = DIR_UP and DIR_DOWN and DIR_RIGHT
# CROSS_TBLR = DIR_UP and DIR_DOWN and DIR_LEFT and DIR_RIGHT
# rule_list = [CROSS_TL, CROSS_TR, CROSS_TLR, CROSS_BL, CROSS_BR, CROSS_BLR, CROSS_TBL, CROSS_TBR, CROSS_TBLR]
# 因为延长的线可能出问题,这里用的是原始的线
print('points_and_lines:', points_and_lines)
points = points_and_lines[0]
raw_line = points_and_lines[1]
point_x = int(points[0])
point_y = int(points[1])
# if point_x == 1620 and point_y == 3260:
bbox = box_polygon.bounds
left_boarder = int(bbox[0]) - point_x if int(bbox[0]) - point_x > 0 else 0
right_boarder = int(bbox[2]) + point_x if int(
bbox[2]) + point_x < image_width else image_width
top_boarder = int(bbox[1]) - point_y if int(bbox[1]) - point_y > 0 else 0
bottom_boarder = int(bbox[3]) + point_y if int(
bbox[3]) + point_y < image_height else image_height
cv2.rectangle(image, (left_boarder, top_boarder),
(right_boarder, bottom_boarder), (255, 0, 255), 1)
raw_line_lan_ = raw_line[0]
raw_line_lon_ = raw_line[1]
if abs(raw_line_lan_.bounds[2] - point_x) >= abs(raw_line_lan_.bounds[0] -
point_x):
raw_line_lan = LineString([(point_x, raw_line_lan_.bounds[1]),
(raw_line_lan_.bounds[2],
raw_line_lan_.bounds[3])])
else:
raw_line_lan = LineString([(raw_line_lan_.bounds[0],
raw_line_lan_.bounds[1]),
(point_x, raw_line_lan_.bounds[3])])
if abs(raw_line_lon_.bounds[3] - point_y) <= abs(point_y -
raw_line_lon_.bounds[1]):
raw_line_lon = LineString([(raw_line_lon_.bounds[0],
raw_line_lon_.bounds[1]),
(raw_line_lon_.bounds[2], point_y)])
else:
raw_line_lon = LineString([(raw_line_lon_.bounds[0], point_y),
(raw_line_lon_.bounds[2],
raw_line_lon_.bounds[3])])
# box_polygon = Polygon([(xmin, ymin), (xmax, ymin), (xmax, ymax), (xmin, ymax)])
right_area = Polygon([(point_x, point_y - 10),
(right_boarder, point_y - 10),
(right_boarder, point_y + 10),
(point_x, point_y + 10)])
# 横线
cv2.line(image, (int(raw_line_lan.bounds[0]), int(raw_line_lan.bounds[1])),
(int(raw_line_lan.bounds[2]), int(raw_line_lan.bounds[3])),
(255, 0, 0), 1, cv2.LINE_AA)
# cv2.line(image, (int(raw_line_lan_.bounds[0]), int(raw_line_lan_.bounds[1])),
# (int(raw_line_lan_.bounds[2]), int(raw_line_lan_.bounds[3])), (255, 255, 0), 1, cv2.LINE_AA)
# 纵线
cv2.line(image, (int(raw_line_lon.bounds[0]), int(raw_line_lon.bounds[1])),
(int(raw_line_lon.bounds[2]), int(raw_line_lon.bounds[3])),
(255, 0, 0), 1, cv2.LINE_AA)
# cv2.line(image, (int(raw_line_lon_.bounds[0]), int(raw_line_lon_.bounds[1])),
# (int(raw_line_lon_.bounds[2]), int(raw_line_lon_.bounds[3])), (255, 255, 0), 1, cv2.LINE_AA)
cv2.rectangle(image,
(int(right_area.bounds[0]), int(right_area.bounds[1])),
(int(right_area.bounds[2]), int(right_area.bounds[3])),
(255, 0, 255), 1)
# cv2.imwrite(r'E:\December\math_12_18\1_18\test_img\save3\extend_lines_by_bbox.jpg', image)
# 水平向右
decision = [
raw_line_lan.within(right_area),
raw_line_lan.contains(right_area),
raw_line_lan.crosses(right_area)
]
if True in decision:
DIR_RIGHT = True
# 水平向左
left_area = Polygon([(left_boarder, point_y - 10), (point_x, point_y - 10),
(point_x, point_y + 10),
(left_boarder, point_y + 10)])
decision = [
raw_line_lan.within(left_area),
raw_line_lan.contains(left_area),
raw_line_lan.crosses(left_area)
]
if True in decision:
DIR_LEFT = True
# box_polygon = Polygon([(xmin, ymin), (xmax, ymin), (xmax, ymax), (xmin, ymax)])
# 垂直向上
top_area = Polygon([(point_x - 10, top_boarder),
(point_x + 10, top_boarder), (point_x + 10, point_y),
(point_x - 10, point_y)])
decision = [
raw_line_lon.within(top_area),
raw_line_lon.contains(top_area),
raw_line_lon.crosses(top_area)
]
if True in decision:
DIR_UP = True
# 垂直向下
bottom_area = Polygon([(point_x - 10, point_y), (point_x + 10, point_y),
(point_x + 10, bottom_boarder),
(point_x - 10, bottom_boarder)])
decision = [
raw_line_lon.within(bottom_area),
raw_line_lon.contains(bottom_area),
raw_line_lon.crosses(bottom_area)
]
if True in decision:
DIR_DOWN = True
CROSS_TL = DIR_UP and DIR_LEFT
CROSS_TR = DIR_UP and DIR_RIGHT
CROSS_TLR = DIR_UP and DIR_LEFT and DIR_RIGHT
CROSS_BL = DIR_DOWN and DIR_LEFT
CROSS_BR = DIR_DOWN and DIR_RIGHT
CROSS_BLR = DIR_DOWN and DIR_LEFT and DIR_RIGHT
CROSS_TBL = DIR_UP and DIR_DOWN and DIR_LEFT
CROSS_TBR = DIR_UP and DIR_DOWN and DIR_RIGHT
CROSS_TBLR = DIR_UP and DIR_DOWN and DIR_LEFT and DIR_RIGHT
direction_dict = {
0: 'CROSS_TL',
1: 'CROSS_TR',
2: 'CROSS_TLR',
3: 'CROSS_BL',
4: 'CROSS_BR',
5: 'CROSS_BLR',
6: 'CROSS_TBL',
7: 'CROSS_TBR',
8: 'CROSS_TBLR',
}
rule_list_tmp = [
CROSS_TL, CROSS_TR, CROSS_TLR, CROSS_BL, CROSS_BR, CROSS_BLR,
CROSS_TBL, CROSS_TBR, CROSS_TBLR
]
index_ = np.where(np.array(rule_list_tmp) == True)[0]
if len(index_) == 0:
print('没找到方向:', points)
return None
else:
print('index_:', index_)
direction = direction_dict[index_[0]]
return direction
def seilingstid(hale_df, tellelinje1, tellelinje2, returner_hale_index=False):
"""
MODIFISERT MENON-KODE
Klipper alle haler i hale_df som passerer både tellelinje 1 og 2 og
returnerer segmentene som ligger mellom, samt tilhørende seilingstid brukt
mellom linjene, lengde på seilasen mellom linjene i m og gjennomsnittlig
hastighet i m/s.
