本文整理汇总了Python中cv2.pyrMeanShiftFiltering方法的典型用法代码示例。如果您正苦于以下问题：Python cv2.pyrMeanShiftFiltering方法的具体用法？Python cv2.pyrMeanShiftFiltering怎么用？Python cv2.pyrMeanShiftFiltering使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类cv2的用法示例。
在下文中一共展示了cv2.pyrMeanShiftFiltering方法的3个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: hough_circles
# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import pyrMeanShiftFiltering [as 别名]
def hough_circles(self):
src = self.cv_read_img(self.src_file)
if src is None:
return
dst = cv.pyrMeanShiftFiltering(src, 10, 100)
cimage = cv.cvtColor(dst, cv.COLOR_BGR2GRAY)
circles = cv.HoughCircles(cimage, cv.HOUGH_GRADIENT, 1, 20, param1=50, param2=30, minRadius=0, maxRadius=0)
circles = np.uint16(np.around(circles))
for i in circles[0, :]:
cv.circle(src, (i[0], i[1]), i[2], (0, 0, 255), 2)
cv.circle(src, (i[0], i[1]), 2, (255, 0, 255), 2)
self.decode_and_show_dst(src)
# 轮廓发现 示例2: roiMask
# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import pyrMeanShiftFiltering [as 别名]
def roiMask(image, boundaries):
scale = max([1.0, np.average(np.array(image.shape)[0:2] / 400.0)])
shape = (int(round(image.shape[1] / scale)), int(round(image.shape[0] / scale)))
small_color = cv2.resize(image, shape, interpolation=cv2.INTER_LINEAR)
# reduce details and remove noise for better edge detection
small_color = cv2.bilateralFilter(small_color, 8, 64, 64)
small_color = cv2.pyrMeanShiftFiltering(small_color, 8, 64, maxLevel=1)
small = cv2.cvtColor(small_color, cv2.COLOR_BGR2HSV)
hue = small[::, ::, 0]
intensity = cv2.cvtColor(small_color, cv2.COLOR_BGR2GRAY)
edges = extractEdges(hue, intensity)
roi = roiFromEdges(edges)
weight_map = weightMap(hue, intensity, edges, roi)
_, final_mask = cv2.threshold(roi, 5, 255, cv2.THRESH_BINARY)
small = cv2.bitwise_and(small, small, mask=final_mask)
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (4, 4))
for (lower, upper) in boundaries:
lower = np.array([lower, 80, 50], dtype="uint8")
upper = np.array([upper, 255, 255], dtype="uint8")
# find the colors within the specified boundaries and apply
# the mask
mask = cv2.inRange(small, lower, upper)
mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel, iterations=3)
mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel, iterations=1)
final_mask = cv2.bitwise_and(final_mask, mask)
# blur the mask for better contour extraction
final_mask = cv2.GaussianBlur(final_mask, (5, 5), 0)
return (final_mask, weight_map, scale) 示例3: mean_shift_filter
# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import pyrMeanShiftFiltering [as 别名]
def mean_shift_filter(self):
src = self.cv_read_img(self.src_file)
if src is None:
return
dst = cv.pyrMeanShiftFiltering(src, 10, 50) # 均值偏移滤波
self.decode_and_show_dst(dst)
# 图像二值化