[Python] OpenCV matchTemplate

程式語言：Python
Package：opencv-python
官方網址
OpenCV-Python Tutorials

功能：找出圖片中類似的東西

演算法

輸入兩張影像，分別為 image、template
不斷滑動 template，得到 image 上各個位置的比較值，比較值代表相似程度
然後將 image 左上角位置，作為 result 比較值的存放位置
完成後可得到 result
可用 minMaxLoc() 函式，找出結果圖的最大或最小值，定位出搜尋位置

限制

物體有旋轉時，會找不到
物體大小改變時，會找不到

result = cv2.matchTemplate(image, templ, method[, result])

參數

image

被尋找的圖片
必須為 8-bit or 32-bit

templ

尋找的物品圖片
size 不能大於 image，且格式需一致

method

比對的方法

result

比較的結果，格式為 numpy.ndarray (dtype=float32)
可傳入想儲存結果的 array
因 image 大小為 $W \times H$ 且 templ 為 $w \times h$ ，所以大小為 $(W - w + 1) \times (H - h + 1)$

比對方法

I

表示 image，

T

表示 template，

R

表示 result

CV_TM_SQDIFF

平方差，越小越相似
$R (x, y) = \sum_{x^{'}, y^{'}} (T (x^{'}, y^{'}) - I (x + x^{'}, y + y^{'}))^{2}$

CV_TM_SQDIFF_NORMED

正規化平方差，越小越相似
保證當 pixel 亮度都乘上同一係數時，相似度不變
$R (x, y) = \frac{\sum_{x^{'}, y^{'}} (T (x^{'}, y^{'}) - I (x + x^{'}, y + y^{'}))^{2}}{\sqrt{\sum_{x^{'}, y^{'}} T (x^{'}, y^{'})^{2} \cdot \sum_{x^{'}, y^{'}} I (x + x^{'}, y + y^{'})^{2}}}$

CV_TM_CCORR

相關係數，越大越相似
$R (x, y) = \sum_{x^{'}, y^{'}} (T (x^{'}, y^{'}) \cdot I (x + x^{'}, y + y^{'}))$

CV_TM_CCORR_NORMED

正規化相關係數，越大越相似
保證當 pixel 亮度都乘上同一係數時，相似度不變
$R (x, y) = \frac{\sum_{x^{'}, y^{'}} (T (x^{'}, y^{'}) \cdot I (x + x^{'}, y + y^{'}))}{\sqrt{\sum_{x^{'}, y^{'}} T (x^{'}, y^{'})^{2} \cdot \sum_{x^{'}, y^{'}} I (x + x^{'}, y + y^{'})^{2}}}$

CV_TM_CCOEFF

去掉直流成份的相關係數，越大越相似
$R (x, y) = \sum_{x^{'}, y^{'}} (T^{'} (x^{'}, y^{'}) \cdot I^{'} (x + x^{'}, y + y^{'}))$
where
$\begin{array}{l} T^{'} (x^{'}, y^{'}) = T (x^{'}, y^{'}) - 1 / (w \cdot h) \cdot \sum_{x^{″}, y^{″}} T (x^{″}, y^{″}) \\ I^{'} (x + x^{'}, y + y^{'}) = I (x + x^{'}, y + y^{'}) - 1 / (w \cdot h) \cdot \sum_{x^{″}, y^{″}} I (x + x^{″}, y + y^{″}) \end{array}$

CV_TM_CCOEFF_NORMED

正規化去掉直流成份的相關係數
保證當 pixel 亮度都乘上同一係數時，相似度不變
計算出的相關係數被限制在了 -1 到 1 之間

1 表示完全相同
-1 表示亮度正好相反
0 表示没有線性相關

$R (x, y) = \frac{\sum_{x^{'}, y^{'}} (T^{'} (x^{'}, y^{'}) \cdot I^{'} (x + x^{'}, y + y^{'}))}{\sqrt{\sum_{x^{'}, y^{'}} T^{'} (x^{'}, y^{'})^{2} \cdot \sum_{x^{'}, y^{'}} I^{'} (x + x^{'}, y + y^{'})^{2}}}$
where
$\begin{array}{l} T^{'} (x^{'}, y^{'}) = T (x^{'}, y^{'}) - 1 / (w \cdot h) \cdot \sum_{x^{″}, y^{″}} T (x^{″}, y^{″}) \\ I^{'} (x + x^{'}, y + y^{'}) = I (x + x^{'}, y + y^{'}) - 1 / (w \cdot h) \cdot \sum_{x^{″}, y^{″}} I (x + x^{″}, y + y^{″}) \end{array}$