Funksjonen er så lang som den er for å håndtere edge-cases som i stor grad
resulterer av at shapely.split() har en bug ved selv-kryssende linjer. Om
denne skulle bli fikset så kan funksjonen potensielt skrives om.
Input:
hale_df: pandas dataframe med haler
tellelinje 1/2: i vilkårlig rekkefølge, shapely geometrier
returner_hale_index: hvis True, returnerer en liste indexer av samme
lengde som klipp-outputen som korresponderer til
halene brukt i hvert av klippene.
Output:
klipp_df: pandas dataframe som inneholder klippene
[hale_indices]: liste med indexer (se returner_hale_index)
ADVARSEL: behandler kun ca. 50 haler i sekundet
"""
#projeksjonen antar at input er en epsg:4326-projeksjon, som er standard for geosatelittdata
#denne brukes for å regne om klippenes lengde fra epsg:4326-koordinater til nautiske mil
#projeksjon = partial(pyproj.transform,
#pyproj.Proj(init='epsg:4326'),
#pyproj.Proj(init='epsg:32632'))
klipp_df = gpd.GeoDataFrame()
#hvis returner_hale_index er True vil denne fylles med indexer fra hale_df korresponderende til klippene i klipp_df
hale_indices = []
#iterer over haler
for idx, hale_serie in hale_df.iterrows():
hale = hale_serie.geometry
hale_punkter = [Point(p) for p in hale.coords]
#sjekker først om halen krysser begge tellelinjene
if hale.intersects(tellelinje1) and hale.intersects(tellelinje2):
#henter ut punktene der halen og tellelinjene krysser
intersect_punkter_1 = hale.intersection(tellelinje1)
intersect_punkter_2 = hale.intersection(tellelinje2)
#dersom det kun finnes ett krysningspunkt per tellelinje puttes det i en liste for å funke i for-loopen senere
if isinstance(intersect_punkter_1, Point):
intersect_punkter_1 = [intersect_punkter_1]
elif isinstance(
intersect_punkter_1,
LineString): #for aa vaere kompatibel med polygoner
intersect_punkter_1 = [
Point(p) for p in intersect_punkter_1.coords
]
elif isinstance(
intersect_punkter_1[0],
LineString): #for aa vaere kompatibel med polygoner
temp = []
for l in intersect_punkter_1:
temp.extend([Point(p) for p in l.coords])
intersect_punkter_1 = temp
if isinstance(intersect_punkter_2, Point):
intersect_punkter_2 = [intersect_punkter_2]
elif isinstance(
intersect_punkter_2,
LineString): #for aa vaere kompatibel med polygoner
intersect_punkter_2 = [
Point(p) for p in intersect_punkter_2.coords
]
elif isinstance(
intersect_punkter_2[0],
LineString): #for aa vaere kompatibel med polygoner
temp = []
for l in intersect_punkter_2:
temp.extend([Point(p) for p in l.coords])
intersect_punkter_2 = temp
intersect_idx_1 = []
intersect_idx_2 = []
intersect_time_1 = []
intersect_time_2 = []
#spar på punktene der halen krysser tellelinjene så de kan legges inn i klippet
krysningspunkter_1 = []
krysningspunkter_2 = []
#iterer over krysningspunktene
for punkt in intersect_punkter_1:
#regn ut avstander fra hvert punkt i halen til krysningspunktene
#(kan effektiviseres ved å filtrere ut hale-punkter utenfor loopen)
point_distances = np.array([
hale_punkt.distance(punkt) for hale_punkt in hale_punkter
])
#henter lokale minimum i avstand for å finne potensielle krysningspunkter.
#NB: antar at halen ikke begynner eller slutter på et krysningspunkt
local_minima = np.where(
np.r_[True, point_distances[1:] <= point_distances[:-1]]
& np.r_[point_distances[:-1] <= point_distances[1:],
True])[0]
#iterer over de potensielle krysningspunktene
for minima in local_minima:
#hent avstand til forrige og neste punkt
linje_bak, linje_frem = None, None
if minima > 0:
linje_bak = LineString(
[hale_punkter[minima],
hale_punkter[minima - 1]]).buffer(0.0001)
if minima < len(hale_punkter) - 1:
linje_frem = LineString(
[hale_punkter[minima],
hale_punkter[minima + 1]]).buffer(0.0001)
if linje_bak is not None and linje_bak.contains(punkt):
#legger til indexen til det nærmeste hale-punktet til krysningspunktet
#(tiden hentes ut her fordi det tillater inerpolering,
#men for øyeblikket støttes ikke dette)
intersect_idx_1.append(minima)
krysningspunkter_1.append(punkt)
weight = point_distances[minima - 1] / hale_punkter[
minima - 1].distance(hale_punkter[minima])
intersect_time_1.append(
hale_serie.times[minima - 1] +
((hale_serie.times[minima] -
hale_serie.times[minima - 1]) * weight))
elif linje_frem is not None and linje_frem.contains(punkt):
#krysning funnet
intersect_idx_1.append(minima)
krysningspunkter_1.append(punkt)
weight = point_distances[minima] / hale_punkter[
minima].distance(hale_punkter[minima + 1])
intersect_time_1.append(hale_serie.times[minima] + (
(hale_serie.times[minima + 1] -
hale_serie.times[minima]) * weight))
#se kommentarer for intersect_punkter_1
for punkt in intersect_punkter_2:
point_distances = np.array([
hale_punkt.distance(punkt) for hale_punkt in hale_punkter
])
local_minima = np.where(
np.r_[True, point_distances[1:] <= point_distances[:-1]]
& np.r_[point_distances[:-1] <= point_distances[1:],
True])[0]
for minima in local_minima:
linje_bak, linje_frem = None, None
if minima > 0:
linje_bak = LineString(
[hale_punkter[minima],
hale_punkter[minima - 1]]).buffer(0.01)
if minima < len(hale_punkter) - 1:
linje_frem = LineString(
[hale_punkter[minima],
hale_punkter[minima + 1]]).buffer(0.01)
if linje_bak is not None and linje_bak.contains(punkt):
intersect_idx_2.append(minima)
krysningspunkter_2.append(punkt)
weight = point_distances[minima - 1] / hale_punkter[
minima - 1].distance(hale_punkter[minima])
intersect_time_2.append(
hale_serie.times[minima - 1] +
((hale_serie.times[minima] -
hale_serie.times[minima - 1]) * weight))
elif linje_frem is not None and linje_frem.contains(punkt):
intersect_idx_2.append(minima)
krysningspunkter_2.append(punkt)
weight = point_distances[minima] / hale_punkter[
minima].distance(hale_punkter[minima + 1])
intersect_time_2.append(hale_serie.times[minima] + (
(hale_serie.times[minima + 1] -
hale_serie.times[minima]) * weight))
#henter ut de unike hale-punkt-indexene
unq1, unq_idx1 = np.unique(intersect_idx_1, return_index=True)
unq2, unq_idx2 = np.unique(intersect_idx_2, return_index=True)
intersect_idx_1 = unq1
intersect_idx_2 = unq2
krysningspunkter_1 = [krysningspunkter_1[u] for u in unq_idx1]
krysningspunkter_2 = [krysningspunkter_2[u] for u in unq_idx2]
#henter ut de tilsvarende hale-tid indexene
intersect_time_1 = np.array(intersect_time_1)[unq_idx1]
intersect_time_2 = np.array(intersect_time_2)[unq_idx2]
#i tilfelle punktene ikke er sortert etter tid
time_sort_idx_1 = np.argsort(intersect_time_1)
time_sort_idx_2 = np.argsort(intersect_time_2)
#sorterer etter tid
intersect_idx_1 = list(intersect_idx_1[time_sort_idx_1])
intersect_idx_2 = list(intersect_idx_2[time_sort_idx_2])
intersect_time_1 = list(intersect_time_1[time_sort_idx_1])
intersect_time_2 = list(intersect_time_2[time_sort_idx_2])
krysningspunkter_1 = [
krysningspunkter_1[t] for t in time_sort_idx_1
]
krysningspunkter_2 = [
krysningspunkter_2[t] for t in time_sort_idx_2
]
#iterer over krysningspunktene og hent ut krysningsparene.
