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from urllib.request import urlopen |
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import matplotlib.pyplot as plt |
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import numpy as np |
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import tensorflow as tf |
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from official.vision.ops.preprocess_ops import resize_and_crop_image |
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from official.vision.utils.object_detection import visualization_utils |
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from PIL import Image |
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from six import BytesIO |
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from shared import category_index, show_batch, tf_ex_decoder, export_dir, HEIGHT, WIDTH |
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def load_image_into_numpy_array(path): |
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"""Load an image from file into a numpy array. |
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Puts image into numpy array to feed into tensorflow graph. |
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Note that by convention we put it into a numpy array with shape |
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(height, width, channels), where channels=3 for RGB. |
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Args: |
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path: the file path to the image |
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Returns: |
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uint8 numpy array with shape (img_height, img_width, 3) |
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""" |
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image = None |
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if (path.startswith('http')): |
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response = urlopen(path) |
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image_data = response.read() |
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image_data = BytesIO(image_data) |
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image = Image.open(image_data) |
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else: |
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image_data = tf.io.gfile.GFile(path, 'rb').read() |
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image = Image.open(BytesIO(image_data)) |
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(im_width, im_height) = image.size |
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return np.array(image.getdata()).reshape( |
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(1, im_height, im_width, 3)).astype(np.uint8) |
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def build_inputs_for_object_detection(image, input_image_size): |
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"""Builds Object Detection model inputs for serving.""" |
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image, _ = resize_and_crop_image( |
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image, |
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input_image_size, |
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padded_size=input_image_size, |
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aug_scale_min=1.0, |
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aug_scale_max=1.0) |
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return image |
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num_of_examples = 3 |
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test_data_input_path = './tfrecords/test-00000-of-00001.tfrecord' |
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test_ds = tf.data.TFRecordDataset( |
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test_data_input_path).take( |
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num_of_examples) |
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show_batch(test_ds) |
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imported = tf.saved_model.load(export_dir) |
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model_fn = imported.signatures['serving_default'] |
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input_image_size = (HEIGHT, WIDTH) |
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plt.figure(figsize=(20, 20)) |
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min_score_thresh = 0.30 |
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for i, serialized_example in enumerate(test_ds): |
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plt.subplot(1, 3, i+1) |
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decoded_tensors = tf_ex_decoder.decode(serialized_example) |
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image = build_inputs_for_object_detection( |
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decoded_tensors['image'], input_image_size) |
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image = tf.expand_dims(image, axis=0) |
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image = tf.cast(image, dtype=tf.uint8) |
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image_np = image[0].numpy() |
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result = model_fn(image) |
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visualization_utils.visualize_boxes_and_labels_on_image_array( |
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image_np, |
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result['detection_boxes'][0].numpy(), |
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result['detection_classes'][0].numpy().astype(int), |
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result['detection_scores'][0].numpy(), |
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category_index=category_index, |
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use_normalized_coordinates=False, |
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max_boxes_to_draw=200, |
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min_score_thresh=min_score_thresh, |
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agnostic_mode=False, |
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instance_masks=None, |
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line_thickness=4) |
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im = Image.fromarray(image_np) |
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im.save(f'SerializedExampleImage-{i+1}.png') |
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