Zi 字媒體

使用OpenCV實現攝像頭測距 [Find distance from camera to object/marker using Python and OpenCV]  (GOOGLE:OPENCV 測距) 資料來源: https://zhuanlan.zhihu.com/p/63149294 https://www.pyimagesearch.com/2015/01/19/find-distance-camera-objectmarker-using-python-opencv/ https://github.com/zxdefying/OpenCV_project/tree/master/distance_to_camera GITHUB:https://github.com/jash-git/Find-distance-from-camera-to-object-using-Python-and-OpenCV 前置動作+原理說明:(使用相似三角形計算物體到相機的距離)     假設物體的寬度為W，將其放到離相機距離為D 的位置，然後對物體進行拍照。在照片上量出物體的像素寬度P，於是可以得出計算相機焦距F 的公式： F =（P x D）/ W     比如我在相機前24 英寸距離（D=24 inches）的位置橫著放了一張8.5 x 11 英寸（W=11 inches）的紙，拍照後通過圖像處理得出照片上紙的像素寬度P=248 pixels。 所以焦距F 等於： F =（248px x 24in）/ 11in = 543.45     此時移動相機離物體更近或者更遠，我們可以應用相似三角形得到計算物體到相機的距離的公式： D’=（寬x F）/ P PDF Code: from imutils import paths import numpy as np import imutils import cv2 # initialize the known distance from the camera to the object, which # in this case is 24 inches KNOWN_DISTANCE = 24.0 # initialize the known object width, which in this case, the piece of # paper is 12 inches wide KNOWN_WIDTH = 11.0 def get_focalLength(): # 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("./2ft.jpg") marker = find_marker(image) focalLength = (marker[1][0] * KNOWN_DISTANCE) / KNOWN_WIDTH return focalLength 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) # the contour of paper is not closed, so apply close operation(dilate and erode) kernel = np.ones((3, 3), np.uint8) close = cv2.morphologyEx(edged, cv2.MORPH_CLOSE, kernel) # 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(close.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE) cnts = imutils.grab_contours(cnts) c = max(cnts, key = cv2.contourArea) # compute the bounding box of the of the paper region and return it return cv2.minAreaRect(c) def distance_to_camera(knownWidth, focalLength, perWidth): # compute and return the distance from the maker to the camera return (knownWidth * focalLength) / perWidth if __name__ == "__main__": focalLength = get_focalLength() # loop over the images for imagePath in sorted(paths.list_images("images")): # load the image, find the marker in the image, then compute the # distance to the marker from the camera image = cv2.imread(imagePath) marker = find_marker(image) inches = distance_to_camera(KNOWN_WIDTH, focalLength, marker[1][0]) # draw a bounding box around the image and display it box = cv2.cv.BoxPoints(marker) if imutils.is_cv2() else cv2.boxPoints(marker) box = np.int0(box) cv2.drawContours(image, [box], -1, (0, 255, 0), 2) cv2.putText(image, "%.2fft" % (inches / 12), (image.shape[1] - 200, image.shape[0] - 20), cv2.FONT_HERSHEY_SIMPLEX, 2.0, (0, 255, 0), 3) cv2.imshow(imagePath.split('/')[-1], image) if cv2.waitKey(0) & 0xFF == ord('q'): cv2.destroyAllWindows()