#lag så en dataframe av geometri, tidspunkter, utregnet avstand
#samt hastighet.
while len(intersect_idx_1) > 0 and len(intersect_idx_2) > 0:
start_idx = None
slutt_idx = None
klipp_start = None
klipp_slutt = None
hale_klippet = None
foerste_kryss = None
if intersect_idx_1[0] < intersect_idx_2[0]:
if len(intersect_idx_1
) > 1 and intersect_idx_1[1] < intersect_idx_2[0]:
krysningspunkter_1 = krysningspunkter_1[1:]
intersect_idx_1 = intersect_idx_1[1:]
intersect_time_1 = intersect_time_1[1:]
else:
start_idx = intersect_idx_1[0]
slutt_idx = intersect_idx_2[0]
klipp_start = intersect_time_1[0]
klipp_slutt = intersect_time_2[0]
foerste_kryss = 1
krysningspunkter_1 = krysningspunkter_1[1:]
krysningspunkter_2 = krysningspunkter_2[1:]
intersect_idx_1 = intersect_idx_1[1:]
intersect_idx_2 = intersect_idx_2[1:]
intersect_time_1 = intersect_time_1[1:]
intersect_time_2 = intersect_time_2[1:]
else:
if len(intersect_idx_2
) > 1 and intersect_idx_2[1] < intersect_idx_1[0]:
krysningspunkter_2 = krysningspunkter_2[1:]
intersect_idx_2 = intersect_idx_2[1:]
intersect_time_2 = intersect_time_2[1:]
else:
start_idx = intersect_idx_2[0]
slutt_idx = intersect_idx_1[0]
intersect_idx_2 = intersect_idx_2[1:]
intersect_idx_1 = intersect_idx_1[1:]
klipp_start = intersect_time_2[0]
klipp_slutt = intersect_time_1[0]
intersect_time_2 = intersect_time_2[1:]
intersect_time_1 = intersect_time_1[1:]
foerste_kryss = 2
krysningspunkter_1 = krysningspunkter_1[1:]
krysningspunkter_2 = krysningspunkter_2[1:]
if slutt_idx is None or slutt_idx - start_idx <= 1:
continue
hale_klippet = LineString(hale_punkter[start_idx:slutt_idx])
lengde = distance_points(
hale_punkter[start_idx],
hale_punkter[slutt_idx]) * 1000 #metrisk meter
tid_brukt = (klipp_slutt - klipp_start).total_seconds()
klipp_df = klipp_df.append(
gpd.GeoDataFrame([[
hale_serie.mmsi, klipp_start, klipp_slutt, lengde,
lengde / tid_brukt, foerste_kryss, hale_klippet
]],
columns=[
'mmsi', 'start_tid', 'slutt_tid',
'lengde', 'obs_speed',
'første_linje_krysset', 'geometry'
],
geometry='geometry'))
if returner_hale_index:
hale_indices.append(idx)
if klipp_df.shape[0] > 0:
klipp_df = klipp_df.set_geometry('geometry')
if returner_hale_index:
return klipp_df, hale_indices
return klipp_df
def filter_long_distance_lines(lines, sheet_dict):
# 去掉roi里面的线 也去掉mark里的线
lines_tmp1 = []
for index, region_box in enumerate(sheet_dict):
if region_box['class_name'] in class_above_edge:
coordinates = region_box['bounding_box']
xmin = coordinates['xmin'] + 10
ymin = coordinates['ymin'] + 10
xmax = coordinates['xmax'] - 10
ymax = coordinates['ymax'] - 10
box_polygon = Polygon([(xmin, ymin), (xmax, ymin), (xmax, ymax),
(xmin, ymax)])
for line in lines:
line_tmp = LineString([(line[0], line[1]), (line[2], line[3])])
# line_tmp = LineString([(750, 502), (1035, 502)])
# decision = [line_tmp.within(box_polygon), line_tmp.contains(box_polygon), line_tmp.overlaps(box_polygon)]
decision = [
line_tmp.within(box_polygon),
line_tmp.contains(box_polygon)
]
# attention:[179, 440, 1092, 729]
# 横线 750, 502, 1035, 509
if True in decision:
lines_tmp1.append(line)
elif region_box['class_name'] == 'mark':
coordinates = region_box['bounding_box']
xmin = coordinates['xmin']
ymin = coordinates['ymin']
xmax = coordinates['xmax']
ymax = coordinates['ymax']
box_polygon = Polygon([(xmin, ymin), (xmax, ymin), (xmax, ymax),
(xmin, ymax)])
for line in lines:
line_tmp = LineString([(line[0], line[1]), (line[2], line[3])])
# line_tmp = LineString([(750, 502), (1035, 502)])
decision = [
line_tmp.within(box_polygon),
line_tmp.contains(box_polygon)
]
# attention:[179, 440, 1092, 729]
# 横线 750, 502, 1035, 509
if True in decision:
lines_tmp1.append(line)
lines_tmp = [line for line in lines if line not in lines_tmp1]
# 这里能去掉所有ROI里面的线,下面还要去掉离ROI很远的线,不然遍历费时间
lines_apart_from_box = []
for line0 in lines_tmp:
# 考虑的是点而不是线
middle_point = [
1 / 2 * (line0[2] - line0[0]), 1 / 2 * (line0[3] - line0[1])
]
middle_point = list(np.maximum(np.array(middle_point), 0))
start_point = [line0[0], line0[1]]
end_point = [line0[2], line0[3]]
key_point = [middle_point, end_point, start_point]
for index, region_box in enumerate(sheet_dict):
if region_box['class_name'] in class_above_edge:
coordinates = region_box['bounding_box']
xmin = coordinates['xmin'] - 10
ymin = coordinates['ymin'] - 10
xmax = coordinates['xmax'] + 10
ymax = coordinates['ymax'] + 10
for pp in key_point:
if xmin <= pp[0] <= xmax and ymin <= pp[1] < ymax:
lines_apart_from_box.append(line0)
# line_tmp_ = LineString([(line[0], line[1]), (line[2], line[3])])
# for index, region_box in enumerate(sheet_dict):
# if region_box['class_name'] in class_above_edge:
# coordinates = region_box['bounding_box']
# xmin = coordinates['xmin'] + 10
# ymin = coordinates['ymin'] + 10
# xmax = coordinates['xmax'] - 10
# ymax = coordinates['ymax'] - 10
# box_polygon_ = Polygon([(xmin, ymin), (xmax, ymin), (xmax, ymax), (xmin, ymax)])
# decision = [line_tmp_.contains(box_polygon_), line_tmp_.within(box_polygon_),
# line_tmp_.crosses(box_polygon_), line_tmp_.overlaps(box_polygon_)]
# if True in decision:
# lines_apart_from_box.append(line)
# 去掉重复线
lines_apart_from_box_array = np.array(lines_apart_from_box)
former_one = lines_apart_from_box_array[1:, :]
rear_one = lines_apart_from_box_array[:-1, :]
differ = former_one - rear_one
index_ = sorted(list(set(np.where(differ != 0)[0])))
lines_apart_from_box_ = [lines_apart_from_box[ele] for ele in index_]
return lines_apart_from_box_
def fld_demo4(image, sheet_dict, name, save_path):
img = Image.fromarray(image)
