# -*- coding: utf-8 -*-
"""
Created on Thu Feb 27 11:50:49 2020

@author: Welp
"""

import numpy as np
import os
import six.moves.urllib as urllib
import sys
import tarfile
import tensorflow as tf
import zipfile


from distutils.version import StrictVersion
from collections import defaultdict
from io import StringIO
import tkinter
from matplotlib import pyplot as plt
from PIL import Image
import time


# This is needed since the notebook is stored in the object_detection folder.
sys.path.append("..")
from object_detection.utils import ops as utils_ops

if StrictVersion(tf.__version__) < StrictVersion('1.12.0'):
  raise ImportError('Please upgrade your TensorFlow installation to v1.12.*.')

print(tf.__version__)
# This is needed to display the images.
#%matplotlib inline
#plt.plot([0,0],[1,1],linewidth=4,label='random diagonal')
#plt.show()


from utils import label_map_util

from utils import visualization_utils as vis_util



# What model to download.
#MODEL_NAME = 'ssd_mobilenet_v1_coco_2017_11_17'
MODEL_NAME = 'trained-inference-graphs'
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_FROZEN_GRAPH = MODEL_NAME + '/frozen_inference_graph_SP3.pb'

# List of the strings that is used to add correct label for each box.
PATH_TO_LABELS = os.path.join('data', 'label_map_SP3.pbtxt')

detection_graph = tf.Graph()
with detection_graph.as_default():
  od_graph_def = tf.GraphDef()
  with tf.gfile.GFile(PATH_TO_FROZEN_GRAPH, 'rb') as fid:
    serialized_graph = fid.read()
    od_graph_def.ParseFromString(serialized_graph)
    tf.import_graph_def(od_graph_def, name='')
    
category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS, use_display_name=True)

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)

# 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 = 'SP3_images'
TEST_IMAGE_PATHS = [ os.path.join(PATH_TO_TEST_IMAGES_DIR, 'image{}.jpg'.format(i)) for i in range(1, 3) ]
print(TEST_IMAGE_PATHS)
# Size, in inches, of the output images.
IMAGE_SIZE = (12, 8)
file_path = os.path.abspath(sys.argv[1])
image_path = os.path.join(file_path, "aktuelleSzene.jpg")
image_path_detected = os.path.join(file_path, "gefundeneObjekte.jpg")
text_path_detected = os.path.join(file_path, "gefundeneObjekte.txt")


def run_inference_for_single_image(image, graph):
  with graph.as_default():
    with tf.Session() as sess:
      # Get handles to input and output tensors
      ops = tf.get_default_graph().get_operations()
      all_tensor_names = {output.name for op in ops for output in op.outputs}
      tensor_dict = {}
      for key in [
          'num_detections', 'detection_boxes', 'detection_scores',
          'detection_classes', 'detection_masks'
      ]:
        tensor_name = key + ':0'
        if tensor_name in all_tensor_names:
          tensor_dict[key] = tf.get_default_graph().get_tensor_by_name(
              tensor_name)
      if 'detection_masks' in tensor_dict:
        # The following processing is only for single image
        detection_boxes = tf.squeeze(tensor_dict['detection_boxes'], [0])
        detection_masks = tf.squeeze(tensor_dict['detection_masks'], [0])
        # Reframe is required to translate mask from box coordinates to image coordinates and fit the image size.
        real_num_detection = tf.cast(tensor_dict['num_detections'][0], tf.int32)
        detection_boxes = tf.slice(detection_boxes, [0, 0], [real_num_detection, -1])
        detection_masks = tf.slice(detection_masks, [0, 0, 0], [real_num_detection, -1, -1])
        detection_masks_reframed = utils_ops.reframe_box_masks_to_image_masks(
            detection_masks, detection_boxes, image.shape[1], image.shape[2])
        detection_masks_reframed = tf.cast(
            tf.greater(detection_masks_reframed, 0.5), tf.uint8)
        # Follow the convention by adding back the batch dimension
        tensor_dict['detection_masks'] = tf.expand_dims(
            detection_masks_reframed, 0)
      image_tensor = tf.get_default_graph().get_tensor_by_name('image_tensor:0')

