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import pickle
import cv2
import skimage
import numpy as np
from shapely.geometry import Polygon
from concern.config import Configurable, State
def binary_search_smallest_width(poly):
if len(poly) < 3:
return 0
poly = Polygon(poly)
low = 0
high = 65536
while high - low > 0.1:
mid = (high + low) / 2
mid_poly = poly.buffer(-mid)
if mid_poly.geom_type == 'Polygon' and mid_poly.area > 0.1:
low = mid
else:
high = mid
height = (low + high) / 2
if height < 0.1:
return 0
else:
return height
def project_point_to_line(x, u, v, axis=0):
n = v - u
n = n / (np.linalg.norm(n, axis=axis, keepdims=True) + np.finfo(np.float32).eps)
p = u + n * np.sum((x - u) * n, axis=axis, keepdims=True)
return p
def project_point_to_segment(x, u, v, axis=0):
p = project_point_to_line(x, u, v, axis=axis)
outer = np.greater_equal(np.sum((u - p) * (v - p), axis=axis, keepdims=True), 0)
near_u = np.less_equal(
np.linalg.norm(u - p, axis=axis, keepdims=True),
np.linalg.norm(v - p, axis=axis, keepdims=True)
)
o = np.where(outer, np.where(near_u, u, v), p)
return o
class MakeSimpleDetectionData(Configurable):
center_shrink = State(default=0.5)
background_weight = State(default=3.0)
def __init__(self, **kwargs):
self.load_all(**kwargs)
def get_mask(self, w, h, polys, ignores):
mask = np.ones((h, w), np.float32)
for poly, ignore in zip(polys, ignores):
if ignore:
cv2.fillPoly(mask, np.array([poly], np.int32), 0.0)
return mask
def get_line_height(self, poly):
return binary_search_smallest_width(poly)
def get_regions_coords(self, w, h, polys, heights, shrink):
label_map = np.zeros((h, w), np.int32)
for line_id, (poly, height) in enumerate(zip(polys, heights)):
if height > 0:
shrinked_poly = Polygon(poly).buffer(-height * shrink)
if shrinked_poly.geom_type == 'Polygon' and not shrinked_poly.is_empty:
shrinked_poly = np.array(list(shrinked_poly.exterior.coords), np.int32)
cv2.fillPoly(label_map, shrinked_poly[np.newaxis], line_id + 1)
regions = skimage.measure.regionprops(label_map)
regions_coords = [
region.coords[:, ::-1] for region in regions
] + [
np.zeros((0, 2), 'int32')
] * (len(polys) - len(regions))
return regions_coords
def get_coords_poly_projection(self, coords, poly):
start_points = np.array(poly)
end_points = np.concatenate([poly[1:], poly[:1]], axis=0)
region_points = coords
projected_points = project_point_to_segment(
region_points[:, np.newaxis],
start_points[np.newaxis],
end_points[np.newaxis],
axis=2,
)
projection_distances = np.linalg.norm(region_points[:, np.newaxis] - projected_points, axis=2)
best_projected_points = projected_points[np.arange(len(region_points)), np.argmin(projection_distances, axis=1)]
return best_projected_points
def get_coords_poly_distance(self, coords, poly):
projection = self.get_coords_poly_projection(coords, poly)
return np.linalg.norm(projection - coords, axis=1)
def get_normalized_weight(self, heatmap, mask):
pos = np.greater_equal(heatmap, 0.5)
neg = 1 - pos
pos = np.logical_and(pos, mask)
neg = np.logical_and(neg, mask)
npos = pos.sum()
nneg = neg.sum()
smooth = (npos + nneg + 1) * 0.05
wpos = (nneg + smooth) / (npos + smooth)
weight = np.zeros_like(heatmap)
weight[neg] = self.background_weight
weight[pos] = wpos
return weight
def draw_maps(self, w, h, polys, ignores):
raise NotImplementedError()
def __call__(self, data, *args, **kwargs):
image, label, meta = data
lines = label['polys']
h, w = image.shape[:2]
polys = []
ignores = []
for line in lines:
if len(line['points']) >= 4:
polys.append(line['points'])
ignores.append(line['ignore'])
maps = self.draw_maps(w, h, polys, ignores)
# to pytorch channel sequence
image = image.transpose(2, 0, 1)
label = maps
return image, label, pickle.dumps(meta)
class MakeSimpleSegData(MakeSimpleDetectionData):
def draw_maps(self, w, h, polys, ignores):
heatmap = np.zeros((h, w), np.float32)
heights = [self.get_line_height(poly) for poly in polys]