t = time.time()
img_gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
# ret, binary = cv2.threshold(img_gray, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)
fld = cv2.ximgproc.createFastLineDetector()
dlines = fld.detect(img_gray)
lines = [line[0] for line in dlines.tolist()]
lines_arr = np.array(lines)
width_ = lines_arr[:, 2] - lines_arr[:, 0]
height_ = lines_arr[:, 3] - lines_arr[:, 1]
lines_index1 = np.where(width_ > 50)[0]
lines_index2 = np.where(height_ > 50)[0]
lines_index = np.hstack([lines_index1, lines_index2])
new_lines = [lines[ele] for ele in lines_index]
new_lines = clean_repeat_lines(new_lines)
lines_tmp = filter_long_distance_lines(new_lines, sheet_dict)
# lines_without_mark = filter_line_in_mark(new_lines, sheet_dict)
# 检验滤线这一步骤有没有问题
for dline in lines_tmp:
x0 = int(round(dline[0]))
y0 = int(round(dline[1]))
x1 = int(round(dline[2]))
y1 = int(round(dline[3]))
cv2.line(image, (x0, y0), (x1, y1), (0, 255, 0), 1, cv2.LINE_AA)
text = str([x0, y0, x1, y1])
cv2.putText(image, text, (x1, y1), cv2.FONT_HERSHEY_COMPLEX, 1,
(0, 0, 255), 1)
write_single_img(image,
os.path.join(save_path, name + '_raw_lines' + '.jpg'))
# get roi polygon 适当放大一点区域,使的线能包含在面里
roi_box_polygon = []
for index, region_box in enumerate(sheet_dict):
if region_box['class_name'] in class_above_edge:
coordinates = region_box['bounding_box']
xmin = coordinates['xmin'] - 5
ymin = coordinates['ymin'] - 5
xmax = coordinates['xmax'] + 5
ymax = coordinates['ymax'] + 5
box_polygon = Polygon([(xmin, ymin), (xmax, ymin), (xmax, ymax),
(xmin, ymax)])
roi_box_polygon.append(box_polygon)
cv2.rectangle(image, (xmin, ymin), (xmax, ymax), (0, 0, 255), 1)
cv2.imwrite(r'E:\111_4_26_test_img\aa\save\enlarge_box.jpg', image)
# print(roi_box_polygon)
# 对line进行分横向纵向
landscape_list_tmp = [] # 横向
longitudinal_list_tmp = [] # 纵向
for ele in lines_tmp:
if int(ele[2]) - int(ele[0]) < 10: # 纵向
longitudinal_list_tmp.append(ele)
elif int(ele[3]) - int(ele[1]) < 10: # 横向
landscape_list_tmp.append(ele)
# print(longitudinal_list_tmp)
points_all_list = []
points_list = []
for index, box_polygon in enumerate(roi_box_polygon):
extend_line_lan = LineString()
extend_line_lon = LineString()
# points_list = []
for line1 in landscape_list_tmp:
for line2 in longitudinal_list_tmp:
# 这部分对原先出来的线进行增大
raw_line_lan = LineString([(line1[0], line1[1]),
(line1[2], line1[3])]) # 横向增加5个像素
line_start_lan, line_end_lan = raw_line_lan.bounds[
0], raw_line_lan.bounds[1]
raw_line_lon = LineString([(line2[0], line2[1]),
(line2[2], line2[3])]) # 纵向的增加5个像素
line_start_lon, line_end_lon = raw_line_lon.bounds[
0], raw_line_lon.bounds[1]
# decision1 = [raw_line_lan.within(box_polygon), raw_line_lan.contains(box_polygon), raw_line_lan.overlaps(box_polygon)]
# decision2 = [raw_line_lon.within(box_polygon), raw_line_lon.contains(box_polygon), raw_line_lon.overlaps(box_polygon)]
decision1 = [
raw_line_lan.within(box_polygon),
raw_line_lan.contains(box_polygon)
]
decision2 = [
raw_line_lon.within(box_polygon),
raw_line_lon.contains(box_polygon)
]
if True in decision1 and True in decision2:
# 这里需要考虑延长左边还是右边
bbox_xmin, bbox_ymin, bbox_xmax, bbox_ymax = box_polygon.bounds[0], box_polygon.bounds[1], \
box_polygon.bounds[2], box_polygon.bounds[3]
# if abs(line_start_lan - bbox_xmin) < abs(bbox_xmax - line_end_lan):
extend_line_lan = LineString([
(box_polygon.bounds[0], line1[1]),
(box_polygon.bounds[2], line1[3])
])
# elif abs(line_start_lon - bbox_ymin) < abs(bbox_ymax - line_end_lon):
extend_line_lon = LineString([
(line2[0], box_polygon.bounds[1]),
(line2[2], box_polygon.bounds[3])
])
cond1 = raw_line_lan.intersects(raw_line_lon)
cond2 = raw_line_lan.crosses(raw_line_lon)
cond3 = extend_line_lan.intersects(extend_line_lon) # 十字交叉
cond4 = extend_line_lan.crosses(extend_line_lon) # 十字交叉
xp, yp = 0.0, 0.0
if cond1:
(xp, yp
) = raw_line_lan.intersection(raw_line_lon).bounds[:2]
elif cond3:
(xp, yp) = extend_line_lan.intersection(
extend_line_lon).bounds[:2]
points_list.append([(xp, yp), (raw_line_lan, raw_line_lon),
(extend_line_lan, extend_line_lon),
box_polygon])
# TODO 验证找所有点的正确性
# template = r'F:\exam\sheet_resolve\exam_info\000000-template.xml'
# tree = ET.parse(template)
# for ele in points_list:
# # for points2 in ele:
# # points1 = points_[1][0].bounds
# # points2 = points_[1][1].bounds
# # create_xml(str(points_[0]), tree, int(points1[0]), int(points1[1]), int(points1[2]), int(points1[3]))
# # create_xml(str(points_[0]), tree, int(points2[0]), int(points2[1]), int(points2[2]), int(points2[3]))
# points2 = ele[0]
# create_xml('points', tree, int(points2[0]), int(points2[1]), int(points2[0] + 1), int(points2[1] + 1))
# tree.write(os.path.join(save_path, name + '.xml'))
# 找出角点最优的那个
# check points 判断点周围有没有黑色像素
points_list = sorted(points_list, key=lambda k: k[0][0])
points_all1 = [ele[0] for ele in points_list]
differ_new1 = np.array(points_all1[1:]) - np.array(points_all1[:-1])
index_new1 = np.where(differ_new1 > 6.0)[0]
points_list = [points_list[ele] for ele in index_new1]
print('points_list:', points_list)
points_new = []
for points_ in points_list:
pixel_point = img.getpixel((int(points_[0][0]), int(points_[0][1])))
if int(np.mean(np.array(pixel_point))) <= 245:
points_new.append(points_)
# print(points_new)
# 验证这里每个点是不是黑色像素
template = r'F:\exam\sheet_resolve\exam_info\000000-template.xml'
tree = ET.parse(template)
for ele in points_list:
points2 = ele[0]
create_xml('points', tree, int(points2[0]), int(points2[1]),
int(points2[0] + 1), int(points2[1] + 1))
tree.write(os.path.join(save_path, 'points_' + name + '.xml'))
