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samples/python/tutorial_code/ml/introduction_to_pca/introduction_to_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()