import numpy as np
import math
import kneron_preprocessing

def check_img_size(img_size, s=32):
    # Verify img_size is a multiple of stride s
    new_size = math.ceil(img_size / int(s)) * int(s)
    if new_size != img_size:
        print('WARNING: --img-size %g must be multiple of max stride %g, updating to %g' % (img_size, s, new_size))
    return new_size

def preprocess_(image, input_shape, **kwargs) : 
    model_stride_max = 32
    imgsz_h, imgsz_w = input_shape
    imgsz_h = check_img_size(imgsz_h, s=model_stride_max)  # check img_size
    imgsz_w = check_img_size(imgsz_w, s=model_stride_max)  # check img_size 
    if isinstance(image, str):
        try:
            image = kneron_preprocessing.API.load_image(image) #RGB
        except:
            try:
                image = kneron_preprocessing.API.load_bin(image, **kwargs)
            except:
                print('input format error')
                assert 0
    else:
        assert isinstance(image, np.ndarray) or isinstance(image, list)
        image = np.asarray(image)
    h_ori, w_ori = image.shape[:2]
    img = image
    new_shape=(imgsz_h,imgsz_w)
    # Resize image to a 32-pixel-multiple rectangle https://github.com/ultralytics/yolov3/issues/232
    shape = img.shape[:2]  # current shape [height, width]
    if isinstance(new_shape, int):
        new_shape = (new_shape, new_shape)

    # Scale ratio (new / old)
    r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
    new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r)) # width, height 

    if shape[::-1] != new_unpad:  # resize
        img = kneron_preprocessing.API.resize(img,size=new_unpad, keep_ratio = False)

    img = kneron_preprocessing.API.pad_corner(img, size=(imgsz_w, imgsz_h), pad_val=0)

    img = kneron_preprocessing.API.norm(img)  
    if img.shape[-1] == 3:
        img = np.expand_dims(img, axis=0)
    pre_info = {'w_ori': w_ori, 'h_ori': h_ori}
    return img, pre_info