      # Run inference
      output_dict = sess.run(tensor_dict,
                             feed_dict={image_tensor: image})

      # all outputs are float32 numpy arrays, so convert types as appropriate
      output_dict['num_detections'] = int(output_dict['num_detections'][0])
      output_dict['detection_classes'] = output_dict[
          'detection_classes'][0].astype(np.int64)
      output_dict['detection_boxes'] = output_dict['detection_boxes'][0]
      output_dict['detection_scores'] = output_dict['detection_scores'][0]
      if 'detection_masks' in output_dict:
        output_dict['detection_masks'] = output_dict['detection_masks'][0]
  return output_dict

#for image_path in TEST_IMAGE_PATHS:
#  image = Image.open(image_path)
#  print(image_path)
#  # the array based representation of the image will be used later in order to prepare the
#  # result image with boxes and labels on it.
#  image_np = load_image_into_numpy_array(image)
#  # 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.
#  output_dict = run_inference_for_single_image(image_np_expanded, detection_graph)
#  print(output_dict['detection_classes'])
#  print(output_dict['detection_scores'])
#  print(output_dict['detection_boxes'])
#  # Visualization of the results of a detection.
#  vis_util.visualize_boxes_and_labels_on_image_array(
#      image_np,
#      output_dict['detection_boxes'],
#      output_dict['detection_classes'],
#      output_dict['detection_scores'],
#      category_index,
#      instance_masks=output_dict.get('detection_masks'),
#      use_normalized_coordinates=True,
#      line_thickness=8)
#
#  plt.figure(figsize=IMAGE_SIZE)
#  plt.imshow(image_np)
#  image_path_detected = image_path[:-4]+'d'+image_path[-4:]
#  plt.savefig(image_path_detected, bbox_inches='tight')
#  
while(True):
    if(os.path.isfile(image_path)):
        print('gefunden')
        image=Image.open(image_path)
        image_np = load_image_into_numpy_array(image)
        # detect objects in image
        # 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.
        output_dict = run_inference_for_single_image(image_np_expanded, detection_graph)
        print(output_dict['detection_classes'])
        print(output_dict['detection_scores'])
        print(output_dict['detection_boxes'])
        # Visualization of the results of a detection.
        vis_util.visualize_boxes_and_labels_on_image_array(
            image_np,
            output_dict['detection_boxes'],
            output_dict['detection_classes'],
            output_dict['detection_scores'],
            category_index,
            instance_masks=output_dict.get('detection_masks'),
            use_normalized_coordinates=True,
            line_thickness=8)
        # save gefundeneObjekte.jpg
        im = Image.fromarray(image_np)
        im.save(image_path_detected)
        # save gefundeneObjekte.txt
        file = open(text_path_detected,"w")
        file.write("detection_classes\n")
        file.write(str(output_dict['detection_classes']))
        file.write(" \ndetection_scores\n")
        file.write(str(output_dict['detection_scores']))
        file.write("\ndetection_boxes\n")
        file.write(str(output_dict['detection_boxes']))
        file.close()
        # remove aktuelleSzene.jpg
        os.remove(image_path)
    else:
        print('nicht gefunden')
        # warte 1 Sekunde
        time.sleep(1)        
6d71e098f2ba9d46d7e9ac1714a53d2a21bd3d0f
<mask token>


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)


