图片批量化进行弹性形变 仿射变换 旋转变换python代码

以下是使用Python进行图片批量化进行弹性形变、仿射变换、旋转变换的示例代码：

弹性形变：

import numpy as np
import cv2
import os

def elastic_transform(image, alpha, sigma, alpha_affine, random_state=None):
    if random_state is None:
        random_state = np.random.RandomState(None)

    shape = image.shape
    shape_size = shape[:2]

    # Random affine
    center_square = np.float32(shape_size) // 2
    square_size = min(shape_size) // 3
    pts1 = np.float32([center_square + square_size, [center_square[0]+square_size, center_square[1]-square_size], center_square - square_size])
    pts2 = pts1 + random_state.uniform(-alpha_affine, alpha_affine, size=pts1.shape).astype(np.float32)
    M = cv2.getAffineTransform(pts1, pts2)
    image = cv2.warpAffine(image, M, shape_size[::-1], borderMode=cv2.BORDER_REFLECT_101)

    dx = gaussian_filter((random_state.rand(*shape) * 2 - 1), sigma) * alpha
    dy = gaussian_filter((random_state.rand(*shape) * 2 - 1), sigma) * alpha
    dz = np.zeros_like(dx)

    x, y, z = np.meshgrid(np.arange(shape[1]), np.arange(shape[0]), np.arange(shape[2]))
    indices = np.reshape(y+dy, (-1, 1)), np.reshape(x+dx,(-1,1)), np.reshape(z+dz,(-1,1))

    return map_coordinates(image, indices, order=1, mode='reflect').reshape(shape)

def gaussian_filter(X, sigma):
    filter_shape = 2 * np.ceil(2 * sigma) + 1
    x = np.arange(-filter_shape/2, filter_shape/2+1)
    gauss_filter = np.exp(-x**2/(2*sigma**2))
    gauss_filter /= np.sum(gauss_filter)
    return np.apply_along_axis(lambda m: np.convolve(m, gauss_filter, mode='valid'), 0, X)

def map_coordinates(X, indices, order=1, mode='reflect'):
    output_shape = indices[0].shape
    indices = np.reshape(indices, (len(X.shape), -1))
    indices = np.concatenate((indices, np.zeros((len(X.shape)-indices.shape[0], indices.shape[1]))))
    X = np.pad(X, ((1,1),(1,1),(1,1)), mode=mode)
    output = np.zeros(output_shape)
    for i, coord in enumerate(indices):
        output += X[tuple([coord + 1])] * _bspline_kernel(coord - indices[i], order=order)
    return output

def _bspline_kernel(x, order):
    if order == 0:
        return np.where(np.abs(x) < 1, 1, 0)
    elif order == 1:
        return np.where(np.abs(x) < 0.5, 0.75 - x**2, np.where(np.abs(x) < 1.5, 0.5 * (1.5 - np.abs(x))**2, 0))
    elif order == 2:
        return np.where(np.abs(x) < 1, 1/3 - x**2 + 0.5*np.abs(x)**3, np.where(np.abs(x) < 2, 1/6*(2-x)**3, 0))
    elif order == 3:
        return np.where(np.abs(x) < 0.5, 2/3 - 4/3*x**2 + 0.5*np.abs(x)**3, np.where(np.abs(x) < 1.5, 1/12*(2-x)**3 - 2/3*(1-x)**3 + 0.75*(1-np.abs(x))**3, np.where(np.abs(x) < 2.5, 1/12*(2-x)**3 + 2/3*(1-x)**3 + 0.75*(1+np.abs(x))**3 - 2*(2+np.abs(x))**3/3/np.abs(x)**3, 0)))

if __name__ == '__main__':
    path = 'path/to/images/'
    alpha = 2000
    sigma = 20
    alpha_affine = 50
    for filename in os.listdir(path):
        img = cv2.imread(os.path.join(path, filename))
        img = elastic_transform(img, alpha, sigma, alpha_affine)
        cv2.imwrite(os.path.join(path, 'elastic_' + filename), img)

仿射变换：

import cv2
import numpy as np
import os

def affine_transform(image, alpha_affine, random_state=None):
    if random_state is None:
        random_state = np.random.RandomState(None)

    shape = image.shape
    shape_size = shape[:2]

    center_square = np.float32(shape_size) // 2
    square_size = min(shape_size) // 3
    pts1 = np.float32([center_square + square_size, [center_square[0]+square_size, center_square[1]-square_size], center_square - square_size])
    pts2 = pts1 + random_state.uniform(-alpha_affine, alpha_affine, size=pts1.shape).astype(np.float32)
    M = cv2.getAffineTransform(pts1, pts2)
    image = cv2.warpAffine(image, M, shape_size[::-1], borderMode=cv2.BORDER_REFLECT_101)

    return image

if __name__ == '__main__':
    path = 'path/to/images/'
    alpha_affine = 50
    for filename in os.listdir(path):
        img = cv2.imread(os.path.join(path, filename))
        img = affine_transform(img, alpha_affine)
        cv2.imwrite(os.path.join(path, 'affine_' + filename), img)

旋转变换：

import cv2
import numpy as np
import os

def rotate_transform(image, angle, scale=1.0):
    (h, w) = image.shape[:2]
    center = (w / 2, h / 2)
    M = cv2.getRotationMatrix2D(center, angle, scale)
    rotated = cv2.warpAffine(image, M, (w, h))
    return rotated

if __name__ == '__main__':
    path = 'path/to/images/'
    angle = 30
    for filename in os.listdir(path):
        img = cv2.imread(os.path.join(path, filename))
        img = rotate_transform(img, angle)
        cv2.imwrite(os.path.join(path, 'rotate_' + filename), img)