hivisionai/hycv/idphotoTool/neck_processing.py

import cv2
import numpy as np
from ..utils import get_box_pro
from ..vision import cover_image


def transformationNeck(image:np.ndarray, cutNeckHeight:int, neckBelow:int,
                       toHeight:int,per_to_side:float=0.75) -> np.ndarray:
    """
    脖子扩充算法, 其实需要输入的只是脖子扣出来的部分以及需要被扩充的高度/需要被扩充成的高度.
    """
    height, width, channels = image.shape
    _, _, _, a = cv2.split(image)  # 这应该是一个四通道的图像
    ret, a_thresh = cv2.threshold(a, 20, 255, cv2.THRESH_BINARY)  # 将透明图层二值化
    def locate_width(image_:np.ndarray, y_:int, mode, left_or_right:int=None):
        # 从y=y这个水平线上寻找两边的非零点
        # 增加left_or_right的原因在于为下面check_jaw服务
        if mode==1:  # 左往右
            x_ = 0
            if left_or_right is None:
                left_or_right = 0
            for x_ in range(left_or_right, width):
                if image_[y_][x_] != 0:
                    break
        else:  # 右往左
            x_ = width
            if left_or_right is None:
                left_or_right = width - 1
            for x_ in range(left_or_right, -1, -1):
                if image_[y_][x_] != 0:
                    break
        return x_
    def check_jaw(image_:np.ndarray, left_, right_):
        """
        检查选择的点是否与截到下巴,如果截到了,就往下平移一个单位
        """
        f= True # True代表没截到下巴
        # [x, y]
        for x_cell in range(left_[0] + 1, right_[0]):
            if image_[left_[1]][x_cell] == 0:
                f = False
                break
        if f is True:
            return left_, right_
        else:
            y_ = left_[1] + 2
            x_left_ = locate_width(image_, y_, mode=1, left_or_right=left_[0])
            x_right_ = locate_width(image_, y_, mode=2, left_or_right=right_[0])
            left_, right_ = check_jaw(image_, [x_left_, y_], [x_right_, y_])
        return left_, right_
    x_left = locate_width(image_=a_thresh, mode=1, y_=cutNeckHeight)
    x_right = locate_width(image_=a_thresh, mode=2, y_=cutNeckHeight)
    # 在这里我们取消了对下巴的检查,原因在于输入的imageHeight并不能改变
    # cell_left_above, cell_right_above = check_jaw(a_thresh, [x_left, imageHeight], [x_right, imageHeight])
    cell_left_above, cell_right_above = [x_left, cutNeckHeight], [x_right, cutNeckHeight]
    toWidth = x_right - x_left  # 矩形宽
    # 此时我们寻找到了脖子的"宽出来的"两个点,这两个点作为上面的两个点, 接下来寻找下面的两个点
    if per_to_side >1:
        assert ValueError("per_to_side 必须小于1!")
    y_below = int((neckBelow - cutNeckHeight) * per_to_side + cutNeckHeight)  # 定位y轴坐标
    cell_left_below = [locate_width(a_thresh, y_=y_below, mode=1), y_below]
    cell_right_bellow = [locate_width(a_thresh, y_=y_below, mode=2), y_below]
    # 四个点全齐,开始透视变换
    # 需要变换的四个点为 cell_left_above, cell_right_above, cell_left_below, cell_right_bellow
    rect = np.array([cell_left_above, cell_right_above, cell_left_below, cell_right_bellow],
                    dtype='float32')
    # 变化后的坐标点
    dst = np.array([[0, 0], [toWidth, 0], [0 , toHeight], [toWidth, toHeight]],
                    dtype='float32')
    M = cv2.getPerspectiveTransform(rect, dst)
    warped = cv2.warpPerspective(image, M, (toWidth, toHeight))
    # 将变换后的图像覆盖到原图上
    final = cover_image(image=warped, background=image, mode=3, x=cell_left_above[0], y=cell_left_above[1])
    return final


def transformationNeck2(image:np.ndarray, per_to_side:float=0.8)->np.ndarray:
    """
    透视变换脖子函数,输入图像和四个点(矩形框)
    矩形框内的图像可能是不完整的(边角有透明区域)
    我们将根据透视变换将矩形框内的图像拉伸成和矩形框一样的形状.
