我想要一份结肠息肉图像目标检测的精度和敏感性指标python代码，标签格式为xml

好的，你需要先安装以下库：

- opencv-python
- lxml

然后按照以下步骤进行操作：

1. 将标签文件夹和图片文件夹放在同一目录下，并将标签文件夹命名为"Annotations"，图片文件夹命名为"JPEGImages"。

2. 运行以下代码：

import cv2
import os
import numpy as np
import xml.etree.ElementTree as ET

# 检测结果存储路径
result_path = "detection_results"
if not os.path.exists(result_path):
    os.makedirs(result_path)

# 标签和图片路径
annotation_path = "Annotations"
image_path = "JPEGImages"

# 目标类别
classes = ["polyp"]

# 读取标签文件
def parse_annotation(annotation_path, image_path):
    tree = ET.parse(annotation_path)
    root = tree.getroot()
    boxes = []
    for obj in root.iter("object"):
        cls = obj.find("name").text
        if cls not in classes:
            continue
        xmlbox = obj.find("bndbox")
        xmin = int(xmlbox.find("xmin").text)
        ymin = int(xmlbox.find("ymin").text)
        xmax = int(xmlbox.find("xmax").text)
        ymax = int(xmlbox.find("ymax").text)
        boxes.append([xmin, ymin, xmax, ymax])
    if not boxes:
        return None, None
    else:
        img = cv2.imread(os.path.join(image_path, root.find("filename").text))
        return img, np.array(boxes)

# 绘制检测框
def draw_box(img, box):
    xmin, ymin, xmax, ymax = box.astype(np.int)
    cv2.rectangle(img, (xmin, ymin), (xmax, ymax), (0, 255, 0), 2)

# 计算IOU
def iou(box1, box2):
    xmin1, ymin1, xmax1, ymax1 = box1
    xmin2, ymin2, xmax2, ymax2 = box2
    inter_xmin = max(xmin1, xmin2)
    inter_ymin = max(ymin1, ymin2)
    inter_xmax = min(xmax1, xmax2)
    inter_ymax = min(ymax1, ymax2)
    inter_area = max(0, inter_xmax - inter_xmin + 1) * max(0, inter_ymax - inter_ymin + 1)
    area1 = (xmax1 - xmin1 + 1) * (ymax1 - ymin1 + 1)
    area2 = (xmax2 - xmin2 + 1) * (ymax2 - ymin2 + 1)
    iou = inter_area / float(area1 + area2 - inter_area)
    return iou

# 计算AP
def compute_ap(recall, precision):
    recall = np.concatenate(([0.], recall, [1.]))
    precision = np.concatenate(([0.], precision, [0.]))
    for i in range(precision.size - 2, -1, -1):
        precision[i] = max(precision[i], precision[i+1])
    idx = np.where(recall[1:] != recall[:-1])[0]
    ap = np.sum((recall[idx + 1] - recall[idx]) * precision[idx + 1])
    return ap

# 计算mAP
def compute_map(det_results, gt_boxes):
    det_results = sorted(det_results, key=lambda x: x[1], reverse=True)
    tp = np.zeros(len(det_results))
    fp = np.zeros(len(det_results))
    for i, det in enumerate(det_results):
        box, score = det
        ious = [iou(box, gt_box) for gt_box in gt_boxes]
        max_iou = np.max(ious)
        max_idx = np.argmax(ious)
        if max_iou >= 0.5:
            if not tp[max_idx]:
                tp[i] = 1
            else:
                fp[i] = 1
        else:
            fp[i] = 1
    tp = np.cumsum(tp)
    fp = np.cumsum(fp)
    recall = tp / float(len(gt_boxes))
    precision = tp / np.maximum(tp + fp, np.finfo(np.float64).eps)
    ap = compute_ap(recall, precision)
    return ap

# 加载模型
net = cv2.dnn.readNet("model.weights", "model.cfg")

# 获取输出层信息
layer_names = net.getLayerNames()
output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]

# 遍历图片和标签
detection_results = []
gt_boxes = []
for i, filename in enumerate(os.listdir(annotation_path)):
    print("processing %d/%d" % (i+1, len(os.listdir(annotation_path))), end="\r")
    annotation_file = os.path.join(annotation_path, filename)
    image_file = os.path.join(image_path, os.path.splitext(filename)[0] + ".jpg")
    img, boxes = parse_annotation(annotation_file, image_path)
    if img is None:
        continue
    gt_boxes += list(boxes)
    # 图像预处理
    blob = cv2.dnn.blobFromImage(img, 0.00392, (416, 416), (0, 0, 0), True, crop=False)
    # 模型推理
    net.setInput(blob)
    outs = net.forward(output_layers)
    class_ids = []
    confidences = []
    boxes = []
    for out in outs:
        for detection in out:
            scores = detection[5:]
            class_id = np.argmax(scores)
            confidence = scores[class_id]
            if confidence > 0.5 and class_id == 0:
                center_x = int(detection[0] * img.shape[1])
                center_y = int(detection[1] * img.shape[0])
                w = int(detection[2] * img.shape[1])
                h = int(detection[3] * img.shape[0])
                xmin = int(center_x - w / 2)
                ymin = int(center_y - h / 2)
                xmax = int(center_x + w / 2)
                ymax = int(center_y + h / 2)
                boxes.append([xmin, ymin, xmax, ymax])
                confidences.append(float(confidence))
                class_ids.append(class_id)
    # 非极大抑制
    indices = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)
    for i in indices:
        i = i[0]
        box = boxes[i]
        score = confidences[i]
        detection_results.append([box, score])
        draw_box(img, box)
        cv2.putText(img, "%.2f" % score, (box[0], box[1]-5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1)
    # 保存检测结果
    cv2.imwrite(os.path.join(result_path, os.path.splitext(filename)[0] + ".jpg"), img)

# 计算指标
ap = compute_map(detection_results, gt_boxes)
recall = np.linspace(0, 1, 11)
precision = []
for r in recall:
    p = compute_ap(np.array([r]), np.array([1]))
    precision.append(p)
precision = np.array(precision)
mAP = np.mean(precision)

# 输出结果
print("mAP: %.4f" % mAP)
print("AP: %.4f" % ap)

这段代码会遍历标签文件夹和图片文件夹，进行目标检测，并计算指标。检测结果会保存在"detection_results"文件夹中，指标会在控制台输出。

希望对你有帮助！