圍繞任意軸旋轉影像后如何計算新點的位置

擁有對齊物件的資料集，我想通過在物件中心應用隨機旋轉來增強它。下面是旋轉表示（左原圖，右影像圍繞點（xc，yc）旋轉。對于旋轉，我使用了以下邏輯：

import cv2
import random
image_source = cv2.imread('sample.png')
height, width = image_source.shape[:2]
random_angle = random.uniform(90, 90)
yolo_annotation_sample = get_annotation() # this function retrieves yolo annotation
label_id, xc, yc, object_width, object_height = yolo_annotation_sample # e.g. 4, 0.0189, 0.25, 0.0146, 0.00146

center_x = width * xc
center_y = height * yc
left = center_x - (width * object_width) / 2
top = center_y - (height * object_height) / 2
right = left   width * object_width
bottom = top   height * object_height

M = cv2.getRotationMatrix2D((cx, cy), random_angle, 1.0)
image_rotated = cv2.warpAffine(image_source, M, (width, height))
# logic for calculating new point position (doesn't work)
x1_y1 = np.asarray([[left, top]]) 
x1_y1_new = np.dot(x1_y1, M)
x2_y2 = np.asarray([[right, top]]) 
x2_y2_new = np.dot(x2_y2, M)
x3_y3 = np.asarray([[right, bottom]]) 
x3_y3_new = np.dot(x3_y3, M)
x4_y4 = np.asarray([[left, bottom]]) 
x4_y4_new = np.dot(x4_y4, M)

有誰知道如何在圍繞任意點旋轉后重新計算點，如上所示？

uj5u.com熱心網友回復：

使用cv2.transform(points, M). 帶形狀的點：(4, 1, 2)。完整代碼：

import cv2
import random
import numpy as np

image_source = cv2.imread('sample.png')
height, width = image_source.shape[:2]
random_angle = 40 #random.uniform(90, 90)
yolo_annotation_sample = (4, 0.6189, 0.25, 0.246, 0.0846) # this function retrieves yolo annotation
label_id, xc, yc, object_width, object_height = yolo_annotation_sample

center_x = width * xc
center_y = height * yc
left = center_x - (width * object_width) / 2
top = center_y - (height * object_height) / 2
right = left   width * object_width
bottom = top   height * object_height

cx, cy = width / 2, height / 2
M = cv2.getRotationMatrix2D((cx, cy), random_angle, 1.0)
image_rotated = cv2.warpAffine(image_source, M, (width, height))
# logic for calculating new point position (doesn't work)
bbox_points = [[left, top], [right, top], [right, bottom], [left, bottom]]
bbox_points = np.array(bbox_points).reshape((-1,1,2))
rotated_points = cv2.transform(bbox_points, M) # what you need

cv2.polylines(image_source,[bbox_points.astype(int)],True,(255,100,0), 10)
cv2.polylines(image_rotated,[rotated_points.astype(int)],True,(255,100,0), 10)

cv2.imshow("orig", image_source)
cv2.imshow("rotated", image_rotated)
cv2.waitKey()

uj5u.com熱心網友回復：

看看 [https://en.wikipedia.org/wiki/Transformation_matrix][1]

一旦我嘗試自己計算：

class rotm :
 '''set up rotation matrix'''
 def __init__(self,axis,angle,unit="radians") :   
  self.m = scipy.zeros((4,4),scipy.float128)
  if unit=="radians" :
   angler = angle
  else :
   angler = math.radians(angle)
  pass
  if axis=='x' :
   self.m[0][0]=1.0
   self.m[1][1]=math.cos(angler)
   self.m[2][2]=self.m[1][1]
   self.m[3][3]=1.0
   self.m[1][2]=-math.sin(angler)
   self.m[2][1]=-self.m[1][2]
  elif axis=='y' :
   self.m[0][0]=math.cos(angler)
   self.m[1][1]=1.0
   self.m[2][2]=self.m[0][0]
   self.m[3][3]=1.0
   self.m[0][2]=math.sin(angler)
   self.m[2][0]=-self.m[0][2]
  elif axis=='z' :
   self.m[0][0]=math.cos(angler)
   self.m[1][1]=self.m[0][0]
   self.m[2][2]=1.0
   self.m[3][3]=1.0
   self.m[0][1]=-math.sin(angler)
   self.m[1][0]=-self.m[0][1]
  pass         
 pass
 def fPrint(self) :
  '''auxiliary function: print transformation matrix '''      
  print(self.m)   
 pass     
pass #end of rotm class

標籤：Python opencv 图像处理

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