如何匹配模板透明图像opencv
How to matchtemplate a transparent image opencv
我尝试了这个答案中的方法:,这正是我要找的,但对我来说没有用,我在 alpha 之后一直得到黑色图像处理中。
我试过了
result = cv2.matchTemplate(Image, Template, cv2.TM_CCOEFF_NORMED)
和
base = template[:,:,0:3]
alpha = template[:,:,3]
alpha = cv2.merge([alpha,alpha,alpha])
# do masked template matching and save correlation image
correlation = cv2.matchTemplate(img, base, cv2.TM_CCORR_NORMED, mask=alpha)
模板:
1
图片:
2
模板在左下方供参考
试试这个:
import cv2 as cv
import numpy as np
from matplotlib import pyplot as plt
original = cv.imread('Original_game.png')
img = cv.imread('Original_game.png',0)
img2 = img.copy()
template = cv.imread('template.png',0)
w, h = template.shape[::-1]
# All the 3 methods for comparison in a list
methods = ['cv.TM_CCOEFF', 'cv.TM_CCOEFF_NORMED', 'cv.TM_CCORR_NORMED']#,'cv.TM_CCORR','cv.TM_SQDIFF', 'cv.TM_SQDIFF_NORMED'
for meth in methods:
img = img2.copy()
method = eval(meth)
# Apply template Matching
res = cv.matchTemplate(img,template,method)
min_val, max_val, min_loc, max_loc = cv.minMaxLoc(res)
print(f"meth={meth} , min_val={min_val}, max_val={max_val}, min_loc={min_loc}, max_loc={max_loc}")
# If the method is TM_SQDIFF or TM_SQDIFF_NORMED, take minimum
if method in [cv.TM_SQDIFF, cv.TM_SQDIFF_NORMED]:
top_left = min_loc
else:
top_left = min_loc#max_loc
bottom_right = (top_left[0] + w, top_left[1] + h)
cv.rectangle(original,top_left, bottom_right, 255, 2)
fig = plt.figure(figsize=(10, 7))
plt.imshow(original)
plt.show()
示例结果:
关注算法:
- 更改阈值以找到不同的匹配位置
- 更改匹配算法
检查为什么有时应该使用最大值而有时使用最小值找到位置匹配。
有用的链接:
OpenCV Template Matching ( cv2.matchTemplate )
Template matching using OpenCV in Python
更新 #1
如果您想获得更好的结果,您应该使用“HOG”、“Surf”、“SIFT”和...等特征描述符。或者像 YOLO 这样的最先进的对象检测模型是您的问题最广为人知的。
我尝试了这个答案中的方法:
我试过了
result = cv2.matchTemplate(Image, Template, cv2.TM_CCOEFF_NORMED)
和
base = template[:,:,0:3]
alpha = template[:,:,3]
alpha = cv2.merge([alpha,alpha,alpha])
# do masked template matching and save correlation image
correlation = cv2.matchTemplate(img, base, cv2.TM_CCORR_NORMED, mask=alpha)
模板: 1
图片: 2
模板在左下方供参考
试试这个:
import cv2 as cv
import numpy as np
from matplotlib import pyplot as plt
original = cv.imread('Original_game.png')
img = cv.imread('Original_game.png',0)
img2 = img.copy()
template = cv.imread('template.png',0)
w, h = template.shape[::-1]
# All the 3 methods for comparison in a list
methods = ['cv.TM_CCOEFF', 'cv.TM_CCOEFF_NORMED', 'cv.TM_CCORR_NORMED']#,'cv.TM_CCORR','cv.TM_SQDIFF', 'cv.TM_SQDIFF_NORMED'
for meth in methods:
img = img2.copy()
method = eval(meth)
# Apply template Matching
res = cv.matchTemplate(img,template,method)
min_val, max_val, min_loc, max_loc = cv.minMaxLoc(res)
print(f"meth={meth} , min_val={min_val}, max_val={max_val}, min_loc={min_loc}, max_loc={max_loc}")
# If the method is TM_SQDIFF or TM_SQDIFF_NORMED, take minimum
if method in [cv.TM_SQDIFF, cv.TM_SQDIFF_NORMED]:
top_left = min_loc
else:
top_left = min_loc#max_loc
bottom_right = (top_left[0] + w, top_left[1] + h)
cv.rectangle(original,top_left, bottom_right, 255, 2)
fig = plt.figure(figsize=(10, 7))
plt.imshow(original)
plt.show()
示例结果:
关注算法:
- 更改阈值以找到不同的匹配位置
- 更改匹配算法
检查为什么有时应该使用最大值而有时使用最小值找到位置匹配。
有用的链接:
OpenCV Template Matching ( cv2.matchTemplate )
Template matching using OpenCV in Python
更新 #1
如果您想获得更好的结果,您应该使用“HOG”、“Surf”、“SIFT”和...等特征描述符。或者像 YOLO 这样的最先进的对象检测模型是您的问题最广为人知的。