import numpy as np


def bbox_iou(box1, box2, x1y1x2y2=True):
    if x1y1x2y2:
        mx = min(box1[0], box2[0])
        Mx = max(box1[2], box2[2])
        my = min(box1[1], box2[1])
        My = max(box1[3], box2[3])
        w1 = box1[2] - box1[0]
        h1 = box1[3] - box1[1]
        w2 = box2[2] - box2[0]
        h2 = box2[3] - box2[1]
    else:
        mx = min(float(box1[0]-box1[2]/2.0), float(box2[0]-box2[2]/2.0))
        Mx = max(float(box1[0]+box1[2]/2.0), float(box2[0]+box2[2]/2.0))
        my = min(float(box1[1]-box1[3]/2.0), float(box2[1]-box2[3]/2.0))
        My = max(float(box1[1]+box1[3]/2.0), float(box2[1]+box2[3]/2.0))
        w1 = box1[2]
        h1 = box1[3]
        w2 = box2[2]
        h2 = box2[3]
    uw = Mx - mx
    uh = My - my
    cw = w1 + w2 - uw
    ch = h1 + h2 - uh
    carea = 0
    if cw <= 0 or ch <= 0:
        return 0.0

    area1 = w1 * h1
    area2 = w2 * h2
    carea = cw * ch
    uarea = area1 + area2 - carea
    return carea/uarea


def area2d(b):
    return (b[:,2]-b[:,0]+1)*(b[:,3]-b[:,1]+1)


def overlap2d(b1, b2):
    xmin = np.maximum( b1[:,0], b2[:,0] )
    xmax = np.minimum( b1[:,2]+1, b2[:,2]+1)
    width = np.maximum(0, xmax-xmin)
    ymin = np.maximum( b1[:,1], b2[:,1] )
    ymax = np.minimum( b1[:,3]+1, b2[:,3]+1)
    height = np.maximum(0, ymax-ymin)   
    return width*height


def nms_3d(detections, overlap=0.5):
    # detections: [(tube1, score1), (tube2, score2)]
    if len(detections) == 0:
        return np.array([], dtype=np.int32)
    I = np.argsort([d[1] for d in detections])
    indices = np.zeros(I.size, dtype=np.int32)
    counter = 0
    while I.size>0:
        i = I[-1]
        indices[counter] = i
        counter += 1
        ious = np.array([ iou3dt(detections[ii][0],detections[i][0]) for ii in I[:-1] ])
        I  = I[np.where(ious<=overlap)[0]]
    return indices[:counter]


def iou3d(b1, b2):
    assert b1.shape[0] == b2.shape[0]
    assert np.all(b1[:,0] == b2[:,0])
    o = overlap2d(b1[:,1:5],b2[:,1:5])
    return np.mean( o/(area2d(b1[:,1:5])+area2d(b2[:,1:5])-o) )


def iou3dt(b1, b2):
    tmin = max(b1[0,0], b2[0,0])
    tmax = min(b1[-1,0], b2[-1,0])
    if tmax <= tmin: return 0.0    
    temporal_inter = tmax-tmin+1
    temporal_union = max(b1[-1,0], b2[-1,0]) - min(b1[0,0], b2[0,0]) + 1 
    return iou3d( b1[np.where(b1[:,0]==tmin)[0][0]:np.where(b1[:,0]==tmax)[0][0]+1,:] , b2[np.where(b2[:,0]==tmin)[0][0]:np.where(b2[:,0]==tmax)[0][0]+1,:]  ) * temporal_inter / temporal_union


def voc_ap(pr, use_07_metric=False):
    """ ap = voc_ap(rec, prec, [use_07_metric])
    Compute VOC AP given precision and recall.
    If use_07_metric is true, uses the
    VOC 07 11 point method (default:False).
    """
    rec, prec = pr[:,1], pr[:,0]
    if use_07_metric:
        # 11 point metric
        ap = 0.
        for t in np.arange(0., 1.1, 0.1):
            if np.sum(rec >= t) == 0:
                p = 0
            else:
                p = np.max(prec[rec >= t])
            ap = ap + p / 11.
    else:
        # correct AP calculation
        # first append sentinel values at the end
        mrec = np.concatenate(([0.], rec, [1.]))
        mpre = np.concatenate(([0.], prec, [0.]))

        # compute the precision envelope
        for i in range(mpre.size - 1, 0, -1):
            mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i])

        # to calculate area under PR curve, look for points
        # where X axis (recall) changes value
        i = np.where(mrec[1:] != mrec[:-1])[0]

        # and sum (\Delta recall) * prec
        ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])
    return ap