博客
关于我
数据增强--对于目标检测(边框、图像)
阅读量:559 次
发布时间:2019-03-09

本文共 6607 字,大约阅读时间需要 22 分钟。

import torchfrom PIL import Image, ImageFont, ImageDrawfrom functools import reduceimport scipy.io as sciofrom PIL import Imageimport cv2 as cvimport numpy as npimport randomimport imutilsfrom imgaug import augmenters as iaadef horisontal_flip(images, targets):    images = torch.flip(images, [-1])    targets[:, 2] = 1 - targets[:, 2]    return images, targetsdef compose(*funcs):    """Compose arbitrarily many functions, evaluated left to right.    Reference: https://mathieularose.com/function-composition-in-python/    """    # return lambda x: reduce(lambda v, f: f(v), funcs, x)    if funcs:        return reduce(lambda f, g: lambda *a, **kw: g(f(*a, **kw)), funcs)    else:        raise ValueError('Composition of empty sequence not supported.')def letterbox_image(image, size):    '''resize image with unchanged aspect ratio using padding'''    iw, ih = image.size    w, h = size    scale = min(w / iw, h / ih)    nw = int(iw * scale)    nh = int(ih * scale)    image = image.resize((nw, nh), Image.BICUBIC)    new_image = Image.new('RGB', size, (128, 128, 128))    new_image.paste(image, ((w - nw) // 2, (h - nh) // 2))    return new_imagedef rand(a=0, b=1):    return np.random.rand() * (b - a) + adef resize_3D_data(ima, size=(416, 416)):    dat = np.zeros([416, 416, 15])    for i in range(15):        slice = np.squeeze(ima[:, :, i])        re_slice = cv.resize(slice.astype('uint8'), size, interpolation=cv.INTER_AREA)        dat[:, :, i] = re_slice    return datdef random_crop(cell, boxes):    dx = random.randint(15, 20)    dy = random.randint(15, 20)    shape = cell.shape    nx = shape[0]    ny = shape[1]    boxes[:, [0, 2]] = boxes[:, [0, 2]] - dx    boxes[:, [1, 3]] = boxes[:, [1, 3]] - dy    new_cell = np.zeros_like(cell)    new_cell[0:int(nx - dx), 0:int(ny - dy)] = cell[dx:, dy:]    new_cell = Image.fromarray(new_cell.astype('uint8')).convert('RGB')    new_cell = cv.cvtColor(np.asarray(new_cell), cv.COLOR_RGB2BGR)    boxes = np.where(boxes < 0, 0, boxes)    return new_cell, boxesdef random_noise(cell, boxes):    image = Image.fromarray(cell.astype('uint8')).convert('RGB')    image = cv.cvtColor(np.asarray(image), cv.COLOR_RGB2BGR)    seq = iaa.Sequential(        [            iaa.AdditiveGaussianNoise(scale=0.05 * 255),            iaa.LinearContrast((0.75, 1.5)),            iaa.GaussianBlur(sigma=(0, 4.0)),            iaa.Dropout(p=(0, 0.2)),            iaa.CoarseDropout(0.02, size_percent=0.5)        ], random_order=True    )    images_aug = seq.augment_images([image])[0]    new_cell = Image.fromarray(cv.cvtColor(images_aug, cv.COLOR_BGR2RGB))    return new_cell, boxesdef gray_level_crop(cell, boxes):    max_val = random.randint(80, 255) / 255    new_cell = cell * max_val    new_cell = Image.fromarray(new_cell.astype('uint8')).convert('RGB')    new_cell = cv.cvtColor(np.asarray(new_cell), cv.COLOR_RGB2BGR)    return new_cell, boxesdef rotate_box(box, M, shape):    # print(box)    y1, x1, y2, x2 = box    p1 = np.array([x1, y1, 1]).reshape((3, 1))    p2 = np.array([x1, y2, 1]).reshape((3, 1))    p3 = np.array([x2, y2, 1]).reshape((3, 1))    p4 = np.array([x2, y1, 1]).reshape((3, 1))    p1 = np.matmul(M, p1)    p2 = np.matmul(M, p2)    p3 = np.matmul(M, p3)    p4 = np.matmul(M, p4)    x1 = np.min([p1[0, 0], p2[0, 0], p3[0, 0], p4[0, 0]])    x2 = np.max([p1[0, 0], p2[0, 0], p3[0, 0], p4[0, 0]])    y1 = np.min([p1[1, 0], p2[1, 0], p3[1, 0], p4[1, 0]])    y2 = np.max([p1[1, 0], p2[1, 0], p3[1, 0], p4[1, 0]])    if x1 < 0:        x1 = 0    if x1 > shape[1]:        x1 = shape[1] - 1    if x2 < 0:        x2 = 0    if x2 > shape[1]:        x2 = shape[1] - 1    if y1 < 0:        y1 = 0    if y1 > shape[0]:        y1 = shape[0] - 1    if y2 < 0:        y2 = 0    if y2 > shape[0]:        y2 = shape[0] - 1    box = [y1, x1, y2, x2]    # print(box)    # print('--------------')    return boxdef random_rotate(cell, boxes, angle=45):    (h, w) = cell.shape    (cX, cY) = (w // 2, h // 2)    new_cell = imutils.rotate_bound(cell.astype('uint8'), angle)    M = cv.getRotationMatrix2D((cX, cY), -angle, 1.0)    cos = np.abs(M[0, 0])    sin = np.abs(M[0, 1])    # compute the new bounding dimensions of the image    nW = int((h * sin) + (w * cos))    nH = int((h * cos) + (w * sin))    # adjust the rotation matrix to take into account translation    M[0, 2] += (nW / 2) - cX    M[1, 2] += (nH / 2) - cY    new_boxes = []    for i in range(len(boxes)):        new_boxes.append(rotate_box(boxes[i], M, new_cell.shape))    if len(new_boxes) > 0:        new_boxes = np.array(new_boxes)    new_cell = Image.fromarray(new_cell.astype('uint8')).convert('RGB')    new_cell = cv.cvtColor(np.asarray(new_cell), cv.COLOR_RGB2BGR)    return new_cell, new_boxesdef random_argument(data, cell, boxes):    # crop region ...    if rand() < .5:        cell, boxes = random_crop(cell, boxes)    # add noise ...    if rand() < .5:        cell, boxes = random_noise(cell, boxes)    # add noise ...    if rand() < .5:        cell, boxes = gray_level_crop(cell, boxes)    # rotate ...    if rand() < .5:       cell, boxes = random_rotate(cell, boxes, random.randint(0, 90))    return cell, boxesdef pad_to_square(img, pad_value):    h, w, c = img.shape    dim_diff = np.abs(h - w)    # (upper / left) padding and (lower / right) padding    pad1, pad2 = dim_diff // 2, dim_diff - dim_diff // 2    # Determine padding    pad = ((0, 0), (pad1, pad2), (0, 0)) if w <= h else ((pad1, pad2), (0, 0), (0, 0))    # Add padding    img = np.pad(img, pad, "constant", constant_values=pad_value)    return img, padif __name__ == '__main__':    dat = scio.loadmat(        '/home/xuxu/Data/CTC_Signal_Datasets/Processed/20200616_cell_signalmark_39frames/Green/653802_650142_0_675.mat')    cell = dat['signal'].astype('uint8')    boxe = dat['rects']    print('boxe', boxe)    # image = Image.fromarray(cell.astype('uint8')).convert('RGB')    # image = cv.cvtColor(np.asarray(image), cv.COLOR_RGB2BGR)    # for x1, y1, x2, y2 in boxes:    #     cv.rectangle(image, (y1, x1), (y2, x2), (255, 255, 255), thickness=1)    # cv.imwrite('1.jpg', image)    new_cell, new_boxes = random_rotate(cell, boxe)    # new_cell, new_boxes = gray_level_crop(cell, boxe)    # print('new_boxes', new_boxes)    # new_boxes = np.where(new_boxes < 0, 0, new_boxes)    # print('new_boxes', new_boxes)    # image = Image.fromarray(new_cell.astype('uint8')).convert('RGB')    # image = cv.cvtColor(np.asarray(image), cv.COLOR_RGB2BGR)    for x1, y1, x2, y2 in new_boxes:        cv.rectangle(new_cell, (int(y1), int(x1)), (int(y2), int(x2)), (255, 255, 255), thickness=1)    cv.imwrite('1.jpg', new_cell)