points_new = sorted(points_new, key=lambda k: k[0][0])
points_new_list = []
if len(points_new) == 1:
points_new_list.extend(points_new)
else:
points_all_x = [ele[0][0] for ele in points_new]
rear_one_x = np.array(points_all_x)[1:]
former_one_x = np.array(points_all_x)[:-1]
differ_x = rear_one_x - former_one_x
index_x = list(
np.where(differ_x >= 6.0)[0]) # 符合条件的说明不是同一个点,这些点都要加进列表里去
# 头和尾加进去
# if points_all_x[1] - points_all_x[0] >= 6.0:
# index_x.insert(0, 0)
# elif points_all_x[-1] - points_all_x[-1] >= 6.0:
# index_x.append(len(points_all_x) - 1)
points_all_y = [ele[0][1] for ele in points_new]
rear_one_y = np.array(points_all_y)[1:]
former_one_y = np.array(points_all_y)[:-1]
differ_y = rear_one_y - former_one_y
index_y = list(
np.where(differ_y >= 6.0)[0]) # 符合条件的说明不是同一个点,这些点都要加进列表里去
# 头和尾加进去
# if points_all_y[1] - points_all_y[0] >= 6.0:
# index_y.insert(0, 0)
# elif points_all_y[-1] - points_all_y[-1] >= 6.0:
# index_y.append(len(points_all_y) - 1)
while len(index_x) == 0 and len(index_y) == 0: # 说明每个点都不重合,都是需要的点
points_new_list.extend(points_new)
break
if len(index_x) != 0:
if len(index_x) > 2:
index_x = list(index_x)
index_x.insert(0, 0)
index_x.append(len(points_new))
for index_a, ele in enumerate(index_x[1:]):
block_one = points_new[index_x[index_a]:index_x[index_a +
1]]
if len(block_one) == 1:
points_new_list.extend(block_one)
else:
# 取均值
x_mean = np.mean(
np.array([ele[0][0] for ele in block_one]))
y_mean = np.mean(
np.array([ele[0][1] for ele in block_one]))
# 取最长的线
# max_index_lan = np.argsort(np.array([int(ele[1][0]) for ele in block_one]))
# max_index_lon = np.argsort(np.array([int(ele[1][1]) for ele in block_one]))
xx = [int(ele[1][0].length) for ele in block_one]
max_index_lan = np.argsort(
[int(ele[1][0].length) for ele in block_one])
yy = [int(ele[1][1].length) for ele in block_one]
max_index_lon = np.argsort(
[int(ele[1][1].length) for ele in block_one])
zz = block_one[max_index_lan[-1]][1][0]
cc = block_one[max_index_lon[-1]][1][1]
points_new_list.append([
(x_mean, y_mean),
(block_one[max_index_lan[-1]][1][0],
block_one[max_index_lon[-1]][1][1]),
block_one[-1][-1]
])
print('points_new_list:', points_new_list)
elif len(index_y) != 0:
index_y = list(index_y)
index_y.insert(0, 0)
index_y.append(len(points_new))
if len(index_y) > 2:
for index_a, ele in enumerate(index_y[1:]):
block_one = points_new[index_y[index_a]:index_y[index_a +
1]]
if len(block_one) == 1:
points_new_list.extend(block_one)
else:
# 取均值
x_mean = np.mean(
np.array([ele[0][0] for ele in block_one]))
y_mean = np.mean(
np.array([ele[0][1] for ele in block_one]))
# 取最长的线
# max_index_lan = np.argsort(np.array([int(ele[1][0]) for ele in block_one]))
# max_index_lon = np.argsort(np.array([int(ele[1][1]) for ele in block_one]))
xx = [int(ele[1][0].length) for ele in block_one]
max_index_lan = np.argsort(
[int(ele[1][0].length) for ele in block_one])
yy = [int(ele[1][1].length) for ele in block_one]
max_index_lon = np.argsort(
[int(ele[1][1].length) for ele in block_one])
zz = block_one[max_index_lan[-1]][1][0]
cc = block_one[max_index_lon[-1]][1][1]
points_new_list.append([
(x_mean, y_mean),
(block_one[max_index_lan[-1]][1][0],
block_one[max_index_lon[-1]][1][1]),
block_one[-1][-1]
])
print('points_new_list:', points_new_list)
else:
points_all_list.extend(points_new)
# 合并points_new_list成一个list
# points_new_list_tmp = [ele for ele in points_new_list]
for points_and_lines in points_new_list:
direction = get_direction(points_and_lines, points_and_lines[-1],
image)
if direction != None:
points_dict = {}
points_dict['points'] = points_and_lines[0]
points_dict['direction'] = direction
points_dict['extend_line'] = points_and_lines[-2]
points_all_list.append(points_dict)
print(points_all_list)
template = r'F:\exam\sheet_resolve\exam_info\000000-template.xml'
tree = ET.parse(template)
for points_ in points_all_list:
points = points_['points']
create_xml(
str(points) + '_' + points_['direction'], tree, int(points[0]),
int(points[1]), int(points[0] + 1), int(points[1] + 1))
tree.write(os.path.join(save_path, name + '.xml'))
def compare_range(standard, compare, buf):
rangement = LineString(standard).buffer(buf)
compare_line = LineString(compare)
return rangement.contains(compare_line)
hull_pts = hull.exterior.coords.xy
hull_points = []
for i in range(0, len(hull_pts[0])):
hull_points.append((hull_pts[0][i], hull_pts[1][i]))
polyg = Polygon(hull_points)
line_polygon = LineString(polyg.exterior.coords)
# (minx, miny, maxx, maxy)
bounds = list(map(int, list(polyg.bounds)))
polni_nivo[_nivo] = []
outer_nivo[_nivo] = []
for i in range(bounds[0], bounds[2]+1):
for j in range(bounds[1], bounds[3]+1):
if line_polygon.contains(Point(i,j)):
outer_nivo[_nivo].append((i,j))
elif polyg.contains(Point(i,j)):
polni_nivo[_nivo].append((i,j))
if _nivo == min_z or _nivo == max_z:
outer_nivo[_nivo].append((i, j))
with open(f"polninivo{time.time()}.dat", "wb+") as f:
pickle.dump(polni_nivo, f)
with open(f"outernivo{time.time()}.dat", "wb+") as f:
pickle.dump(outer_nivo, f)
xdata_final = []
ydata_final = []
def filter_contained_points(line: LineString,
points: List[Point]) -> List[Point]:
return [point for point in points if line.contains(point)]
# Ponto de referência
p1 = vertices[p1_num]
for m in range(i - nivel_conect * espacamento,
i + nivel_conect * espacamento + 1):
if not (0 <= m <= malha_gs.shape[0] - 1):
continue
for n in range(j - nivel_conect * espacamento,
j + nivel_conect * espacamento + 1):
if 0 <= n <= malha_gs.shape[1] - 1:
if (p2_num := malha_gs[m, n]) != 0:
# Ponto de comparação
p2 = vertices[p2_num]
