对图片做透视变换，borderMode=cv2.BORDER_REFLECT,# 对称法，以图像边缘为轴进行对称填充

# -*- coding:utf-8 -*-
import cv2
import numpy as np

# class wraper():
#     def __init__(self):

def rad(x):#弧度，角度数×﹙π/180°﹚
    return x * np.pi / 180

def rotate_3(img, angle_vari=30):
    w, h = img.shape[0:2]
    fov = 42
    # anglex = np.random.uniform(-angle_vari, angle_vari)
    # angley = np.random.uniform(-angle_vari, angle_vari)
    # anglez = np.random.uniform(-angle_vari + 10, angle_vari - 10)
    anglex = -60# plan angle
    angley = -3.5 # align left-right
    anglez = 5
    #tan（FOV/ 2） = （实际视场宽度 / 2） / 实际视距 = （底片宽度 / 2） / 焦距
    # 镜头与图像间的距离，21为半可视角，算z的距离是为了保证在此可视角度下恰好显示整幅图像
    z = np.sqrt(w ** 2 + h ** 2) / 2 / np.tan(rad(fov / 2))
    # 齐次变换矩阵
    rx = np.array([[1, 0, 0, 0],
                   [0, np.cos(rad(anglex)), -np.sin(rad(anglex)), 0],
                   [0, -np.sin(rad(anglex)), np.cos(rad(anglex)), 0, ],
                   [0, 0, 0, 1]], np.float32)

    ry = np.array([[np.cos(rad(angley)), 0, np.sin(rad(angley)), 0],
                   [0, 1, 0, 0],
                   [-np.sin(rad(angley)), 0, np.cos(rad(angley)), 0, ],
                   [0, 0, 0, 1]], np.float32)

    rz = np.array([[np.cos(rad(anglez)), np.sin(rad(anglez)), 0, 0],
                   [-np.sin(rad(anglez)), np.cos(rad(anglez)), 0, 0],
                   [0, 0, 1, 0],
                   [0, 0, 0, 1]], np.float32)
    r = rx.dot(ry).dot(rz)
    # 四对点的生成
    pcenter = np.array([h / 2, w / 2, 0, 0], np.float32)

    p1 = np.array([0, 0, 0, 0], np.float32) - pcenter
    p2 = np.array([w, 0, 0, 0], np.float32) - pcenter
    p3 = np.array([0, h, 0, 0], np.float32) - pcenter
    p4 = np.array([w, h, 0, 0], np.float32) - pcenter
    dst1 = r.dot(p1)
    dst2 = r.dot(p2)
    dst3 = r.dot(p3)
    dst4 = r.dot(p4)
    list_dst = [dst1, dst2, dst3, dst4]
    org = np.array([[0, 0],
                    [w, 0],
                    [0, h],
                    [w, h]], np.float32)
    dst = np.zeros((4, 2), np.float32)

    # 投影至成像平面
    for i in range(4):
        dst[i, 0] = list_dst[i][0] * z / (z - list_dst[i][2]) + pcenter[0]
        dst[i, 1] = list_dst[i][1] * z / (z - list_dst[i][2]) + pcenter[1]
    # 计算变换矩阵
    warpR = cv2.getPerspectiveTransform(org, dst)

    result = cv2.warpPerspective(img, warpR, (h, w),
                                 flags=cv2.INTER_NEAREST,
                                 borderMode=cv2.BORDER_REFLECT,# 对称法，以图像边缘为轴进行对称填充
                                 borderValue = 0)

    return result


import os
i = 0
if __name__ == '__main__':

    floor_dir = '/home/yao/Documents/webfloor/wood_floor_resize640'
    savePath = '/home/yao/Documents/webfloor/wraper_floor_crop2/'
    floor_list = [img for img in os.listdir(floor_dir)]
    n = len(floor_list)
    # im1 = '/home/yao/Documents/webfloor/wood_floor_resize1024/000008.jpg'
    for im in floor_list:
        imPath = os.path.join(floor_dir,im)
        img = cv2.imread(imPath)
        angle_vari = 60
        # while True:
        result = rotate_3(img, angle_vari=angle_vari)
        tem = result.copy()
        # result[tem==0]=100
        # result_resize = cv2.resize(result,())
        cv2.imwrite('{}/floor_{}.jpg'.format(savePath,i), result)
        i += 1
        # cv2.imwrite('0001.jpg' , result)
        print(result.shape)
        # cv2.imshow("result", result)
        # c = cv2.waitKey(0)
        # # exit(00)