<mask token>


def run_inference_for_single_image(image, graph):
    with graph.as_default():
        with tf.Session() as sess:
            ops = tf.get_default_graph().get_operations()
            all_tensor_names = {output.name for op in ops for output in op.
                outputs}
            tensor_dict = {}
            for key in ['num_detections', 'detection_boxes',
                'detection_scores', 'detection_classes', 'detection_masks']:
                tensor_name = key + ':0'
                if tensor_name in all_tensor_names:
                    tensor_dict[key] = tf.get_default_graph(
                        ).get_tensor_by_name(tensor_name)
            if 'detection_masks' in tensor_dict:
                detection_boxes = tf.squeeze(tensor_dict['detection_boxes'],
                    [0])
                detection_masks = tf.squeeze(tensor_dict['detection_masks'],
                    [0])
                real_num_detection = tf.cast(tensor_dict['num_detections'][
                    0], tf.int32)
                detection_boxes = tf.slice(detection_boxes, [0, 0], [
                    real_num_detection, -1])
                detection_masks = tf.slice(detection_masks, [0, 0, 0], [
                    real_num_detection, -1, -1])
                detection_masks_reframed = (utils_ops.
                    reframe_box_masks_to_image_masks(detection_masks,
                    detection_boxes, image.shape[1], image.shape[2]))
                detection_masks_reframed = tf.cast(tf.greater(
                    detection_masks_reframed, 0.5), tf.uint8)
                tensor_dict['detection_masks'] = tf.expand_dims(
                    detection_masks_reframed, 0)
            image_tensor = tf.get_default_graph().get_tensor_by_name(
                'image_tensor:0')
            output_dict = sess.run(tensor_dict, feed_dict={image_tensor: image}
                )
            output_dict['num_detections'] = int(output_dict[
                'num_detections'][0])
            output_dict['detection_classes'] = output_dict['detection_classes'
                ][0].astype(np.int64)
            output_dict['detection_boxes'] = output_dict['detection_boxes'][0]
            output_dict['detection_scores'] = output_dict['detection_scores'][0
                ]
            if 'detection_masks' in output_dict:
                output_dict['detection_masks'] = output_dict['detection_masks'
                    ][0]
    return output_dict


<mask token>

<mask token>
sys.path.append('..')
<mask token>
if StrictVersion(tf.__version__) < StrictVersion('1.12.0'):
    raise ImportError('Please upgrade your TensorFlow installation to v1.12.*.'
        )
print(tf.__version__)
<mask token>
with detection_graph.as_default():
    od_graph_def = tf.GraphDef()
    with tf.gfile.GFile(PATH_TO_FROZEN_GRAPH, 'rb') as fid:
        serialized_graph = fid.read()
        od_graph_def.ParseFromString(serialized_graph)
        tf.import_graph_def(od_graph_def, name='')
<mask token>


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)


<mask token>
print(TEST_IMAGE_PATHS)
<mask token>


def run_inference_for_single_image(image, graph):
    with graph.as_default():
        with tf.Session() as sess:
            ops = tf.get_default_graph().get_operations()
            all_tensor_names = {output.name for op in ops for output in op.
                outputs}
            tensor_dict = {}
            for key in ['num_detections', 'detection_boxes',
                'detection_scores', 'detection_classes', 'detection_masks']:
                tensor_name = key + ':0'
                if tensor_name in all_tensor_names:
                    tensor_dict[key] = tf.get_default_graph(
                        ).get_tensor_by_name(tensor_name)
            if 'detection_masks' in tensor_dict:
                detection_boxes = tf.squeeze(tensor_dict['detection_boxes'],
                    [0])
                detection_masks = tf.squeeze(tensor_dict['detection_masks'],
                    [0])
                real_num_detection = tf.cast(tensor_dict['num_detections'][
                    0], tf.int32)
                detection_boxes = tf.slice(detection_boxes, [0, 0], [
                    real_num_detection, -1])
                detection_masks = tf.slice(detection_masks, [0, 0, 0], [
                    real_num_detection, -1, -1])
                detection_masks_reframed = (utils_ops.
                    reframe_box_masks_to_image_masks(detection_masks,
                    detection_boxes, image.shape[1], image.shape[2]))
                detection_masks_reframed = tf.cast(tf.greater(
                    detection_masks_reframed, 0.5), tf.uint8)
                tensor_dict['detection_masks'] = tf.expand_dims(
                    detection_masks_reframed, 0)
            image_tensor = tf.get_default_graph().get_tensor_by_name(
                'image_tensor:0')
            output_dict = sess.run(tensor_dict, feed_dict={image_tensor: image}
                )
            output_dict['num_detections'] = int(output_dict[
                'num_detections'][0])
            output_dict['detection_classes'] = output_dict['detection_classes'
                ][0].astype(np.int64)
            output_dict['detection_boxes'] = output_dict['detection_boxes'][0]
            output_dict['detection_scores'] = output_dict['detection_scores'][0
                ]
            if 'detection_masks' in output_dict:
                output_dict['detection_masks'] = output_dict['detection_masks'
                    ][0]
    return output_dict


