import os
from pathlib import Path

import PIL
import dlib
import numpy as np
import scipy
import scipy.ndimage
import torch
from PIL import Image
from torchvision import transforms as T

from utils.drive import open_url

"""
brief: face alignment with FFHQ method (https://github.com/NVlabs/ffhq-dataset)
author: lzhbrian (https://lzhbrian.me)
date: 2020.1.5
note: code is heavily borrowed from
    https://github.com/NVlabs/ffhq-dataset
    http://dlib.net/face_landmark_detection.py.html

requirements:
    apt install cmake
    conda install Pillow numpy scipy
    pip install dlib
    # download face landmark model from:
    # http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2
"""


def get_landmark(filepath, predictor):
    """get landmark with dlib
    :return: np.array shape=(68, 2)
    """
    detector = dlib.get_frontal_face_detector()

    img = dlib.load_rgb_image(filepath)
    dets = detector(img, 1)
    filepath = Path(filepath)
    print(f"{filepath.name}: Number of faces detected: {len(dets)}")
    shapes = [predictor(img, d) for k, d in enumerate(dets)]

    lms = [np.array([[tt.x, tt.y] for tt in shape.parts()]) for shape in shapes]

    return lms


def get_landmark_from_tensors(tensors: list[torch.Tensor | Image.Image | np.ndarray], predictor):
    detector = dlib.get_frontal_face_detector()
    transform = T.ToPILImage()
    images = []
    lms = []

    for k, tensor in enumerate(tensors):
        if isinstance(tensor, torch.Tensor):
            img_pil = transform(tensor)
        else:
            img_pil = tensor
        img = np.array(img_pil)
        images.append(img_pil)

        dets = detector(img, 1)
        if len(dets) == 0:
            raise ValueError(f"No faces detected in the image {k}.")
        elif len(dets) == 1:
            print(f"Number of faces detected: {len(dets)}")
        else:
            print(f"Number of faces detected: {len(dets)}, get largest face")

        # Find the largest face
        dets = sorted(dets, key=lambda det: det.width() * det.height(), reverse=True)
        shape = predictor(img, dets[0])
        lm = np.array([[tt.x, tt.y] for tt in shape.parts()])
        lms.append(lm)

    return images, lms


def align_face(data, predictor=None, is_filepath=False, return_tensors=True):
    """
    :param data: filepath or list torch Tensors
    :return: list of PIL Images
    """
    if predictor is None:
        predictor_path = 'pretrained_models/ShapeAdaptor/shape_predictor_68_face_landmarks.dat'

        if not os.path.isfile(predictor_path):
            print("Downloading Shape Predictor")
            data_io = open_url("https://drive.google.com/uc?id=1huhv8PYpNNKbGCLOaYUjOgR1pY5pmbJx")
            with open(predictor_path, 'wb') as f:
                f.write(data_io.getbuffer())

        predictor = dlib.shape_predictor(predictor_path)

    if is_filepath:
        lms = get_landmark(data, predictor)
    else:
        if not isinstance(data, list):
            data = [data]
        images, lms = get_landmark_from_tensors(data, predictor)

    imgs = []
    for num_img, lm in enumerate(lms):
        lm_chin = lm[0: 17]  # left-right
        lm_eyebrow_left = lm[17: 22]  # left-right
        lm_eyebrow_right = lm[22: 27]  # left-right
        lm_nose = lm[27: 31]  # top-down
        lm_nostrils = lm[31: 36]  # top-down
        lm_eye_left = lm[36: 42]  # left-clockwise
        lm_eye_right = lm[42: 48]  # left-clockwise
        lm_mouth_outer = lm[48: 60]  # left-clockwise
        lm_mouth_inner = lm[60: 68]  # left-clockwise

        # Calculate auxiliary vectors.
        eye_left = np.mean(lm_eye_left, axis=0)
        eye_right = np.mean(lm_eye_right, axis=0)
        eye_avg = (eye_left + eye_right) * 0.5
        eye_to_eye = eye_right - eye_left
        mouth_left = lm_mouth_outer[0]
        mouth_right = lm_mouth_outer[6]
        mouth_avg = (mouth_left + mouth_right) * 0.5
        eye_to_mouth = mouth_avg - eye_avg

        # Choose oriented crop rectangle.
        x = eye_to_eye - np.flipud(eye_to_mouth) * [-1, 1]
        x /= np.hypot(*x)
        x *= max(np.hypot(*eye_to_eye) * 2.0, np.hypot(*eye_to_mouth) * 1.8)
        y = np.flipud(x) * [-1, 1]
        c = eye_avg + eye_to_mouth * 0.1
        quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y])
        qsize = np.hypot(*x) * 2

        # read image
        if is_filepath:
            img = PIL.Image.open(data)
        else:
            img = images[num_img]

        output_size = 1024
        # output_size = 256
        transform_size = 4096
        enable_padding = True

        # Shrink.
        shrink = int(np.floor(qsize / output_size * 0.5))
        if shrink > 1:
            rsize = (int(np.rint(float(img.size[0]) / shrink)), int(np.rint(float(img.size[1]) / shrink)))
            img = img.resize(rsize, PIL.Image.ANTIALIAS)
            quad /= shrink
            qsize /= shrink

        # Crop.
        border = max(int(np.rint(qsize * 0.1)), 3)
        crop = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))),
                int(np.ceil(max(quad[:, 1]))))
        crop = (max(crop[0] - border, 0), max(crop[1] - border, 0), min(crop[2] + border, img.size[0]),
                min(crop[3] + border, img.size[1]))
        if crop[2] - crop[0] < img.size[0] or crop[3] - crop[1] < img.size[1]:
            img = img.crop(crop)
            quad -= crop[0:2]

        # Pad.
        pad = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))),
               int(np.ceil(max(quad[:, 1]))))
        pad = (max(-pad[0] + border, 0), max(-pad[1] + border, 0), max(pad[2] - img.size[0] + border, 0),
               max(pad[3] - img.size[1] + border, 0))
        if enable_padding and max(pad) > border - 4:
            pad = np.maximum(pad, int(np.rint(qsize * 0.3)))
            img = np.pad(np.float32(img), ((pad[1], pad[3]), (pad[0], pad[2]), (0, 0)), 'reflect')
            h, w, _ = img.shape
            y, x, _ = np.ogrid[:h, :w, :1]
            mask = np.maximum(1.0 - np.minimum(np.float32(x) / pad[0], np.float32(w - 1 - x) / pad[2]),
                              1.0 - np.minimum(np.float32(y) / pad[1], np.float32(h - 1 - y) / pad[3]))
            blur = qsize * 0.02
            img += (scipy.ndimage.gaussian_filter(img, [blur, blur, 0]) - img) * np.clip(mask * 3.0 + 1.0, 0.0, 1.0)
            img += (np.median(img, axis=(0, 1)) - img) * np.clip(mask, 0.0, 1.0)
            img = PIL.Image.fromarray(np.uint8(np.clip(np.rint(img), 0, 255)), 'RGB')
            quad += pad[:2]

        # Transform.
        img = img.transform((transform_size, transform_size), PIL.Image.QUAD, (quad + 0.5).flatten(),
                            PIL.Image.BILINEAR)
        if output_size < transform_size:
            img = img.resize((output_size, output_size), PIL.Image.LANCZOS)

        # Save aligned image.
        imgs.append(img)

    if return_tensors:
        transform = T.ToTensor()
        tensors = [transform(img).clamp(0, 1) for img in imgs]
        return tensors
    return imgs
