文章目录
概要
模板匹配是一种在图像处理领域广泛应用的技术，旨在寻找目标模板在源图像中的位置。该算法的核心思想是通过比较模板与源图像的局部区域，逐像素滑动，创建一个相似度图，反映了模板与源图像各位置的相似程度。
使用OpenCV库实现模板匹配，该过程类似于在卷积神经网络中使用卷积核的方式。
模板匹配的基本原理：
模板匹配的基本原理是在源图像上通过逐像素滑动的方式，将模板与源图像的每个部分进行比较。这相当于在图像上进行卷积操作，其中每个像素值表示了模板与源图像相应区域的相似性。这个过程生成了一个相似度图，通过分析该图，可以识别出峰值，这些峰值对应于源图像中与模板相匹配的位置。
OpenCV中的模板匹配实现：
在OpenCV中，模板匹配通过cv2.matchTemplate()函数实现。该函数采用源图像和模板图像作为输入，返回一个相似度图。通过分析相似度图，可以使用cv2.minMaxLoc()函数找到最大匹配值的位置，从而确定模板在源图像中的位置。
相似性度量的选择：
模板匹配的实现可能基于不同的相似性度量方法，例如平方差匹配、相关性匹配或归一化互相关匹配。选择适当的相似性度量方法取决于具体的应用场景和需求。
整体架构流程
首先导入原图像并且显示出来：
bash
# Import libraries
import numpy as np
import matplotlib.pyplot as plt
from skimage.io import imread, imshow
from skimage.color import rgb2gray
from skimage.feature import match_template
from skimage.feature import peak_local_max
# Load the original image
original_image = imread('121.jpg')
# Show the original image with a smaller window
plt.figure(figsize=(10, 10))
plt.imshow(original_image)
plt.title('Original Image', fontsize=16, weight='bold')
plt.axis('off')
plt.show()
图像灰度化
虽然模板匹配适用于彩色图像，但让我们简化并将图像转换为灰度图来减少计算量。
这个可以调节窗口大小：
改（5，5）即可
bash
plt.figure(figsize=(5,5))
bash
# Import libraries
import numpy as np
import matplotlib.pyplot as plt
from skimage.io import imread, imshow
from skimage.color import rgb2gray
from skimage.feature import match_template
from skimage.feature import peak_local_max
# Load the original image
original_image = imread('121.jpg')
# Convert the image to grayscale
gray_image = rgb2gray(original_image[:,:,:3])
plt.figure(figsize=(5,5))
plt.imshow(gray_image, cmap='gray')
plt.title('Grayscale Image', fontsize=20, weight='bold')
plt.axis('off')
plt.show()
加载模板
现在，让我们从灰度图中截取一个心动的表情作为我们的目标模板，代码如下
bash
# Import libraries
import numpy as np
import matplotlib.pyplot as plt
from skimage.io import imread, imshow
from skimage.color import rgb2gray
from skimage.feature import match_template
from skimage.feature import peak_local_max
# Load the original image
original_image = imread('121.jpg')
# Convert the image to grayscale
gray_image = rgb2gray(original_image[:,:,:3])
plt.figure(figsize=(10,10))
plt.imshow(gray_image, cmap='gray')
plt.title('Grayscale Image', fontsize=20, weight='bold')
plt.axis('off')
plt.show()
template = gray_image[:80, -80:]
plt.figure(figsize=(5,5))
plt.imshow(template, cmap='gray')
plt.title('Template Image', fontsize=20, weight='bold')
plt.axis('off')
plt.show();
print("Original image shape:", original_image.shape)
print("Gray image shape:", gray_image.shape)
print("Template shape:", template.shape)取表情包的代码：template = gray_image[:80, -80:]
是指一个区域，这是最右上角的表情
这个是最左下角，可以自行适当调整。
bash
template = gray_image[-80:, :80]
模板匹配
通过使用 skimage库中的match_template函数 , 我们可以得到衡量模板图和原图的相似度的热力图，如下：
bash
result = match_template(gray_image, template)
plt.figure(figsize=(10,10))
imshow(result, cmap='viridis')
plt.show();
全部代码：
bash
# Import libraries
import numpy as np
import matplotlib.pyplot as plt
from skimage.io import imread, imshow
from skimage.color import rgb2gray
from skimage.feature import match_template
from skimage.feature import peak_local_max
# Load the original image
original_image = imread('121.jpg')
# Convert the image to grayscale
gray_image = rgb2gray(original_image[:,:,:3])
plt.figure(figsize=(10,10))
plt.imshow(gray_image, cmap='gray')
plt.title('Grayscale Image', fontsize=20, weight='bold')
plt.axis('off')
plt.show()
# Define the template
template = gray_image[:80, -80:]
# Show the template
plt.figure(figsize=(5,5))
plt.imshow(template, cmap='gray')
