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pca.py
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pca.py
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from __future__ import print_function
from __future__ import division
import cv2 as cv
import numpy as np
import argparse
from math import atan2, cos, sin, sqrt, pi
def drawAxis(img, p_, q_, colour, scale):
p = list(p_)
q = list(q_)
## [visualization1]
angle = atan2(p[1] - q[1], p[0] - q[0]) # angle in radians
hypotenuse = sqrt((p[1] - q[1]) * (p[1] - q[1]) + (p[0] - q[0]) * (p[0] - q[0]))
# Here we lengthen the arrow by a factor of scale
q[0] = p[0] - scale * hypotenuse * cos(angle)
q[1] = p[1] - scale * hypotenuse * sin(angle)
cv.line(img, (int(p[0]), int(p[1])), (int(q[0]), int(q[1])), colour, 1, cv.LINE_AA)
# create the arrow hooks
p[0] = q[0] + 9 * cos(angle + pi / 4)
p[1] = q[1] + 9 * sin(angle + pi / 4)
cv.line(img, (int(p[0]), int(p[1])), (int(q[0]), int(q[1])), colour, 1, cv.LINE_AA)
p[0] = q[0] + 9 * cos(angle - pi / 4)
p[1] = q[1] + 9 * sin(angle - pi / 4)
cv.line(img, (int(p[0]), int(p[1])), (int(q[0]), int(q[1])), colour, 1, cv.LINE_AA)
## [visualization1]
def getOrientation(pts, img):
## [pca]
# Construct a buffer used by the pca analysis
sz = len(pts)
data_pts = np.empty((sz, 2), dtype=np.float64)
for i in range(data_pts.shape[0]):
data_pts[i,0] = pts[i,0,0]
data_pts[i,1] = pts[i,0,1]
# Perform PCA analysis
mean = np.empty((0))
mean, eigenvectors, eigenvalues = cv.PCACompute2(data_pts, mean)
# Store the center of the object
cntr = (int(mean[0,0]), int(mean[0,1]))
## [pca]
## [visualization]
# Draw the principal components
cv.circle(img, cntr, 3, (255, 0, 255), 2)
p1 = (cntr[0] + 0.02 * eigenvectors[0,0] * eigenvalues[0,0], cntr[1] + 0.02 * eigenvectors[0,1] * eigenvalues[0,0])
p2 = (cntr[0] - 0.02 * eigenvectors[1,0] * eigenvalues[1,0], cntr[1] - 0.02 * eigenvectors[1,1] * eigenvalues[1,0])
drawAxis(img, cntr, p1, (0, 255, 0), 1)
drawAxis(img, cntr, p2, (255, 255, 0), 5)
angle = atan2(eigenvectors[0,1], eigenvectors[0,0]) # orientation in radians
## [visualization]
return angle
## [pre-process]
# Load image
parser = argparse.ArgumentParser(description='Code for Introduction to Principal Component Analysis (PCA) tutorial.\
This program demonstrates how to use OpenCV PCA to extract the orientation of an object.')
parser.add_argument('--input', help='Path to input image.', default='pca_test1.jpg')
args = parser.parse_args()
src = cv.imread(cv.samples.findFile(args.input))
# Check if image is loaded successfully
if src is None:
print('Could not open or find the image: ', args.input)
exit(0)
cv.imshow('src', src)
# Convert image to grayscale
gray = cv.cvtColor(src, cv.COLOR_BGR2GRAY)
# Convert image to binary
_, bw = cv.threshold(gray, 50, 255, cv.THRESH_BINARY | cv.THRESH_OTSU)
## [pre-process]
## [contours]
# Find all the contours in the thresholded image
contours, _ = cv.findContours(bw, cv.RETR_LIST, cv.CHAIN_APPROX_NONE)
for i, c in enumerate(contours):
# Calculate the area of each contour
area = cv.contourArea(c)
# Ignore contours that are too small or too large
if area < 1e2 or 1e5 < area:
continue
# Draw each contour only for visualisation purposes
cv.drawContours(src, contours, i, (0, 0, 255), 2)
# Find the orientation of each shape
getOrientation(c, src)
## [contours]
cv.imshow('output', src)
cv.waitKey()