Wie programmiert man eine Objekterkennung im Kamerabild?

#!/usr/bin/env python

# coding: utf-8

# Imports

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

from matplotlib import pyplot as plt

from PIL import Image

print(tf.__version__)

if tf.__version__ < '1.10.0':

  raise ImportError('Please upgrade your tensorflow installation to v1.4.* or later!')

# ## Env setup

# This is needed to display the images.

#get_ipython().run_line_magic('matplotlib', 'inline')

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

#sys.path.append("..")

# ## Object detection imports

# Here are the imports from the object detection module.

from utils import label_map_util

from 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/research/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.

# 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

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())

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

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)

import cv2

cap=cv2.VideoCapture(0) # 0 stands for very first webcam attach

cap.set(3,640)

cap.set(4,480)

with detection_graph.as_default():

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

    ret=True

    while (ret):

        ret, image_np=cap.read() 

        # Definite input and output Tensors for detection_graph

        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.

        detection_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.

        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')

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

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

          # Actual detection.

        (boxes, scores, classes, num) = sess.run(

              [detection_boxes, detection_scores, detection_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('live_detection',image_np)

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

            break

            cv2.destroyAllWindows()

            cap.release()