while True:
    if os.path.isfile(image_path):
        print('gefunden')
        image = Image.open(image_path)
        image_np = load_image_into_numpy_array(image)
        image_np_expanded = np.expand_dims(image_np, axis=0)
        output_dict = run_inference_for_single_image(image_np_expanded,
            detection_graph)
        print(output_dict['detection_classes'])
        print(output_dict['detection_scores'])
        print(output_dict['detection_boxes'])
        vis_util.visualize_boxes_and_labels_on_image_array(image_np,
            output_dict['detection_boxes'], output_dict['detection_classes'
            ], output_dict['detection_scores'], category_index,
            instance_masks=output_dict.get('detection_masks'),
            use_normalized_coordinates=True, line_thickness=8)
        im = Image.fromarray(image_np)
        im.save(image_path_detected)
        file = open(text_path_detected, 'w')
        file.write('detection_classes\n')
        file.write(str(output_dict['detection_classes']))
        file.write(' \ndetection_scores\n')
        file.write(str(output_dict['detection_scores']))
        file.write('\ndetection_boxes\n')
        file.write(str(output_dict['detection_boxes']))
        file.close()
        os.remove(image_path)
    else:
        print('nicht gefunden')
        time.sleep(1)

<mask token>
sys.path.append('..')
<mask token>
if StrictVersion(tf.__version__) < StrictVersion('1.12.0'):
    raise ImportError('Please upgrade your TensorFlow installation to v1.12.*.'
        )
print(tf.__version__)
<mask token>
MODEL_NAME = 'trained-inference-graphs'
MODEL_FILE = MODEL_NAME + '.tar.gz'
DOWNLOAD_BASE = 'http://download.tensorflow.org/models/object_detection/'
PATH_TO_FROZEN_GRAPH = MODEL_NAME + '/frozen_inference_graph_SP3.pb'
PATH_TO_LABELS = os.path.join('data', 'label_map_SP3.pbtxt')
detection_graph = tf.Graph()
with detection_graph.as_default():
    od_graph_def = tf.GraphDef()
    with tf.gfile.GFile(PATH_TO_FROZEN_GRAPH, 'rb') as fid:
        serialized_graph = fid.read()
        od_graph_def.ParseFromString(serialized_graph)
        tf.import_graph_def(od_graph_def, name='')
category_index = label_map_util.create_category_index_from_labelmap(
    PATH_TO_LABELS, use_display_name=True)


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)


PATH_TO_TEST_IMAGES_DIR = 'SP3_images'
TEST_IMAGE_PATHS = [os.path.join(PATH_TO_TEST_IMAGES_DIR, 'image{}.jpg'.
    format(i)) for i in range(1, 3)]
print(TEST_IMAGE_PATHS)
IMAGE_SIZE = 12, 8
file_path = os.path.abspath(sys.argv[1])
image_path = os.path.join(file_path, 'aktuelleSzene.jpg')
image_path_detected = os.path.join(file_path, 'gefundeneObjekte.jpg')
text_path_detected = os.path.join(file_path, 'gefundeneObjekte.txt')