plt.title('Template Image', fontsize=20, weight='bold')
plt.axis('off')
plt.show()
# Perform template matching
result = match_template(gray_image, template)
# Display the result
plt.figure(figsize=(10,10))
imshow(result, cmap='viridis')
plt.show()
# Find the location of the template
x, y = np.unravel_index(np.argmax(result), result.shape)
# Display the original image with the rectangle around the matched region
imshow(gray_image)
template_width, template_height = template.shape
rect = plt.Rectangle((y, x), template_height, template_width, color='y',
fc='none')
plt.gca().add_patch(rect)
plt.show()
bash
# Import libraries
import numpy as np
import matplotlib.pyplot as plt
from skimage.io import imread, imshow
from skimage.color import rgb2gray
from skimage.feature import match_template
from skimage.feature import peak_local_max
# Load the original image
original_image = imread('121.jpg')
# Convert the image to grayscale
gray_image = rgb2gray(original_image[:,:,:3])
plt.figure(figsize=(10,10))
plt.imshow(gray_image, cmap='gray')
plt.title('Grayscale Image', fontsize=20, weight='bold')
plt.axis('off')
plt.show()
# Define the template
template = gray_image[:80, -80:]
# Show the template
plt.figure(figsize=(5,5))
plt.imshow(template, cmap='gray')
plt.title('Template Image', fontsize=20, weight='bold')
plt.axis('off')
plt.show()
# Perform template matching
result = match_template(gray_image, template)
# Display the result
plt.figure(figsize=(10,10))
imshow(result, cmap='viridis')
plt.show()
# Set a threshold for peak detection
# Set a lower threshold for peak detection
threshold = 0.9
# Find the location of the template with peak_local_max
imshow(gray_image)
template_width, template_height = template.shape
for x, y in peak_local_max(result, threshold_abs=threshold):
rect = plt.Rectangle((y, x), template_height, template_width, color='red',
fc='none')
plt.gca().add_patch(rect)
plt.show()可以设置阈值
bash
threshold = 0.5
bash
threshold = 0.9
至于最右上角的为什么识别不出来，是因为上面模板匹配找设置的时候，右边的笑脸框进去了，但是最右上角的没有左半边笑脸，就自动被排除在外了。
原图显示;
bash
# Import libraries
import numpy as np
import matplotlib.pyplot as plt
from skimage.io import imread, imshow
from skimage.color import rgb2gray
from skimage.feature import match_template
from skimage.feature import peak_local_max
# Load the original image
original_image = imread('121.jpg')
# Convert the image to grayscale
gray_image = rgb2gray(original_image[:,:,:3])
plt.figure(figsize=(10,10))
plt.imshow(gray_image, cmap='gray')
plt.title('Grayscale Image', fontsize=20, weight='bold')
plt.axis('off')
plt.show()
# Define the template
template = gray_image[:80, -80:]
# Perform template matching
result = match_template(gray_image, template)
# Set a lower threshold for peak detection
threshold = 0.75
# Find the location of the template with peak_local_max
imshow(gray_image)
template_width, template_height = template.shape
for x, y in peak_local_max(result, threshold_abs=threshold):
rect = plt.Rectangle((y, x), template_height, template_width, color='red',
fc='none')
plt.gca().add_patch(rect)
# Overlay the result on the original image
plt.figure(figsize=(20, 20))
plt.imshow(original_image)
plt.title('We found our heart eyes emojis!', fontsize=20, weight='bold', color='red')
for x, y in peak_local_max(result, threshold_abs=threshold):
rect = plt.Rectangle((y, x), template_height, template_width, color='red',
fc='none')
plt.gca().add_patch(rect)
plt.show()
结论
通过使用OpenCV进行模板匹配，我们可以有效地在图像中定位目标模板的位置。该方法广泛应用于目标检测、物体识别和图像分析等领域。在实际应用中，根据具体需求选择合适的相似性度量方法和调整算法参数，可以获得更精确的匹配结果。