coco数据集图像识别完整代码

很抱歉，根据提供的引用内容，无法回答关于coco数据集图像识别的完整代码。因为coco数据集图像识别是一个相对复杂的任务，需要使用深度学习框架进行实现，而且需要根据具体的模型和算法进行选择和调整。不过，我可以为您提供一些相关的代码片段和参考资料，帮助您更好地了解coco数据集图像识别的实现过程。

1. 使用Python API加载和处理COCO数据集

from pycocotools.coco import COCO
import numpy as np
import skimage.io as io
import matplotlib.pyplot as plt

# 初始化COCO API
dataDir = '/path/to/coco/dataset'
dataType = 'train2017'
annFile = '{}/annotations/instances_{}.json'.format(dataDir, dataType)
coco = COCO(annFile)

# 获取所有类别
cats = coco.loadCats(coco.getCatIds())
nms = [cat['name'] for cat in cats]
print('COCO categories: \n{}\n'.format(' '.join(nms)))

# 获取所有图像ID
imgIds = coco.getImgIds()
print('Total images: {}'.format(len(imgIds)))

# 加载并显示一张图像
img = coco.loadImgs(imgIds[np.random.randint(0, len(imgIds))])[0]
I = io.imread('{}/images/{}/{}'.format(dataDir, dataType, img['file_name']))
plt.axis('off')
plt.imshow(I)
plt.show()

2. 使用TensorFlow Object Detection API进行目标检测

import tensorflow as tf
from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as vis_util

# 加载模型和标签映射文件
PATH_TO_CKPT = '/path/to/frozen_inference_graph.pb'
PATH_TO_LABELS = '/path/to/label_map.pbtxt'
NUM_CLASSES = 90
detection_graph = tf.Graph()
with detection_graph.as_default():
    od_graph_def = tf.GraphDef()
    with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
        serialized_graph = fid.read()
        od_graph_def.ParseFromString(serialized_graph)
        tf.import_graph_def(od_graph_def, name='')
    label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
    categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True)
    category_index = label_map_util.create_category_index(categories)

# 进行目标检测
with detection_graph.as_default():
    with tf.Session(graph=detection_graph) as sess:
        image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
        detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
        detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')
        detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')
        num_detections = detection_graph.get_tensor_by_name('num_detections:0')
        image = io.imread('/path/to/image.jpg')
        image_expanded = np.expand_dims(image, axis=0)
        (boxes, scores, classes, num) = sess.run(
            [detection_boxes, detection_scores, detection_classes, num_detections],
            feed_dict={image_tensor: image_expanded})
        vis_util.visualize_boxes_and_labels_on_image_array(
            image,
            np.squeeze(boxes),
            np.squeeze(classes).astype(np.int32),
            np.squeeze(scores),
            category_index,
            use_normalized_coordinates=True,
            line_thickness=8)
        plt.imshow(image)
        plt.show()

参考资料：
1. COCO API官方文档：https://cocodataset.org/#home
2. TensorFlow Object Detection API官方文档：https://github.com/tensorflow/models/tree/master/research/object_detection