def run_inference_for_single_image(image, graph):
    with graph.as_default():
        with tf.Session() as sess:
            ops = tf.get_default_graph().get_operations()
            all_tensor_names = {output.name for op in ops for output in op.
                outputs}
            tensor_dict = {}
            for key in ['num_detections', 'detection_boxes',
                'detection_scores', 'detection_classes', 'detection_masks']:
                tensor_name = key + ':0'
                if tensor_name in all_tensor_names:
                    tensor_dict[key] = tf.get_default_graph(
                        ).get_tensor_by_name(tensor_name)
            if 'detection_masks' in tensor_dict:
                detection_boxes = tf.squeeze(tensor_dict['detection_boxes'],
                    [0])
                detection_masks = tf.squeeze(tensor_dict['detection_masks'],
                    [0])
                real_num_detection = tf.cast(tensor_dict['num_detections'][
                    0], tf.int32)
                detection_boxes = tf.slice(detection_boxes, [0, 0], [
                    real_num_detection, -1])
                detection_masks = tf.slice(detection_masks, [0, 0, 0], [
                    real_num_detection, -1, -1])
                detection_masks_reframed = (utils_ops.
                    reframe_box_masks_to_image_masks(detection_masks,
                    detection_boxes, image.shape[1], image.shape[2]))
                detection_masks_reframed = tf.cast(tf.greater(
                    detection_masks_reframed, 0.5), tf.uint8)
                tensor_dict['detection_masks'] = tf.expand_dims(
                    detection_masks_reframed, 0)
            image_tensor = tf.get_default_graph().get_tensor_by_name(
                'image_tensor:0')
            output_dict = sess.run(tensor_dict, feed_dict={image_tensor: image}
                )
            output_dict['num_detections'] = int(output_dict[
                'num_detections'][0])
            output_dict['detection_classes'] = output_dict['detection_classes'
                ][0].astype(np.int64)
            output_dict['detection_boxes'] = output_dict['detection_boxes'][0]
            output_dict['detection_scores'] = output_dict['detection_scores'][0
                ]
            if 'detection_masks' in output_dict:
                output_dict['detection_masks'] = output_dict['detection_masks'
                    ][0]
    return output_dict


while True:
    if os.path.isfile(image_path):
        print('gefunden')
        image = Image.open(image_path)
        image_np = load_image_into_numpy_array(image)
        image_np_expanded = np.expand_dims(image_np, axis=0)
        output_dict = run_inference_for_single_image(image_np_expanded,
            detection_graph)
        print(output_dict['detection_classes'])
        print(output_dict['detection_scores'])
        print(output_dict['detection_boxes'])
        vis_util.visualize_boxes_and_labels_on_image_array(image_np,
            output_dict['detection_boxes'], output_dict['detection_classes'
            ], output_dict['detection_scores'], category_index,
            instance_masks=output_dict.get('detection_masks'),
            use_normalized_coordinates=True, line_thickness=8)
        im = Image.fromarray(image_np)
        im.save(image_path_detected)
        file = open(text_path_detected, 'w')
        file.write('detection_classes\n')
        file.write(str(output_dict['detection_classes']))
        file.write(' \ndetection_scores\n')
        file.write(str(output_dict['detection_scores']))
        file.write('\ndetection_boxes\n')
        file.write(str(output_dict['detection_boxes']))
        file.close()
        os.remove(image_path)
    else:
        print('nicht gefunden')
        time.sleep(1)

<mask token>
import numpy as np
import os
import six.moves.urllib as urllib
import sys
import tarfile
import tensorflow as tf
import zipfile
from distutils.version import StrictVersion
from collections import defaultdict
from io import StringIO
import tkinter
from matplotlib import pyplot as plt
from PIL import Image
import time
sys.path.append('..')
from object_detection.utils import ops as utils_ops
if StrictVersion(tf.__version__) < StrictVersion('1.12.0'):
    raise ImportError('Please upgrade your TensorFlow installation to v1.12.*.'
        )
print(tf.__version__)
from utils import label_map_util
from utils import visualization_utils as vis_util
MODEL_NAME = 'trained-inference-graphs'
MODEL_FILE = MODEL_NAME + '.tar.gz'
DOWNLOAD_BASE = 'http://download.tensorflow.org/models/object_detection/'
PATH_TO_FROZEN_GRAPH = MODEL_NAME + '/frozen_inference_graph_SP3.pb'
PATH_TO_LABELS = os.path.join('data', 'label_map_SP3.pbtxt')
detection_graph = tf.Graph()
with detection_graph.as_default():
    od_graph_def = tf.GraphDef()
    with tf.gfile.GFile(PATH_TO_FROZEN_GRAPH, 'rb') as fid:
        serialized_graph = fid.read()
        od_graph_def.ParseFromString(serialized_graph)
        tf.import_graph_def(od_graph_def, name='')
category_index = label_map_util.create_category_index_from_labelmap(
    PATH_TO_LABELS, use_display_name=True)


