#######
# Legacy code extracted from: https://github.com/DuinoDu/mtcnn
#######

from time import time
import caffe
import cv2
import numpy as np


def bbreg(boundingbox, reg):
    reg = reg.T

    # calibrate bouding boxes
    if reg.shape[1] == 1:
        pass  # reshape of reg
    w = boundingbox[:, 2] - boundingbox[:, 0] + 1
    h = boundingbox[:, 3] - boundingbox[:, 1] + 1

    bb0 = boundingbox[:, 0] + reg[:, 0] * w
    bb1 = boundingbox[:, 1] + reg[:, 1] * h
    bb2 = boundingbox[:, 2] + reg[:, 2] * w
    bb3 = boundingbox[:, 3] + reg[:, 3] * h

    boundingbox[:, 0:4] = np.array([bb0, bb1, bb2, bb3]).T
    # print "bb", boundingbox
    return boundingbox


def pad(boxesA, w, h):
    boxes = boxesA.copy()  # shit, value parameter!!!
    tmph = boxes[:, 3] - boxes[:, 1] + 1
    tmpw = boxes[:, 2] - boxes[:, 0] + 1
    numbox = boxes.shape[0]

    dx = np.ones(numbox)
    dy = np.ones(numbox)
    edx = tmpw
    edy = tmph

    x = boxes[:, 0:1][:, 0]
    y = boxes[:, 1:2][:, 0]
    ex = boxes[:, 2:3][:, 0]
    ey = boxes[:, 3:4][:, 0]

    tmp = np.where(ex > w)[0]
    if tmp.shape[0] != 0:
        edx[tmp] = -ex[tmp] + w - 1 + tmpw[tmp]
        ex[tmp] = w - 1

    tmp = np.where(ey > h)[0]
    if tmp.shape[0] != 0:
        edy[tmp] = -ey[tmp] + h - 1 + tmph[tmp]
        ey[tmp] = h - 1

    tmp = np.where(x < 1)[0]
    if tmp.shape[0] != 0:
        dx[tmp] = 2 - x[tmp]
        x[tmp] = np.ones_like(x[tmp])

    tmp = np.where(y < 1)[0]
    if tmp.shape[0] != 0:
        dy[tmp] = 2 - y[tmp]
        y[tmp] = np.ones_like(y[tmp])

    # for python index from 0, while matlab from 1
    dy = np.maximum(0, dy - 1)
    dx = np.maximum(0, dx - 1)
    y = np.maximum(0, y - 1)
    x = np.maximum(0, x - 1)
    edy = np.maximum(0, edy - 1)
    edx = np.maximum(0, edx - 1)
    ey = np.maximum(0, ey - 1)
    ex = np.maximum(0, ex - 1)

    return [dy, edy, dx, edx, y, ey, x, ex, tmpw, tmph]


def rerec(bboxA):
    # convert bboxA to square
    w = bboxA[:, 2] - bboxA[:, 0]
    h = bboxA[:, 3] - bboxA[:, 1]
    max_length = np.maximum(w, h).T

    bboxA[:, 0] = bboxA[:, 0] + w * 0.5 - max_length * 0.5
    bboxA[:, 1] = bboxA[:, 1] + h * 0.5 - max_length * 0.5
    bboxA[:, 2:4] = bboxA[:, 0:2] + np.repeat([max_length], 2, axis=0).T
    return bboxA


def nms(boxes, threshold, type):
    """nms
    :boxes: [:,0:5]
    :threshold: 0.5 like
    :type: 'Min' or others
    :returns: TODO
    """
    if boxes.shape[0] == 0:
        return np.array([])
    x1 = boxes[:, 0]
    y1 = boxes[:, 1]
    x2 = boxes[:, 2]
    y2 = boxes[:, 3]
    s = boxes[:, 4]
    area = np.multiply(x2 - x1 + 1, y2 - y1 + 1)
    I = np.array(s.argsort())  # read s using I

    pick = []
    while len(I) > 0:
        xx1 = np.maximum(x1[I[-1]], x1[I[0:-1]])
        yy1 = np.maximum(y1[I[-1]], y1[I[0:-1]])
        xx2 = np.minimum(x2[I[-1]], x2[I[0:-1]])
        yy2 = np.minimum(y2[I[-1]], y2[I[0:-1]])
        w = np.maximum(0.0, xx2 - xx1 + 1)
        h = np.maximum(0.0, yy2 - yy1 + 1)
        inter = w * h
        if type == 'Min':
            o = inter / np.minimum(area[I[-1]], area[I[0:-1]])
        else:
            o = inter / (area[I[-1]] + area[I[0:-1]] - inter)
        pick.append(I[-1])
        I = I[np.where(o <= threshold)[0]]
    return pick


