yigitcolakoglu
/
MyCity


								#!/usr/bin/python3


								import numpy as np

								import os

								import sys

								import tensorflow as tf

								import cv2

								from distutils.version import StrictVersion

								import socket

								from utils import label_map_util


								from utils import visualization_utils as vis_util

								import psutil

								import json


								import base64

								from PIL import Image

								from io import BytesIO

								import psutil


								switch = 1


								import io

								import socket

								import struct

								import time

								import pickle

								import zlib


								# 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.


								encode_param = [int(cv2.IMWRITE_JPEG_QUALITY), 90]


								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

								data = {"gpu_temp":"10C","gpu_load":"15%","cpu_temp":"47C","cpu_load":"15%","mem_temp":"NaN","mem_load":"17%","fan_speed":"10000RPM"}


								def get_temps():

								    global data

								    temps = psutil.sensors_temperatures()

								    data["cpu_temp"] = str(int(temps["dell_smm"][0][1]))+"°C"

								    data["cpu_load"] = str(psutil.cpu_percent())+"%"

								    data["mem_load"] = str(dict(psutil.virtual_memory()._asdict())["percent"])+"%"

								    data["fan_speed"] = str(psutil.sensors_fans()["dell_smm"][0][1])+"RPM"


								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]

								cut_send = [0,0,0,0]

								a = 1

								img_counter = 0

								socket_switch = True

								cam = cv2.VideoCapture(0)

								with detection_graph.as_default():

								    sess = tf.Session()

								switch = 0

								get_temps()

								while 1:

								    if(True):

								        try:

								            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

								            send_image = cv2.cvtColor(image,cv2.COLOR_BGR2RGB)

								            cv2.imshow("Cam",image)

								            cv2.imshow("Cut",image_np)


								            if socket_switch:

								                try:

								                    client_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)

								                    client_socket.connect(('127.0.0.1', 8485))

								                    connection = client_socket.makefile('wb')

								                    socket_switch = False

								                except:

								                    socket_switch=True

								                    continue

								                try:

								                    crop_img = send_image.copy(order='C')


								                    crop_img = Image.fromarray(crop_img,"RGB")

								                    buffered = BytesIO()

								                    crop_img.save(buffered, format="JPEG")

								                    img = base64.b64encode(buffered.getvalue()).decode("ascii")

								                    client_socket.sendall(json.dumps({"image_full":img,"image_sizes":{"x":cut_send[2],"y":cut_send[0],"width":cut_send[3],"height":cut_send[1]},"load":data}).encode('gbk')+b"\n")

								                    img_counter += 1


								                except:

								                    socket_switch=True


								            if img_counter % 10 ==0:

								                get_temps()

								            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

								        except KeyboardInterrupt:

								            if not socket_switch:

								                client_socket.sendall(b"Bye\n")

								                cam.release()

								                exit(0)


								cv2.destroyAllWindows()

								cam.release()