我有一段代码，是对二值图进行聚类，然后画框，但是大框中包含许多小框，请你帮我添加一段代码在我的代码基础上，做一个nms抑制，优化这种情况，我的代码如下：_, binary_image = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY) connectivity = 4 output = cv2.connectedComponentsWithStats(binary_image, connectivity, cv2.CV_32S) num_labels = output[0] labels = output[1] stats = output[2] centroids = output[3] print(num_labels) # 遍历每个聚类 for i in range(1, num_labels): x, y, w, h, area = stats[i]

好的，你需要添加的是一个非极大值抑制（Non-Maximum Suppression，NMS）的过程，用来筛选掉重叠的小框，只保留最大的一个框。以下是代码示例：

_, binary_image = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY)
connectivity = 4
output = cv2.connectedComponentsWithStats(binary_image, connectivity, cv2.CV_32S)
num_labels = output[0]
labels = output[1]
stats = output[2]
centroids = output[3]
print(num_labels)

# 定义NMS函数
def nms(bboxes, scores, threshold):
    if len(bboxes) == 0:
        return []
    x1 = bboxes[:, 0]
    y1 = bboxes[:, 1]
    x2 = bboxes[:, 0] + bboxes[:, 2]
    y2 = bboxes[:, 1] + bboxes[:, 3]
    areas = (x2 - x1 + 1) * (y2 - y1 + 1)
    order = scores.argsort()[::-1]
    keep = []
    while order.size > 0:
        i = order[0]
        keep.append(i)
        xx1 = np.maximum(x1[i], x1[order[1:]])
        yy1 = np.maximum(y1[i], y1[order[1:]])
        xx2 = np.minimum(x2[i], x2[order[1:]])
        yy2 = np.minimum(y2[i], y2[order[1:]])
        w = np.maximum(0.0, xx2 - xx1 + 1)
        h = np.maximum(0.0, yy2 - yy1 + 1)
        overlap = w * h / areas[order[1:]]
        inds = np.where(overlap <= threshold)[0]
        order = order[inds + 1]
    return keep

# 初始化框和得分列表
bboxes = []
scores = []

# 遍历每个聚类
for i in range(1, num_labels):
    x, y, w, h, area = stats[i]
    score = area  # 以面积作为得分
    bboxes.append([x, y, w, h])
    scores.append(score)

# 进行NMS
keep = nms(np.array(bboxes), np.array(scores), threshold=0.5)

# 画出保留的框
for i in keep:
    x, y, w, h = bboxes[i]
    cv2.rectangle(img, (x, y), (x+w, y+h), (0, 255, 0), 2)

其中，`nms`函数是一个常用的NMS实现，输入是框的坐标和得分，输出是经过抑制后保留的框的索引。在代码中，我们先将每个聚类的区域作为一个框，以其面积作为得分，然后调用`nms`函数进行抑制，最后在原图上画出保留的框。你可以自行调整`threshold`参数来控制抑制的严格程度。