七、图像金字塔与轮廓检测

七、图像金字塔与轮廓检测

1. 图像轮廓

（1）概念

cv2.findContours(img, mode, method)

mode（轮廓检测模式）：

• RETR_EXTERNAL：只检测最外面的轮廓
• RETR_LIST：检测所有的轮廓，并将其保存到链表中
• RETR_CCOMP：检索所有的轮廓，并将他们组织为两层，第一层为各部分的外部边界，第二层为空洞的边界
• RETR_TREE：检索所有的轮廓，并重构嵌套轮廓的整个层次

method（轮廓逼近的方法）：

• CHAIN_APPROX_NONE：以 Freeman 链码的方式输出轮廓，所有其他方法输出多边形（顶点的序列）
• CHAIN_APPROX_SIMPLE：压缩水平的、垂直的和斜的部分，即函数只保留他们的终点部分

import cv2 # OpenCV 读取的格式是 BGR
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline

img = cv2.imread('ysg.png')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# 使用二值图像提高准确率
ret, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)
plt.imshow(thresh, cmap='gray')
plt.show()

binary, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)

（2）轮廓绘制

img_cp = img.copy()
# 参数：需绘制的图像，轮廓（-1为绘制所有轮廓），颜色模式，线条厚度
res = cv2.drawContours(img_cp, contours, -1, (0, 0, 255), 1)
plt.imshow(cv2.cvtColor(res, cv2.COLOR_BGR2RGB))
plt.show()

（3）轮廓特征

cnt = contours[0]

# 面积
cv2.contourArea(cnt)
136.0

# 周长，True表示闭合
cv2.arcLength(cnt, True)
49.65685415267944

2. 模板匹配

（1）概念

模板匹配类似于卷积原理，模板在原图像上从原点开始滑动，计算模板与（图像被覆盖的地方）的差别程度（OpenCV中共6种计算方法），然后将每一次计算结果放入一个矩阵，作为结果输出。

6种计算方法：

• TM_SQDIFF：计算平方不同，计算结果值越小，越相关
• TM_CCORR：计算相关性，计算结果值越大，越相关
• TM_CCOEFF：计算相关系数，计算结果值越大，越相关
• TM_SQDIFF_NORMED：计算归一化平方不同，计算结果值越接近0，越相关
• TM_CCORR_NORMED：计算归一化相关性，计算结果值越接近1，越相关

• TM_CCOEFF_NORMED：计算归一化相关系数，计算结果值越接近1，越相关

  

  如：原图像大小为 A x B，模板大小为 a x b，则输出结果矩阵是(A - a + 1) x (B - b + 1)

img = cv2.imread('ysg.png', 0)
template = cv2.imread('ysg_template.png', 0)
h, w = template.shape[:2]

img.shape
(310, 341)

template.shape
(161, 144)

res = cv2.matchTemplate(img, template, cv2.TM_SQDIFF)
res.shape
(150, 198)

min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
# 最小值
min_val
0.0

# 最大值
max_val
217423616.0

# 最小值位置
min_loc
(67, 10)

# 最大值位置
max_loc
(189, 97)

（2）示例

methods = ['cv2.TM_SQDIFF', 'cv2.TM_CCORR', 'cv2.TM_CCOEFF', 'cv2.TM_SQDIFF_NORMED', 'cv2.TM_CCORR_NORMED', 'cv2.TM_CCOEFF_NORMED']
for item in methods:
    img_copy = img.copy()
    
    # 匹配方法的真值
    method = eval(item)
    print(method)
    res = cv2.matchTemplate(img, template, method)
    min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)

    # 如果平方差匹配TM_SQDIFF，或归一化平方差匹配TM_SQDIFF_NORMED，取最小值
    if method in [cv2.TM_SQDIFF, cv2.TM_SQDIFF_NORMED]:
        top_left = min_loc
    else:
        top_left = max_loc
    bottom_right = (top_left[0] + w, top_left[1] + h)
    
    cv2.rectangle(img_copy, top_left, bottom_right, 255, 2)
    plt.subplot(121), plt.imshow(res, cmap='gray')
    plt.xticks([]), plt.yticks([])
    plt.subplot(122), plt.imshow(img_copy, cmap='gray')
    plt.xticks([]), plt.yticks([])
    plt.suptitle(item)
    plt.show()

