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import cv2
print(cv2.__version__)
Deep learning Framework from Google It is an open source library for numerical computation using data flow graphs.
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!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/
!rm -rf models
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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
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# What model to download.
# MODEL_NAME = 'ssd_mobilenet_v1_coco_2017_11_17'
# model with more accurancy but up to you use a diferent 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/'
# 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('object_detection/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())
## Load a (frozen) Tensorflow model into memory.
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, Example, so that when our conolutional network predicts '5', we know that this corresponds to 'airp'
# 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)
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## the array based representations of the image will be used later in order to prepare the
## result image with boxes and labels on it.
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)
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!mkdir images
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!wget https://img.cutenesscdn.com/640/photos.demandstudios.com/getty/article/225/225/478872471.jpg -O images/custom_img_1.jpg
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!wget https://s.aolcdn.com/dims-global/dims3/GLOB/legacy_thumbnail/916x515/quality/95/https://s.blogcdn.com/slideshows/images/slides/727/101/1/S7271011/slug/l/01-2019-mercedes-benz-cls450-fd-1.jpg -O images/custom_img_2.jpg
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!ls ./images/
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PATH_TO_TEST_IMAGES_DIR = 'images'
TEST_IMAGE_PATHS = [ os.path.join(PATH_TO_TEST_IMAGES_DIR, 'custom_img_{}.jpg'.format(i)) for i in range(1, 3) ]
IMAGE_SIZE = (12, 8)
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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')
## Each box represents a part of the image where a particular object was detected
detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')
## Each score represents how level of confidence for each of the objects
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)
## Each score represent how level of confidence for each of the objects.
(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)
plt.figure(figsize=IMAGE_SIZE)
plt.imshow(image_np)