範例

尋找特定物品

程式碼

import cv2
import numpy as np
from matplotlib import pyplot as plt
 
img = cv2.imread('image.png')
img2 = img.copy()
template = cv2.imread('template.jpg')
w = template.shape[1]
h = template.shape[0]
 
# All the 6 methods for comparison in a list
methods = ['cv2.TM_CCOEFF', 'cv2.TM_CCOEFF_NORMED', 'cv2.TM_CCORR',
            'cv2.TM_CCORR_NORMED', 'cv2.TM_SQDIFF', 'cv2.TM_SQDIFF_NORMED']
 
for meth in methods:
    img = img2.copy()
    method = eval(meth)
 
    # Apply template Matching
    res = cv2.matchTemplate(img,template,method)
    min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
 
    # If the method is TM_SQDIFF or TM_SQDIFF_NORMED, take minimum
    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,top_left, bottom_right, 255, 2)
 
    plt.subplot(121),plt.imshow(res,cmap = 'gray')
    plt.title('Matching Result'), plt.xticks([]), plt.yticks([])
    plt.subplot(122),plt.imshow(img)
    plt.title('Detected Point'), plt.xticks([]), plt.yticks([])
    plt.suptitle(meth)
 
    plt.show()

結果

標示所有相同物品

程式碼

import cv2
import numpy as np
from matplotlib import pyplot as plt
 
def getPoints(img, template, threshold, method=cv2.TM_CCOEFF_NORMED):
    result = cv2.matchTemplate(img, template, method)
 
    if method in [cv2.TM_SQDIFF, cv2.TM_SQDIFF_NORMED]:
        loc = np.where(result <= threshold) #回傳為 y, x
    else:
        loc = np.where(result >= threshold) #回傳為 y, x
    
    pts = zip(*loc[::-1])
    
    return removeSame(pts, min(template.shape[0], template.shape[1]))
    
 
# 去掉太過接近的座標
def removeSame(pts, threshold):
    elements = []
    for x,y in pts:
        for ele in elements:
            if ((x-ele[0])**2 + (y-ele[1])**2) < threshold**2:
                break
        else:
            elements.append((x,y))
    
    return elements
    
    
def drawRectangle(img, pts, w, h, color=(0,0,255), lineW=2):
    for pt in pts:
        cv2.rectangle(img, pt, (pt[0] + w, pt[1] + h), color, lineW) #因後面會反向顏色順序，所以這邊顯示紅色要反向
    
 
if __name__ == '__main__':
    img = cv2.imread('image.png')
    img_result = img.copy()
    template = cv2.imread('template.png')
    w = template.shape[1]
    h = template.shape[0]
    
    threshold = 0.4
    
    elements = getPoints(img, template, threshold)  
    total_n = len(elements)
    drawRectangle(img_result, elements, w=template.shape[1], h=template.shape[0])  
 
    # 逆時針旋轉 90
    template_r90 = cv2.transpose(template)
    cv2.flip(template_r90, 0)
    pts1 = getPoints(img, template_r90, threshold)
    # 順時針旋轉 90
    template_r90 = cv2.transpose(template)
    cv2.flip(template_r90, 1)
    pts2 = getPoints(img, template_r90, threshold)
    
    elements = removeSame(pts1+pts2, w)  
    total_90 = len(elements)
    drawRectangle(img_result, elements, w=template_r90.shape[1], h=template_r90.shape[0], color=(0,255,0)) 
        
    plt.subplot(121)
    plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB)) #因 openCV 記錄的顏色順序是反過來的
    plt.title('Original'), plt.xticks([]), plt.yticks([])
    plt.subplot(122)
    plt.imshow(cv2.cvtColor(img_result, cv2.COLOR_BGR2RGB)) #因 openCV 記錄的顏色順序是反過來的
    plt.title('Detected Point'), plt.xticks([]), plt.yticks([])
    plt.suptitle('Total:{}\nnomral:{}, 90:{}'.format(total_n+total_90, total_n, total_90))
 
    plt.show()
    # cv2.imwrite('result.png', img_result)

參考

Template Matching
Template Matching

子風的知識庫

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[Python] OpenCV matchTemplate

演算法

限制

result = cv2.matchTemplate(image, templ, method[, result])

比對方法

範例

參考

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