工作学习第二--PCB板短路检测渐入佳境

本文介绍了使用图像配准和形态学处理方法来检测短路的技术流程。通过对比两张图像并利用ECC算法进行图像对齐，之后进行图像减法及开运算去除噪声，最后运用轮廓检测方法标记出短路位置。

摘要生成于 C知道，由 DeepSeek-R1 满血版支持，前往体验 >

反思一下，以后不能拿到任务相当然的开始直接试，多收集资料，先看看论文，吸收知识再有的放矢较为明智。

早上先了解一下行业相关，毕竟刚进来什么都不懂：https://zhuanlan.zhihu.com/p/31319878

对于行业及其相关识别方法有了一定的了解，开始进行图像配准+形态学处理识别短路的测试。

参考大佬的github和论文：https://blog.youkuaiyun.com/full_speed_turbo/article/details/68484450

将前面无法直接做异或的图像进行对齐，首先对图像进行前期的预处理：

im1 = cv2.imread("./short_circuit_2/0_13_211_c.bmp")
im2 = cv2.imread("./short_circuit_2/0_13_211_g.bmp")

#大小调整
#im1 = cv2.resize(im1,(im2.shape[1],im2.shape[0]), interpolation = cv2.INTER_CUBIC)

kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))  # 矩形结构
# Convert images to grayscale
im1_gray1 = cv2.cvtColor(im1, cv2.COLOR_BGR2GRAY)
im1_gray = cv2.morphologyEx(im1_gray1, cv2.MORPH_CLOSE,kernel)  # 闭运算
im2_gray = cv2.cvtColor(im2, cv2.COLOR_BGR2GRAY)

ECC图像配准大法：

# Find size of image1
sz = im1_gray.shape

# Define the motion model
warp_mode = cv2.MOTION_TRANSLATION

# Define 2x3 or 3x3 matrices and initialize the matrix to identity
if warp_mode == cv2.MOTION_HOMOGRAPHY:
	warp_matrix = np.eye(3, 3, dtype=np.float32)
else:
	warp_matrix = np.eye(2, 3, dtype=np.float32)

# Specify the number of iterations.
number_of_iterations = 5000

# Specify the threshold of the increment
# in the correlation coefficient between two iterations
termination_eps = 1e-10

# Define termination criteria
criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, number_of_iterations, termination_eps)

im1_gray = cv2.resize(im1_gray, (im2_gray.shape[1], im2_gray.shape[0]), interpolation=cv2.INTER_CUBIC)
# Run the ECC algorithm. The results are stored in warp_matrix.
(cc, warp_matrix) = cv2.findTransformECC(im1_gray, im2_gray, warp_matrix, warp_mode, criteria)

if warp_mode == cv2.MOTION_HOMOGRAPHY:
	# Use warpPerspective for Homography
	im2_aligned = cv2.warpPerspective(im2, warp_matrix, (sz[1], sz[0]), flags=cv2.INTER_LINEAR + cv2.WARP_INVERSE_MAP)
else:
	# Use warpAffine for Translation, Euclidean and Affine
	im2_aligned = cv2.warpAffine(im2, warp_matrix, (sz[1], sz[0]), flags=cv2.INTER_LINEAR + cv2.WARP_INVERSE_MAP)

图像对齐后，对图像进行减法，并后续做一个开运算消除噪点，后续用cv的findcontours函数找连通域化矩形框进行标记：

img_diff = cv2.subtract(im1, im2_aligned)
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))  # 矩形结构
img_diff2 = cv2.morphologyEx(img_diff, cv2.MORPH_OPEN,kernel)  # 开运算
cv2.namedWindow('subtracted', cv2.WINDOW_NORMAL)
cv2.imshow("subtracted", img_diff)  # 减运算图
#cv2.imwrite("img_diff.bmp",img_diff,None)
cv2.namedWindow('subtracted2', cv2.WINDOW_NORMAL)
cv2.imshow("subtracted2", img_diff2)


img_diff2 = cv2.cvtColor(img_diff2, cv2.COLOR_BGR2GRAY)
_,contours, hierarchy = cv2.findContours(img_diff2, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

for j in range(0,len(contours)):
	x, y, w, h = cv2.boundingRect(contours[j])
	cv2.rectangle(im1, (x,y), (x+w,y+h), (0,0,255), 4)


cv2.namedWindow('Image 1', cv2.WINDOW_NORMAL)
cv2.imshow("Image 1", im1)
#cv2.imwrite("./result_2/img_result%d.jpg"%(i),im1,None)
cv2.waitKey(0)
cv2.destroyAllWindows()

结果图：