# Copyright 2020 NXP
# SPDX-License-Identifier: MIT

import pyarmnn as ann
import numpy as np
from PIL import Image
import example_utils as eu


def preprocess_onnx(img: Image, width: int, height: int, data_type, scale: float, mean: list,
                    stddev: list):
    """Preprocessing function for ONNX imagenet models based on:
    https://github.com/onnx/models/blob/master/vision/classification/imagenet_inference.ipynb

    Args:
        img (PIL.Image): Loaded PIL.Image
        width (int): Target image width
        height (int): Target image height
        data_type: Image datatype (np.uint8 or np.float32)
        scale (float): Scaling factor
        mean: RGB mean values
        stddev: RGB standard deviation

    Returns:
        np.array: Preprocess image as Numpy array
    """
    img = img.resize((256, 256), Image.BILINEAR)
    # first rescale to 256,256 and then center crop
    left = (256 - width) / 2
    top = (256 - height) / 2
    right = (256 + width) / 2
    bottom = (256 + height) / 2
    img = img.crop((left, top, right, bottom))
    img = img.convert('RGB')
    img = np.array(img)
    img = np.reshape(img, (-1, 3))  # reshape to [RGB][RGB]...
    img = ((img / scale) - mean) / stddev
    # NHWC to NCHW conversion, by default NHWC is expected
    # image is loaded as [RGB][RGB][RGB]... transposing it makes it [RRR...][GGG...][BBB...]
    img = np.transpose(img)
    img = img.flatten().astype(data_type)  # flatten into a 1D tensor and convert to float32
    return img


if __name__ == "__main__":
    # Download resources
    kitten_filename = eu.download_file('https://s3.amazonaws.com/model-server/inputs/kitten.jpg')
    labels_filename = eu.download_file('https://s3.amazonaws.com/onnx-model-zoo/synset.txt')
    model_filename = eu.download_file(
        'https://s3.amazonaws.com/onnx-model-zoo/mobilenet/mobilenetv2-1.0/mobilenetv2-1.0.onnx')

    # Create a network from a model file
    net_id, parser, runtime = eu.create_onnx_network(model_filename)

    # Load input information from the model and create input tensors
    input_binding_info = parser.GetNetworkInputBindingInfo("data")

    # Load output information from the model and create output tensors
    output_binding_info = parser.GetNetworkOutputBindingInfo("mobilenetv20_output_flatten0_reshape0")
    output_tensors = ann.make_output_tensors([output_binding_info])

    # Load labels
    labels = eu.load_labels(labels_filename)

    # Load images and resize to expected size
    image_names = [kitten_filename]
    images = eu.load_images(image_names,
                            224, 224,
                            np.float32,
                            255.0,
                            [0.485, 0.456, 0.406],
                            [0.229, 0.224, 0.225],
                            preprocess_onnx)

    for idx, im in enumerate(images):
        # Create input tensors
        input_tensors = ann.make_input_tensors([input_binding_info], [im])

        # Run inference
        print("Running inference on '{0}' ...".format(image_names[idx]))
        runtime.EnqueueWorkload(net_id, input_tensors, output_tensors)

        # Process output
        out_tensor = ann.workload_tensors_to_ndarray(output_tensors)[0][0]
        results = np.argsort(out_tensor)[::-1]
        eu.print_top_n(5, results, labels, out_tensor)