使用单目摄像头测量距离

#!usr/bin/python # -*- coding: utf-8 -*- #import the necessary packages from __future__ import print_function from imutils.object_detection import non_max_suppression from imutils import paths import numpy as np import argparse import imutils import cv2 def find_marker(image): # convert the image to grayscale, blur it, and detect edges 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; # we'll assume that this is our piece of paper in the image (cnts, _) = 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 # 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) def find_person(image): frame = imutils.resize(image, width=min(400, image.shape[1])) # detect people in the image (rects, weights) = hog.detectMultiScale(image, winStride=(4, 4), padding=(8, 8), scale=1.05) rects = np.array([[x, y, x + w, y + h] for (x, y, w, h) in rects]) #非极大抑制 消除多余的框 找到最佳人体位置 pick = non_max_suppression(rects, probs=None, overlapThresh=0.65) return pick def distance_to_camera(knownWidth, focalLength, perWidth): # compute and return the distance from the maker to the camera return (knownWidth * focalLength) / perWidth # initialize the known distance from the camera to the object, which # in this case is 24 inches KNOWN_DISTANCE = 24.0 #KNOWN_DISTANCE = 610 # initialize the known object width, which in this case, the piece of # paper is 11 inches wide KNOWN_WIDTH = 11.69 KNOWN_HEIGHT = 8.27 KNOW_PERSON_HEIGHT = 70.87 #KNOWN_WIDTH = 297 #KNOWN_HEIGHT = 210 # initialize the list of images that we'll be using IMAGE_PATHS = ["Picture1.jpg", "Picture2.jpg", "Picture3.jpg","Picture4.jpg","Picture5.jpg","Picture6.jpg","Picture7.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 image = cv2.imread(IMAGE_PATHS[0]) marker = find_marker(image) focalLength = (marker[1][0] * KNOWN_DISTANCE) / KNOWN_WIDTH #focalLength = 811.82 print('focalLength = ',focalLength) camera = cv2.VideoCapture(0) # initialize the HOG descriptor/person detector hog = cv2.HOGDescriptor() hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector()) ya_max = 0 yb_max = 0 while camera.isOpened(): # get a frame (grabbed, frame) = camera.read() # 如果不能抓取到一帧，说明我们到了视频的结尾 if not grabbed: break frame = imutils.resize(frame, width=min(400, frame.shape[1])) #marker = find_marker(frame) marker = find_person(frame) #inches = distance_to_camera(KNOWN_WIDTH, focalLength, marker[1][0]) for (xA, yA, xB, yB) in marker: cv2.rectangle(frame, (xA, yA), (xB, yB), (0, 255, 0), 2) ya_max = yA yb_max = yB pix_person_height = yb_max - ya_max if pix_person_height == 0: #pix_person_height = 1 continue print (pix_person_height) #print (pix_person_height) inches = distance_to_camera(KNOW_PERSON_HEIGHT, focalLength, pix_person_height) print("%.2fcm" % (inches *30.48/ 12)) # draw a bounding box around the image and display it #box = np.int0(cv2.cv.BoxPoints(marker)) #cv2.drawContours(frame, [box], -1, (0, 255, 0), 2) 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) # show a frame cv2.imshow("capture", frame) if cv2.waitKey(1) & 0xFF == ord('q'): break camera.release() cv2.destroyAllWindows()