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process.py
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process.py
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# -*- coding: utf-8 -*-
# @Time : 2020/7/22 14:16
# @Author : cos0sin0
# @Email : [email protected]
from collections import OrderedDict
import numpy as np
import cv2
import torch
from shapely.geometry import Polygon
import pyclipper
def _draw_polygons(image, polygons):
for i in range(len(polygons)):
polygon = polygons[i].reshape(-1, 2).astype(np.int32)
color = (0, 0, 255) # depict polygons in red
cv2.polylines(image, [polygon], True, color, 1)
cv2.imshow('polygon',image)
cv2.waitKey(0)
def make_polyons(data,debug=False):
polygons = []
annotations = data['polys']
ignore_tags = data['ignore_tags']
for annotation in annotations:
polygons.append(np.array(annotation['points']))
polygons = np.array(polygons)
ignore_tags = np.array(ignore_tags)
filename = data.get('filename', data['data_id'])
if debug:
_draw_polygons(data['image'], polygons)
return OrderedDict(image=data['image'],
polygons=polygons,
ignore_tags = ignore_tags,
filename=filename)
def make_seg_map(data, min_text_size=8,shrink_ratio=0.4):
'''
data: a dict typically returned from `MakeICDARData`,
where the following keys are contrains:
image*, polygons*, ignore_tags*, shape, filename
* means required.
'''
def validate_polygons(polygons,ignore_tags, h, w):
'''
polygons (numpy.array, required): of shape (num_instances, num_points, 2)
'''
if len(polygons) == 0:
return polygons
polygons[:, :, 0] = np.clip(polygons[:, :, 0], 0, w - 1)
polygons[:, :, 1] = np.clip(polygons[:, :, 1], 0, h - 1)
for i in range(polygons.shape[0]):
area = polygon_area(polygons[i])
if abs(area) < 1:
ignore_tags[i] = True
if area > 0:
polygons[i] = polygons[i][(0, 3, 2, 1), :]
return polygons
def polygon_area( polygon):
edge = [
(polygon[1][0] - polygon[0][0]) * (polygon[1][1] + polygon[0][1]),
(polygon[2][0] - polygon[1][0]) * (polygon[2][1] + polygon[1][1]),
(polygon[3][0] - polygon[2][0]) * (polygon[3][1] + polygon[2][1]),
(polygon[0][0] - polygon[3][0]) * (polygon[0][1] + polygon[3][1])
]
return np.sum(edge) / 2.
polygons = data['polygons']
image = data['image']
filename = data['filename']
ignore_tags = data['ignore_tags']
h, w = image.shape[:2]
polygons = validate_polygons(polygons,ignore_tags, h, w)
gt = np.zeros((1, h, w), dtype=np.float32)
mask = np.ones((h, w), dtype=np.float32)
for i in range(polygons.shape[0]):
polygon = polygons[i]
height = min(np.linalg.norm(polygon[0] - polygon[3]),
np.linalg.norm(polygon[1] - polygon[2]))
width = min(np.linalg.norm(polygon[0] - polygon[1]),
np.linalg.norm(polygon[2] - polygon[3]))
if min(height, width) < min_text_size:
cv2.fillPoly(mask, polygon.astype(
np.int32)[np.newaxis, :, :], 0)
ignore_tags[i] = True
else:
polygon_shape = Polygon(polygon)
distance = polygon_shape.area * \
(1 - np.power(shrink_ratio, 2)) / polygon_shape.length
subject = [tuple(l) for l in polygons[i]]
padding = pyclipper.PyclipperOffset()
padding.AddPath(subject, pyclipper.JT_ROUND,
pyclipper.ET_CLOSEDPOLYGON)
shrinked = padding.Execute(-distance)
if shrinked == []:
cv2.fillPoly(mask, polygon.astype(
np.int32)[np.newaxis, :, :], 0)
ignore_tags[i] = True
continue
shrinked = np.array(shrinked[0]).reshape(-1, 2)
cv2.fillPoly(gt[0], [shrinked.astype(np.int32)], 1)
if filename is None:
filename = ''
data.update(image=image,
polygons=polygons,
gt=gt, mask=mask,
filename=filename)
return data
def make_seg_border(data, shrink_ratio=0.4, thresh_max=0.7,thresh_min=0.3):
'''
data: a dict typically returned from `MakeICDARData`,
where the following keys are contrains:
image*, polygons*, ignore_tags*, shape, filename
* means required.
'''
def draw_border_map(polygon, canvas, mask):
polygon = np.array(polygon)
assert polygon.ndim == 2
assert polygon.shape[1] == 2
polygon_shape = Polygon(polygon)
distance = polygon_shape.area * \
(1 - np.power(shrink_ratio, 2)) / polygon_shape.length
subject = [tuple(l) for l in polygon]
padding = pyclipper.PyclipperOffset()
padding.AddPath(subject, pyclipper.JT_ROUND,
pyclipper.ET_CLOSEDPOLYGON)
padded_polygon = np.array(padding.Execute(distance)[0])
cv2.fillPoly(mask, [padded_polygon.astype(np.int32)], 1.0)
xmin = padded_polygon[:, 0].min()
xmax = padded_polygon[:, 0].max()
ymin = padded_polygon[:, 1].min()
ymax = padded_polygon[:, 1].max()
width = xmax - xmin + 1
height = ymax - ymin + 1
polygon[:, 0] = polygon[:, 0] - xmin
polygon[:, 1] = polygon[:, 1] - ymin
xs = np.broadcast_to(
np.linspace(0, width - 1, num=width).reshape(1, width), (height, width))
ys = np.broadcast_to(
np.linspace(0, height - 1, num=height).reshape(height, 1), (height, width))
distance_map = np.zeros(
(polygon.shape[0], height, width), dtype=np.float32)
for i in range(polygon.shape[0]):
j = (i + 1) % polygon.shape[0]
absolute_distance = point_line_distance(xs, ys, polygon[i], polygon[j])
distance_map[i] = np.clip(absolute_distance / distance, 0, 1)
distance_map = distance_map.min(axis=0)
xmin_valid = min(max(0, xmin), canvas.shape[1] - 1)
xmax_valid = min(max(0, xmax), canvas.shape[1] - 1)
ymin_valid = min(max(0, ymin), canvas.shape[0] - 1)
ymax_valid = min(max(0, ymax), canvas.shape[0] - 1)
canvas[ymin_valid:ymax_valid + 1, xmin_valid:xmax_valid + 1] = np.fmax(
1 - distance_map[
ymin_valid-ymin:ymax_valid-ymax+height,
xmin_valid-xmin:xmax_valid-xmax+width],
canvas[ymin_valid:ymax_valid + 1, xmin_valid:xmax_valid + 1])
def point_line_distance(xs, ys, point_1, point_2):
'''
compute the distance from point to a line
ys: coordinates in the first axis
xs: coordinates in the second axis
point_1, point_2: (x, y), the end of the line
'''
height, width = xs.shape[:2]
square_distance_1 = np.square(xs - point_1[0]) + np.square(ys - point_1[1])
square_distance_2 = np.square(xs - point_2[0]) + np.square(ys - point_2[1])
square_distance = np.square(point_1[0] - point_2[0]) + np.square(point_1[1] - point_2[1])
cosin = (square_distance - square_distance_1 - square_distance_2) / (2 * np.sqrt(square_distance_1 * square_distance_2))
square_sin = 1 - np.square(cosin)
square_sin = np.nan_to_num(square_sin)
result = np.sqrt(square_distance_1 * square_distance_2 *square_sin / square_distance)
# print('distance',square_distance_1,square_distance_2,square_sin,square_distance)
result[cosin < 0] = np.sqrt(np.fmin(square_distance_1, square_distance_2))[cosin < 0]
# self.extend_line(point_1, point_2, result)
return result
image = data['image']
polygons = data['polygons']
ignore_tags = data['ignore_tags']
canvas = np.zeros(image.shape[:2], dtype=np.float32)
mask = np.zeros(image.shape[:2], dtype=np.float32)
for i in range(len(polygons)):
if ignore_tags[i]:
continue
draw_border_map(polygons[i], canvas, mask=mask)
canvas = canvas * (thresh_max - thresh_min) + thresh_min
data['thresh_map'] = canvas
data['thresh_mask'] = mask
return data
_RGB_MEAN = np.array([122.67891434, 116.66876762, 104.00698793])
def normalize_image( data):
def add_pos_channel(image):
image_shape = image.shape[-2:]
height, width = image_shape
w = torch.arange(1, width + 1, dtype=torch.float32)
h = torch.arange(1, height + 1, dtype=torch.float32)
y, x = torch.meshgrid(h, w)
x = x / width
y = y / height
z = torch.cat((x.unsqueeze(0), y.unsqueeze(0)), 0)
z = z.to(torch.device('cpu'))
image = torch.cat((image, z), 0)
return image
assert 'image' in data, '`image` in data is required by this process'
image = data['image']
image = image.astype("float32")
image -= _RGB_MEAN
image /= 255.
# image = torch.from_numpy(image).permute(2, 0, 1).float()
image = np.transpose(image,(2,0,1))
# image = add_pos_channel(image)
data['image'] = image
return data