if not np.allclose(p1, p2):
linha = LineString([p1, p2])
if not any(
linha.contains(e)
for e in elementos.values()):
if poligono.contains(
linha
) or contorno_com_buff.contains(
linha):
elementos[(p1_num,
p2_num)] = linha
return elementos, vertices
@staticmethod
def verificar_vertices_ociosos(vertices, elementos) -> tuple:
# Apagar os vértices que não são utilizados
verts_usados = []
for e in elementos:
# Defining the points for the obstacle
obs_pt = [[2, 2], [2, 4], [4, 4], [4, 2]]
obs_poly = Polygon(obs_pt)
plt.gca().add_patch(plt.Polygon(obs_pt, fill=True, color='lightgrey'))
# Inside point
pt_i = [3, 3]
plt.plot(pt_i[0], pt_i[1], 'ok')
# Point on the boundary
pt_b = [2, 3]
plt.plot(pt_b[0], pt_b[1], 'ob')
# Point outside
pt_o = [1, 3]
plt.plot(pt_o[0], pt_o[1], 'or')
# Condition to check if point lies inside polygon
# using polygon.contains(point)
print(obs_poly.contains(Point(pt_i[0], pt_i[1])),
obs_poly.contains(Point(pt_b[0], pt_b[1])),
obs_poly.contains(Point(pt_o[0], pt_o[1])))
# Creating a linestring to verify the same
line = LineString(obs_pt)
print(line.contains(Point(pt_i[0], pt_i[1])),
line.contains(Point(pt_b[0], pt_b[1])),
line.contains(Point(pt_o[0], pt_o[1])))
plt.show()
def intersection(x1, y1, x2, y2, x3, y3, x4, y4):
# wektory
first_third_x = x3 - x1
first_third_y = y3 - y1
rx = x2 - x1
ry = y2 - y1
sx = x4 - x3
sy = y4 - y3
# obliczanie wyznaczników potrzebnych do wykorzystania metody Cramer'a
numerator_t = determinant(first_third_x, first_third_y, sx, sy)
numerator_u = determinant(first_third_x, first_third_y, rx, ry)
denominator = determinant(rx, ry, sx, sy)
try:
t = numerator_t / denominator
u = numerator_u / denominator
if denominator != 0 and 0 <= t <= 1 and 0 <= u <= 1:
i_x = x1 + t * rx
i_y = y1 + u * ry
msg = f"Odcinek przecina się w punkcie: ({i_x}, {i_y})"
messagebox.showinfo('Odpowiedź', msg)
plot_intersection(x1, y1, x2, y2, x3, y3, x4, y4, i_x, i_y)
else:
msg = "Odcninki nie przecinają się oraz nie są równoległe.\nKliknij 'OK', żeby wyświetlić wizualizację."
messagebox.showinfo('Odpowiedź', msg)
plot_intersection(x1, y1, x2, y2, x3, y3, x4, y4)
except ZeroDivisionError:
if denominator == 0 and numerator_u != 0:
msg = "Odcinki są równoległe.\nKliknij 'OK', żeby wyświetlić wizualizację."
messagebox.showinfo('Odpowiedź', msg)
plot_intersection(x1, y1, x2, y2, x3, y3, x4, y4)
if numerator_u == 0 and denominator == 0:
# parametry potrzebne do ocenienia wspólniniowości odcinków
t_0 = dot_product(first_third_x, first_third_y, rx,
ry) / dot_product(rx, ry, rx, ry)
t_1 = t_0 + dot_product(sx, sy, rx, ry) / dot_product(
rx, ry, rx, ry)
# zdefiniowanie punktów oraz odcinków (bibliotek shapely)
segment_AB = LineString([(x1, y1), (x2, y2)])
segment_CD = LineString([(x3, y3), (x4, y4)])
point_A = Point(x1, y1)
point_B = Point(x2, y2)
point_C = Point(x3, y3)
point_D = Point(x4, y4)
if dot_product(sx, sy, rx, ry) > 0:
# sprawdzenie czy wyliczone t_0 oraz t_1 mają część wspólną z [0,1]
interval = portion.closed(t_0, t_1)
if interval.overlaps(portion.closed(0, 1)):
if segment_AB.contains(point_C) and segment_AB.contains(
point_D):
msg = f"Odcinki są współliniowe i odcinek CD zawiera się w odcinku AB.\n" \
f"Przedział wspólny: C({x3}, {y3}) - D({x4}, {y4})\nKliknij 'OK', żeby wyświetlić wizualizację."
messagebox.showinfo('Odpowiedź', msg)
elif segment_CD.contains(point_A) and segment_CD.contains(
point_B):
msg = f"Odcinki są współliniowe i odcinek AB zawiera się w odcinku CD.\n" \
f"Przedział wspólny: D({x1}, {y1}) - B({x2}, {y2})\nKliknij 'OK', żeby wyświetlić wizualizację."
messagebox.showinfo('Odpowiedź', msg)
elif segment_AB.contains(point_C):
if distance.euclidean(point_D,
point_A) > distance.euclidean(
point_D, point_B):
msg = f"Odcinki są współliniowe i pokrywają się w przedziale CB\n" \
f"C({x3}, {y3}) - B({x2}, {y2})\nKliknij 'OK', żeby wyświetlić wizualizację."
messagebox.showinfo('Odpowiedź', msg)
else:
msg = f"Odcinki są współliniowe i pokrywają się w przedziale AC\n" \
f"A({x1}, {y1}) - C({x3}, {y3})\nKliknij 'OK', żeby wyświetlić wizualizację."
messagebox.showinfo('Odpowiedź', msg)
elif segment_AB.contains(point_D):
if distance.euclidean(point_C,
point_A) > distance.euclidean(
point_C, point_B):
msg = f"Odcinki są współliniowe i pokrywają się w przedziale DB\n" \
f"D({x4}, {y4}) - B({x2}, {y2})\nKliknij 'OK', żeby wyświetlić wizualizację."
messagebox.showinfo('Odpowiedź', msg)
else:
msg = f"Odcinki są współliniowe i pokrywają się w przedziale AD\n" \
f"A({x1}, {y1}) - D({x4}, {y4})\nKliknij 'OK', żeby wyświetlić wizualizację."
messagebox.showinfo('Odpowiedź', msg)
elif segment_CD.contains(point_A):
if distance.euclidean(point_B,
point_C) > distance.euclidean(
point_B, point_D):
msg = f"Odcinki są współliniowe i pokrywają się w przedziale AD\n" \
f"A({x1}, {y1}) - D({x4}, {y4})\nKliknij 'OK', żeby wyświetlić wizualizację."
messagebox.showinfo('Odpowiedź', msg)
else:
msg = f"Odcinki są współliniowe i pokrywają się w przedziale CA\n" \
f"C({x3}, {y3}) - A({x1}, {y1})\nKliknij 'OK', żeby wyświetlić wizualizację."
messagebox.showinfo('Odpowiedź', msg)
elif segment_CD.contains(point_B):
if distance.euclidean(point_A,
point_C) > distance.euclidean(
point_A, point_D):
msg = f"Odcinki są współliniowe i pokrywają się w przedziale BD\n" \
f"B({x2}, {y2}) - D({x4}, {y4})\nKliknij 'OK', żeby wyświetlić wizualizację."
messagebox.showinfo('Odpowiedź', msg)
else:
msg = f"Odcinki są współliniowe i pokrywają się w przedziale CB\n" \
f"C({x3}, {y3}) - B({x2}, {y2})\nKliknij 'OK', żeby wyświetlić wizualizację."
