opencvを使用して画像の向きを補正するためのPCAを計算する

Using Opencv Calculate Pca

opencvを使用したPca画像の向きの修正

#coding=utf-8 from __future__ import print_function from __future__ import division import cv2 as cv import numpy as np import matplotlib.pyplot as plt import argparse from math import * ''' The code is modified based on the opencv routine, adding image correction capabilities Need opencv3.4.3 or higher ''' def drawAxis(img, p_, q_, colour, scale): p = list(p_) q = list(q_) 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) def getOrientation(pts, img): 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) # mean, eigenvectors, eigenvalues = cv.PCACompute(data_pts, mean, 2) #image, mean=None, maxComponents=10 # Store the center of the object cntr = (int(mean[0,0]), int(mean[0,1])) cv.circle(img, cntr, 3, (255, 0, 255), 2) #Draw a circle at the center of PCA 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), 1) #yellow angle = atan2(eigenvectors[0,1], eigenvectors[0,0]) # orientation in radians #PCA first dimension angle  return angle def rotate_img(img,angle): ''' center rotates the image, the input angle is radians ''' angle_o=(angle-pi/2)*180/pi #  height = img.shape[0] # original image height width = img.shape[1] # original image width rotateMat = cv.getRotationMatrix2D((width / 2, height / 2), angle_o, 1) # Rotate the image by angle heightNew = int(width * math.fabs(math.sin(angle)) + height * math.fabs(math.cos(angle))) widthNew = int(height * math.fabs(math.sin(angle)) + width * math.fabs(math.cos(angle))) rotateMat[0, 2] += (widthNew - width) / 2 rotateMat[1, 2] += (heightNew - height) / 2 imgRotation = cv.warpAffine(img, rotateMat, (widthNew, heightNew), borderValue=(255, 255, 255)) plt.figure(4)plt.imshow(imgRotation,cmap='gray') return imgRotation 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='3.jpg') args = parser.parse_args() src = cv.imread(args.input) # Check if image is loaded successfully if src is None: print('Could not open or find the image: ', args.input) exit(0) src=cv.resize(src,(200,200)) # cv.imshow('src', src) plt.figure(1)plt.imshow(src,cmap='gray') # Convert image to grayscale gray = cv.cvtColor(src, cv.COLOR_BGR2GRAY) # Convert image to binary _, bw = cv.threshold(gray, 150, 255, cv.THRESH_BINARY | cv.THRESH_OTSU) plt.figure(2)plt.imshow(bw,cmap='gray') _, contours, _ = cv.findContours(bw, cv.RETR_LIST, cv.CHAIN_APPROX_NONE) areas=[] areas_angle=[] 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 areas.append(area) # Draw each contour only for visualisation purposes cv.drawContours(src, contours, i, (0, 0, 255), 2) # Find the orientation of each shape angle=getOrientation(c, src) areas_angle.append(angle) # cv.imshow('output', src) plt.figure(3)plt.imshow(src,cmap='gray') # cv.waitKey() # Calculate the direction of the largest connected domain ind=np.argmax(areas) imgRotation=rotate_img(src,areas_angle[ind]) plt.show() print('done')