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- def run_inference_for_single_image(image, graph):
- with graph.as_default():
- with tf.Session() as sess:
- 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:
- detection_boxes = tf.squeeze(tensor_dict['detection_boxes'], [0])
- detection_masks = tf.squeeze(tensor_dict['detection_masks'], [0])
- 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[0], image.shape[1])
- detection_masks_reframed = tf.cast(
- tf.greater(detection_masks_reframed, 0.5), tf.uint8)
- tensor_dict['detection_masks'] = tf.expand_dims(
- detection_masks_reframed, 0)
- image_tensor = tf.get_default_graph().get_tensor_by_name('image_tensor:0')
- # Запуск поиска объектов
- output_dict = sess.run(tensor_dict,
- feed_dict={image_tensor: np.expand_dims(image, 0)})
- # Преобразование выходных данных из массивов float32 в нужный формат
- output_dict['num_detections'] = int(output_dict['num_detections'][0])
- output_dict['detection_classes'] = output_dict[
- 'detection_classes'][0].astype(np.uint8)
- 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]
- test = output_dict.copy()
- for i, v in enumerate(output_dict['detection_classes']):
- if v != 1:
- test['detection_boxes'][i] = np.zeros((4))
- test['detection_scores'][i] = 0
- return test
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