regions_center_coords = self.get_regions_coords(w, h, polys, heights, self.center_shrink)
train_mask = self.get_mask(w, h, polys, ignores)
for region_center_coords in regions_center_coords:
x, y = region_center_coords[:, 0], region_center_coords[:, 1]
heatmap[y, x] = 1.0
heatmap_weight = self.get_normalized_weight(heatmap, train_mask)
return {
'heatmap': heatmap[np.newaxis],
'heatmap_weight': heatmap_weight[np.newaxis],
}
class MakeSimpleEASTData(MakeSimpleDetectionData):
def draw_maps(self, w, h, polys, ignores):
heatmap = np.zeros((h, w), np.float32)
densebox = np.zeros((8, h, w), np.float32)
densebox_weight = np.zeros((h, w), np.float32)
heights = [self.get_line_height(poly) for poly in polys]
regions_center_coords = self.get_regions_coords(w, h, polys, heights, self.center_shrink)
train_mask = self.get_mask(w, h, polys, ignores)
for poly, region_center_coords in zip(polys, regions_center_coords):
x, y = region_center_coords[:, 0], region_center_coords[:, 1]
heatmap[y, x] = 1.0
densebox_weight[y, x] = 1.0
for i in range(0, 4):
densebox[i * 2, y, x] = float(poly[i][0]) - x
densebox[i * 2 + 1, y, x] = float(poly[i][1]) - y
heatmap_weight = self.get_normalized_weight(heatmap, train_mask)
densebox_weight = densebox_weight * train_mask
return {
'heatmap': heatmap[np.newaxis],
'heatmap_weight': heatmap_weight[np.newaxis],
'densebox': densebox,
'densebox_weight': densebox_weight[np.newaxis],
}
class MakeSimpleTextsnakeData(MakeSimpleDetectionData):
def draw_maps(self, w, h, polys, ignores):
heatmap = np.zeros((h, w), np.float32)
radius = np.zeros((h, w), np.float32)
radius_weight = np.zeros((h, w), np.float32)
heights = [self.get_line_height(poly) for poly in polys]
regions_center_coords = self.get_regions_coords(w, h, polys, heights, self.center_shrink)
train_mask = self.get_mask(w, h, polys, ignores)
for poly, region_center_coords in zip(polys, regions_center_coords):
x, y = region_center_coords[:, 0], region_center_coords[:, 1]
heatmap[y, x] = 1.0
distance = self.get_coords_poly_distance(region_center_coords, poly)
radius[y, x] = distance
radius_weight[y, x] = 1.0
heatmap_weight = self.get_normalized_weight(heatmap, train_mask)
radius_weight = radius_weight * train_mask
return {
'heatmap': heatmap[np.newaxis],
'heatmap_weight': heatmap_weight[np.newaxis],
'radius': radius[np.newaxis],
'radius_weight': radius_weight[np.newaxis],
}
class MakeSimpleMSRData(MakeSimpleDetectionData):
def draw_maps(self, w, h, polys, ignores):
heatmap = np.zeros((h, w), np.float32)
offset = np.zeros((2, h, w), np.float32)
offset_weight = np.zeros((h, w), np.float32)
heights = [self.get_line_height(poly) for poly in polys]
regions_center_coords = self.get_regions_coords(w, h, polys, heights, self.center_shrink)
train_mask = self.get_mask(w, h, polys, ignores)
for poly, region_center_coords in zip(polys, regions_center_coords):
x, y = region_center_coords[:, 0], region_center_coords[:, 1]
heatmap[y, x] = 1.0
projection_points = self.get_coords_poly_projection(region_center_coords, poly)
offset[0, y, x] = projection_points[:, 0] - x
offset[1, y, x] = projection_points[:, 1] - y
offset_weight[y, x] = 1.0
heatmap_weight = self.get_normalized_weight(heatmap, train_mask)
offset_weight = offset_weight * train_mask
return {
'heatmap': heatmap[np.newaxis],
'heatmap_weight': heatmap_weight[np.newaxis],
'offset': offset,
'offset_weight': offset_weight[np.newaxis],
}
class SimpleDetectionCropper(Configurable):
patch_cropper = State()
def __init__(self, **kwargs):
self.load_all(**kwargs)
def crop(self, batch, output):
img, label, meta = batch
images_polys = []
images_patches = []
for polys, image_meta in zip(output['polygons_pred'], meta):
image_meta = pickle.loads(image_meta)
images_polys.append(polys)
images_patches.append([self.patch_cropper.crop(image_meta['image'], p) for p in polys])
return images_polys, images_patches
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