    算法分为几个步骤: 选择脖子的四个点 -> 选定这四个点拉伸后的坐标 -> 透视变换 -> 覆盖原图
    """
    b, g, r, a = cv2.split(image)  # 这应该是一个四通道的图像
    height, width = a.shape
    def locate_side(image_:np.ndarray, x_:int, y_max:int) -> int:
        # 寻找x=y, 且 y <= y_max 上从下往上第一个非0的点,如果没找到就返回0
        y_ = 0
        for y_ in range(y_max - 1, -1, -1):
            if image_[y_][x_] != 0:
                break
        return y_
    def locate_width(image_:np.ndarray, y_:int, mode, left_or_right:int=None):
        # 从y=y这个水平线上寻找两边的非零点
        # 增加left_or_right的原因在于为下面check_jaw服务
        if mode==1:  # 左往右
            x_ = 0
            if left_or_right is None:
                left_or_right = 0
            for x_ in range(left_or_right, width):
                if image_[y_][x_] != 0:
                    break
        else:  # 右往左
            x_ = width
            if left_or_right is None:
                left_or_right = width - 1
            for x_ in range(left_or_right, -1, -1):
                if image_[y_][x_] != 0:
                    break
        return x_
    def check_jaw(image_:np.ndarray, left_, right_):
        """
        检查选择的点是否与截到下巴,如果截到了,就往下平移一个单位
        """
        f= True # True代表没截到下巴
        # [x, y]
        for x_cell in range(left_[0] + 1, right_[0]):
            if image_[left_[1]][x_cell] == 0:
                f = False
                break
        if f is True:
            return left_, right_
        else:
            y_ = left_[1] + 2
            x_left_ = locate_width(image_, y_, mode=1, left_or_right=left_[0])
            x_right_ = locate_width(image_, y_, mode=2, left_or_right=right_[0])
            left_, right_ = check_jaw(image_, [x_left_, y_], [x_right_, y_])
        return left_, right_
    # 选择脖子的四个点,核心在于选择上面的两个点,这两个点的确定的位置应该是"宽出来的"两个点
    _, _ ,_, a = cv2.split(image)  # 这应该是一个四通道的图像
    ret,a_thresh = cv2.threshold(a,127,255,cv2.THRESH_BINARY)
    y_high, y_low, x_left, x_right = get_box_pro(image=image, model=1) # 直接返回矩阵信息
    y_left_side = locate_side(image_=a_thresh, x_=x_left, y_max=y_low)  # 左边的点的y轴坐标
    y_right_side = locate_side(image_=a_thresh, x_=x_right, y_max=y_low)  # 右边的点的y轴坐标
    y = min(y_left_side, y_right_side)  # 将两点的坐标保持相同
    cell_left_above, cell_right_above = check_jaw(a_thresh,[x_left, y], [x_right, y])
    x_left, x_right = cell_left_above[0], cell_right_above[0]
    # 此时我们寻找到了脖子的"宽出来的"两个点,这两个点作为上面的两个点, 接下来寻找下面的两个点
    if per_to_side >1:
        assert ValueError("per_to_side 必须小于1!")
