import numpy as np
import torch
from torch.autograd import Variable
from .get_nets import PNet, RNet, ONet
from .box_utils import nms, calibrate_box, get_image_boxes, convert_to_square
from .first_stage import run_first_stage


def detect_faces(image, min_face_size=20.0,
                 thresholds=[0.6, 0.7, 0.8],
                 nms_thresholds=[0.7, 0.7, 0.7]):
    """
    Arguments:
        image: an instance of PIL.Image.
        min_face_size: a float number.
        thresholds: a list of length 3.
        nms_thresholds: a list of length 3.

    Returns:
        two float numpy arrays of shapes [n_boxes, 4] and [n_boxes, 10],
        bounding boxes and facial landmarks.
    """

    # LOAD MODELS
    pnet = PNet()
    rnet = RNet()
    onet = ONet()
    # device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
    device = 'cpu'
    pnet.to(device)
    rnet.to(device)
    onet.to(device)
    onet.eval()

    # BUILD AN IMAGE PYRAMID
    width, height = image.size
    min_length = min(height, width)

    min_detection_size = 12
    factor = 0.707  # sqrt(0.5)

    # scales for scaling the image
    scales = []

    # scales the image so that
    # minimum size that we can detect equals to
    # minimum face size that we want to detect
    m = min_detection_size/min_face_size
    min_length *= m

    factor_count = 0
    while min_length > min_detection_size:
        scales.append(m*factor**factor_count)
        min_length *= factor
        factor_count += 1

    # STAGE 1

    # it will be returned
    bounding_boxes = []
    
    with torch.no_grad():
        # run P-Net on different scales
        for s in scales:
            boxes = run_first_stage(image, pnet, scale=s, threshold=thresholds[0])
            bounding_boxes.append(boxes)

        # collect boxes (and offsets, and scores) from different scales
        bounding_boxes = [i for i in bounding_boxes if i is not None]
        bounding_boxes = np.vstack(bounding_boxes)

        keep = nms(bounding_boxes[:, 0:5], nms_thresholds[0])
        bounding_boxes = bounding_boxes[keep]

        # use offsets predicted by pnet to transform bounding boxes
        bounding_boxes = calibrate_box(bounding_boxes[:, 0:5], bounding_boxes[:, 5:])
        # shape [n_boxes, 5]

        bounding_boxes = convert_to_square(bounding_boxes)
        bounding_boxes[:, 0:4] = np.round(bounding_boxes[:, 0:4])

        # STAGE 2

        img_boxes = get_image_boxes(bounding_boxes, image, size=24)
        img_boxes = torch.FloatTensor(img_boxes).to(device)

        output = rnet(img_boxes)
        offsets = output[0].cpu().data.numpy()  # shape [n_boxes, 4]
        probs = output[1].cpu().data.numpy()  # shape [n_boxes, 2]

        keep = np.where(probs[:, 1] > thresholds[1])[0]
        bounding_boxes = bounding_boxes[keep]
        bounding_boxes[:, 4] = probs[keep, 1].reshape((-1,))
        offsets = offsets[keep]

        keep = nms(bounding_boxes, nms_thresholds[1])
        bounding_boxes = bounding_boxes[keep]
        bounding_boxes = calibrate_box(bounding_boxes, offsets[keep])
        bounding_boxes = convert_to_square(bounding_boxes)
        bounding_boxes[:, 0:4] = np.round(bounding_boxes[:, 0:4])

        # STAGE 3

        img_boxes = get_image_boxes(bounding_boxes, image, size=48)
        if len(img_boxes) == 0: 
            return [], []
        img_boxes = torch.FloatTensor(img_boxes).to(device)
        output = onet(img_boxes)
        landmarks = output[0].cpu().data.numpy()  # shape [n_boxes, 10]
        offsets = output[1].cpu().data.numpy()  # shape [n_boxes, 4]
        probs = output[2].cpu().data.numpy()  # shape [n_boxes, 2]

        keep = np.where(probs[:, 1] > thresholds[2])[0]
        bounding_boxes = bounding_boxes[keep]
        bounding_boxes[:, 4] = probs[keep, 1].reshape((-1,))
        offsets = offsets[keep]
        landmarks = landmarks[keep]

        # compute landmark points
        width = bounding_boxes[:, 2] - bounding_boxes[:, 0] + 1.0
        height = bounding_boxes[:, 3] - bounding_boxes[:, 1] + 1.0
        xmin, ymin = bounding_boxes[:, 0], bounding_boxes[:, 1]
        landmarks[:, 0:5] = np.expand_dims(xmin, 1) + np.expand_dims(width, 1)*landmarks[:, 0:5]
        landmarks[:, 5:10] = np.expand_dims(ymin, 1) + np.expand_dims(height, 1)*landmarks[:, 5:10]

        bounding_boxes = calibrate_box(bounding_boxes, offsets)
        keep = nms(bounding_boxes, nms_thresholds[2], mode='min')
        bounding_boxes = bounding_boxes[keep]
        landmarks = landmarks[keep]

    return bounding_boxes, landmarks
