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- image = Image.open(image_path)
- # the array based representation of the image will be used later in order to prepare the
- # result image with boxes and labels on it.
- image_np = load_image_into_numpy_array(image)
- # Expand dimensions since the model expects images to have shape: [1, None, None, 3]
- image_np_expanded = np.expand_dims(image_np, axis=0)
- # Actual detection.
- output_dict = run_inference_for_single_image(image_np, 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=2)
- plt.figure(figsize=IMAGE_SIZE)
- plt.imshow(image_np)
- width = 1024
- height = 600
- for i,j in zip(output_dict['detection_boxes'],output_dict['detection_scores']):
- if(j>0.5):
- print(i[0]*width,i[2]*height,i[1]*width,i[3]*height)
- 456.1627197265625 433.97676944732666 659.828125 562.1794939041138
- 430.4501953125 364.17006254196167 612.30224609375 440.01832008361816
- 453.7798156738281 326.9976854324341 584.5558471679688 374.18121099472046
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