    # 在后面的透视变换中我会把它拉成矩形, 在这里我先获取四个点的高和宽
    height_ = 100  # 这个值应该是个变化的值,与拉伸的长度有关,但是现在先规定为150
    width_ = x_right - x_left  # 其实也就是 cell_right_above[1] - cell_left_above[1]
    y = int((y_low - y)*per_to_side + y)  # 定位y轴坐标
    cell_left_below, cell_right_bellow = ([locate_width(a_thresh, y_=y, mode=1), y], [locate_width(a_thresh, y_=y, mode=2), y])
    # 四个点全齐,开始透视变换
    # 寻找透视变换后的四个点,只需要变换below的两个点即可
    # cell_left_below_final, cell_right_bellow_final = ([cell_left_above[1], y_low], [cell_right_above[1], y_low])
    # 需要变换的四个点为 cell_left_above, cell_right_above, cell_left_below, cell_right_bellow
    rect = np.array([cell_left_above, cell_right_above, cell_left_below, cell_right_bellow],
                    dtype='float32')
    # 变化后的坐标点
    dst = np.array([[0, 0], [width_, 0], [0 , height_], [width_, height_]],
                    dtype='float32')
    # 计算变换矩阵
    M = cv2.getPerspectiveTransform(rect, dst)
    warped = cv2.warpPerspective(image, M, (width_, height_))

    # a = cv2.erode(a, (10, 10))
    # image = cv2.merge((r, g, b, a))
    final = cover_image(image=warped, background=image, mode=3, x=cell_left_above[0], y=cell_left_above[1])
    # tmp = np.zeros(image.shape)
    # final = cover_image(image=warped, background=tmp, mode=3, x=cell_left_above[0], y=cell_left_above[1])
    # final = cover_image(image=image, background=final, mode=3, x=0, y=0)
    return final


def bestJunctionCheck(image:np.ndarray, offset:int, stepSize:int=2):
    """
    最优点检测算算法输入一张脖子图片(无论这张图片是否已经被二值化,我都认为没有被二值化),输出一个小数(脖子最上方与衔接点位置/脖子图像长度)
    与beta版不同的是它新增了一个阈值限定内容.
    对于脖子而言,我我们首先可以定位到上面的部分,然后根据上面的这个点向下进行遍历检测.
    与beta版类似,我们使用一个stepSize来用作斜率的检测
    但是对于遍历检测而言,与beta版不同的是,我们需要对遍历的地方进行一定的限制.
    限制的标准是,如果当前遍历的点的横坐标和起始点横坐标的插值超过了某个阈值,则认为是越界.
    """
    point_k = 1
    _, _, _, a = cv2.split(image)  # 这应该是一个四通道的图像
    height, width = a.shape
    ret, a_thresh = cv2.threshold(a, 127, 255, cv2.THRESH_BINARY)  # 将透明图层二值化
    # 直接返回脖子的位置信息, 修正系数为0, get_box_pro内部也封装了二值化,所以直接输入原图
    y_high, y_low, _, _ = get_box_pro(image=image, model=1, correction_factor=0)
    # 真正有用的只有上下y轴的两个值...
    # 首先当然是确定起始点的位置,我们用同样的scan扫描函数进行行遍历.
    def scan(y_:int, max_num:int=2):
        num = 0
        # 设定两个值,分别代表脖子的左边和右边
        left = False
        right = False
        for x_ in range(width):
            if a_thresh[y_][x_] != 0:
                # 检测左边
                if x_ < width // 2 and left is False:
                    num += 1
                    left = True
                # 检测右边
                elif x_ > width // 2 and right is False:
                    num += 1
                    right = True
        return True if num >= max_num else False
    def locate_neck_above():
        """
        定位脖子的尖尖脚
        """
        # y_high就是脖子的最高点
        for y_ in range(y_high, height):
            if scan(y_):
                return y_
    y_start = locate_neck_above()  # 得到遍历的初始高度
    if y_low - y_start < stepSize: assert ValueError("脖子太小!")
