聽同學說Tensorflow

最近才開始學習不過沒關西還是完成他，下一個框架，可能挑PyTorch 之類的可以去找看看。

Clone TensorFlow Models

PIP Install

pip install pillow
pip install lxml
pip install jupyter
pip install matplotlib

Install protoc
"C:/Program Files/protoc/bin/protoc" object_detection/protos/*.proto --python_out=.

Cd D:\Programming\python\protoc-3.4.0-win32\bin

切錯囉

跟影片有出入新版的下載完research 才是影片的models

錯誤情況 0x1

Traceback (most recent call last):
File "C:\Users\x2132\Desktop\pyhton\tesorflow\test1\test2.py", line 33, in <module>
from utils import label_map_util
ModuleNotFoundError: No module named 'utils'

from utils import label_map_util
from utils import visualization_utils as vis_util

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

錯誤情況 0x2

Traceback (most recent call last):

File "C:\Users\x2132\Desktop\pyhton\tesorflow\test1\test2.py", line 93, in <module>

label_map = label_map_util.load_labelmap(PATH_TO_LABELS)

File "C:\Users\x2132\AppData\Local\Programs\Python\Python36\lib\site-packages\object_detection-0.1-py3.6.egg\object_detection\utils\label_map_util.py", line 131, in load_labelmap

label_map_string = fid.read()

File "C:\Users\x2132\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\lib\io\file_io.py", line 119, in read

self._preread_check()

File "C:\Users\x2132\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\lib\io\file_io.py", line 79, in _preread_check

compat.as_bytes(self.__name), 1024 * 512, status)

File "C:\Users\x2132\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\framework\errors_impl.py", line 473, in __exit__

c_api.TF_GetCode(self.status.status))

tensorflow.python.framework.errors_impl.NotFoundError: NewRandomAccessFile failed to Create/Open: data\mscoco_label_map.pbtxt : \udca8t\udcb2Χ䤣\udca8\udcec\udcab\udcfc\udca9w\udcaa\udcba\udcb8\udcf4\udcae|\udca1C

; No such process

這邊可以看到我跟影片的不一樣，額切到D:\Programming\python\model\research並且下指令

python setup.py build
python setup.py install

然後再切到這邊可以看到我們的python也自動裝上了

接下來我們跑一下程式碼可以看到我們安裝的python 套件資料夾有了一個object_detection-0.1-py3.6.egg 然後呢我們點進去。

這邊裡面原本沒有data 這個資料夾，所以呢,我們呢從我們下載下來的tensorflow/research/data我們把它複製過去非常重要。

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

然後呢這就是以上兩種可能發生的錯誤。stackoverflow.com 挖了 1天呢。

啟動

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

可以發現運行得非常順利我們來上代碼。

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

view raw test.py hosted with ❤ by GitHub

物種分類

可以分類幾種物種呢?我們來看一下。

範例裡面可以分類90種

參考

https://pythonprogramming.net/video-tensorflow-object-detection-api-tutorial/
https://github.com/tensorflow/models/issues/1990
https://github.com/tensorflow/models/issues/1832

Coding の ORZ

Friday, March 30, 2018

Tensorflow Object Detection