将图像转换成灰度图像，像素值在0~255之间
# 转换为矩阵格式 big = np.array(big_image) small = np.array(small_image) print("打印图像尺寸:") print("big:",big.shape) print("small:",small.shape) #从small中随机取50个点,可能用不完,多了备用 rand_point = [] for i in range(50): rand_point.append((np.random.randint(0,small.shape[0]-1) ,np.random.randint(0,small.shape[1]-1)))从[0-模板图片的长]随机取50个数->x 从[0-模板图片的宽]随机取50个数->y
# 矩阵R 用来保存误差次数 R = np.zeros([big.shape[0]-small.shape[0],big.shape[1]-small.shape[1]]) sMean = np.mean(small) # 计算small的均值 print("正在进行计算，请稍后……") for i in range(R.shape[0]): for j in range(R.shape[1]): loss = 0 #误差 mean = np.mean(big[i:i+small.shape[0],j:j+small.shape[1]]) # s[i,j]的均值 for n in range(len(rand_point)): point = rand_point[n] #从备选的随机点中取出一个随机点 # 计算并叠加误差 # 误差 = |备选区域中随机点的灰度值-备选区域均值 - small中随机点的灰度值 + small均值 |2 loss += np.abs( big[i+point[0],j+point[1]] - mean - small[point[0],point[1]] + sMean) # 当误差超过一定的限度，记录下使用的随机点个数 if loss >= 150 or n==len(rand_point)-1: R[i,j] = n break # 跳出n循环 # 找到迭代次数最多的点，对应的索引即为小图对应左上角位置坐标 index = np.unravel_index(R.argmax(), R.shape) print("左上角左边：",index) xy = [(index[1],index[0]),(index[1]+small.shape[1],index[0]+small.shape[0])] # 在big图中画出对应的位置 big_image = big_image_rgb #将big还原为RGB图像 x = index[0] y = index[1] print("x,y =",x,y) x0 = xy[0][0] y0 = xy[0][1] x1 = xy[1][0] y1 = xy[1][1] xm = int((x0 + x1)/2) ym = int((y0 + y1)/2) print("xy",xy) draw = ImageDraw.Draw(big_image) draw.rectangle(xy,outline='red',width=3) big_image = cv2.cvtColor(np.asarray(big_image),cv2.COLOR_RGB2BGR) print("big_image",big_image.shape) cv2.circle(big_image,(xm,ym),10,(255,0,0),3) cv2.circle(big_image,(x0,y0),10,(0,0,225),3) cv2.circle(big_image,(x1,y1),10,(0,0,225),3) cv2.namedWindow("big",1) cv2.imshow("big",big_image) cv2.waitKey(80000) #big_image.show() #print("已保存图像结果") #big_image.save("output.jpg")