Pytorch学习记录（十一）：数据增强、torchvision.transforms各函数讲解

常用的数据增强方法
常用的数据增强方法如下：
1.对图片进行一定比例缩放
2.对图片进行随机位置的截取
3.对图片进行随机的水平和竖直翻转
4.对图片进行随机角度的旋转
5.对图片进行亮度、对比度和颜色的随机变化

import sys
sys.path.append('..')from PIL import Image
from torchvision import transforms as tfs

# 读入一张图片
im = Image.open('./cat.png')
im

随机比例放缩

随机比例缩放主要使用的是 torchvision.transforms.Resize()
第一个参数可以是一个整数，那么图片会保存现在的宽和高的比例，并将更短的边缩放到这个整数的大小，第一个参数也可以是一个 tuple，那么图片会直接把宽和高缩放到这个大小；第二个参数表示放缩图片使用的方法，比如最邻近法，或者双线性差值等，一般双线性差值能够保留图片更多的信息，所以 pytorch 默认使用的是双线性差值。

# 比例缩放
print('before scale, shape: {}'.format(im.size))
new_im = tfs.Resize((100, 200))(im)
print('after scale, shape: {}'.format(new_im.size))
new_im

随机位置截取

torchvision.transforms.RandomCrop()，传入的参数就是截取出的图片的长和宽，对图片在随机位置进行截取，torchvision.transforms.CenterCrop()，同样传入介曲初的图片的大小作为参数，会在图片的中心进行截取。

# 随机裁剪出 100 x 100 的区域
random_im1 = tfs.RandomCrop(100)(im)
random_im1

# 中心裁剪出 100 x 100 的区域
center_im = tfs.CenterCrop(100)(im)
center_im

随机的水平和竖直方向翻转

随机翻转使用的是 torchvision.transforms.RandomHorizontalFlip() 和 torchvision.transforms.RandomVerticalFlip()

# 随机水平翻转
h_filp = tfs.RandomHorizontalFlip()(im)
h_filp

# 随机竖直翻转
v_flip = tfs.RandomVerticalFlip()(im)
v_flip

随机角度旋转

一些角度的旋转仍然是非常有用的数据增强方式，在 torchvision 中，使用 torchvision.transforms.RandomRotation() 来实现，其中第一个参数就是随机旋转的角度，比如填入 10，那么每次图片就会在 -10 ~ 10 度之间随机旋转。

rot_im = tfs.RandomRotation(45)(im)
rot_im

亮度、对比度和颜色的变化

torchvision 中主要使用 torchvision.transforms.ColorJitter() 来实现的，第一个参数就是亮度的比例，第二个是对比度，第三个是颜色。

# 亮度
bright_im = tfs.ColorJitter(brightness=1)(im) # 随机从 0 ~ 2 之间亮度变化，1 表示原图
bright_im

# 对比度
contrast_im = tfs.ColorJitter(contrast=1)(im) # 随机从 0 ~ 2 之间对比度变化，1 表示原图
contrast_im

# 颜色
color_im = tfs.ColorJitter(hue=0.5)(im) # 随机从 -0.5 ~ 0.5 之间对颜色变化
color_im

比如先做随机翻转，然后随机截取，再做对比度增强等等，torchvision 里面有个非常方便的函数能够将这些变化合起来，就是torchvision.transforms.Compose()，下面我们举个例子

im_aug = tfs.Compose([tfs.Resize(120),tfs.RandomHorizontalFlip(),tfs.RandomCrop(96),tfs.ColorJitter(brightness=0.5, contrast=0.5, hue=0.5)
])

nrows = 3
ncols = 3
figsize = (8, 8)
_, figs = plt.subplots(nrows, ncols, figsize=figsize)
for i in range(nrows):for j in range(ncols):figs[i][j].imshow(im_aug(im))figs[i][j].axes.get_xaxis().set_visible(False)figs[i][j].axes.get_yaxis().set_visible(False)
plt.show()

