本文整理汇总了Python中box.Box.getContourRep方法的典型用法代码示例。如果您正苦于以下问题：Python Box.getContourRep方法的具体用法？Python Box.getContourRep怎么用？Python Box.getContourRep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box.Box的用法示例。
在下文中一共展示了Box.getContourRep方法的1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: findRoi
# 需要导入模块: from box import Box [as 别名]
# 或者: from box.Box import getContourRep [as 别名]
def findRoi(filename):
image = cv.imread(filename)
image = cv.resize(image, (0, 0), fx = 0.2, fy = 0.2) # TODO: Just set a general bound on image size
gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
ret, thresh = cv.threshold(gray,0,255,cv.THRESH_BINARY_INV + cv.THRESH_OTSU)
LOWAREA_THRESH = 10
UPAREA_THRESH = 3000
contours, heirachy = cv.findContours(thresh, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)
contours = [con for con in contours if LOWAREA_THRESH < cv.contourArea(con) < UPAREA_THRESH]
contours = [cv.approxPolyDP(con, 10, True) for con in contours]
contours = [cv.convexHull(con) for con in contours]
contours = [con for con in contours if len(con) != 1]
# After initialization, follow this algorithm:
# 1. Consider a box B in the set.
# 2. For every other box C in the set
# 3. If dist(B, C) < THRESHOLD, meld B's set with C's set
# 4. Repeat for each box C
# 5. For each disjoint set, meld each box in the set together. These are your clusters.
objects = []
for i, con in enumerate(contours):
x, y, w, h = cv.boundingRect(con)
box = Box(x, y, w, h)
objects.append({"box": box, "set": set([i]), "id": i})
THRESH = 12
AREA_THRESH = 200
SMALL_DIST_THRESH = 15
for obj1 in objects:
for obj2 in objects:
# If the box is not in box 1's cluster yet
if obj2["id"] not in obj1["set"]:
area1 = obj1["box"].area()
area2 = obj2["box"].area()
if obj1["box"].dist(obj2["box"]) < THRESH:
# TODO: Need to fix these bugs
# if obj1["box"].dist(obj2["box"]) != obj2["box"].dist(obj1["box"]):
# print "WARNING: Antisymmetric distance for {0} and {1}".format(obj1["id"], obj2["id"])
# print "Distances are: {0} and {1}".format(obj1["box"].dist(obj2["box"]), obj2["box"].dist(obj1["box"]))
# print "{0}, {1}".format(obj1["box"], obj2["box"])
obj1["set"] = obj1["set"].union(obj2["set"])
for id in obj1["set"]:
objects[id]["set"] = obj1["set"] # Shallow copy; all point to obj1's set
# Groups the members of the clusters together
clusters = set([tuple(obj["set"]) for obj in objects])
outputBoxes = []
for tup in clusters:
boxes = [objects[i]["box"] for i in tup]
outputBoxes.append(Box.meld(*boxes))
outputBoxes = [box.getContourRep() for box in outputBoxes]
PADDING = 5
return [image[box[2][0][1]-PADDING:box[0][0][1]+PADDING, box[1][0][0]-PADDING:box[0][0][0]+PADDING] for box in outputBoxes]