def generateBoundingBox(map, reg, scale, t):
    stride = 2
    cellsize = 12
    map = map.T
    dx1 = reg[0, :, :].T
    dy1 = reg[1, :, :].T
    dx2 = reg[2, :, :].T
    dy2 = reg[3, :, :].T
    (x, y) = np.where(map >= t)

    yy = y
    xx = x

    score = map[x, y]
    reg = np.array([dx1[x, y], dy1[x, y], dx2[x, y], dy2[x, y]])

    if reg.shape[0] == 0:
        pass
    boundingbox = np.array([yy, xx]).T

    # matlab index from 1, so with "boundingbox-1"
    bb1 = np.fix((stride * (boundingbox) + 1) / scale).T
    bb2 = np.fix((stride * (boundingbox) + cellsize - 1 + 1) /
                 scale).T  # while python don't have to
    score = np.array([score])

    boundingbox_out = np.concatenate((bb1, bb2, score, reg), axis=0)

    return boundingbox_out.T


def drawBoxes(im, boxes):
    x1 = boxes[:, 0]
    y1 = boxes[:, 1]
    x2 = boxes[:, 2]
    y2 = boxes[:, 3]
    for i in range(x1.shape[0]):
        cv2.rectangle(im, (int(x1[i]), int(y1[i])),
                      (int(x2[i]), int(y2[i])), (0, 255, 0), 1)
    return im


_tstart_stack = []


def tic():
    _tstart_stack.append(time())


def toc(fmt="Elapsed: %s s"):
    print(fmt % (time() - _tstart_stack.pop()))


def detect_face(img, minsize, PNet, RNet, ONet, threshold, fastresize, factor):

    factor_count = 0
    total_boxes = np.zeros((0, 9), np.float)
    points = []
    h = img.shape[0]
    w = img.shape[1]
    minl = min(h, w)
    img = img.astype(float)
    m = 12.0 / minsize
    minl = minl * m

    # create scale pyramid
    scales = []
    while minl >= 12:
        scales.append(m * pow(factor, factor_count))
        minl *= factor
        factor_count += 1

    # first stage
    # print("#############################")
    for scale in scales:
        hs = int(np.ceil(h * scale))
        ws = int(np.ceil(w * scale))

        if fastresize:
            im_data = (img - 127.5) * 0.0078125  # [0,255] -> [-1,1]
            im_data = cv2.resize(im_data, (ws, hs))  # default is bilinear
        else:
            im_data = cv2.resize(img, (ws, hs))  # default is bilinear
            im_data = (im_data - 127.5) * 0.0078125  # [0,255] -> [-1,1]
        # im_data = imResample(img, hs, ws); print "scale:", scale

        im_data = np.swapaxes(im_data, 0, 2)
        im_data = np.array([im_data], dtype=np.float)
        # print(im_data.shape)
        PNet.blobs['data'].reshape(1, 3, ws, hs)
        PNet.blobs['data'].data[...] = im_data
        out = PNet.forward()

        boxes = generateBoundingBox(
            out['prob1'][0, 1, :, :], out['conv4-2'][0], scale, threshold[0])
        if boxes.shape[0] != 0:
            pick = nms(boxes, 0.5, 'Union')

            if len(pick) > 0:
                boxes = boxes[pick, :]

        if boxes.shape[0] != 0:
            total_boxes = np.concatenate((total_boxes, boxes), axis=0)

    numbox = total_boxes.shape[0]
    if numbox > 0:
        # nms
        pick = nms(total_boxes, 0.7, 'Union')
        total_boxes = total_boxes[pick, :]

        # revise and convert to square
        regh = total_boxes[:, 3] - total_boxes[:, 1]
        regw = total_boxes[:, 2] - total_boxes[:, 0]
        t1 = total_boxes[:, 0] + total_boxes[:, 5] * regw
        t2 = total_boxes[:, 1] + total_boxes[:, 6] * regh
        t3 = total_boxes[:, 2] + total_boxes[:, 7] * regw
        t4 = total_boxes[:, 3] + total_boxes[:, 8] * regh
        t5 = total_boxes[:, 4]
        total_boxes = np.array([t1, t2, t3, t4, t5]).T

        total_boxes = rerec(total_boxes)  # convert box to square
        total_boxes[:, 0:4] = np.fix(total_boxes[:, 0:4])

        [dy, edy, dx, edx, y, ey, x, ex, tmpw, tmph] = pad(total_boxes, w, h)

    numbox = total_boxes.shape[0]
    if numbox > 0:
        # second stage

        # construct input for RNet
        tempimg = np.zeros((numbox, 24, 24, 3))  # (24, 24, 3, numbox)
        for k in range(numbox):
            tmp = np.zeros((int(tmph[k]) + 1, int(tmpw[k]) + 1, 3))
            tmp[int(dy[k]):int(edy[k]) + 1, int(dx[k]):int(edx[k]) +
                1] = img[int(y[k]):int(ey[k]) + 1, int(x[k]):int(ex[k]) + 1]
            tempimg[k, :, :, :] = cv2.resize(tmp, (24, 24))

