OpenCV半小时掌握基本操作之图像轮廓

cv2.findContours(image, mode, method, contours=None, hierarchy=None, offset=None)

cv2.drawContours(image, contours, contourIdx, color, thickness=None, lineType=None, hierarchy=None, maxLevel=None, offset=None): 

# 读取图片
img = cv2.imread("contours.jpg")

# 转换成灰度图
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# 二值化
ret, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)

# 获取轮廓 (所有)
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)

# 绘制轮廓
draw_img = img.copy()
res = cv2.drawContours(draw_img, contours, -1, (0, 0, 255), 2)

# 图片展示
cv2.imshow("res", res)
cv2.waiTKEy(0)
cv2.destroyAllwindows()

# 读取图片
img = cv2.imread("contours.jpg")

# 转换成灰度图
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# 二值化
ret, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)

# 获取轮廓
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)

# 绘制轮廓 (单一)
draw_img = img.copy()
res = cv2.drawContours(draw_img, contours, 0, (0, 0, 255), 2)

# 图片展示
cv2.imshow("res", res)
cv2.waitKey(0)
cv2.destroyAllWindows()

# 获取轮廓
cnt = contours[0]  # 取第一个轮廓

# 面积
area = cv2.contourArea(cnt)
print("轮廓面积:", area)

# 周长, True表示合并
perimeter = cv2.arcLength(cnt, True)
print("轮廓周长:", perimeter)

# 读取图片
img = cv2.imread("contours2.jpg")

# 转换成灰度图
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# 二值化
ret, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)

# 获取轮廓
contours, hieratchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)

# 绘制轮廓
draw_img = img.copy()
res = cv2.drawContours(draw_img, contours, 0, (0, 0, 255), 2)

# 图片展示
cv2.imshow("res", res)
cv2.waitKey(0)
cv2.destroyAllWindows()

# 取外围轮廓
cnt = contours[0]

# 轮廓近似
epsilon = 0.1 * cv2.arcLength(cnt, True)
approx = cv2.approxPolyDP(cnt, epsilon, True)

# 绘制轮廓
draw_img = img.copy()
res = cv2.drawContours(draw_img, [approx], -1, (0, 0, 255), 2)

# 图片展示
cv2.imshow("res", res)
cv2.waitKey(0)
cv2.destroyAllWindows()

# 读取图片
img = cv2.imread("contours.jpg")

# 转换成灰度图
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# 二值化
ret, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)

# 获取轮廓
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)

# 获取第一个轮廓
cnt = contours[0]

# 获取正方形坐标长宽
x, y, w, h = cv2.boundingRect(cnt)

# 图片展示
img = cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)
cv2.imshow("img", img)
cv2.waitKey(0)
cv2.destroyAllWindows()

# 轮廓面积
area = cv2.contourArea(cnt)

# 边界矩形面积
rect_area = w * h

# 占比
extent = area / rect_area
print('轮廓面积与边界矩形比:', extent)

# 读取图片
img = cv2.imread("contours.jpg")

# 转换成灰度图
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# 二值化
ret, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)

# 获取轮廓
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)

# 获取第一个轮廓
cnt = contours[0]

# 获取外接圆
(x, y), radius = cv2.minEnclosingCircle(cnt)

# 获取图片
img = cv2.circle(img, (int(x), int(y)), int(radius), (255, 100, 0), 2)

# 图片展示
cv2.imshow("img", img)
cv2.waitKey(0)
cv2.destroyAllWindows()