import numpy as np
import cv2
import os
import torch

annotator_ckpts_path = os.path.join(os.path.dirname(__file__), 'ckpts')

def get_control(type):
    if type == 'canny':
        from .canny import CannyDetector
        apply_control = CannyDetector()
    elif type == 'openpose':
        from .openpose import OpenposeDetector
        apply_control = OpenposeDetector()
    elif type == 'dwpose':
        from .dwpose import DWposeDetector
        apply_control = DWposeDetector()
    elif type == 'depth' or type == 'normal':
        from .midas import MidasDetector
        apply_control = MidasDetector()
    elif type == 'depth_zoe':
        from .zoe import ZoeDetector
        apply_control = ZoeDetector()
    elif type == 'hed':
        from .hed import HEDdetector
        apply_control = HEDdetector()
    elif type == 'scribble':
        apply_control = None
    elif type == 'seg':
        from .uniformer import UniformerDetector
        apply_control = UniformerDetector()
    elif type == 'mlsd':
        from .mlsd import MLSDdetector
        apply_control = MLSDdetector()
    else:
        raise TypeError(type)
    return apply_control


def HWC3(x):
    assert x.dtype == np.uint8
    if x.ndim == 2:
        x = x[:, :, None]
    assert x.ndim == 3
    H, W, C = x.shape
    assert C == 1 or C == 3 or C == 4
    if C == 3:
        return x
    if C == 1:
        return np.concatenate([x, x, x], axis=2)
    if C == 4:
        color = x[:, :, 0:3].astype(np.float32)
        alpha = x[:, :, 3:4].astype(np.float32) / 255.0
        y = color * alpha + 255.0 * (1.0 - alpha)
        y = y.clip(0, 255).astype(np.uint8)
        return y


def resize_image(input_image, resolution):
    H, W, C = input_image.shape
    H = float(H)
    W = float(W)
    k = float(resolution) / min(H, W)
    H *= k
    W *= k
    H = int(np.round(H / 64.0)) * 64
    W = int(np.round(W / 64.0)) * 64
    img = cv2.resize(input_image, (W, H), interpolation=cv2.INTER_LANCZOS4 if k > 1 else cv2.INTER_AREA)
    return img