messagebox.showinfo('Odpowiedź', msg)
else:
msg = "Odcinki są współliniowe, ale nie nachodzą na siebie.\nKliknij 'OK', żeby wyświetlić wizualizację."
messagebox.showinfo('Odpowiedź', msg)
plot_intersection(x1, y1, x2, y2, x3, y3, x4, y4)
if dot_product(sx, sy, rx, ry) < 0:
interval = portion.closed(t_1, t_0)
if interval.overlaps(portion.closed(0, 1)):
if segment_AB.contains(point_C) and segment_AB.contains(
point_D):
msg = f"Odcinki są współliniowe i odcinek CD zawiera się w odcinku AB.\n" \
f"Przedział wspólny: C({x3}, {y3}) - D({x4}, {y4})\nKliknij 'OK', żeby wyświetlić wizualizację."
messagebox.showinfo('Odpowiedź', msg)
elif segment_CD.contains(point_A) and segment_CD.contains(
point_B):
msg = f"Odcinki są współliniowe i odcinek AB zawiera się w odcinku CD.\n" \
f"Przedział wspólny: D({x1}, {y1}) - B({x2}, {y2})\nKliknij 'OK', żeby wyświetlić wizualizację."
messagebox.showinfo('Odpowiedź', msg)
elif segment_AB.contains(point_C):
if distance.euclidean(point_D,
point_A) > distance.euclidean(
point_D, point_B):
msg = f"Odcinki są współliniowe i pokrywają się w przedziale CB\n" \
f"C({x3}, {y3}) - B({x2}, {y2})\nKliknij 'OK', żeby wyświetlić wizualizację."
messagebox.showinfo('Odpowiedź', msg)
else:
msg = f"Odcinki są współliniowe i pokrywają się w przedziale AC\n" \
f"A({x1}, {y1}) - C({x3}, {y3})\nKliknij 'OK', żeby wyświetlić wizualizację."
messagebox.showinfo('Odpowiedź', msg)
elif segment_AB.contains(point_D):
if distance.euclidean(point_C,
point_A) > distance.euclidean(
point_C, point_B):
msg = f"Odcinki są współliniowe i pokrywają się w przedziale DB\n" \
f"D({x4}, {y4}) - B({x2}, {y2})\nKliknij 'OK', żeby wyświetlić wizualizację."
messagebox.showinfo('Odpowiedź', msg)
else:
msg = f"Odcinki są współliniowe i pokrywają się w przedziale AD\n" \
f"A({x1}, {y1}) - D({x4}, {y4})\nKliknij 'OK', żeby wyświetlić wizualizację."
messagebox.showinfo('Odpowiedź', msg)
elif segment_CD.contains(point_A):
if distance.euclidean(point_B,
point_C) > distance.euclidean(
point_B, point_D):
msg = f"Odcinki są współliniowe i pokrywają się w przedziale AD\n" \
f"A({x1}, {y1}) - D({x4}, {y4})\nKliknij 'OK', żeby wyświetlić wizualizację."
messagebox.showinfo('Odpowiedź', msg)
else:
msg = f"Odcinki są współliniowe i pokrywają się w przedziale CA\n" \
f"C({x3}, {y3}) - A({x1}, {y1})\nKliknij 'OK', żeby wyświetlić wizualizację."
messagebox.showinfo('Odpowiedź', msg)
elif segment_CD.contains(point_B):
if distance.euclidean(point_A,
point_C) > distance.euclidean(
point_A, point_D):
msg = f"Odcinki są współliniowe i pokrywają się w przedziale BD\n" \
f"B({x2}, {y2}) - D({x4}, {y4})\nKliknij 'OK', żeby wyświetlić wizualizację."
messagebox.showinfo('Odpowiedź', msg)
else:
msg = f"Odcinki są współliniowe i pokrywają się w przedziale CB\n" \
f"C({x3}, {y3}) - B({x2}, {y2})\nKliknij 'OK', żeby wyświetlić wizualizację."
messagebox.showinfo('Odpowiedź', msg)
else:
msg = "Odcinki są współliniowe, ale nie nachodzą na siebie.\nKliknij 'OK', żeby wyświetlić wizualizację."
messagebox.showinfo('Odpowiedź', msg)
plot_intersection(x1, y1, x2, y2, x3, y3, x4, y4)
sys.exit()
def on_segment(p, q, r):
line = LineString([Point(p.x, p.y), Point(r.x, r.y)])
return line.contains(Point(q.x, q.y))
class Polyline:
"""!
@brief Custom (open or closed) polyline class
"""
def __init__(self,
coordinates,
attributes=None,
z_array=None,
m_array=None,
id=None):
self._nb_points = len(coordinates)
self._is_2d = len(coordinates[0]) == 2
if z_array is not None:
self._is_2d = False
self._is_closed = False
if tuple(coordinates[0]) == tuple(coordinates[-1]):
self._is_closed = len(
coordinates
) > 2 # line with 2 coordinates which are identical can not be a polygon
if z_array is not None:
self._is_closed = z_array[-1] == z_array[0]
if z_array is None:
coord = coordinates
else:
coord = [(x, y, z) for (x, y), z in zip(coordinates, z_array)]
if self._is_closed:
self._polyline = ClosedPolyline(coord)
else:
self._polyline = OpenPolyline(coord)
if attributes is None:
self._attributes = []
else:
self._attributes = attributes[:]
if m_array is None:
self.m = [None] * self._nb_points
else:
if m_array:
self.m = m_array[:]
else:
self.m = [None] * self._nb_points
self.id = id
def set_id(self, id):
self.id = id
def to_3d(self, z_array):
return Polyline(self.coords(), self.attributes(), z_array)
def to_2d(self):
if self.is_2d():
return Polyline(self.coords(), self.attributes(), m_array=self.m)
return Polyline(list(map(tuple,
np.array(self.coords())[:, :2])),
self.attributes(),
m_array=self.m)
def is_2d(self):
return self._is_2d
def is_closed(self):
return self._is_closed
def nb_points(self):
return self._nb_points
def attributes(self):
return self._attributes
def add_attribute(self, attribute):
self._attributes.append(attribute)
def coords(self):
if self.is_closed():
return self._polyline.exterior.coords
return self._polyline.coords
def polyline(self):
return self._polyline
def project(self, x, y):
return self._polyline.project(Point(x, y))
def segments(self):
prev_x, prev_y = None, None
for coord in self.coords():
x, y = coord[:2] # ignore elevation if 3D
if prev_x is None:
prev_x, prev_y = x, y
else:
yield x > prev_x, y > prev_y, Polyline([(prev_x, prev_y),
(x, y)])
prev_x, prev_y = x, y
def __str__(self):
return ['Open', 'Closed'][self.is_closed(
)] + ' polyline with coordinates %s' % str(list(self.coords()))
def contains(self, item):
return self._polyline.contains(item)
def bounds(self):
return self._polyline.bounds
def length(self):
return self._polyline.length
def polygon_intersection(self, triangle):
"""!
