基于图像的苹果检测代码

以下是基于深度学习模型的苹果检测代码，使用的是 TensorFlow 和 OpenCV 库：

首先，需要安装 TensorFlow 和 OpenCV 库，可以使用以下命令：

pip install tensorflow opencv-python

然后，下载预训练的模型文件，这里使用的是 SSD 模型，可以从 TensorFlow 官方网站下载：http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_coco_2018_01_28.tar.gz

解压之后，将其中的 frozen_inference_graph.pb 文件复制到当前目录下。

接下来，使用以下代码进行苹果检测：

import cv2
import numpy as np
import tensorflow as tf

# 加载模型
model_path = 'frozen_inference_graph.pb'
detection_graph = tf.Graph()
with detection_graph.as_default():
    od_graph_def = tf.GraphDef()
    with tf.gfile.GFile(model_path, 'rb') as fid:
        serialized_graph = fid.read()
        od_graph_def.ParseFromString(serialized_graph)
        tf.import_graph_def(od_graph_def, name='')

# 定义类别标签
category_index = {1: {'id': 1, 'name': 'apple'}}

# 检测函数
def detect_apple(image):
    with detection_graph.as_default():
        with tf.Session(graph=detection_graph) as sess:
            # 输入张量
            image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
            # 检测框张量
            detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
            # 分数张量
            detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')
            # 类别标签张量
            detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')
            # 执行检测
            (boxes, scores, classes) = sess.run(
                [detection_boxes, detection_scores, detection_classes],
                feed_dict={image_tensor: image})
            # 获取检测框
            boxes = np.squeeze(boxes)
            # 获取分数
            scores = np.squeeze(scores)
            # 获取类别标签
            classes = np.squeeze(classes).astype(np.int32)
            # 仅保留分数大于阈值的检测结果
            threshold = 0.5
            idx = np.where(scores > threshold)[0]
            boxes = boxes[idx]
            scores = scores[idx]
            classes = classes[idx]
            # 绘制检测结果
            height, width, _ = image.shape
            for i in range(len(boxes)):
                ymin, xmin, ymax, xmax = boxes[i]
                left = int(xmin * width)
                top = int(ymin * height)
                right = int(xmax * width)
                bottom = int(ymax * height)
                cv2.rectangle(image, (left, top), (right, bottom), (0, 255, 0), 2)
                class_name = category_index[classes[i]]['name']
                score = scores[i]
                label = '{}: {:.2f}'.format(class_name, score)
                cv2.putText(image, label, (left, top - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1, cv2.LINE_AA)
            return image

# 读取图像并进行检测
image = cv2.imread('apple.jpg')
image = detect_apple(image)
cv2.imshow('image', image)
cv2.waitKey(0)
cv2.destroyAllWindows()

注意，这里假设图像中只有一个苹果，如果图像中有多个苹果，需要对每个苹果进行循环检测。