广州竞价托管代运营,一点优化,广告牌制作培训学校,做本地网站赚钱吗？1、下载models。
https://github.com/tensorflow/models 并文件解压。 2、下载protos文件
https://github.com/protocolbuffers/protobuf/releases?afterv3.9.1
我这里下载的3.7.0版本。注意一定要下载protoc-xxx-win64.zip版本。必须是带有win64的压缩包#xff0c;否则可…1、下载models。
https://github.com/tensorflow/models 并文件解压。 2、下载protos文件
https://github.com/protocolbuffers/protobuf/releases?afterv3.9.1
我这里下载的3.7.0版本。注意一定要下载protoc-xxx-win64.zip版本。必须是带有win64的压缩包否则可能没有需要的bin文件 下载后解压如下并将bin下的protoc.exe文件复制到C:\Windows\System32文件夹下。 打开cmd输入protoc。如果出现以下界面 则表示配置protoc成功 3、编译proto文件
打开windows PowerShell注意这里必须是PowerShell运行cmd会报错。cd到research文件目录下
输入 Get-ChildItem object_detection/protos/*.proto | Resolve-Path -Relative | %{ protoc $_ --python_out. } 运行成功后查看research下object_detection文件夹下protos文件如果每个proto文件都成了对应的以py为后缀的python源码就说明编译成功了。 4、配置环境变量
在Anaconda\Lib\site-packages新建一个路径文件tensorflow_model.pth必须以.pth为后缀写上你要加入的模块文件所在的目录名称如下图 5、运行models/research下的setup.py python setup.py build python setup.py install 6、测试
在object_detection文件夹下建立object_detection_demo.py 文件
代码如下这里模型下载链接链接https://pan.baidu.com/s/1dxzU4YMpF93qwkXF0x-3JA提取码uhju
# 一定要保存为UTF8的格式哦
import numpy as np
import os
import six.moves.urllib as urllib
import sys
import tarfile
import tensorflow as tf
import zipfile
import matplotlib
import cv2# Matplotlib chooses Xwindows backend by default.
matplotlib.use(Agg)from collections import defaultdict
from io import StringIO
from matplotlib import pyplot as plt
from PIL import Image
from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as vis_util##################### Download Model如果本地已下载也可修改成本地路径
# What model to download.
MODEL_NAME ssd_mobilenet_v1_coco_2017_11_17
MODEL_FILE MODEL_NAME .tar.gz
DOWNLOAD_BASE http://download.tensorflow.org/models/object_detection/# Path to frozen detection graph. This is the actual model that is used for the object detection.
PATH_TO_CKPT MODEL_NAME /frozen_inference_graph.pb# List of the strings that is used to add correct label for each box.
PATH_TO_LABELS os.path.join(data, mscoco_label_map.pbtxt)NUM_CLASSES 90# Download model if not already downloaded
if not os.path.exists(PATH_TO_CKPT):print(Downloading model... (This may take over 5 minutes))opener urllib.request.URLopener()opener.retrieve(DOWNLOAD_BASE MODEL_FILE, MODEL_FILE)print(Extracting...)tar_file tarfile.open(MODEL_FILE)for file in tar_file.getmembers():file_name os.path.basename(file.name)if frozen_inference_graph.pb in file_name:tar_file.extract(file, os.getcwd())
else:print(Model already downloaded.)##################### Load a (frozen) Tensorflow model into memory.
print(Loading model...)
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)##################### Loading label map
print(Loading label map...)
label_map label_map_util.load_labelmap(PATH_TO_LABELS)
categories label_map_util.convert_label_map_to_categories(label_map, max_num_classesNUM_CLASSES,use_display_nameTrue)
category_index label_map_util.create_category_index(categories)##################### Helper code
def load_image_into_numpy_array(image):(im_width, im_height) image.sizereturn np.array(image.getdata()).reshape((im_height, im_width, 3)).astype(np.uint8)##################### Detection
# 测试图片的路径可以根据自己的实际情况修改
TEST_IMAGE_PATH test_images/image1.jpg# Size, in inches, of the output images.
IMAGE_SIZE (12, 8)print(Detecting...)
config tf.ConfigProto()
config.gpu_options.allow_growth True
with detection_graph.as_default():with tf.Session(graphdetection_graph,configconfig) as sess:print(TEST_IMAGE_PATH)image Image.open(TEST_IMAGE_PATH)image_np load_image_into_numpy_array(image)image_np_expanded np.expand_dims(image_np, axis0)image_tensor detection_graph.get_tensor_by_name(image_tensor:0)boxes detection_graph.get_tensor_by_name(detection_boxes:0)scores detection_graph.get_tensor_by_name(detection_scores:0)classes detection_graph.get_tensor_by_name(detection_classes:0)num_detections detection_graph.get_tensor_by_name(num_detections:0)# Actual detection.(boxes, scores, classes, num_detections) sess.run([boxes, scores, classes, num_detections],feed_dict{image_tensor: image_np_expanded})# Visualization of the results of a detection.vis_util.visualize_boxes_and_labels_on_image_array(image_np,np.squeeze(boxes),np.squeeze(classes).astype(np.int32),np.squeeze(scores),category_index,use_normalized_coordinatesTrue,line_thickness8)print(TEST_IMAGE_PATH.split(.)[0] _labeled.jpg)plt.figure(figsizeIMAGE_SIZE, dpi300)# 不知道为什么在我的机器上没显示出图片有知道的朋友指点下谢谢plt.imshow(image_np)# 保存标记图片plt.savefig(TEST_IMAGE_PATH.split(.)[0] _labeled.jpg)
运行后
在object_detection文件夹下test_images文件下多了一张image1_labeled.jpg则证明配置成功。 参考自https://blog.csdn.net/zhongxianjin/article/details/103269901
https://blog.csdn.net/qq_28019591/article/details/82023949