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)


PATH_TO_TEST_IMAGES_DIR = 'SP3_images'
TEST_IMAGE_PATHS = [os.path.join(PATH_TO_TEST_IMAGES_DIR, 'image{}.jpg'.
    format(i)) for i in range(1, 3)]
print(TEST_IMAGE_PATHS)
IMAGE_SIZE = 12, 8
file_path = os.path.abspath(sys.argv[1])
image_path = os.path.join(file_path, 'aktuelleSzene.jpg')
image_path_detected = os.path.join(file_path, 'gefundeneObjekte.jpg')
text_path_detected = os.path.join(file_path, 'gefundeneObjekte.txt')


def run_inference_for_single_image(image, graph):
    with graph.as_default():
        with tf.Session() as sess:
            ops = tf.get_default_graph().get_operations()
            all_tensor_names = {output.name for op in ops for output in op.
                outputs}
            tensor_dict = {}
            for key in ['num_detections', 'detection_boxes',
                'detection_scores', 'detection_classes', 'detection_masks']:
                tensor_name = key + ':0'
                if tensor_name in all_tensor_names:
                    tensor_dict[key] = tf.get_default_graph(
                        ).get_tensor_by_name(tensor_name)
            if 'detection_masks' in tensor_dict:
                detection_boxes = tf.squeeze(tensor_dict['detection_boxes'],
                    [0])
                detection_masks = tf.squeeze(tensor_dict['detection_masks'],
                    [0])
                real_num_detection = tf.cast(tensor_dict['num_detections'][
                    0], tf.int32)
                detection_boxes = tf.slice(detection_boxes, [0, 0], [
                    real_num_detection, -1])
                detection_masks = tf.slice(detection_masks, [0, 0, 0], [
                    real_num_detection, -1, -1])
                detection_masks_reframed = (utils_ops.
                    reframe_box_masks_to_image_masks(detection_masks,
                    detection_boxes, image.shape[1], image.shape[2]))
                detection_masks_reframed = tf.cast(tf.greater(
                    detection_masks_reframed, 0.5), tf.uint8)
                tensor_dict['detection_masks'] = tf.expand_dims(
                    detection_masks_reframed, 0)
            image_tensor = tf.get_default_graph().get_tensor_by_name(
                'image_tensor:0')
            output_dict = sess.run(tensor_dict, feed_dict={image_tensor: image}
                )
            output_dict['num_detections'] = int(output_dict[
                'num_detections'][0])
            output_dict['detection_classes'] = output_dict['detection_classes'
                ][0].astype(np.int64)
            output_dict['detection_boxes'] = output_dict['detection_boxes'][0]
            output_dict['detection_scores'] = output_dict['detection_scores'][0
                ]
            if 'detection_masks' in output_dict:
                output_dict['detection_masks'] = output_dict['detection_masks'
                    ][0]
    return output_dict


