Esempio n. 1
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def predict(img_bytes):

    # Decode
    x = image.decode_jpeg(img_bytes, channels=3)
    print('decoded')

    # Normalize
    x = image.convert_image_dtype(x, float32)
    print('normalized')

    # Resize
    np_x = image.resize(x, [224, 224]).numpy()
    x = np_x.tolist()
    print('resized')

    # Predict
    pred = model.predict(reshape(x, [-1, 224, 224, 3]))

    # Map
    top_k_values, top_k_indices = math.top_k(pred, k=3, sorted=True, name=None)

    top_k_values = top_k_values.numpy().tolist()[0]
    top_k_labels = [d[idx] for idx in top_k_indices.numpy()[0]]
    results = {'prob': top_k_values, 'prob_labels': top_k_labels}
    print(results)

    # Return
    return json.dumps(results)
Esempio n. 2
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def get_img(img_path):
    img = read_file(img_path)
    img = decode_jpeg(img, channels=3)
    img = resize(img, [image_size, image_size])
    img = 1 - img/255. # We would rather have the whole white void area be full of zeros than ones
    img = tf.convert_to_tensor([img])
    return img
Esempio n. 3
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def process_path(file_path):
    print(file_path)
    file = read_file(file_path)
    file = image.decode_jpeg(file, channels=3)
    file = cast(file, float32)
    file = preprocess_input(file)
    file = image.resize(file, [ROW, COL])

    return file
Esempio n. 4
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 def __call__(self, img):
     if self.is_encoded:
         img = tfimg.decode_jpeg(img)
         img = tf.image.convert_image_dtype(img, tf.float32)
     for cbr in self.comburant:
         img = cbr(img)
     if not tf.is_tensor(img):
         img = tf.convert_to_tensor(img)
     return img
Esempio n. 5
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 def preprocess_image(self, image):
     image = tf_img.decode_jpeg(image, channels=3)
     image = tf_img.resize(image, [224, 224])
     image /= 255.0  # normalize to [0,1] range
     return image
Esempio n. 6
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def handle_image_path(img_path):
    img = read_file(img_path)
    img = decode_jpeg(img, channels=3)
    img = resize_images(img, [image_size, image_size])
    img = img / 255.0
    return img
Esempio n. 7
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def load_image(image_path, img_width=299, img_height=299):
    img = io.read_file(image_path)
    img = image.decode_jpeg(img, channels=3)
    img = image.resize(img, (img_width, img_height))
    return img
Esempio n. 8
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 def from_raw(cls, image_bytes: bytes):
     image_tensor = decode_jpeg(image_bytes, channels=3)
     return cls(image_tensor)