python setup.py build
python setup.py install

PATH_TO_LABELS = os.path.join('C:/Users/x2132/AppData/Local/Programs/Python/Python36/Lib/site-packages/object_detection-0.1-py3.6.egg/object_detection/data', 'mscoco_label_map.pbtxt')

pip install -e slim

python D:\Programming\python\model\research\object_detection\builders\model_builder_test.py

import numpy as np


import os


import six.moves.urllib as urllib


import sys


import tarfile


import tensorflow as tf


import zipfile


from collections import defaultdict


from io import StringIO


import matplotlib


matplotlib.use('Agg')


import matplotlib.pyplot as plt


from PIL import Image


import cv2


cap = cv2.VideoCapture(0) # 這邊改成攝像頭第幾顆


# This is needed since the notebook is stored in the object_detection folder.


#sys.path.append("D:/Programming/python/model/research")


#sys.path.append("C:/Users/x2132/AppData/Local/Programs/Python/Python36/Lib/site-packages/object_detection-0.1-py3.6.egg/object_detection")


# ## Object detection imports


# Here are the imports from the object detection module.


# In[3]:


#from utils import label_map_util


from object_detection.utils import label_map_util


from object_detection.utils import visualization_utils as vis_util


# # Model preparation 


# ## Variables


# 


# Any model exported using the `export_inference_graph.py` tool can be loaded here simply by changing `PATH_TO_CKPT` to point to a new .pb file.  


# 


# By default we use an "SSD with Mobilenet" model here. See the [detection model zoo](https://github.com/tensorflow/models/blob/master/object_detection/g3doc/detection_model_zoo.md) for a list of other models that can be run out-of-the-box with varying speeds and accuracies.


# In[4]:


# What model to download.


MODEL_NAME = 'ssd_mobilenet_v1_coco_11_06_2017'


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')


PATH_TO_LABELS = os.path.join('C:/Users/x2132/AppData/Local/Programs/Python/Python36/Lib/site-packages/object_detection-0.1-py3.6.egg/object_detection/data', 'mscoco_label_map.pbtxt')


NUM_CLASSES = 90


# ## Download Model


# In[5]:


opener = urllib.request.URLopener()


opener.retrieve(DOWNLOAD_BASE + MODEL_FILE, MODEL_FILE)


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())


print ('asdasd')


# ## Load a (frozen) Tensorflow model into memory.


# In[6]:


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


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


# In[7]:


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)


# ## Helper code


# In[8]:


def load_image_into_numpy_array(image):


(im_width, im_height) = image.size


return np.array(image.getdata()).reshape(


(im_height, im_width, 3)).astype(np.uint8)


# # Detection


# In[9]:


# For the sake of simplicity we will use only 2 images:


# image1.jpg


# image2.jpg


# If you want to test the code with your images, just add path to the images to the TEST_IMAGE_PATHS.


PATH_TO_TEST_IMAGES_DIR = 'test_images'


TEST_IMAGE_PATHS = [ os.path.join(PATH_TO_TEST_IMAGES_DIR, 'image{}.jpg'.format(i)) for i in range(1, 3) ]


# Size, in inches, of the output images.


IMAGE_SIZE = (12, 8)


# In[10]:


print('s')


with detection_graph.as_default():


with tf.Session(graph=detection_graph) as sess:


while True:


ret, image_np = cap.read()




# Expand dimensions since the model expects images to have shape: [1, None, None, 3]


image_np_expanded = np.expand_dims(image_np, axis=0)


image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')


# Each box represents a part of the image where a particular object was detected.


boxes = detection_graph.get_tensor_by_name('detection_boxes:0')


# Each score represent how level of confidence for each of the objects.


# Score is shown on the result image, together with the class label.


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_coordinates=True,


line_thickness=8)


cv2.imshow('object detection', cv2.resize(image_np, (800,600)))


if cv2.waitKey(25) & 0xFF == ord('q'):


cv2.destroyAllWindows()


break