while True:
    if os.path.isfile(image_path):
        print('gefunden')
        image = Image.open(image_path)
        image_np = load_image_into_numpy_array(image)
        image_np_expanded = np.expand_dims(image_np, axis=0)
        output_dict = run_inference_for_single_image(image_np_expanded,
            detection_graph)
        print(output_dict['detection_classes'])
        print(output_dict['detection_scores'])
        print(output_dict['detection_boxes'])
        vis_util.visualize_boxes_and_labels_on_image_array(image_np,
            output_dict['detection_boxes'], output_dict['detection_classes'
            ], output_dict['detection_scores'], category_index,
            instance_masks=output_dict.get('detection_masks'),
            use_normalized_coordinates=True, line_thickness=8)
        im = Image.fromarray(image_np)
        im.save(image_path_detected)
        file = open(text_path_detected, 'w')
        file.write('detection_classes\n')
        file.write(str(output_dict['detection_classes']))
        file.write(' \ndetection_scores\n')
        file.write(str(output_dict['detection_scores']))
        file.write('\ndetection_boxes\n')
        file.write(str(output_dict['detection_boxes']))
        file.close()
        os.remove(image_path)
    else:
        print('nicht gefunden')
        time.sleep(1)

# -*- coding: utf-8 -*-
"""
Created on Thu Feb 27 11:50:49 2020

@author: Welp
"""

import numpy as np
import os
import six.moves.urllib as urllib
import sys
import tarfile
import tensorflow as tf
import zipfile


from distutils.version import StrictVersion
from collections import defaultdict
from io import StringIO
import tkinter
from matplotlib import pyplot as plt
from PIL import Image
import time


# This is needed since the notebook is stored in the object_detection folder.
sys.path.append("..")
from object_detection.utils import ops as utils_ops

if StrictVersion(tf.__version__) < StrictVersion('1.12.0'):
  raise ImportError('Please upgrade your TensorFlow installation to v1.12.*.')

print(tf.__version__)
# This is needed to display the images.
#%matplotlib inline
#plt.plot([0,0],[1,1],linewidth=4,label='random diagonal')
#plt.show()


from utils import label_map_util

from utils import visualization_utils as vis_util



# What model to download.
#MODEL_NAME = 'ssd_mobilenet_v1_coco_2017_11_17'
MODEL_NAME = 'trained-inference-graphs'
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_FROZEN_GRAPH = MODEL_NAME + '/frozen_inference_graph_SP3.pb'

# List of the strings that is used to add correct label for each box.
PATH_TO_LABELS = os.path.join('data', 'label_map_SP3.pbtxt')

detection_graph = tf.Graph()
with detection_graph.as_default():
  od_graph_def = tf.GraphDef()
  with tf.gfile.GFile(PATH_TO_FROZEN_GRAPH, 'rb') as fid:
    serialized_graph = fid.read()
    od_graph_def.ParseFromString(serialized_graph)
    tf.import_graph_def(od_graph_def, name='')
    
category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS, use_display_name=True)

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)

# 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 = 'SP3_images'
TEST_IMAGE_PATHS = [ os.path.join(PATH_TO_TEST_IMAGES_DIR, 'image{}.jpg'.format(i)) for i in range(1, 3) ]
print(TEST_IMAGE_PATHS)
# Size, in inches, of the output images.
IMAGE_SIZE = (12, 8)
file_path = os.path.abspath(sys.argv[1])
image_path = os.path.join(file_path, "aktuelleSzene.jpg")
image_path_detected = os.path.join(file_path, "gefundeneObjekte.jpg")
text_path_detected = os.path.join(file_path, "gefundeneObjekte.txt")


def run_inference_for_single_image(image, graph):
  with graph.as_default():
    with tf.Session() as sess:
      # Get handles to input and output tensors
      ops = tf.get_default_graph().get_operations()
      all_tensor_names = {output.name for op in ops for output in op.outputs}
      tensor_dict = {}
      for key in [
          'num_detections', 'detection_boxes', 'detection_scores',
          'detection_classes', 'detection_masks'
      ]:
        tensor_name = key + ':0'
        if tensor_name in all_tensor_names:
          tensor_dict[key] = tf.get_default_graph().get_tensor_by_name(
              tensor_name)
      if 'detection_masks' in tensor_dict:
        # The following processing is only for single image
        detection_boxes = tf.squeeze(tensor_dict['detection_boxes'], [0])
        detection_masks = tf.squeeze(tensor_dict['detection_masks'], [0])
        # Reframe is required to translate mask from box coordinates to image coordinates and fit the image size.
        real_num_detection = tf.cast(tensor_dict['num_detections'][0], tf.int32)
        detection_boxes = tf.slice(detection_boxes, [0, 0], [real_num_detection, -1])
        detection_masks = tf.slice(detection_masks, [0, 0, 0], [real_num_detection, -1, -1])
        detection_masks_reframed = utils_ops.reframe_box_masks_to_image_masks(
            detection_masks, detection_boxes, image.shape[1], image.shape[2])
        detection_masks_reframed = tf.cast(
            tf.greater(detection_masks_reframed, 0.5), tf.uint8)
        # Follow the convention by adding back the batch dimension
        tensor_dict['detection_masks'] = tf.expand_dims(
            detection_masks_reframed, 0)
      image_tensor = tf.get_default_graph().get_tensor_by_name('image_tensor:0')

