排序后如何对等高线区域求和?

Question

我想在排序后对最大的五个轮廓的区域求和，或者如果小于五个，则对所有轮廓的区域求和。

机器人根据颜色跟随人们的基础，但有时人们有相同的颜色，我想使用该区域从他们中选择一个。我将这条线用于两个轮廓，但这种方法不好area1 = cv2.contourArea(cnts[0]) + cv2.contourArea(cnts[1])

完整代码：

import cv2
import numpy as np
from imutils.video import FPS
import time
cap = cv2.VideoCapture(0)
width = cap.get(3)  # float
height = cap.get(4)  # float
print width, height
time.sleep(2.0)
fps = FPS().start()
while (1):
_, img = cap.read()
if _ is True:
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
else:
continue
black_lower = np.array([0,0,0], np.uint8)
black_upper = np.array([180,255,30], np.uint8)
black = cv2.inRange(hsv, black_lower, black_upper)
kernal = np.ones((5, 5), "uint8")
black = cv2.dilate(black, kernal)
res_black = cv2.bitwise_and(img, img, mask=black)
# Tracking black
(_, contours, hierarchy) = cv2.findContours(black, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
cnts = sorted(contours, key=cv2.contourArea, reverse=True)[:2000]  # get largest 2000 contour area
area1 = cv2.contourArea(cnts[0]) + cv2.contourArea(cnts[1])
# area2 = cv2.contourArea(cnts[0])
# total = area1 +area2
print 'area', area1,   type(cnts)
rects = []
print len(cnts) , type(cnts[1])
for c in cnts:
peri = cv2.arcLength(c, True)
approx = cv2.approxPolyDP(c, 0.02 * peri, True)
x, y, w, h = cv2.boundingRect(approx)
if h >= 15:
rect = (x, y, w, h)
rects.append(rect)
img = cv2.rectangle(img, (x, y), (x + w, y + h), (0, 0, 0), 2)
cv2.putText(img, "Black Colour", (x, y), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 0))
cv2.imshow("Color Tracking", img)
if cv2.waitKey(10) & 0xFF == ord('q'):
cap.release()
cv2.destroyAllWindows()
break

任何帮助或建议将不胜感激。

Answer 1

您可以使用 list = [] 对它们求和，但也许您面临另一个问题，即所有人的区域总和。

import cv2
import numpy as np
from imutils.video import FPS
import time
cap = cv2.VideoCapture(0)
width = cap.get(3)  # float
height = cap.get(4)  # float
print width, height
time.sleep(2.0)
fps = FPS().start()
while (1):
_, img = cap.read()
if _ is True:
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
else:
continue
black_lower = np.array([0,0,0], np.uint8)
black_upper = np.array([180,255,30], np.uint8)
black = cv2.inRange(hsv, black_lower, black_upper)
kernal = np.ones((5, 5), "uint8")
black = cv2.dilate(black, kernal)
res_black = cv2.bitwise_and(img, img, mask=black)
# Tracking black
(_, contours, hierarchy) = cv2.findContours(black, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
cnts = sorted(contours, key=cv2.contourArea, reverse=True)[:5]  # get largest five contour area
areas = []
for contour in cnts:
area = cv2.contourArea(contour)
if area > 300:
areas.append(area)
x, y, w, h = cv2.boundingRect(contour)
img = cv2.rectangle(img, (x, y), (x + w, y + h), (0, 0, 0), 2)
cv2.putText(img, "Black Colour", (x, y), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 0))
a = sum(areas)
print areas
print a
cv2.imshow("Color Tracking", img)
if cv2.waitKey(10) & 0xFF == ord('q'):
cap.release()
cv2.destroyAllWindows()
break

Answer 2

你可以只使用这一行：

area = sum([cv2.contourArea(cnt) for cnt in sorted(cnts, key=cv2.contourArea, reverse=True)[:5]])

我将添加完整的代码来比较它们。

import cv2
import numpy as np
from imutils.video import FPS
import time
cap = cv2.VideoCapture(0)
width = cap.get(3)  # float
height = cap.get(4)  # float
print width, height
time.sleep(2.0)
fps = FPS().start()
while (1):
_, img = cap.read()
if _ is True:
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
else:
continue
black_lower = np.array([0,0,0], np.uint8)
black_upper = np.array([180,255,30], np.uint8)
black = cv2.inRange(hsv, black_lower, black_upper)
kernal = np.ones((5, 5), "uint8")
black = cv2.dilate(black, kernal)
res_black = cv2.bitwise_and(img, img, mask=black)
# Tracking black
(_, contours, hierarchy) = cv2.findContours(black, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
cnts = sorted(contours, key=cv2.contourArea, reverse=True)[:5]  # get largest five contour area
area = sum([cv2.contourArea(cnt) for cnt in sorted(cnts, key=cv2.contourArea, reverse=True)[:5]])
print 'area_method1', area
areas = []
for contour in cnts:
area = cv2.contourArea(contour)
if area > 300:
areas.append(area)
x, y, w, h = cv2.boundingRect(contour)
img = cv2.rectangle(img, (x, y), (x + w, y + h), (0, 0, 0), 2)
cv2.putText(img, "Black Colour", (x, y), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 0))
a = sum(areas)
# print areas
print 'area_method2',  a
cv2.imshow("Color Tracking", img)
if cv2.waitKey(10) & 0xFF == ord('q'):
cap.release()
cv2.destroyAllWindows()
break