詳解Python使用OpenCV如何確定一個(gè)對(duì)象的方向

更新時(shí)間：2022年10月27日 08:36:33 作者：求則得之，舍則失之

在本教程中，我們將構(gòu)建一個(gè)程序，該程序可以使用流行的計(jì)算機(jī)視覺庫 OpenCV 確定對(duì)象的方向（即以度為單位的旋轉(zhuǎn)角度），感興趣的小伙伴可以了解一下

1.代碼實(shí)現(xiàn)

接受一個(gè)名為input_img.jpg的圖像，并輸出一個(gè)名為output_img.jpg的帶標(biāo)記的圖像。部分代碼來自官方的OpenCV實(shí)現(xiàn)。

import cv2 as cv
from math import atan2, cos, sin, sqrt, pi
import numpy as np
 
def drawAxis(img, p_, q_, color, 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])), color, 3, 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])), color, 3, 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])), color, 3, 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, (255, 255, 0), 1)
  drawAxis(img, cntr, p2, (0, 0, 255), 5)
 
  angle = atan2(eigenvectors[0,1], eigenvectors[0,0]) # orientation in radians
  ## [visualization]
 
  # Label with the rotation angle
  label = "  Rotation Angle: " + str(-int(np.rad2deg(angle)) - 90) + " degrees"
  textbox = cv.rectangle(img, (cntr[0], cntr[1]-25), (cntr[0] + 250, cntr[1] + 10), (255,255,255), -1)
  cv.putText(img, label, (cntr[0], cntr[1]), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0,0,0), 1, cv.LINE_AA)
 
  return angle
 
# Load the image
img = cv.imread("input_img.jpg")
 
# Was the image there?
if img is None:
  print("Error: File not found")
  exit(0)
 
cv.imshow('Input Image', img)
 
# Convert image to grayscale
gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
 
# Convert image to binary
_, bw = cv.threshold(gray, 50, 255, cv.THRESH_BINARY | cv.THRESH_OTSU)
 
# 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 < 3700 or 100000 < area:
    continue
 
  # Draw each contour only for visualisation purposes
  cv.drawContours(img, contours, i, (0, 0, 255), 2)
 
  # Find the orientation of each shape
  getOrientation(c, img)
 
cv.imshow('Output Image', img)
cv.waitKey(0)
cv.destroyAllWindows()
  
# Save the output image to the current directory
cv.imwrite("output_img.jpg", img)

2.輸出圖像

3.了解坐標(biāo)軸

紅線表示每個(gè)物體的正x軸。藍(lán)線表示每個(gè)物體的正y軸。

全局正x軸從左到右橫貫圖像。整體正z軸指向這一頁外。全局正y軸從圖像的底部垂直指向圖像的頂部。

使用右手法則來測(cè)量旋轉(zhuǎn)，你將你的四個(gè)手指(食指到小指)筆直地指向全局正x軸的方向。

然后逆時(shí)針旋轉(zhuǎn)四個(gè)手指90度。指尖指向y軸正方向，大拇指指向紙外z軸正方向。

4.計(jì)算0到180度之間的方向

如果我們想計(jì)算一個(gè)對(duì)象的方向，并確保結(jié)果總是在0到180度之間，我們可以使用以下代碼:

# This programs calculates the orientation of an object.
# The input is an image, and the output is an annotated image
# with the angle of otientation for each object (0 to 180 degrees)
 
import cv2 as cv
from math import atan2, cos, sin, sqrt, pi
import numpy as np
 
# Load the image
img = cv.imread("input_img.jpg")
 
# Was the image there?
if img is None:
  print("Error: File not found")
  exit(0)
 
cv.imshow('Input Image', img)
 
# Convert image to grayscale
gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
 
# Convert image to binary
_, bw = cv.threshold(gray, 50, 255, cv.THRESH_BINARY | cv.THRESH_OTSU)
 
# 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 < 3700 or 100000 < area:
    continue
 
  # cv.minAreaRect returns:
  # (center(x, y), (width, height), angle of rotation) = cv2.minAreaRect(c)
  rect = cv.minAreaRect(c)
  box = cv.boxPoints(rect)
  box = np.int0(box)
 
  # Retrieve the key parameters of the rotated bounding box
  center = (int(rect[0][0]),int(rect[0][1])) 
  width = int(rect[1][0])
  height = int(rect[1][1])
  angle = int(rect[2])
 
     
  if width < height:
    angle = 90 - angle
  else:
    angle = -angle
         
  label = "  Rotation Angle: " + str(angle) + " degrees"
  textbox = cv.rectangle(img, (center[0]-35, center[1]-25), 
    (center[0] + 295, center[1] + 10), (255,255,255), -1)
  cv.putText(img, label, (center[0]-50, center[1]), 
    cv.FONT_HERSHEY_SIMPLEX, 0.7, (0,0,0), 1, cv.LINE_AA)
  cv.drawContours(img,[box],0,(0,0,255),2)
 
cv.imshow('Output Image', img)
cv.waitKey(0)
cv.destroyAllWindows()
  
# Save the output image to the current directory
cv.imwrite("min_area_rec_output.jpg", img)

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