yigitcolakoglu
/
MyCity


								#!/usr/bin/python3


								import numpy as np

								import os

								import six.moves.urllib as urllib

								import sys

								import tarfile

								import tensorflow as tf

								import zipfile

								import cv2

								from distutils.version import StrictVersion

								from collections import defaultdict

								from io import StringIO


								from utils import label_map_util


								from utils import visualization_utils as vis_util

								from PIL import Image

								switch = 1

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

								sys.path.append("..")

								import time

								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.*.')


								# What model to download.


								#MODEL_NAME = 'ssd_mobilenet_v1_coco_2017_11_17' #not even worth trying

								MODEL_NAME="ssd_inception_v2_coco_11_06_2017" # not bad and fast

								MODEL_NAME="rfcn_resnet101_coco_11_06_2017" # WORKS BEST BUT takes 4 times longer per image

								#MODEL_NAME = "faster_rcnn_resnet101_coco_11_06_2017" # too slow

								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.pb'


								# List of the strings that is used to add correct label for each box.

								PATH_TO_LABELS = os.path.join('object_detection/data', 'mscoco_label_map.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 = 'object_detection/test_images'

								TEST_IMAGE_PATHS = [ os.path.join(PATH_TO_TEST_IMAGES_DIR, 'image{}.jpg'.format(i)) for i in range(3, 6) ]


								# Size, in inches, of the output images.

								sess = 0

								switch = 1

								def run_inference_for_single_image(image, graph):

								  global switch

								  global sess

								  with graph.as_default():

								    if(switch):

								        sess = tf.Session()

								        switch = 0

								      # 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

								cut=[-175,-1,-175,-1]

								a = 1

								cam = cv2.VideoCapture(0)

								with detection_graph.as_default():

								    sess = tf.Session()

								    switch = 0

								while 1:

								    if(True):


								        ret,image = cam.read()

								        image_np = image[cut[0]:cut[1],cut[2]:cut[3]]

								        #image_np = image_np[int(r[1]):int(r[1]+r[3]),int(r[0]):int(r[0]+r[2])]

								        # the array based representation of the image will be used later in order to prepare the

								        # result image with boxes and labels on it.


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

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

								        t1 = time.time()

								        # Actual detection.

								        output_dict = run_inference_for_single_image(image_np_expanded, detection_graph)

								        # 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)

								        image[cut[0]:cut[1],cut[2]:cut[3]] = image_np

								        cv2.imshow("Cam",np.concatenate((image,image_np),axis=0))

								        t2 = time.time()

								        print("time taken for {}".format(t2-t1))


								        ex_c = [27, ord("q"), ord("Q")]

								        if cv2.waitKey(1) & 0xFF in ex_c:

								            break

								cv2.destroyAllWindows()

								cam.release()