其中图像加噪使用的是imgaug库

转载地址:http://ewhsz.baihongyu.com/

你可能感兴趣的文章
Mysql 拼接多个字段作为查询条件查询方法
查看>>
mysql 排序id_mysql如何按特定id排序
查看>>
Mysql 提示:Communication link failure
查看>>
mysql 插入是否成功_PDO mysql:如何知道插入是否成功
查看>>
Mysql 数据库InnoDB存储引擎中主要组件的刷新清理条件:脏页、RedoLog重做日志、Insert Buffer或ChangeBuffer、Undo Log
查看>>
mysql 数据库中 count(*),count(1),count(列名)区别和效率问题
查看>>
mysql 数据库备份及ibdata1的瘦身
查看>>
MySQL 数据库备份种类以及常用备份工具汇总
查看>>
mysql 数据库存储引擎怎么选择?快来看看性能测试吧
查看>>
MySQL 数据库操作指南:学习如何使用 Python 进行增删改查操作
查看>>
MySQL 数据库的高可用性分析
查看>>
MySQL 数据库设计总结
查看>>
Mysql 数据库重置ID排序
查看>>
Mysql 数据类型一日期
查看>>
MySQL 数据类型和属性
查看>>
mysql 敲错命令 想取消怎么办?
查看>>
Mysql 整形列的字节与存储范围
查看>>
mysql 断电数据损坏,无法启动
查看>>
MySQL 日期时间类型的选择
查看>>
Mysql 时间操作(当天,昨天,7天,30天,半年,全年,季度)
查看>>