input_fn.py

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# Modification made by Xilinx, Inc.
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# Origin code:https://github.com/lji72/inference/blob/master/others/cloud/single_stage_detector/tensorflow/train/eval_ssd_large.py 

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from PIL import Image
import numpy as np


def letterbox_image(image, size):
    '''resize image with unchanged aspect ratio using padding'''
    iw, ih = image.size
    w, h = size
    scale = min(w/iw, h/ih)
    nw = int(iw*scale)
    nh = int(ih*scale)

    image = image.resize((nw,nh), Image.BICUBIC)
    new_image = Image.new('RGB', size, (128,128,128))
    new_image.paste(image, ((w-nw)//2, (h-nh)//2))
    return new_image

#image = Image.open(img_path)

def preprocessing_fn(image, model_image_size=(416,416)):
    if model_image_size != (None, None):
        assert model_image_size[0]%32 == 0, 'Multiples of 32 required'
        assert model_image_size[1]%32 == 0, 'Multiples of 32 required'
        boxed_image = letterbox_image(image, tuple(reversed(model_image_size)))
    else:
        new_image_size = (image.width - (image.width % 32), image.height - (image.height % 32))
        boxed_image = letterbox_image(image, new_image_size)
    image_data = np.array(boxed_image, dtype='float32')
    image_data /= 255.
    return image_data

calib_image_dir = "./images/"
calib_image_list = "./list.txt"
calib_batch_size = 1
def calib_input(iter):
  images = []
  line = open(calib_image_list).readlines()
  for index in range(0, calib_batch_size):
    curline = line[iter * calib_batch_size + index]
    image_name = curline.strip()
    image = Image.open(calib_image_dir + image_name)
    image = preprocessing_fn(image)
    images.append(image)
  return {"input_1": images}