# coding = utf-8
# @Time    : 2020-11-17
# @Author  : 郭冰洋
# @File    : data_aug.py
# @Desc    : 数据增强处理
# @Usual   : Compose RandomHflip RandomVflip Reszie RandomCrop Normalize Rotate RandomRotatefrom __future__ import division
import cv2
import numpy as np
from numpy import random
import math
from torchvision import transforms# 所有处理类型
__all__ = ['Compose',# 综合处理多个并写以下的处理,'RandomHflip', 'RandomUpperCrop', 'Resize', 'UpperCrop', 'RandomBottomCrop',"RandomErasing",'BottomCrop', 'Normalize', 'RandomSwapChannels', 'RandomRotate', 'RandomHShift',"CenterCrop","RandomVflip",'ExpandBorder', 'RandomResizedCrop','RandomDownCrop', 'DownCrop', 'ResizedCrop',"FixRandomRotate"]# 旋转（rotate）
def rotate_nobound(image, angle, center=None, scale=1.):(h, w) = image.shape[:2]# if the center is None, initialize it as the center of the imageif center is None:center = (w // 2, h // 2)    # perform the rotationM = cv2.getRotationMatrix2D(center, angle, scale)rotated = cv2.warpAffine(image, M, (w, h))return rotated# 缩放（scale）
def scale_down(src_size, size):w, h = sizesw, sh = src_sizeif sh < h:w, h = float(w * sh) / h, shif sw < w:w, h = sw, float(h * sw) / wreturn int(w), int(h)# 固定裁剪（fixed crop）
def fixed_crop(src, x0, y0, w, h, size=None):out = src[y0:y0 + h, x0:x0 + w]if size is not None and (w, h) != size:out = cv2.resize(out, (size[0], size[1]), interpolation=cv2.INTER_CUBIC)return out# 随机旋转（random rotate）
class FixRandomRotate(object):def __init__(self, angles=[0,90,180,270], bound=False):self.angles = anglesself.bound = bounddef __call__(self,img):do_rotate = random.randint(0, 4)angle=self.angles[do_rotate]if self.bound:img = rotate_bound(img, angle)else:img = rotate_nobound(img, angle)return img# 中心裁剪（center crop）
def center_crop(src, size):h, w = src.shape[0:2]new_w, new_h = scale_down((w, h), size)x0 = int((w - new_w) / 2)y0 = int((h - new_h) / 2)out = fixed_crop(src, x0, y0, new_w, new_h, size)return out# 底部裁剪（bottom crop）
def bottom_crop(src, size):h, w = src.shape[0:2]new_w, new_h = scale_down((w, h), size)x0 = int((w - new_w) / 2)y0 = int((h - new_h) * 0.75)out = fixed_crop(src, x0, y0, new_w, new_h, size)return out# 旋转约束（rotate bound）
def rotate_bound(image, angle):# grab the dimensions of the image and then determine the centerh, w = image.shape[:2](cX, cY) = (w // 2, h // 2)M = cv2.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 imagenW = int((h * sin) + (w * cos))nH = int((h * cos) + (w * sin))# adjust the rotation matrix to take into account translationM[0, 2] += (nW / 2) - cXM[1, 2] += (nH / 2) - cYrotated = cv2.warpAffine(image, M, (nW, nH))return rotated# 排序（compose）
class Compose(object):def __init__(self, transforms):self.transforms = transformsdef __call__(self, img):for t in self.transforms:img = t(img)return img# 随机旋转（random rotate）
class RandomRotate(object):def __init__(self, angles, bound=False):self.angles = anglesself.bound = bounddef __call__(self,img):do_rotate = random.randint(0, 2)if do_rotate:angle = np.random.uniform(self.angles[0], self.angles[1])if self.bound:img = rotate_bound(img, angle)else:img = rotate_nobound(img, angle)return img# 随机亮度增强（random brightness）
class RandomBrightness(object):def __init__(self, delta=10):assert delta >= 0assert delta <= 255self.delta = deltadef __call__(self, image):if random.randint(2):delta = random.uniform(-self.delta, self.delta)image = (image + delta).clip(0.0, 255.0)# print('RandomBrightness,delta ',delta)return image# 随机对比度增强（random contrast）
class RandomContrast(object):def __init__(self, lower=0.9, upper=1.05):self.lower = lowerself.upper = upperassert self.upper >= self.lower, "contrast upper must be >= lower."assert self.lower >= 0, "contrast lower must be non-negative."# expects float imagedef __call__(self, image):if random.randint(2):alpha = random.uniform(self.lower, self.upper)# print('contrast:', alpha)image = (image * alpha).clip(0.0,255.0)return image# 随机饱和度增强（random saturation）
class RandomSaturation(object):def __init__(self, lower=0.8, upper=1.2):self.lower = lowerself.upper = upperassert self.upper >= self.lower, "contrast upper must be >= lower."assert self.lower >= 0, "contrast lower must be non-negative."def __call__(self, image):if random.randint(2):alpha = random.uniform(self.lower, self.upper)image[:, :, 1] *= alpha# print('RandomSaturation,alpha',alpha)return image
# 随机
class RandomHue(object):def __init__(self, delta=18.0):assert delta >= 0.0 and delta <= 360.0self.delta = deltadef __call__(self, image):if random.randint(2):alpha = random.uniform(-self.delta, self.delta)image[:, :, 0] += alphaimage[:, :, 0][image[:, :, 0] > 360.0] -= 360.0image[:, :, 0][image[:, :, 0] < 0.0] += 360.0# print('RandomHue,alpha:', alpha)return image
# 随机色彩通道转换（convert color）
class ConvertColor(object):def __init__(self, current='BGR', transform='HSV'):self.transform = transformself.current = currentdef __call__(self, image):if self.current == 'BGR' and self.transform == 'HSV':image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)elif self.current == 'HSV' and self.transform == 'BGR':image = cv2.cvtColor(image, cv2.COLOR_HSV2BGR)else:raise NotImplementedErrorreturn image
# 色彩通道随机交换（random swap channels）
class RandomSwapChannels(object):def __call__(self, img):if np.random.randint(2):order = np.random.permutation(3)return img[:,:,order]return img
# 随机裁剪（random crop）
class RandomCrop(object):def __init__(self, size):self.size = sizedef __call__(self, image):h, w, _ = image.shapenew_w, new_h = scale_down((w, h), self.size)if w == new_w:x0 = 0else:x0 = random.randint(0, w - new_w)if h == new_h:y0 = 0else:y0 = random.randint(0, h - new_h)out = fixed_crop(image, x0, y0, new_w, new_h, self.size)return out
# 随机大小裁剪（random resized crop）
class RandomResizedCrop(object):def __init__(self, size,scale=(0.49, 1.0), ratio=(1., 1.)):self.size = sizeself.scale = scaleself.ratio = ratiodef __call__(self,img):if random.random() < 0.2:return cv2.resize(img,self.size)h, w, _ = img.shapearea = h * wd=1for attempt in range(10):target_area = random.uniform(self.scale[0], self.scale[1]) * areaaspect_ratio = random.uniform(self.ratio[0], self.ratio[1])new_w = int(round(math.sqrt(target_area * aspect_ratio)))new_h = int(round(math.sqrt(target_area / aspect_ratio)))if random.random() < 0.5:new_h, new_w = new_w, new_hif new_w < w and new_h < h:x0 = random.randint(0, w - new_w)y0 = (random.randint(0, h - new_h))//dout = fixed_crop(img, x0, y0, new_w, new_h, self.size)return out# Fallbackreturn center_crop(img, self.size)
# 下裁剪（down crop）
class DownCrop():def __init__(self, size,  select, scale=(0.36,0.81)):self.size = sizeself.scale = scaleself.select = selectdef __call__(self,img, attr_idx):if attr_idx not in self.select:return img, attr_idxif attr_idx == 0:self.scale=(0.64,1.0)h, w, _ = img.shapearea = h * ws = (self.scale[0]+self.scale[1])/2.0target_area = s * areanew_w = int(round(math.sqrt(target_area)))new_h = int(round(math.sqrt(target_area)))if new_w < w and new_h < h:dw = w-new_wx0 = int(0.5*dw)y0 = h-new_hout = fixed_crop(img, x0, y0, new_w, new_h, self.size)return out, attr_idx# Fallbackreturn center_crop(img, self.size), attr_idx
# 缩放裁剪（resized crop）
class ResizedCrop(object):def __init__(self, size, select,scale=(0.64, 1.0), ratio=(3. / 4., 4. / 3.)):self.size = sizeself.scale = scaleself.ratio = ratioself.select = selectdef __call__(self,img, attr_idx):if attr_idx not in self.select:return img, attr_idxh, w, _ = img.shapearea = h * wd=1if attr_idx == 2:self.scale=(0.36,0.81)d=2if attr_idx == 0:self.scale=(0.81,1.0)target_area = (self.scale[0]+self.scale[1])/2.0 * area# aspect_ratio = random.uniform(self.ratio[0], self.ratio[1])new_w = int(round(math.sqrt(target_area)))new_h = int(round(math.sqrt(target_area)))# if random.random() < 0.5:#     new_h, new_w = new_w, new_hif new_w < w and new_h < h:x0 =  (w - new_w)//2y0 = (h - new_h)//d//2out = fixed_crop(img, x0, y0, new_w, new_h, self.size)# cv2.imshow('{}_img'.format(idx2attr_map[attr_idx]), img)# cv2.imshow('{}_crop'.format(idx2attr_map[attr_idx]), out)## cv2.waitKey(0)return out, attr_idx# Fallbackreturn center_crop(img, self.size), attr_idx
# 随机水平翻转（random h flip）
class RandomHflip(object):def __call__(self, image):if random.randint(2):return cv2.flip(image, 1)else:return image
# 随机垂直翻转（random v flip）
class RandomVflip(object):def __call__(self, image):if random.randint(2):return cv2.flip(image, 0)else:return image
# 水平翻转（h flip）
class Hflip(object):def __init__(self,doHflip):self.doHflip = doHflipdef __call__(self, image):if self.doHflip:return cv2.flip(image, 1)else:return image
# 中心裁剪（center crop）
class CenterCrop(object):def __init__(self, size):self.size = sizedef __call__(self, image):return center_crop(image, self.size)
# 上裁剪（upper crop）
class UpperCrop():def __init__(self, size, scale=(0.09, 0.64)):self.size = sizeself.scale = scaledef __call__(self,img):h, w, _ = img.shapearea = h * ws = (self.scale[0]+self.scale[1])/2.0target_area = s * areanew_w = int(round(math.sqrt(target_area)))new_h = int(round(math.sqrt(target_area)))if new_w < w and new_h < h:dw = w-new_wx0 = int(0.5*dw)y0 = 0out = fixed_crop(img, x0, y0, new_w, new_h, self.size)return out# Fallbackreturn center_crop(img, self.size)
# 随机上裁剪（random upper crop）
class RandomUpperCrop(object):def __init__(self, size, select, scale=(0.09, 0.64), ratio=(3. / 4., 4. / 3.)):self.size = sizeself.scale = scaleself.ratio = ratioself.select = selectdef __call__(self,img, attr_idx):if random.random() < 0.2:return img, attr_idxif attr_idx not in self.select:return img, attr_idxh, w, _ = img.shapearea = h * wfor attempt in range(10):s = random.uniform(self.scale[0], self.scale[1])d = 0.1 + (0.3 - 0.1) / (self.scale[1] - self.scale[0]) * (s - self.scale[0])target_area = s * areaaspect_ratio = random.uniform(self.ratio[0], self.ratio[1])new_w = int(round(math.sqrt(target_area * aspect_ratio)))new_h = int(round(math.sqrt(target_area / aspect_ratio)))# new_w = int(round(math.sqrt(target_area)))# new_h = int(round(math.sqrt(target_area)))if new_w < w and new_h < h:dw = w-new_wx0 = random.randint(int((0.5-d)*dw), int((0.5+d)*dw)+1)y0 = (random.randint(0, h - new_h))//10out = fixed_crop(img, x0, y0, new_w, new_h, self.size)return out, attr_idx# Fallbackreturn center_crop(img, self.size), attr_idx
# 随机下裁剪（random down crop）
class RandomDownCrop(object):def __init__(self, size, select, scale=(0.36, 0.81), ratio=(3. / 4., 4. / 3.)):self.size = sizeself.scale = scaleself.ratio = ratioself.select = selectdef __call__(self,img, attr_idx):if random.random() < 0.2:return img, attr_idxif attr_idx not in self.select:return img, attr_idxif attr_idx == 0:self.scale=(0.64,1.0)h, w, _ = img.shapearea = h * wfor attempt in range(10):s = random.uniform(self.scale[0], self.scale[1])d = 0.1 + (0.3 - 0.1) / (self.scale[1] - self.scale[0]) * (s - self.scale[0])target_area = s * areaaspect_ratio = random.uniform(self.ratio[0], self.ratio[1])new_w = int(round(math.sqrt(target_area * aspect_ratio)))new_h = int(round(math.sqrt(target_area / aspect_ratio)))## new_w = int(round(math.sqrt(target_area)))# new_h = int(round(math.sqrt(target_area)))if new_w < w and new_h < h:dw = w-new_wx0 = random.randint(int((0.5-d)*dw), int((0.5+d)*dw)+1)y0 = (random.randint((h - new_h)*9//10, h - new_h))out = fixed_crop(img, x0, y0, new_w, new_h, self.size)# cv2.imshow('{}_img'.format(idx2attr_map[attr_idx]), img)# cv2.imshow('{}_crop'.format(idx2attr_map[attr_idx]), out)## cv2.waitKey(0)return out, attr_idx# Fallbackreturn center_crop(img, self.size), attr_idx
# 随机水平移动（random h shift）
class RandomHShift(object):def __init__(self, select, scale=(0.0, 0.2)):self.scale = scaleself.select = selectdef __call__(self,img, attr_idx):if attr_idx not in self.select:return img, attr_idxdo_shift_crop = random.randint(0, 2)if do_shift_crop:h, w, _ = img.shapemin_shift = int(w*self.scale[0])max_shift = int(w*self.scale[1])shift_idx = random.randint(min_shift, max_shift)direction = random.randint(0,2)if direction:right_part = img[:, -shift_idx:, :]left_part = img[:, :-shift_idx, :]else:left_part = img[:, :shift_idx, :]right_part = img[:, shift_idx:, :]img = np.concatenate((right_part, left_part), axis=1)# Fallbackreturn img, attr_idx
# 随机底部裁剪（random bottom crop）
class RandomBottomCrop(object):def __init__(self, size, select, scale=(0.4, 0.8)):self.size = sizeself.scale = scaleself.select = selectdef __call__(self,img, attr_idx):if attr_idx not in self.select:return img, attr_idxh, w, _ = img.shapearea = h * wfor attempt in range(10):s = random.uniform(self.scale[0], self.scale[1])d = 0.25 + (0.45 - 0.25) / (self.scale[1] - self.scale[0]) * (s - self.scale[0])target_area = s * areanew_w = int(round(math.sqrt(target_area)))new_h = int(round(math.sqrt(target_area)))if new_w < w and new_h < h:dw = w-new_wdh = h - new_hx0 = random.randint(int((0.5-d)*dw), min(int((0.5+d)*dw)+1,dw))y0 = (random.randint(max(0,int(0.8*dh)-1), dh))out = fixed_crop(img, x0, y0, new_w, new_h, self.size)return out, attr_idx# Fallbackreturn bottom_crop(img, self.size), attr_idx
# 底部裁剪（bottom crop）
class BottomCrop():def __init__(self, size,  select, scale=(0.4, 0.8)):self.size = sizeself.scale = scaleself.select = selectdef __call__(self,img, attr_idx):if attr_idx not in self.select:return img, attr_idxh, w, _ = img.shapearea = h * ws = (self.scale[0]+self.scale[1])/3.*2.target_area = s * areanew_w = int(round(math.sqrt(target_area)))new_h = int(round(math.sqrt(target_area)))if new_w < w and new_h < h:dw = w-new_wdh = h-new_hx0 = int(0.5*dw)y0 = int(0.9*dh)out = fixed_crop(img, x0, y0, new_w, new_h, self.size)return out, attr_idx# Fallbackreturn bottom_crop(img, self.size), attr_idx
# 大小重置（resize）
class Resize(object):def __init__(self, size, inter=cv2.INTER_CUBIC):self.size = sizeself.inter = interdef __call__(self, image):return cv2.resize(image, (self.size[0], self.size[0]), interpolation=self.inter)
# 边界扩充（expand border）
class ExpandBorder(object):def __init__(self,  mode='constant', value=255, size=(336,336), resize=False):self.mode = modeself.value = valueself.resize = resizeself.size = sizedef __call__(self, image):h, w, _ = image.shapeif h > w:pad1 = (h-w)//2pad2 = h - w - pad1if self.mode == 'constant':image = np.pad(image, ((0, 0), (pad1, pad2), (0, 0)),self.mode, constant_values=self.value)else:image = np.pad(image,((0,0), (pad1, pad2),(0,0)), self.mode)elif h < w:pad1 = (w-h)//2pad2 = w-h - pad1if self.mode == 'constant':image = np.pad(image, ((pad1, pad2),(0, 0), (0, 0)),self.mode,constant_values=self.value)else:image = np.pad(image, ((pad1, pad2), (0, 0), (0, 0)),self.mode)if self.resize:image = cv2.resize(image, (self.size[0], self.size[0]),interpolation=cv2.INTER_LINEAR)return image
# 字节整形转换（type to int）
class AstypeToInt():def __call__(self, image, attr_idx):return image.clip(0,255.0).astype(np.uint8), attr_idx
# 字节浮点数转换（type to float）
class AstypeToFloat():def __call__(self, image, attr_idx):return image.astype(np.float32), attr_idximport matplotlib.pyplot as plt# 正则化（normalize）
class Normalize(object):def __init__(self,mean, std):''':param mean: RGB order:param std:  RGB order'''self.mean = np.array(mean).reshape(3,1,1)self.std = np.array(std).reshape(3,1,1)def __call__(self, image):''':param image:  (H,W,3)  RGB:return:'''# plt.figure(1)# plt.imshow(image)# plt.show()return (image.transpose((2, 0, 1)) / 255. - self.mean) / self.std
# 随机擦除（random erasing）
class RandomErasing(object):def __init__(self, select,EPSILON=0.5,sl=0.02, sh=0.09, r1=0.3, mean=[0.485, 0.456, 0.406]):self.EPSILON = EPSILONself.mean = meanself.sl = slself.sh = shself.r1 = r1self.select = selectdef __call__(self, img,attr_idx):if attr_idx not in self.select:return img,attr_idxif random.uniform(0, 1) > self.EPSILON:return img,attr_idxfor attempt in range(100):area = img.shape[1] * img.shape[2]target_area = random.uniform(self.sl, self.sh) * areaaspect_ratio = random.uniform(self.r1, 1 / self.r1)h = int(round(math.sqrt(target_area * aspect_ratio)))w = int(round(math.sqrt(target_area / aspect_ratio)))if w <= img.shape[2] and h <= img.shape[1]:x1 = random.randint(0, img.shape[1] - h)y1 = random.randint(0, img.shape[2] - w)if img.shape[0] == 3:# img[0, x1:x1+h, y1:y1+w] = random.uniform(0, 1)# img[1, x1:x1+h, y1:y1+w] = random.uniform(0, 1)# img[2, x1:x1+h, y1:y1+w] = random.uniform(0, 1)img[0, x1:x1 + h, y1:y1 + w] = self.mean[0]img[1, x1:x1 + h, y1:y1 + w] = self.mean[1]img[2, x1:x1 + h, y1:y1 + w] = self.mean[2]# img[:, x1:x1+h, y1:y1+w] = torch.from_numpy(np.random.rand(3, h, w))else:img[0, x1:x1 + h, y1:y1 + w] = self.mean[1]# img[0, x1:x1+h, y1:y1+w] = torch.from_numpy(np.random.rand(1, h, w))return img,attr_idxreturn img,attr_idxif __name__ == '__main__':import matplotlib.pyplot as pltclass FSAug(object):def __init__(self):self.augment = Compose([AstypeToFloat(),# RandomHShift(scale=(0.,0.2),select=range(8)),# RandomRotate(angles=(-20., 20.), bound=True),ExpandBorder(select=range(8), mode='symmetric'),# symmetric# Resize(size=(336, 336), select=[ 2, 7]),AstypeToInt()])def __call__(self, spct,attr_idx):return self.augment(spct,attr_idx)trans = FSAug()img_path = '/home/by/Orbit.png'img = cv2.cvtColor(cv2.imread(img_path),cv2.COLOR_BGR2RGB)img_trans,_ = trans(img,5)# img_trans2,_ = trans(img,6)print (img_trans.max(), img_trans.min())print (img_trans.dtype)plt.figure()plt.subplot(221)plt.imshow(img)plt.subplot(222)plt.imshow(img_trans)# plt.subplot(223)# plt.imshow(img_trans2)# plt.imshow(img_trans2)plt.show()