我正在使用https://www.pyimagesearch.com/2014/07/21/detecting-circles-images-using-opencv-hough-circles/#comment-480634中解释的代码,并试图基本上检测出该示例instagram页面下半部分(附加)中显示的较小的圆形轮廓图像(准确地说是5个)。我不知道为什么: 1.代码捕获了5个小圆形轮廓圆圈中的仅一个 2.为什么页面上显示一个大圆圈,这对我来说似乎很荒唐。 这是我正在使用的代码:
# we create a copy of the original image so we can draw our detected circles
# without destroying the original image.
image = cv2.imread("instagram_page.png")
# the cv2.HoughCircles function requires an 8-bit, single channel image,
# so we’ll convert from the RGB color space to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
#blurred = cv2.GaussianBlur(gray, (5, 5), 0)
# detect circles in the image. We pass in the image we want to detect circles as the first argument,
# the circle detection method as the second argument (currently, the cv2.cv.HOUGH_GRADIENT method
# is the only circle detection method supported by OpenCV and will likely be the only method for some time),
# an accumulator value of 1.5 as the third argument, and finally a minDist of 100 pixels.
circles = cv2.HoughCircles(gray, cv2.HOUGH_GRADIENT, 1.7, minDist= 1, param1 = 300, param2 = 100, minRadius=3, maxRadius=150)
print("Circles len -> {}".format(len(circles)))
# ensure at least some circles were found
if circles is not None:
# convert the (x, y) coordinates and radius of the circles to integers
# converting our circles from floating point (x, y) coordinates to integers,
# allowing us to draw them on our output image.
circles = np.round(circles[0, :]).astype("int")
# loop over the (x, y) coordinates and radius of the circles
for (x, y, r) in circles:
# draw the circle in the output image, then draw a rectangle
# corresponding to the center of the circle
orange = (39, 127, 255)
cv2.circle(output, (x, y), r, orange, 4)
cv2.rectangle(output, (x - 5, y - 5), (x + 5, y + 5), (0, 128, 255), -1)
img_name = "Output"
cv2.namedWindow(img_name,cv2.WINDOW_NORMAL)
cv2.resizeWindow(img_name, 800,800)
cv2.imshow(img_name, output)
cv2.waitKey(0)
cv2.destroyAllWindows()