faster rcnn实现有丝分裂细胞目标检测，输出python代码

以下是使用Faster RCNN实现有丝分裂细胞目标检测的Python代码：

import numpy as np
import tensorflow as tf
import cv2

#load the model
model = tf.keras.models.load_model('faster_rcnn_model.h5')

#load the image
image = cv2.imread('cells.jpg')

#resize the image
image = cv2.resize(image, (800, 800))

#preprocess the image
image = tf.keras.applications.resnet.preprocess_input(image)

#get the predictions
predictions = model.predict(np.array([image]))

#get the boxes, scores and classes
boxes = predictions[0]['detection_boxes']
scores = predictions[0]['detection_scores']
classes = predictions[0]['detection_classes']

#filter the boxes to only get the cells
cell_boxes = []
for i in range(len(scores)):
    if scores[i] > 0.5 and classes[i] == 1:
        cell_boxes.append(boxes[i])

#draw the boxes on the image
for box in cell_boxes:
    x1 = int(box[1] * 800)
    y1 = int(box[0] * 800)
    x2 = int(box[3] * 800)
    y2 = int(box[2] * 800)
    
    cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2)
    
#display the image
cv2.imshow('image', image)
cv2.waitKey(0)

请注意，这仅仅是快速实现一个有丝分裂细胞目标检测的示例代码，具体实现方式可能因应用场景的不同而有所不同。