OpenCV 轮廓层次结构
OpenCV Contouring hierarchy
如何return 绘制内部正方形的轮廓。这是我的代码:我需要内部正方形的轮廓,以便我可以使用角点来图像扭曲 area.I 已使用黑色胶带帮助检测边缘。 enter image description here
def getBase(img, minarea=100, filter=0, draw=False):
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
mask = cv2.inRange(gray, 0, 50)
result = cv2.bitwise_not(gray, gray, mask=mask)
cv2.imshow("result", result)
_, contours, hierarchy = cv2.findContours(
mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE
)
finalContours = []
for i in contours:
area = cv2.contourArea(i)
if area > minarea:
peri = cv2.arcLength(i, True)
approx = cv2.approxPolyDP(i, 0.02 * peri, True)
bbox = cv2.boundingRect(approx)
if filter > 0:
if len(approx) == filter:
finalContours.append((len(approx), area, approx, bbox, i))
else:
finalContours.append((len(approx), area, approx, bbox, i))
finalContours = sorted(finalContours, key=lambda x: x[1], reverse=True)
if draw:
for con in finalContours:
cv2.drawContours(img, con[4], -1, (0, 0, 255), 3)
return img, finalContours
下面是检测内部矩形并提取其左角点、高度和宽度的解决方案。
import cv2
import numpy as np
def is_rect_contour(contour):
peri = cv2.arcLength(contour, True)
approx = cv2.approxPolyDP(contour, 0.01 * peri, True)
if len(approx) == 4:
return True
else:
return False
image = cv2.imread("./rect.jpg")
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blurred = cv2.GaussianBlur(gray, (7, 7), 0)
thresh = cv2.threshold(blurred, 40, 255, cv2.THRESH_BINARY_INV)[1]
cnts = cv2.findContours(thresh.copy(), cv2.RETR_CCOMP, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0]
refined_contours = []
for cnt in cnts:
if is_rect_contour(cnt):
refined_contours.append(cnt)
inner_rect_cnt = min(refined_contours, key=cv2.contourArea)
(x, y, w, h) = cv2.boundingRect(inner_rect_cnt)
print("x: {}, y:{}, widhth:{}, height:{}".format(x, y, w, h))
cv2.drawContours(image, [inner_rect_cnt], -1, (0, 255, 0), 2)
cv2.imshow('Contours', image)
cv2.waitKey(0)
cv2.destroyAllWindows()
输出:
x: 425, y:126, widhth:1104, height:720
如何return 绘制内部正方形的轮廓。这是我的代码:我需要内部正方形的轮廓,以便我可以使用角点来图像扭曲 area.I 已使用黑色胶带帮助检测边缘。 enter image description here
def getBase(img, minarea=100, filter=0, draw=False):
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
mask = cv2.inRange(gray, 0, 50)
result = cv2.bitwise_not(gray, gray, mask=mask)
cv2.imshow("result", result)
_, contours, hierarchy = cv2.findContours(
mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE
)
finalContours = []
for i in contours:
area = cv2.contourArea(i)
if area > minarea:
peri = cv2.arcLength(i, True)
approx = cv2.approxPolyDP(i, 0.02 * peri, True)
bbox = cv2.boundingRect(approx)
if filter > 0:
if len(approx) == filter:
finalContours.append((len(approx), area, approx, bbox, i))
else:
finalContours.append((len(approx), area, approx, bbox, i))
finalContours = sorted(finalContours, key=lambda x: x[1], reverse=True)
if draw:
for con in finalContours:
cv2.drawContours(img, con[4], -1, (0, 0, 255), 3)
return img, finalContours
下面是检测内部矩形并提取其左角点、高度和宽度的解决方案。
import cv2
import numpy as np
def is_rect_contour(contour):
peri = cv2.arcLength(contour, True)
approx = cv2.approxPolyDP(contour, 0.01 * peri, True)
if len(approx) == 4:
return True
else:
return False
image = cv2.imread("./rect.jpg")
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blurred = cv2.GaussianBlur(gray, (7, 7), 0)
thresh = cv2.threshold(blurred, 40, 255, cv2.THRESH_BINARY_INV)[1]
cnts = cv2.findContours(thresh.copy(), cv2.RETR_CCOMP, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0]
refined_contours = []
for cnt in cnts:
if is_rect_contour(cnt):
refined_contours.append(cnt)
inner_rect_cnt = min(refined_contours, key=cv2.contourArea)
(x, y, w, h) = cv2.boundingRect(inner_rect_cnt)
print("x: {}, y:{}, widhth:{}, height:{}".format(x, y, w, h))
cv2.drawContours(image, [inner_rect_cnt], -1, (0, 255, 0), 2)
cv2.imshow('Contours', image)
cv2.waitKey(0)
cv2.destroyAllWindows()
输出:
x: 425, y:126, widhth:1104, height:720