準備相關環境。如tensorflow環境和object detection相關環境，可以參考上面的文章

tensorflow models 倉庫位置：在github上搜 tensorflow models

準備下載資料（可以使用下面的倉庫，根據readme來調整和生成tfrecord）

生成配置檔案，主要兩個，pipconfig 和 labelmap.pbtxt

labelmap.pbtxt：

item {
  id: 5
  name: 'king'
}

item {
  id: 6
  name: 'ace'
}

piplineconfig 例項位於：

object_detection/samples/configs/

主要修改，學習率，優化器方式，batchsize大小，tfrecord位置，labelmap位置等。

例項中有README和配置檔案中有提示。

若遇到下述問題： “rv = reductor(4) TypeError: can’t pickle dict_values objects” 解決方案為：我們進入到D:\tensorflow1\models\research\object_detection下，然後開啟model_lib.py檔案，接著找到下圖中所標出的位置，最後將category_index.values()改為list(category_index.values())即可。  

訓練使用 model_main.py 訓練：（會沒有列印，但是推薦使用tensorboard進行檢視，資訊非常全）

python object_detection/model_main.py     --pipeline_config_path=object_detection/samples/configs/ssd_resnet50_v1_fpn_shared_box_predictor_640x640_coco14_sync.config     --model_dir=./training/     --num_train_steps=5000000     --sample_1_of_n_eval_examples=1     --alsologtostderr
 

匯出inference圖：

python object_detection/export_inference_graph.py --input_type=image_tensor --pipeline_config_path=object_detection/samples/configs/ssd_mobilenet_v1_coco.config  --trained_checkpoint_prefix=/data/self-trained/model-41915/model.ckpt-xxx --output_directory=inference_graph

預測使用如下程式碼（圖位置和例項圖片位置需要修改）：

######## Image Object Detection Using Tensorflow-trained Classifier #########
#
# Author: Evan Juras
# Date: 1/15/18
# Description:
# This program uses a TensorFlow-trained classifier to perform object detection.
# It loads the classifier uses it to perform object detection on an image.
# It draws boxes and scores around the objects of interest in the image.

## Some of the code is copied from Google's example at
## https://github.com/tensorflow/models/blob/master/research/object_detection/object_detection_tutorial.ipynb

## and some is copied from Dat Tran's example at
## https://github.com/datitran/object_detector_app/blob/master/object_detection_app.py

## but I changed it to make it more understandable to me.

# Import packages
import os
import cv2
import numpy as np
import tensorflow as tf
import sys

# This is needed since the notebook is stored in the object_detection folder.
sys.path.append("..")

# Import utilites
from utils import label_map_util
from utils import visualization_utils as vis_util

# Name of the directory containing the object detection module we're using
MODEL_NAME = './inference_graph'
IMAGE_NAME = './test.jpg'

# Grab path to current working directory
CWD_PATH = os.getcwd()

# Path to frozen detection graph .pb file, which contains the model that is used
# for object detection.
PATH_TO_CKPT = os.path.join(CWD_PATH,MODEL_NAME,'frozen_inference_graph.pb')

# Path to label map file
PATH_TO_LABELS = os.path.join(CWD_PATH,'training','labelmap.pbtxt')

# Path to image
PATH_TO_IMAGE = os.path.join(CWD_PATH,IMAGE_NAME)

# Number of classes the object detector can identify
NUM_CLASSES = 1

# Load the label map.
# Label maps map indices to category names, so that when our convolution
# network predicts `5`, we know that this corresponds to `king`.
# Here we use internal utility functions, but anything that returns a
# dictionary mapping integers to appropriate string labels would be fine
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)

# Load the Tensorflow model into memory.
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='')

    sess = tf.Session(graph=detection_graph)

# Define input and output tensors (i.e. data) for the object detection classifier

# Input tensor is the image
image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')

# Output tensors are the detection boxes, scores, and classes
# Each box represents a part of the image where a particular object was detected
detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')

# Each score represents level of confidence for each of the objects.
# The score is shown on the result image, together with the class label.
detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')
detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')

# Number of objects detected
num_detections = detection_graph.get_tensor_by_name('num_detections:0')

# Load image using OpenCV and
# expand image dimensions to have shape: [1, None, None, 3]
# i.e. a single-column array, where each item in the column has the pixel RGB value
image = cv2.imread(PATH_TO_IMAGE)
cv2.imshow("image", image)
image_expanded = np.expand_dims(image, axis=0)

# Perform the actual detection by running the model with the image as input
import time
start = time.time()
(boxes, scores, classes, num) = sess.run(
    [detection_boxes, detection_scores, detection_classes, num_detections],
    feed_dict={image_tensor: image_expanded})
print("using time for first:{}".format(time.time()-start))
for i in range(100):
    start = time.time()
    (boxes, scores, classes, num) = sess.run(
        [detection_boxes, detection_scores, detection_classes, num_detections],
        feed_dict={image_tensor: image_expanded})
    print("using time for {}:{}".format(i,time.time() - start))
# Draw the results of the detection (aka 'visulaize the results')

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,
    min_score_thresh=0.80)

# All the results have been drawn on image. Now display the image.
cv2.imshow('Object detector', image)

# Press any key to close the image
cv2.waitKey(0)

# Clean up
cv2.destroyAllWindows()