Question

这里有新问题...我一直在关注this guide来检测物体和相机之间的距离。

以下是我目前正在运行的代码：

# import the necessary packages
import numpy as np
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)
    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

# 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 11 inches wide
KNOWN_WIDTH = 11.0

# initialize the list of images that we'll be using
IMAGE_PATHS = ["images/2ft.png", "images/3ft.png", "images/4ft.png"]

# 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

# loop over the images
for imagePath in IMAGE_PATHS:
    # 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 = np.int0(cv2.cv.BoxPoints(marker))
    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("image", image)
    cv2.waitKey(0)

有效。但是，我不确定如何使用代码实时（视频）而不是通过拍摄的照片来检测物体和相机之间的距离。

Answer 1

我目前正在使用具有相同代码的 Tello 无人机。不同之处在于我使用的视频将轮廓插入矩形并跟踪模型火箭发射。我认为您正在寻找的是用于理解视频帧的 OpenCV 代码。此 YouTube 视频使用 Tello 视频源和 OpenCV 计算人脸的计数框：https://www.youtube.com/watch?v=LmEcyQnfpDA&t=7253s。

import cv2
from tracker import *
import math, time, numpy as np

# global variables
_w, _h = 0,0
pid = [.01,.01,0]
pError = 0
cap = cv2.VideoCapture("rocketVideo.mp4")
loop = True
rocketPositionList = []
rocketPositionListArea = []

# initialize the known distance from the camera to the object, which
# in this case is 24 inches
KNOWN_DISTANCE = 480
# initialize the known object width, which in this case, the piece of
# paper is 12 inches wide
KNOWN_WIDTH = 5
inches = 0

# distance tracker from Tracker.py
tracker = EuclideanDistTracker()
#object detector
object_detector = cv2.createBackgroundSubtractorMOG2(history=4000,varThreshold=330)

# calulate frame data to get rocket positions in frame
def getFrameCalculation(frame):
    if len(rocketPositionListArea) != 0:
        i = rocketPositionListArea.index(max(rocketPositionListArea))
        return frame, [rocketPositionList[i], rocketPositionListArea[i]]
    else:
        return frame, [[0,0], 0]

def distance_to_camera(perWidth):
    # compute and return the distance from the maker to the camera
    return (KNOWN_WIDTH * focalLength) / perWidth

while loop:
    # Start Reading OpenCV Video Frame
    ret, frame = cap.read()
    height, width, _ = frame.shape
    w = width
    h = height
    #extract region of interst
    roi = frame[0:1110,0:720]

    
    #Object Detection
    mask = object_detector.apply(roi)
    contours, _ = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
    detections = []
    for cnt in contours:
        #Calcuate area and remove small elements
        area = cv2.contourArea(cnt)
        # Get Relative Area of Rocket Contour
        if area > 100:
            x,y,w,h = cv2.boundingRect(cnt)
            if x > 210:
                detections.append([x,y,w,h])

    # update box ids from tracker->detections
    boxes_ids = tracker.update(detections)
    # create visual data for boxIds
    for boxes_id in boxes_ids:
        x,y,w,h, id = boxes_id
        cv2.putText(roi,str(id),(x,y-15), cv2.FONT_HERSHEY_PLAIN, 1, (255,0,0),2)
        cv2.rectangle(roi, (x, y), (x + w, y + h), (0, 255, 0), 3)
        # get circle center and area
        cx = x + w // 2
        cy = y + h // 2
        area = w * h
        cv2.circle(roi,(cx,cy), 4, (0,0,255), cv2.FILLED)
        rocketPositionList.append([cx,cy])
        rocketPositionListArea.append(area)
        # Get First Known Focal Length
        focalLength = (rocketPositionListArea[0] * KNOWN_DISTANCE) / KNOWN_WIDTH

    # calculate Img Frame in Video
    frame, info = getFrameCalculation(frame)
    feetOut = 0
    if info[1] > 0:
        # Get Inches from Distance to Camera
        inches = distance_to_camera(info[1])
        # give data to Tello to Operate Movement Action
        feetOut = inches / 12
    cv2.putText(roi,str(int(feetOut)) + "ft.",(50,50), cv2.FONT_HERSHEY_PLAIN, 4, (255,0,0),2)

    # display cv2 videos
    cv2.imshow("ROI",resizeR)
    cv2.imshow("Mask",resizeM)
    cv2.imshow("Frame", resizeF)

# clean up cv2 and exit
cap.release()
cv2.destroyAllWindows()
exit()

Raspberry Pi OpenCV-2.4.X USB摄像头距离检测

1 个答案: