些一段Python代码,使用OpenCV,要求已知一张模版指纹图像,自定义函数实现加载指纹图像与模版指纹图像的验证识别。
时间: 2024-10-23 19:19:21 浏览: 10
基于OpenCV和tinker的指纹识别系统python源码+代码注释+项目说明及设计报告(数字图像处理课程设计).zip
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在Python中,你可以使用OpenCV库来实现模板匹配功能,这里提供一个简单的示例,假设我们已经有了模板指纹图像`template_fingerprint.jpg`以及需要检测的目标图像`image_to_match.jpg`:
```python
import cv2
import numpy as np
# 加载模板指纹图像
def load_template_fingerprint(template_path):
template = cv2.imread(template_path, 0) # 读取灰度图像
if template is None:
print("无法加载模板指纹图像")
return None
return template
# 加载目标图像并转换为灰度
def load_image_to_match(image_path):
image = cv2.imread(image_path, 0)
if image is None:
print("无法加载目标图像")
return None
return image
# 使用SIFT特征提取并匹配模板
def fingerprint_verification(template, image):
sift = cv2.xfeatures2d.SIFT_create() # 初始化SIFT对象
kp_template, des_template = sift.detectAndCompute(template, None)
kp_image, des_image = sift.detectAndCompute(image, None)
FLANN_INDEX_KDTREE = 0
index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)
search_params = dict(checks=50) # or pass empty dictionary
flann = cv2.FlannBasedMatcher(index_params, search_params)
matches = flann.knnMatch(des_template, des_image, k=2) # 找到最相似的两个匹配点
good_matches = []
for m, n in matches:
if m.distance < 0.7 * n.distance: # 设置阈值过滤掉不太匹配的点
good_matches.append([m])
if len(good_matches) > 0: # 如果找到一些匹配点,则认为是匹配的
match_percentage = (len(good_matches) / max(len(kp_template), len(kp_image))) * 100
return True, match_percentage
else:
return False, 0
# 示例:
template_path = 'template_fingerprint.jpg'
image_path = 'image_to_match.jpg'
template = load_template_fingerprint(template_path)
image = load_image_to_match(image_path)
if template is not None and image is not None:
matched, match_percent = fingerprint_verification(template, image)
if matched:
print(f"指纹匹配成功,匹配百分比:{match_percent}%")
else:
print("指纹匹配失败")
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