'''图像金字塔'''

def resize(img, scaleFactor):

    # cv2.resize先接收列后接收行，返回亦然

    return cv2.resize(img, (int(img.shape[1] * (1/scaleFactor)),

                            int(img.shape[0] * (1/scaleFactor))),

                      interpolation=cv2.INTER_AREA)

def pyramid(image, scale=1.5, minSize = (200, 80)):

    yield image

    while True:

        image = resize(image, scale)

        if image.shape[0]  < minSize[1] or image.shape[1] < minSize[0]:

            break

        yield  image

'''滑动窗口'''

def sliding_window(image, stepSize, windowSize):

    for y in range(0, image.shape[0], stepSize):

        for x in range(0, image.shape[1], stepSize):

            yield(x, y, image[y:y+windowSize[1], x:x+windowSize[0]])

'''非极大值抑制'''

def non_max_suppression_fast(boxes, overlapThresh):

    # 如果没有box，返回空list

    if len(boxes) == 0:

        return []

    # 修改boxes的格式为float方便处理

    if boxes.dtype.kind == 'i':

        boxes = boxes.astype('float')

    # 使用pick收集boxes

    pick = []

    x1 = boxes[:, 0]

    y1 = boxes[:, 1]

    x2 = boxes[:, 2]

    y2 = boxes[:, 3]

    scores = boxes[:, 4]

    area = (x2 - x1 + 1) * (y2 - y1 + 1)

    # 按照score从小到大的顺序排序indexes

    idxs = np.argsort(scores)[::-1]

    while len(idxs) > 0:

        # 分配最后一个（得分最高）index给i，并使用pick收集这个index（即i）

        last = len(idxs) - 1

        i = idxs[last]

        pick.append(i)

        # 在得分大于当前i的boxes中，

        # 找到重合部分的左上点和右下点

        xx1 = np.maximum(x1[i], x1[idxs[:last]])

        yy1 = np.maximum(y1[i], y1[idxs[:last]])

        xx2 = np.minimum(x2[i], x2[idxs[:last]])

        yy2 = np.minimum(y2[i], y2[idxs[:last]])

        # 计算上面得到的重合面积

        w = np.maximum(0, xx2 - xx1 + 1)

        h = np.maximum(0, yy2 - yy1 + 1)

        # 计算重合度

        overlap = (w * h) / area[idxs[:last]]

        # 删除得分最高的项（循环开始已经收集了），

        # 删除

        idxs = np.delete(idxs, np.concatenate(([last],

                                               np.where(overlap > overlapThresh))))  # [0])))

        # 加上索引之后只删除一个得分最高的过重合矩形，所以不应该加索引

    return boxes[pick].astype('int')