# 匹配多个对象
match = cv2.imread('ysg_match.png')
match_gray = cv2.cvtColor(match, cv2.COLOR_BGR2GRAY)
template = cv2.imread('ysg_template.png', 0)
h, w = template.shape[:2]

res = cv2.matchTemplate(match_gray, template, cv2.TM_CCOEFF_NORMED)
threshold = 0.8
loc = np.where(res >= threshold)
# 筛选匹配程度大于等于80%的
for pt in zip(*loc[::-1]):
    bottom_right = (pt[0] + w, pt[1] + h)
    cv2.rectangle(match, pt, bottom_right, (0, 0, 255), 1)

plt.imshow(cv2.cvtColor(match, cv2.COLOR_BGR2RGB))
plt.show()

3. 图像金字塔

• 高斯金字塔

  

  • 向下采样（缩小）

    

    # 向下采样
    img_down = cv2.pyrDown(img)
    plt.imshow(cv2.cvtColor(img_down, cv2.COLOR_BGR2RGB))
    plt.show()

    

    img.shape
    (310, 341, 3)
    
    img_down.shape
    (155, 171, 3)

  • 向上采样（放大）

    

    # 向上采样
    img = cv2.imread('ysg.png')
    img_up = cv2.pyrUp(img)
    plt.imshow(cv2.cvtColor(img_up, cv2.COLOR_BGR2RGB))
    plt.show()

    

    img.shape
    (310, 341, 3)
    
    img_up.shape
    (620, 682, 3)

• 拉普拉斯金字塔

  

  

  img = cv2.imread('ysg.png')
  down = cv2.pyrDown(img)
  down_up = cv2.pyrUp(down)
  
  if img.shape != down_up.shape:
      img = cv2.resize(img, (down_up.shape[1], down_up.shape[0]))
  res = img - down_up
  plt.imshow(cv2.cvtColor(res, cv2.COLOR_BGR2RGB))
  plt.show()

  
  
  

4. 轮廓近似

outline = cv2.imread('outline.png')

gray = cv2.cvtColor(outline, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)
binary, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
cnt = contours[0]

outline_cp = outline.copy()
res = cv2.drawContours(outline_cp, [cnt], -1, (0, 0, 255), 2)
plt.imshow(cv2.cvtColor(res, cv2.COLOR_BGR2RGB))
plt.show()

# 值越小，越接近原始轮廓
epsilon = 0.1 * cv2.arcLength(cnt, True)
approx = cv2.approxPolyDP(cnt, epsilon, True)

outline_cp1 = outline.copy()
res = cv2.drawContours(outline_cp1, [approx], -1, (0, 0, 255), 2)
plt.imshow(cv2.cvtColor(res, cv2.COLOR_BGR2RGB))
plt.show()

5. 边界矩形

outline = cv2.imread('outline.png')

gray = cv2.cvtColor(outline, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)
binary, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
cnt = contours[0]

x, y, w, h = cv2.boundingRect(cnt)
res = cv2.rectangle(outline, (x, y), (x + w, y + h), (0, 0, 255), 2)
plt.imshow(cv2.cvtColor(res, cv2.COLOR_BGR2RGB))
plt.show()

# 轮廓面积与边界矩形比
area = cv2.contourArea(cnt)
x, y, w, h = cv2.boundingRect(cnt)
rect_area = w * h
extent = float(area) / rect_area
print(extent)

0.5060173697270471

6. 外接圆

(x, y), radius = cv2.minEnclosingCircle(cnt)
center = (int(x), int(y))
radius = int(radius)
res = cv2.circle(res, center, radius, (0, 0, 255), 2)
plt.imshow(cv2.cvtColor(res, cv2.COLOR_BGR2RGB))
plt.show()