Question

我已经阅读了https://www.pyimagesearch.com/2015/11/02/watershed-opencv/教程，这让我惊讶地发现了这种惊人的可能性，现在我正在尝试将其实现到当前的对象跟踪程序中。我正在努力将这种算法实现到我的项目中，因为它需要视频流，并且还创建了一个只能看到红色物体的遮罩。该程序的主要问题是重叠的对象被视为一个对象，而在阅读了本教程之后，我意识到有一个针对该对象的算法，但是我无法弄清楚如何将其实现到我的项目中。

任何人都可以分享一些见识，并希望称我为白痴，并让我睁开眼睛。

我感谢任何评论。非常感谢你

我一直关注的

教程/研究 https://www.pyimagesearch.com/2015/11/02/watershed-opencv/ Image Segmentation using Mean Shift explained https://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_imgproc/py_watershed/py_watershed.html

# This is my main functionality in my code. And I have no idea where I can implement 
# watershed succesfully because of the color filtering and constant background change


while True:
frame = camera.read()  # read camera

if frame is None:
    print('fail with camera. Is it being used? src # correct?')
    break

frame = imutils.resize(frame, width=400)  # resize frame
height = np.size(frame, 0)  # calculates the height of frame
width = np.size(frame, 1)  # calculates the width of frame
blurred = cv2.GaussianBlur(frame, (21, 21), 0)  # blurring image before hsv applied (less noise)
hsv = cv2.cvtColor(blurred, cv2.COLOR_BGR2HSV)  # creating hsv from blurred frame and converting the bgr to hsv

mask = cv2.inRange(hsv, np.array(args["a"]), np.array(args["b"]))  # mask is setting hsv in range of the lower and upper blue ranges
mask = cv2.erode(mask, None, iterations=2)  # erode for less noise / more white
mask = cv2.dilate(mask, None, iterations=2)  # dilate does similar but makes whiteness thicker
res = cv2.bitwise_and(frame, frame, mask=mask)

contours = cv2.findContours(mask.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)  # find contours of mask
contours = imutils.grab_contours(contours)  # get them

middleLine = (height / 2)  # calculate the middleLine
cv2.line(frame, (0, height // 2), (width, height // 2), (100, 200, 100), 2)  # // = int division | draw the line
rects = []

if len(contours) > 0:  # don't pass in empty contour!!!
    for c in contours:  # loop through them
        if cv2.contourArea(c) < args["e"]:  # not big enough to be considered an object
            continue  # go next
        (x, y, w, h) = cv2.boundingRect(c)  # create rect for the object

我希望能够计算一个分水岭算法，以便能够唯一地识别重叠在具有彩色滤镜的网络摄像头流上的对象，但是如果您愿意，我遵循的教程始终将我留在“ cliffhanger”上因为他们使用的方法适用于图像而不适用于视频，并且它们没有颜色过滤功能，所以我似乎无法获得使它适用于视频流和颜色过滤项目所需的图片。

分水岭算法唯一地识别图像中的重叠对象，网络摄像头流中是否可能？

0 个答案: