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
mysqldump数据库备份无法进行操作只能查询 --single-transaction
查看>>
mysqldump的一些用法
查看>>
mysqli
查看>>
MySQLIntegrityConstraintViolationException异常处理
查看>>
mysqlreport分析工具详解
查看>>
MySQLSyntaxErrorException: Unknown error 1146和SQLSyntaxErrorException: Unknown error 1146
查看>>
Mysql_Postgresql中_geometry数据操作_st_astext_GeomFromEWKT函数_在java中转换geometry的16进制数据---PostgreSQL工作笔记007
查看>>
mysql_real_connect 参数注意
查看>>
mysql_secure_installation初始化数据库报Access denied
查看>>
MySQL_西安11月销售昨日未上架的产品_20161212
查看>>
Mysql——深入浅出InnoDB底层原理
查看>>
MySQL“被动”性能优化汇总
查看>>
MySQL、HBase 和 Elasticsearch:特点与区别详解
查看>>
MySQL、Redis高频面试题汇总
查看>>
MYSQL、SQL Server、Oracle数据库排序空值null问题及其解决办法
查看>>
mysql一个字段为空时使用另一个字段排序
查看>>
MySQL一个表A中多个字段关联了表B的ID,如何关联查询?
查看>>
MYSQL一直显示正在启动
查看>>
MySQL一站到底!华为首发MySQL进阶宝典,基础+优化+源码+架构+实战五飞
查看>>
MySQL万字总结!超详细!
查看>>