1.思路 原先图片匹配一般都是缺口匹配全图 优化点: 1.缺口图片匹配缺口所在图片那一行图片可以提高他识别率 2.移动后再进行2次匹配计算距离 2.代码 def get_image_deviation(): ##读取滑块图 block = cv.imread("img.png", -1) #完整图片有个缺口 backimg = cv.imread("bg_img.png") #缺口图片 # block = cv.resize(block, (240, 480)) # backimg = cv.resize(block, (240, 480)) ##灰度化 gray_backimg = cv.cvtColor(backimg, cv.COLOR_RGB2GRAY) blockWidth, blockHeight = block.shape[1], block.shape[0] ##识别滑块图前景 ###由于滑块图为带透明的png，可根据透明通道来判断前景位置 ##识别物体框,生成blockmask left = blockWidth right = 0 top = blockHeight bottom = 0 for i in range(0, blockHeight): for j in range(0, blockWidth): if block[i, j, 3] > 0: if j <= left: left = j if j >= right: right = j if i <= top: top = i if i >= bottom: bottom = i blockBox = block[top:bottom, left:right] blockBox_width, blockBox_height = blockBox.shape[1], blockBox.shape[0] print(blockBox_width) blockMask = np.zeros([blockBox_height, blockBox_width], np.uint8) for i in range(0, blockBox_height): for j in range(0, blockBox_width): if blockBox[i, j, 3] > 0: blockMask[i, j] = 255 blockBox = cv.cvtColor(blockBox, cv.COLOR_RGBA2GRAY) ##由于边界点存在光照影响，为了避免边界点对匹配的影响，进行腐蚀操作 kernel = np.ones((3, 3), np.uint8) blockMask = cv.erode(blockMask, kernel, iterations=1).astype(np.float32) backgroundROI = gray_backimg[top:bottom, :] ##将backgroundROI、blockBox都转化成float型 blockBox = (blockBox * 1.0).astype(np.float32) backgroundROI = (backgroundROI * 1.0).astype(np.float32) ##使用cv的 res = cv.matchTemplate(backgroundROI, blockBox, cv.TM_CCORR_NORMED, mask=blockMask) loc = cv.minMaxLoc(res) print("loc==", loc[3][0]) locs = (loc[3][0]) return locs