@brief (Used in volume calculation) Return the polygon or multipolygon intersection with the triangle
@param triangle <shapely.geometry.Polygon>: A triangle
@return <bool, shapely.geometry.Polygon or shapely.geometry.Multipolygon>: The intersection with the triangle
"""
inter = self._polyline.intersection(triangle)
if inter.geom_type == 'Polygon' or inter.geom_type == 'MultiPolygon':
return True, inter
elif inter.geom_type == 'GeometryCollection':
poly = list(filter(lambda x: x.geom_type == 'Polygon',
inter.geoms))
if not poly:
return False, None
return True, MultiPolygon(poly)
return False, None
@staticmethod
def triangle_difference(triangle, polygon):
"""!
@brief (Used in volume calculation) Return the polygon or multipolygon in triangle but not in polygon
@param triangle <shapely.geometry.Polygon>: A triangle
@param polygon <shapely.geometry.Polygon>: A polygon
@return <bool, shapely.geometry.Polygon or shapely.geometry.Multipolygon>:
The difference between triangle and polygon
"""
diff = triangle.difference(polygon.polyline())
if diff.geom_type == 'Polygon' or diff.geom_type == 'MultiPolygon':
return True, diff
elif diff.geom_type == 'GeometryCollection':
poly = list(filter(lambda x: x.geom_type == 'Polygon', diff.geoms))
if not poly:
return False, None
return True, MultiPolygon(poly)
return False, None
def linestring_intersection(self, triangle):
"""!
@brief (Used in flux calculation) Returns the LinearString intersection with the triangle
@param triangle <shapely.geometry.Polygon>: A triangle
@return <bool, [shapely.geometry.LinearString]>: The intersection with the triangle
"""
inter = triangle.intersection(self._polyline)
if inter.geom_type == 'LineString':
return True, [inter]
elif inter.geom_type == 'MultiLineString':
return True, list(inter.geoms)
elif inter.geom_type == 'GeometryCollection':
return True, list(
filter(lambda x: x.geom_type == 'LineString', inter.geoms))
return False, None
def apply_transformations(self, transformations):
new_coords = np.array(list(self.coords()))
if self.is_2d():
new_coords = np.hstack((new_coords, np.zeros(
(self.nb_points(), 1))))
for t in transformations:
new_coords = np.apply_along_axis(t, 1, new_coords)
if self.is_2d():
new_coords = new_coords[:, :2]
return Polyline(list(map(tuple, new_coords)),
self.attributes(),
m_array=self.m)
def resample(self, max_len):
new_coords = []
new_m = []
coords = list(self.coords())
new_coords.append(coords[0])
new_m.append(self.m[0])
for i in range(self.nb_points() - 1):
first_point, second_point = coords[i], coords[i + 1]
segment = OpenPolyline([first_point, second_point])
nb_segments = int(np.ceil(segment.length / max_len))
inv_nb_segments = 1 / nb_segments
first_m, second_m = self.m[i], self.m[i + 1]
if first_m is None or second_m is None:
interpolate_m = False
else:
interpolate_m = True
for j in range(1, nb_segments):
new_point = list(
segment.interpolate(j * inv_nb_segments,
normalized=True).coords)[0]
new_coords.append(new_point)
if interpolate_m:
m = ((1 - j) * first_m + j * second_m) * inv_nb_segments
new_m.append(m)
else:
new_m.append(None)
new_coords.append(second_point)
new_m.append(second_m)
return Polyline(new_coords, self.attributes(), m_array=new_m)
def __repr__(self):
return "%sPolyline with %i vertices" % ('Closed ' if self.is_closed()
else '', len(self.coords()))
def rings2geojson(rings):
"""Check for holes in the ring and fill them."""
outer_rings = []
holes = []
x = None # iterable
outer_ring = None # current outer ring being evaluated
hole = None # current hole being evaluated
for ring in rings:
if not all(np.isclose(ring[0], ring[-1])):
ring.append(ring[0])
if len(ring) < 4:
continue
total = sum((pt2[0] - pt1[0]) * (pt2[1] + pt1[1])
for pt1, pt2 in zip(ring[:-1], ring[1:]))
# Clock-wise check
if total >= 0:
outer_rings.append([
ring[::-1]
]) # wind outer rings counterclockwise for RFC 7946 compliance
else:
holes.append(
ring[::-1]
) # wind inner rings clockwise for RFC 7946 compliance
uncontained_holes = []
# while there are holes left...
while holes:
# pop a hole off out stack
hole = holes.pop()
# loop over all outer rings and see if they contain our hole.
contained = False
x = len(outer_rings) - 1
while x >= 0:
outer_ring = outer_rings[x][0]
l1, l2 = LineString(outer_ring), LineString(hole)
p2 = Point(hole[0])
intersects = l1.intersects(l2)
contains = l1.contains(p2)
if not intersects and contains:
# the hole is contained push it into our polygon
outer_rings[x].append(hole)
contained = True
break
x = x - 1
# ring is not contained in any outer ring
# sometimes this happens https://github.com/Esri/esri-leaflet/issues/320
if not contained:
uncontained_holes.append(hole)
# if we couldn't match any holes using contains we can try intersects...
while uncontained_holes:
# pop a hole off out stack
hole = uncontained_holes.pop()
# loop over all outer rings and see if any intersect our hole.
intersects = False
x = len(outer_rings) - 1
while x >= 0:
outer_ring = outer_rings[x][0]
l1, l2 = LineString(outer_ring), LineString(hole)
intersects = l1.intersects(l2)
if intersects:
# the hole is contained push it into our polygon
outer_rings[x].append(hole)
intersects = True
break
x = x - 1
if not intersects:
outer_rings.append([hole[::-1]])
if len(outer_rings) == 1:
return {"type": "Polygon", "coordinates": outer_rings[0]}
return {"type": "MultiPolygon", "coordinates": outer_rings}
def get_ex_gyms():
ex_gyms = []
parks = get_all_parks()
try:
ex_gyms = load_pickle('ex_gyms', raise_exception=True)
except (FileNotFoundError, TypeError, KeyError):
for g in get_gym_markers():
g['id'] = 'ex-' + g['id']
gym_point = Point(g['lat'], g['lon'])
cell = Polygon(get_s2_cell_as_polygon(g['lat'], g['lon'],
level=20)) # s2 lvl 20
sponsor_value = 'park'
if g['sponsor'] == 7:
sponsor_value = 'starbucks'
ex_gyms.append({
'id': g['id'],
'external_id': g['external_id'],
'fort_id': g['fort_id'],
'name': g['gym_name'],
'lat': g['lat'],
'lon': g['lon'],
'sponsor': sponsor_value
})
if g['sponsor'] == 9:
sponsor_value = 'sprint'
ex_gyms.append({
'id': g['id'],
'external_id': g['external_id'],
'fort_id': g['fort_id'],
'name': g['gym_name'],
'lat': g['lat'],
'lon': g['lon'],
'sponsor': sponsor_value
})
for p in parks:
coords = p['coords']
# osm polygon can be a line
if len(coords) == 2:
shape = LineString(coords)
if shape.within(cell.centroid):
ex_gyms.append({
'id': g['id'],
'external_id': g['external_id'],
'fort_id': g['fort_id'],
'name': g['gym_name'],
'lat': g['lat'],
'lon': g['lon'],
'sponsor': sponsor_value
})
if len(coords) > 2:
shape = Polygon(coords)
if shape.contains(cell.centroid):
ex_gyms.append({
'id': g['id'],
'external_id': g['external_id'],
'fort_id': g['fort_id'],
'name': g['gym_name'],
'lat': g['lat'],
'lon': g['lon'],
'sponsor': sponsor_value
})
dump_pickle('ex_gyms', ex_gyms)
return ex_gyms