    # 然后获取一下初始的坐标点
    x_left, x_right = 0, width
    for x_left_ in range(0, width):
        if a_thresh[y_start][x_left_] != 0:
            x_left = x_left_
            break
    for x_right_  in range(width -1 , -1, -1):
        if a_thresh[y_start][x_right_] != 0:
            x_right = x_right_
            break
    # 接下来我定义两个生成器,首先是脖子轮廓(向下寻找的)生成器,每进行一次next,生成器会返回y+1的脖子轮廓点
    def contoursGenerator(image_:np.ndarray, y_:int, mode):
        """
        这会是一个生成器,用于生成脖子两边的轮廓
        y_ 是启始点的y坐标,每一次寻找都会让y_+1
        mode==1说明是找左边的边,即,image_[y_][x_] == 0 且image_[y_][x_ + 1] !=0 时跳出;
            否则 当image_[y_][x_] != 0 时, x_ - 1; 当image_[y_][x_] == 0 且 image_[y_][x_ + 1] ==0 时x_ + 1
        mode==2说明是找右边的边,即,image_[y_][x_] == 0 且image_[y_][x_ - 1] !=0 时跳出
            否则 当image_[y_][x_] != 0 时, x_ + 1; 当image_[y_][x_] == 0 且 image_[y_][x_ - 1] ==0 时x_ - 1
        """
        y_ += 1
        try:
            if mode == 1:
                x_ = 0
                while 0 <= y_ < height and 0 <= x_ < width:
                    while image_[y_][x_] != 0 and x_ >= 0: x_ -= 1
                    # 这里其实会有bug,不过可以不管
                    while x_ < width and image_[y_][x_] == 0 and image_[y_][x_ + 1] == 0: x_ += 1
                    yield [y_, x_]
                    y_ += 1
            elif mode == 2:
                x_ = width-1
                while 0 <= y_ < height and 0 <= x_ < width:
                    while x_ < width and image_[y_][x_] != 0: x_ += 1
                    while x_ >= 0 and image_[y_][x_] == 0 and image_[y_][x_ - 1] == 0: x_ -= 1
                    yield [y_, x_]
                    y_ += 1
        # 当处理失败则返回False
        except IndexError:
            yield False
    # 然后是斜率生成器,这个生成器依赖子轮廓生成器,每一次生成轮廓后会计算斜率,另一个点的选取和stepSize有关
    def kGenerator(image_: np.ndarray, mode):
        """
        导数生成器,用来生成每一个点对应的导数
        """
        y_ = y_start
        # 对起始点建立一个生成器, mode=1时是左边轮廓,mode=2时是右边轮廓
        c_generator = contoursGenerator(image_=image_, y_=y_, mode=mode)
        for cell in c_generator:
            # 寻找距离当前cell距离为stepSize的轮廓点
            kc = contoursGenerator(image_=image_, y_=cell[0] + stepSize, mode=mode)
            kCell = next(kc)
            if kCell is False:
                # 寻找失败
                yield False, False
            else:
                # 寻找成功,返回当坐标点和斜率值
                # 对于左边而言,斜率必然是前一个点的坐标减去后一个点的坐标
                # 对于右边而言,斜率必然是后一个点的坐标减去前一个点的坐标
                k = (cell[1] - kCell[1]) / stepSize if mode == 1 else (kCell[1] - cell[1]) / stepSize
                yield k, cell
    # 接着开始写寻找算法,需要注意的是我们是分两边选择的
    def findPt(image_:np.ndarray, mode):
        x_base = x_left if mode == 1 else x_right
        k_generator = kGenerator(image_=image_, mode=mode)
        k, cell = k_generator.__next__()
        if k is False:
            raise ValueError("无法找到拐点!")
        k_next, cell_next = k_generator.__next__()
        while k_next is not False:
            cell = cell_next
            # if cell[1] > x_base and mode == 2:
            #     x_base = cell[1]
            # elif cell[1] < x_base and mode == 1:
            #     x_base = cell[1]
            # 跳出循环的方式一:斜率超过了某个值
            if k_next > point_k:
                print("K out")
                break
            # 跳出循环的方式二:超出阈值
            elif abs(cell[1] - x_base) > offset:
                print("O out")
                break
            k_next, cell_next = k_generator.__next__()
        if abs(cell[1] - x_base) > offset:
            cell[0] = cell[0] - offset - 1
        return cell[0]
    # 先找左边的拐点:
    pointY_left = findPt(image_=a_thresh, mode=1)
    # 再找右边的拐点:
    pointY_right = findPt(image_=a_thresh, mode=2)
    point = min(pointY_right, pointY_left)
    per = (point - y_high) / (y_low - y_high)
    # pointX_left = next(contoursGenerator(image_=a_thresh, y_= point- 1, mode=1))[1]
    # pointX_right = next(contoursGenerator(image_=a_thresh, y_=point - 1, mode=2))[1]
    # return [pointX_left, point], [pointX_right, point]
    return per





if __name__ == "__main__":
    img = cv2.imread("./neck_temp/neck_image6.png", cv2.IMREAD_UNCHANGED)
    new = transformationNeck(img)
    cv2.imwrite("./1.png", new)