使用 OpenCV–Python 进行实时距离估计
原文:https://www . geesforgeks . org/real-distance-estimation-use-opencv-python/
先决条件:OpenCV 简介
在本文中，我们将看到如何使用 Python 中的 OpenCV 通过网络摄像头计算距离。通过使用它，人们可以处理图像和视频来识别物体、人脸，甚至是人类的笔迹。本文着重于检测对象。
在本文中，我们将使用哈尔级联来进行物体检测。
瀑布式头发
哈尔级联分类器是一种有效的目标检测方法。哈尔级联是一种基于机器学习的方法，其中使用大量的正图像和负图像来训练分类器。
文件可以从这里下载:for ontalface。
距离估算步骤:
- Take a reference image: measure the distance from the object (face) to the camera, take a reference image and record the measured distance.
- Measure the width of the object (face) to ensure that the reference image and the width of the object (face) keep the measurement unit. Mine reference image
人脸检测:
该函数将检测人脸并返回人脸宽度的像素值。这个面部数据函数只需要一个参数，即图像，返回以像素为单位的面部宽度，这是焦距探测器和距离探测器功能的要求。
Python 3
def face_data(image):
face_width = 0 # making face width to zero
# converting color image to gray scale image
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# detecting face in the image
faces = face_detector.detectMultiScale(gray_image, 1.3, 5)
# looping through the faces detect in the
# image getting coordinates x, y ,
# width and height
for (x, y, h, w) in faces:
# draw the rectangle on the face
cv2.rectangle(image, (x, y), (x+w, y+h), GREEN, 2)
# getting face width in the pixels
face_width = w
# return the face width in pixel
return face_width
焦距探测器:
焦距探测器功能处理三个参数:
- Measured _ distance: It is the distance between the camera and the object when shooting the reference image, Known _ distance = 72.2 # cm
- real _ width: It measures the width of objects in the real world, and here we measure the width of faces around , which is known as _ width = 14.3 # cm
- 宽度 _ 英寸 _ 射频 _ 英寸
该函数将返回焦距，用于查找距离。
Python 3
# focal length finder function
def FocalLength(measured_distance, real_width, width_in_rf_image):
focal_length = (width_in_rf_image* measured_distance)/ real_width
return focal_length
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