我需要此代码才能使用 Pi 相机而不是网络摄像头

Question

我正在做一个球跟踪项目，我在网上找到了一些可以帮助我入门的示例代码。此代码使用网络摄像头导入视频，但我将使用带有 PiCamera 端口的 go pro。我已经有了适配器，并且视频只能自己运行，但是当我尝试切换此代码以使用它时，我到处都会出错。

代码：

# USAGE
# python ball_tracking.py --video ball_tracking_example.mp4
# python ball_tracking.py

# import the necessary packages
from collections import deque
from imutils.video import VideoStream
import numpy as np
import argparse
import cv2
import imutils
import time

# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-v", "--video",
    help="path to the (optional) video file")
ap.add_argument("-b", "--buffer", type=int, default=64,
    help="max buffer size")
args = vars(ap.parse_args())

# define the lower and upper boundaries of the "green"
# ball in the HSV color space, then initialize the
# list of tracked points
greenLower = (0, 100, 100)
greenUpper = (10, 255, 255)
pts = deque(maxlen=args["buffer"])

# if a video path was not supplied, grab the reference
# to the webcam
if not args.get("video", False):
    vs = VideoStream(src=0).start()

# otherwise, grab a reference to the video file
else:
    vs = cv2.VideoCapture(args["video"])

# allow the camera or video file to warm up
time.sleep(2.0)

# keep looping
while True:
    # grab the current frame
    frame = vs.read()

    # handle the frame from VideoCapture or VideoStream
    frame = frame[1] if args.get("video", False) else frame

    # if we are viewing a video and we did not grab a frame,
    # then we have reached the end of the video
    if frame is None:
        break

    # resize the frame, blur it, and convert it to the HSV
    # color space
    frame = imutils.resize(frame, width=600)
    blurred = cv2.GaussianBlur(frame, (11, 11), 0)
    hsv = cv2.cvtColor(blurred, cv2.COLOR_BGR2HSV)

    # construct a mask for the color "green", then perform
    # a series of dilations and erosions to remove any small
    # blobs left in the mask
    mask = cv2.inRange(hsv, greenLower, greenUpper)
    mask = cv2.erode(mask, None, iterations=2)
    mask = cv2.dilate(mask, None, iterations=2)

    # find contours in the mask and initialize the current
    # (x, y) center of the ball
    cnts = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL,
        cv2.CHAIN_APPROX_SIMPLE)
    cnts = imutils.grab_contours(cnts)
    center = None

    # only proceed if at least one contour was found
    if len(cnts) > 0:
        # find the largest contour in the mask, then use
        # it to compute the minimum enclosing circle and
        # centroid
        c = max(cnts, key=cv2.contourArea)
        ((x, y), radius) = cv2.minEnclosingCircle(c)
        M = cv2.moments(c)
        center = (int(M["m10"] / M["m00"]), int(M["m01"] / M["m00"]))

        # only proceed if the radius meets a minimum size
        if radius > 10:
            # draw the circle and centroid on the frame,
            # then update the list of tracked points
            cv2.circle(frame, (int(x), int(y)), int(radius),
                (0, 255, 255), 2)
            cv2.circle(frame, center, 5, (0, 0, 255), -1)

    # update the points queue
    pts.appendleft(center)

    # loop over the set of tracked points
    for i in range(1, len(pts)):
        # if either of the tracked points are None, ignore
        # them
        if pts[i - 1] is None or pts[i] is None:
            continue

        # otherwise, compute the thickness of the line and
        # draw the connecting lines
        thickness = int(np.sqrt(args["buffer"] / float(i + 1)) * 2.5)
        cv2.line(frame, pts[i - 1], pts[i], (0, 0, 255), thickness)

    # show the frame to our screen
    cv2.imshow("Frame", frame)
    key = cv2.waitKey(1) & 0xFF

    # if the 'q' key is pressed, stop the loop
    if key == ord("q"):
        break

# if we are not using a video file, stop the camera video stream
if not args.get("video", False):
    vs.stop()

# otherwise, release the camera
else:
    vs.release()

# close all windows
cv2.destroyAllWindows()

if not args.get("video", False):
vs = VideoStream(src=0).start()

我相信这是从网络摄像头获取视频源

Answer 1

使用下面的代码。

from picamera.array import PiRGBArray
from picamera import PiCamera
import time
import cv2

# initialize the camera and grab a reference to the raw camera capture
camera = PiCamera()
camera.resolution = (640, 480)
camera.framerate = 32
rawCapture = PiRGBArray(camera, size=(640, 480))

# allow the camera to warmup
time.sleep(0.1)

# capture frames from the camera
for frame in camera.capture_continuous(rawCapture, format="bgr", use_video_port=True):
    # grab the raw NumPy array representing the image, then initialize the timestamp
    # and occupied/unoccupied text
    image = frame.array
    cv2.rectangle(image,(150,70),(490,410),(0,255,0),3)
    # show the frame
    cv2.imshow("Frame", image)
    key = cv2.waitKey(1) & 0xFF

    # clear the stream in preparation for the next frame
    rawCapture.truncate(0)

# if the `q` key was pressed, break from the loop
if key == ord("q"):
    break

也可以直接使用 Pi 摄像头进入 Opencv

sudo modprobe bcm2835-v4l2

为 pi 摄像头加载驱动程序，每次重启 Pi 时写入此命令。 然后，您可以像使用网络摄像头一样使用 Pi 摄像头。 下面的代码也可以工作

import numpy as np
import cv2

cap = cv2.VideoCapture(0)

while(True):
    # Capture frame-by-frame
    ret, frame = cap.read()

    # Our operations on the frame come here
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    # Display the resulting frame
    cv2.imshow('frame',gray)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# When everything done, release the capture
cap.release()
cv2.destroyAllWindows()