#!/usr/bin/env python3
# -*- coding: utf-8 -*-
__author__ = "heider"
__doc__ = r"""
Created on 5/5/22
"""
from collections import defaultdict
from pathlib import Path
import cv2
import numpy
from draugr.opencv_utilities import (
ColorConversionEnum,
KmeansEnum,
NoiseFilterMethodEnum,
TermCriteriaFlag,
is_singular_channel,
noise_filter,
show_image,
to_gray,
)
__all__ = ["draw_parameter_lines", "draw_markers", "find_intersections"]
[docs]def draw_parameter_lines(img, lines, color=(0, 0, 255)):
"""
Draw lines on an image
"""
if is_singular_channel(img):
img = cv2.cvtColor(img, ColorConversionEnum.gray2bgr.value)
for line in lines:
for rho, theta in line:
a = numpy.cos(theta)
b = numpy.sin(theta)
x0 = a * rho
y0 = b * rho
x1 = int(x0 + 1000 * (-b))
y1 = int(y0 + 1000 * a)
x2 = int(x0 - 1000 * (-b))
y2 = int(y0 - 1000 * a)
cv2.line(img, (x1, y1), (x2, y2), color, 1)
return img
[docs]def draw_markers(img, centroids, length=5):
"""
:param img:
:type img:
:param centroids:
:type centroids:
:param length:
:type length:
:return:
:rtype:
"""
if is_singular_channel(img):
img = cv2.cvtColor(img, ColorConversionEnum.gray2bgr.value)
for ct in centroids:
ct = numpy.array(ct, dtype=numpy.int32)
cv2.line(
img,
(ct[0], ct[1] - length),
(ct[0], ct[1] + length),
(255, 0, 255),
1,
) # vertical line
cv2.line(
img,
(ct[0] - length, ct[1]),
(ct[0] + length, ct[1]),
(255, 0, 255),
1,
)
return img
def find_centroids(pts, k=4, **kwargs):
"""
:param pts:
:type pts:
:param k:
:type k:
:param kwargs:
:type kwargs:
:return:
:rtype:
"""
assert len(pts) >= k
# Define criteria = (type, max_iter, epsilon)
default_criteria_type = TermCriteriaFlag.eps.value + TermCriteriaFlag.max_iter.value
criteria = kwargs.get("criteria", (default_criteria_type, 10, 1.0))
flags = kwargs.get("flags", KmeansEnum.random_centers.value)
attempts = kwargs.get("attempts", 10)
return cv2.kmeans(pts, k, None, criteria, attempts, flags)[1:]
def segment_by_angle_kmeans(lines, k=2, **kwargs):
"""
Group lines by their angle using k-means clustering.
Code from here:
https://stackoverflow.com/a/46572063/1755401
"""
# Get angles in [0, pi] radians
angles = numpy.array([line[0][1] for line in lines])
# Multiply the angles by two and find coordinates of that angle on the Unit Circle
pts = numpy.array(
[[numpy.cos(2 * angle), numpy.sin(2 * angle)] for angle in angles],
dtype=numpy.float32,
)
labels, centers = find_centroids(pts, k)
labels = labels.reshape(-1) # Transpose to row vector
# Segment lines based on their label of 0 or 1
segmented = defaultdict(list)
for i, line in zip(range(len(lines)), lines):
segmented[labels[i]].append(line)
segmented = list(segmented.values())
# print(f"Segmented lines into two groups: {len(segmented[0]):d}, {len(segmented[1]):d}")
return segmented
def intersection(line1, line2):
"""
Find the intersection of two lines
specified in Hesse normal form.
Returns the closest integer pixel locations.
See here:
https://stackoverflow.com/a/383527/5087436
"""
rho1, theta1 = line1[0]
rho2, theta2 = line2[0]
A = numpy.array(
[
[numpy.cos(theta1), numpy.sin(theta1)],
[numpy.cos(theta2), numpy.sin(theta2)],
]
)
b = numpy.array([[rho1], [rho2]])
x0, y0 = numpy.linalg.solve(A, b)
return [[int(numpy.round(x0)), int(numpy.round(y0))]]
def segmented_intersections(lines):
"""
Find the intersection between groups of lines.
"""
intersections = []
for i, group in enumerate(lines[:-1]):
for next_group in lines[i + 1 :]:
for line1 in group:
for line2 in next_group:
intersections.append(intersection(line1, line2))
return intersections
[docs]def find_intersections(lines, k_angles=2, k_centers=4):
"""
Find the intersection points of lines.
"""
segmented = segment_by_angle_kmeans(
lines, k=k_angles
) # Cluster line angles into 2 groups (vertical and horizontal)
intersections = segmented_intersections(
segmented
) # Find the intersections of each vertical line with each horizontal line
points = []
for point in intersections:
pt = (point[0][0], point[0][1])
points.append(numpy.array(pt))
labels, centers = find_centroids(
numpy.array(points, dtype=numpy.float32), k=k_centers
)
return centers
if __name__ == "__main__":
# file = "360_F_108702068_Z9VGab1DfiyPzq2v5Xgm2wRljttzRGgq.jpg"
# file = "istockphoto-529081402-170667a.jpg"
# file = "white-paper-table-13912022.jpg"
# file = "2.jpg"
file = "3.jpg"
# file = "NdNLO.jpg"
image = cv2.imread(str(Path.home() / "Pictures" / file))
gray = to_gray(image)
blur = noise_filter(gray, method=NoiseFilterMethodEnum.median_blur, ksize=5)
adapt_type = cv2.ADAPTIVE_THRESH_GAUSSIAN_C
thresh_type = cv2.THRESH_BINARY_INV
bin_img = cv2.adaptiveThreshold(blur, 255, adapt_type, thresh_type, 11, 2)
if False:
show_image(bin_img, wait=True)
lines = cv2.HoughLines(
bin_img, rho=2, theta=numpy.pi / 180, threshold=350
) # Detect lines
"""
if False:
img_with_all_lines = numpy.copy(img)
draw_lines(img_with_all_lines, lines)
show_image(img_with_all_lines, wait=True)
"""
centroids = find_intersections(lines)
show_image(draw_markers(image.copy(), centroids), wait=True)
