OpenCV中的模板匹配
OpenCV中的模板匹配是支持基于NCC相似度查找的，但是不是很好用，一个主要的原因是查找最大阈值，只能匹配一个，自己比对阈值，又导致无法正确设定阈值范围，所以问题很多。于是我重新写了纯Python版本的NCC图像模板匹配的代码实现了一个Python版本的，简单易用，支持多尺度，跟多进程并行！
主要思想
主要是基于NCC实现的像素相似度计算，这个OpenCV官方的模板匹配也有这中方式像素相似度计算支持，它的公式描述如下：
就是参照这个公式，然后基于OpenCV提供的积分图计算函数，实现了NCC相似度比较计算，值在0~1之间，1表示完全相似，0表示完全不相似。
代码实现
我把整个部分搞成了一个类，调用的方法主要是run_match,就可以直接运行，完成模板匹配。大体的功能跟OpenCV实现的模板匹配功能比较相似，改进的地方就是比较方便的实现多个对象匹配的直接输出Box框。该类完整的代码实现如下：
import cv2 as cv
import numpy as np
import time
import concurrent.futures
class NCCTemplateMatch:
def __init__(self, ref_imgs, target_imgs, scores, tpl_sums, tpl_sqr_sums, target_sums, target_sqr_sums):
self.ref_imgs = ref_imgs
self.target_imgs = target_imgs
self.scores = scores
self.tpls_sums = tpl_sums
self.tpls_sqsums = tpl_sqr_sums
self.target_sums = target_sums
self.target_sqsums = target_sqr_sums
self.nms_boxes = []
def run_match(self):
num_ps = min(6, len(self.ref_imgs))
# print("num_ps: ", num_ps)
start = time.perf_counter()
with concurrent.futures.ProcessPoolExecutor(num_ps) as executor:
matched = executor.map(self.ncc_run, self.ref_imgs, self.target_imgs, self.tpls_sums, self.tpls_sqsums, self.target_sums, self.target_sqsums, self.scores)
self.nms_boxes = list(matched)
end = time.perf_counter()
print(f'Finished in {round(end-start, 2)} seconds')
def ncc_run(self, tpl_gray, target_gray, tpl_sum, tpl_sqsum, target_sum, target_sqsum, score):
print("run once~~~~")
th, tw = tpl_gray.shape
min_step = max(1, min(th // 16, tw // 16))
h, w = target_gray.shape
sr = 1 / (th * tw)
t_s1 = tpl_sum[th, tw]
t_s1_2 = t_s1 * t_s1 * sr
t_s1_1 = t_s1 * sr
t_s2 = tpl_sqsum[th, tw]
sum_t = np.sqrt(t_s2 - t_s1_2)
row = 0
boxes = []
confidences = []
while row < (h - th+1):
col = 0
while col < (w - tw+1):
s1 = self.get_block_sum(target_sum, col, row, col + tw, row + th)
s2 = self.get_block_sum(target_sqsum, col, row, col + tw, row + th)
sum1 = t_s1_1 * s1
ss_sqr = s2 - s1 * s1 * sr
if ss_sqr < 0: # fix issue, 精度问题
ss_sqr = 0.0
sum2 = sum_t * np.sqrt(ss_sqr)
sum3 = np.sum(np.multiply(tpl_gray, target_gray[row:row + th, col:col + tw]))
if sum2 == 0.0:
ncc = 0.0
else:
ncc = (sum3 - sum1) / sum2
if ncc > score:
boxes.append([col, row, tw, th])
confidences.append(float(ncc))
col += tw//2
else:
col += min_step
row += min_step
# NMS Process
nms_indices = cv.dnn.NMSBoxes(boxes, confidences, 0.5, 0.5)
det_boxes = []
print(nms_indices)
for i in range(len(nms_indices)):
rect_box = boxes[nms_indices[i]]
det_boxes.append(rect_box)
return det_boxes
def get_block_sum(self, integal_img, x1, y1, x2, y2):
t1 = integal_img[y1, x1]
t2 = integal_img[y1, x2]
t3 = integal_img[y2, x1]
t4 = integal_img[y2, x2]
s = t4 - t2 - t3 + t1
return s 相关的测试与调用代码如下：
print("test ncc......")
tpl_image = cv.imread("D:/images/llk_tpl.png")
target_image = cv.imread("D:/images/llk.jpg")
tpl_gray = cv.cvtColor(tpl_image, cv.COLOR_BGR2GRAY)
target_gray = cv.cvtColor(target_image, cv.COLOR_BGR2GRAY)
tpl_gray = np.float32(tpl_gray / 255.0)
target_gray = np.float32(target_gray / 255.0)
tpl_sum, tpl_sqsum = cv.integral2(tpl_gray)
t_sum, t_sqsum = cv.integral2(target_gray)
matcher = NCCTemplateMatch([tpl_gray], [target_gray], [0.85],
[tpl_sum], [tpl_sqsum], [t_sum], [t_sqsum])
matcher.run_match()
for rect_box in matcher.nms_boxes[0]:
cv.rectangle(target_image, (rect_box[0], rect_box[1]),
(rect_box[0]+rect_box[2], rect_box[1]+rect_box[3]), (0, 0, 255), 2, 8, 0)
cv.imshow("result", target_image)
cv.waitKey(0)
cv.destroyAllWindows() 模板图像：
运行结果如下：