Python’da yapay zekâ ile obje algılama nasıl yapılır?

İlk düzenleme

!git clone https://github.com/tensorflow/models.git
!apt-get -qq install libprotobuf-java protobuf-compiler
!protoc ./models/research/object_detection/protos/string_int_label_map.proto --python_out=.
!cp -R models/research/object_detection/ object_detection/ #Dosya ve klasör kopyalama işlemleri gerçekleştiriyor. cp kaynak-dosya-klasor - hedef-dosya-klasor
!rm -rf models #Dosyayı siler

Kütüphaneleri aktarma

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

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

Model aktarımı

#Daha iyi sonuç veren model
MODEL_NAME = 'faster_rcnn_inception_resnet_v2_atrous_coco_2017_11_08'

MODEL_FILE = MODEL_NAME + '.tar.gz'
DOWNLOAD_BASE = 'http://download.tensorflow.org/models/object_detection/'

#Nesne tespiti için kullanılan model
PATH_TO_CKPT = MODEL_NAME + '/frozen_inference_graph.pb'

#Her kutuya doğru etiket eklemek için kullanılan dizelerin listesi.
PATH_TO_LABELS = os.path.join('object_detection/data', 'mscoco_label_map.pbtxt')

NUM_CLASSES = 90

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='')
    
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)

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)

Resim aktarma

#Make directory images: images adında yeni yön oluşturur
!mkdir images
#Link üzerinden resim edinir
!wget https://files.ekofi.science/Kiz-Kulesi.jpg -O images/resim_1.jpg
#Resim inmiş mi inmemiş mi? Resim listesini görüntüler
!ls ./images/

#Resim klasörü
PATH_TO_TEST_IMAGES_DIR = 'images'
TEST_IMAGE_PATHS = [ os.path.join(PATH_TO_TEST_IMAGES_DIR, 'resim_{}.jpg'.format(i)) for i in range(1, 2) ]

IMAGE_SIZE = (12, 8)

Gösterme

with detection_graph.as_default():
  with tf.Session(graph=detection_graph) as sess:
    image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
    detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
    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')
    for image_path in TEST_IMAGE_PATHS:
      image = Image.open(image_path)
      image_np = load_image_into_numpy_array(image)
      image_np_expanded = np.expand_dims(image_np, axis=0)
      (boxes, scores, classes, num) = sess.run(
          [detection_boxes, detection_scores, detection_classes, num_detections],
          feed_dict={image_tensor: image_np_expanded})
      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)
      plt.figure(figsize=IMAGE_SIZE)
      plt.imshow(image_np)