      # Run inference
      output_dict = sess.run(tensor_dict,
                             feed_dict={image_tensor: image})

      # all outputs are float32 numpy arrays, so convert types as appropriate
      output_dict['num_detections'] = int(output_dict['num_detections'][0])
      output_dict['detection_classes'] = output_dict[
          'detection_classes'][0].astype(np.int64)
      output_dict['detection_boxes'] = output_dict['detection_boxes'][0]
      output_dict['detection_scores'] = output_dict['detection_scores'][0]
      if 'detection_masks' in output_dict:
        output_dict['detection_masks'] = output_dict['detection_masks'][0]
  return output_dict

#for image_path in TEST_IMAGE_PATHS:
#  image = Image.open(image_path)
#  print(image_path)
#  # the array based representation of the image will be used later in order to prepare the
#  # result image with boxes and labels on it.
#  image_np = load_image_into_numpy_array(image)
#  # 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.
#  output_dict = run_inference_for_single_image(image_np_expanded, detection_graph)
#  print(output_dict['detection_classes'])
#  print(output_dict['detection_scores'])
#  print(output_dict['detection_boxes'])
#  # Visualization of the results of a detection.
#  vis_util.visualize_boxes_and_labels_on_image_array(
#      image_np,
#      output_dict['detection_boxes'],
#      output_dict['detection_classes'],
#      output_dict['detection_scores'],
#      category_index,
#      instance_masks=output_dict.get('detection_masks'),
#      use_normalized_coordinates=True,
#      line_thickness=8)
#
#  plt.figure(figsize=IMAGE_SIZE)
#  plt.imshow(image_np)
#  image_path_detected = image_path[:-4]+'d'+image_path[-4:]
#  plt.savefig(image_path_detected, bbox_inches='tight')
#  
while(True):
    if(os.path.isfile(image_path)):
        print('gefunden')
        image=Image.open(image_path)
        image_np = load_image_into_numpy_array(image)
        # detect objects in image
        # 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.
        output_dict = run_inference_for_single_image(image_np_expanded, detection_graph)
        print(output_dict['detection_classes'])
        print(output_dict['detection_scores'])
        print(output_dict['detection_boxes'])
        # Visualization of the results of a detection.
        vis_util.visualize_boxes_and_labels_on_image_array(
            image_np,
            output_dict['detection_boxes'],
            output_dict['detection_classes'],
            output_dict['detection_scores'],
            category_index,
            instance_masks=output_dict.get('detection_masks'),
            use_normalized_coordinates=True,
            line_thickness=8)
        # save gefundeneObjekte.jpg
        im = Image.fromarray(image_np)
        im.save(image_path_detected)
        # save gefundeneObjekte.txt
        file = open(text_path_detected,"w")
        file.write("detection_classes\n")
        file.write(str(output_dict['detection_classes']))
        file.write(" \ndetection_scores\n")
        file.write(str(output_dict['detection_scores']))
        file.write("\ndetection_boxes\n")
        file.write(str(output_dict['detection_boxes']))
        file.close()
        # remove aktuelleSzene.jpg
        os.remove(image_path)
    else:
        print('nicht gefunden')
        # warte 1 Sekunde
        time.sleep(1)        