        # done in imResample function wrapped by python
        tempimg = (tempimg - 127.5) * 0.0078125

        # RNet
        tempimg = np.swapaxes(tempimg, 1, 3)

        RNet.blobs['data'].reshape(numbox, 3, 24, 24)
        RNet.blobs['data'].data[...] = tempimg
        out = RNet.forward()
        score = out['prob1'][:, 1]
        pass_t = np.where(score > threshold[1])[0]

        score = np.array([score[pass_t]]).T
        total_boxes = np.concatenate((total_boxes[pass_t, 0:4], score), axis=1)

        mv = out['conv5-2'][pass_t, :].T

        if total_boxes.shape[0] > 0:
            pick = nms(total_boxes, 0.7, 'Union')
            if len(pick) > 0:
                total_boxes = total_boxes[pick, :]
                total_boxes = bbreg(total_boxes, mv[:, pick])
                total_boxes = rerec(total_boxes)

        numbox = total_boxes.shape[0]
        if numbox > 0:
            # third stage

            total_boxes = np.fix(total_boxes)
            [dy, edy, dx, edx, y, ey, x, ex, tmpw,
                tmph] = pad(total_boxes, w, h)

            tempimg = np.zeros((numbox, 48, 48, 3))
            for k in range(numbox):
                tmp = np.zeros((int(tmph[k]), int(tmpw[k]), 3))
                tmp[int(dy[k]):int(edy[k]) + 1, int(dx[k]):int(edx[k]) + 1] \
                    = img[int(y[k]):int(ey[k]) + 1, int(x[k]):int(ex[k]) + 1]
                tempimg[k, :, :, :] = cv2.resize(tmp, (48, 48))
            tempimg = (tempimg - 127.5) * 0.0078125  # [0,255] -> [-1,1]

            # ONet
            tempimg = np.swapaxes(tempimg, 1, 3)
            ONet.blobs['data'].reshape(numbox, 3, 48, 48)
            ONet.blobs['data'].data[...] = tempimg
            out = ONet.forward()

            score = out['prob1'][:, 1]
            points = out['conv6-3']
            pass_t = np.where(score > threshold[2])[0]
            points = points[pass_t, :]
            score = np.array([score[pass_t]]).T
            total_boxes = np.concatenate(
                (total_boxes[pass_t, 0:4], score), axis=1)

            mv = out['conv6-2'][pass_t, :].T
            w = total_boxes[:, 3] - total_boxes[:, 1] + 1
            h = total_boxes[:, 2] - total_boxes[:, 0] + 1

            points[:, 0:5] = np.tile(
                w, (5, 1)).T * points[:, 0:5] + np.tile(
                total_boxes[:, 0], (5, 1)).T - 1
            points[:, 5:10] = np.tile(
                h, (5, 1)).T * points[:, 5:10] + np.tile(
                total_boxes[:, 1], (5, 1)).T - 1

            if total_boxes.shape[0] > 0:
                total_boxes = bbreg(total_boxes, mv[:, :])
                pick = nms(total_boxes, 0.7, 'Min')

                # print pick
                if len(pick) > 0:
                    total_boxes = total_boxes[pick, :]
                    points = points[pick, :]

    return total_boxes, points


def initFaceDetector():
    minsize = 20
    caffe_model_path = "/home/duino/iactive/mtcnn/model"
    threshold = [0.6, 0.7, 0.7]
    factor = 0.709
    caffe.set_mode_cpu()
    PNet = caffe.Net(caffe_model_path + "/det1.prototxt",
                     caffe_model_path + "/det1.caffemodel", caffe.TEST)
    RNet = caffe.Net(caffe_model_path + "/det2.prototxt",
                     caffe_model_path + "/det2.caffemodel", caffe.TEST)
    ONet = caffe.Net(caffe_model_path + "/det3.prototxt",
                     caffe_model_path + "/det3.caffemodel", caffe.TEST)
    return (minsize, PNet, RNet, ONet, threshold, factor)


def haveFace(img, facedetector):
    minsize = facedetector[0]
    PNet = facedetector[1]
    RNet = facedetector[2]
    ONet = facedetector[3]
    threshold = facedetector[4]
    factor = facedetector[5]

    if max(img.shape[0], img.shape[1]) < minsize:
        return False, []

    img_matlab = img.copy()
    tmp = img_matlab[:, :, 2].copy()
    img_matlab[:, :, 2] = img_matlab[:, :, 0]
    img_matlab[:, :, 0] = tmp

    # tic()
    boundingboxes, points = detect_face(
        img_matlab, minsize, PNet, RNet, ONet, threshold, False, factor)
    # toc()
    containFace = (True, False)[boundingboxes.shape[0] == 0]
    return containFace, boundingboxes