python opencv投影变换增强

一个是投影变换增强，一个是旋转增强

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


def rad(x):
    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)

    # 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]

    warpR = cv2.getPerspectiveTransform(org, dst)

    result = cv2.warpPerspective(img, warpR, (h, w))

    return result



 def rotate(image, angle_vari=30):
     angle = np.random.uniform(-angle_vari, angle_vari)
     rows, cols = image.shape[:2]
     M = cv2.getRotationMatrix2D((cols / 2, rows / 2), angle, 1)
     dst = cv2.warpAffine(image, M, (cols, rows))
     return dst

if __name__ == '__main__':
    img = cv2.imread(r"D:\data\face_mask\1/20200526_173738_452720.jpg")
    angle_vari = 30

    while True:
        result=rotate_3(img, angle_vari=angle_vari)
        cv2.imshow("result", result)
        c = cv2.waitKey()