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- 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='')
- sess = tf.Session(graph=detection_graph)
- # Define input and output tensors (i.e. data) for the object detection classifier
- # Input tensor is the image
- image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
- # Output tensors are the detection boxes, scores, and classes
- # Each box represents a part of the image where a particular object was detected
- detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
- # Each score represents level of confidence for each of the objects.
- # The score is shown on the result image, together with the class label.
- detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')
- detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')
- # Number of objects detected
- num_detections = detection_graph.get_tensor_by_name('num_detections:0')
- # Load image using OpenCV and
- # expand image dimensions to have shape: [1, None, None, 3]
- # i.e. a single-column array, where each item in the column has the pixel RGB value
- image = cv2.imread(PATH_TO_IMAGE)
- image_expanded = np.expand_dims(image, axis=0)
- # Perform the actual detection by running the model with the image as input
- (boxes, scores, classes, num) = sess.run(
- [detection_boxes, detection_scores, detection_classes, num_detections],
- feed_dict={image_tensor: image_expanded})
- # Draw the results of the detection (aka 'visulaize the results')
- vis_util.visualize_boxes_and_labels_on_image_array(
- image,
- np.squeeze(boxes),
- np.squeeze(classes).astype(np.int32),
- np.squeeze(scores),
- category_index,
- use_normalized_coordinates=True,
- line_thickness=8,
- min_score_thresh=0.60)
- # All the results have been drawn on image. Now display the image.
- cv2.imshow('Object detector', image)
- # Press any key to close the image
- cv2.waitKey(0)
- # Clean up
- cv2.destroyAllWindows()
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