dep_camera_单目测距_测距单目_

import numpy as np import cv2 # 找到目标函数 def find_marker(image): # convert the image to grayscale, blur it, and detect edges # 将图像转换成灰度、模糊和检测边缘 gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) gray = cv2.GaussianBlur(gray, (5, 5), 0) edged = cv2.Canny(gray, 35, 125) # find the contours in the edged image and keep the largest one; # 在边缘图像中找到轮廓并保持最大的轮廓 # we'll assume that this is our piece of paper in the image # 我们假设这是我们在图像中的一张纸 cnts, hierarchy = cv2.findContours(edged.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE) # 求最大面积 c = max(cnts, key=cv2.contourArea) # compute the bounding box of the of the paper region and return it # 计算纸张区域的边界框并返回它 # cv2.minAreaRect() c代表点集，返回rect[0]是最小外接矩形中心点坐标， # rect[1][0]是width，rect[1][1]是height，rect[2]是角度 return cv2.minAreaRect(c) # 距离计算函数 def distance_to_camera(knownWidth, focalLength, perWidth): # compute and return the distance from the maker to the camera # 计算并返回从目标到相机的距离 return (knownWidth * focalLength) / perWidth # initialize the known distance from the camera to the object, which # in this case is 24 inches # 初始化已知距离从相机到对象，在这种情况下是24英寸 KNOWN_DISTANCE = 24.0 # initialize the known object width, which in this case, the piece of # paper is 11 inches wide # 初始化已知的物体宽度，在这种情况下，纸是11英寸宽。 # A4纸的长和宽(单位:inches) KNOWN_WIDTH = 11.69 KNOWN_HEIGHT = 8.27 # initialize the list of images that we'll be using # 初始化我们将要使用的图像列表 IMAGE_PATHS = ["Picture1.jpg", "Picture2.jpg", "Picture3.jpg"] # load the furst image that contains an object that is KNOWN TO BE 2 feet # from our camera, then find the paper marker in the image, and initialize # the focal length # 加载包含一个距离我们相机2英尺的物体的第一张图像，然后找到图像中的纸张标记，并初始化焦距 # 读入第一张图，通过已知距离计算相机焦距 # ret, frame = camera.read() image = cv2.imread(IMAGE_PATHS[0]) # 应使用摄像头拍的图 marker = find_marker(image) focalLength = (marker[1][0] * KNOWN_DISTANCE) / KNOWN_WIDTH # D’ = (W x F) / P # 通过摄像头标定获取的像素焦距 # focalLength = 811.82 print('focalLength = ', focalLength) # 打开摄像头 camera = cv2.VideoCapture(0) while camera.isOpened(): # get a frame (grabbed, frame) = camera.read() marker = find_marker(frame) #print(marker) if marker == 0: print(marker) continue inches = distance_to_camera(KNOWN_WIDTH, focalLength, marker[1][0]) # draw a bounding box around the image and display it # 在图像周围绘制一个边界框并显示它 box = cv2.boxPoints(marker) box = np.int0(box) cv2.drawContours(frame, [box], -1, (0, 255, 0), 2) # inches 转换为 cm dis = cv2.putText(frame, "%.2fcm" % (inches * 30.48 / 12), (frame.shape[1] - 200, frame.shape[0] - 20), cv2.FONT_HERSHEY_SIMPLEX, 2.0, (0, 255, 0), 3) # print(dis) # show a frame cv2.imshow("capture", frame) if cv2.waitKey(1) & 0xFF == ord('q'): break camera.release() cv2.destroyAllWindows()