numpy基础知识点
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numpy基础知识点
小編覺得挺不錯的,現(xiàn)在分享給大家,幫大家做個參考.
1. np.squeeze
一,np.squeeze """ np.squeeze 刪除單維度的條 對多維度無效 """ import numpy as np a=np.array([[1,2],[3,4],[4,5]]) print(a) print(a.shape) b=np.squeeze(a) print(b) c=a.reshape(1,6,1) print(c) print(np.squeeze(c)) print(np.squeeze(c).shape) print(np.squeeze(c,axis=0)) print(np.squeeze(c,axis=0).shape) print(np.squeeze(c,axis=1)) print(np.squeeze(c,axis=1).shape)維度不為1，報錯。
print(np.squeeze(c,axis=2)) print(np.squeeze(c,axis=2).shape)2.np.newaxis增加維度
import numpy as np a=np.arange(1,5) print(a) print(a.reshape([-1,1])) b=a[:,np.newaxis] print(b) print(b.shape)3. np.random
import numpy as np """ 0~1之間產(chǎn)生隨機值 """ a=np.random.rand(3,2) print(a) """ 產(chǎn)生標準正態(tài)分布的值 """ a=np.random.randn(3,2) print(a) """ 產(chǎn)生隨機整數(shù)1~3之間 """ a=np.random.randint(low=1,high=3,size=(3,2)) print(a) """ 產(chǎn)生高斯分布:均值 方差 """ a=np.random.normal(loc=0,scale=1,size=(3,2)) print(a)numpy.random.choice(a, size=None, replace=True, p=None)
從給定的一維數(shù)組中生成一個隨機樣本；
| a | 為一維數(shù)組或者int數(shù)據(jù)； |
| size | 為生成的數(shù)組維度； |
| replace | 是否原地替換； |
| p | 為樣本出現(xiàn)的概率； |
replace為true會重復，為false不會重復
a = ['bird', 'meh', 'sad', 'd', '123'] print(np.random.choice(a, 5)) # replace默認為True print(np.random.choice(a, 5, replace=False))4. np.logspace
import numpy as np #等比數(shù)列 9/(5-1)=2.25 10^0 10^2.25 10^5.5 10^7.75 10^9 a=np.logspace(0,9,5) print(a)5. 等差數(shù)列
a=np.linspace(2.0, 3.0, num=5) print(a) b=np.linspace(2.0, 3.0, num=5, endpoint=False) print(b) c=np.linspace(2.0, 3.0, num=5, retstep=True) print(c)6. np.argmax,np.sum
np.max操作也滿足記住, 0列1行, 0從上往下看，1從左往右看
#axis=0 對列操作 axis=1 對行操作 axis=2 對最后一根軸操作 a = np.array([[1, 2,3],[3, 4,5],[4, 5,6],[6, 7,8]]) print(np.mean(a,axis=0)) print(np.sum(a,axis=0)) print(np.argmax(a,axis=0)) print(np.all(a==2,axis=0)) #axis = 2對最后一根軸操作 a=a.reshape((2,2,3)) print(a) print() print(np.mean(a,axis=0)) print() print(np.mean(a, axis=1)) print() print(np.mean(a, axis=-1)) a=np.array([[1,0,0],[0,1,0],[0,0,1],[0,1,0]]) print(a) print(np.argmax(a,1))#對行找最大值索引 b=np.array([[1,0,0],[0,1,0],[1,0,0],[0,0,1]]) print(b) print(np.argmax(b,1))print(np.argmax(a,1)==np.argmax(b,1)) print(np.sum(np.argmax(a,1)==np.argmax(b,1))) img=np.array([[[1,2,3],[6,4,5]]]) print(img) pre=np.argmax(img,axis=2) print(pre) pre=np.expand_dims(pre,axis=-1) print(pre)找出每個channel的最值索引，然后進行分割。
np.sum
import numpy as np a=np.array([[[[1,2],[1,3],[1,4]],[[1,7],[1,6],[1,5]],[[1,2],[1,3],[1,4]]]])print(a.shape) print(np.sum(a,axis=3)) print(np.sum(a,axis=3).shape) print(np.sum(a,axis=3,keepdims=True)) print(np.sum(a,axis=3,keepdims=True).shape)保持維度故變?yōu)?#xff08;1,3,3,1）?
對于三維的話 比如（m,32,32,17）是由每一個channel貢獻loss?
a=np.array([[[[1,2,3]],[[2,3,4]]],[[[1, 2, 3]],[[2, 3, 4]]]]) print(a) print(a.shape) print(np.sum(a, axis=0)) print(np.sum(a,axis=1)) # print(np.sum(a, axis=2)) # print(np.sum(a, axis=3)) print(np.mean(np.sum(a,axis=1))) xe = -tf.reduce_sum(tf.multiply(labels_ * tf.log(logits + epsilon), cb),reduction_indices=[1])loss_total = tf.reduce_mean(xe)+m_IOU_lossnp.all(),和sum一樣axis=1,是行操作。沿著軸的方向都為true，則返回為true。
a=np.all([[True, False],[True, True]]) print(a)a = np.all([[True, False],[True, True]],axis=1) print(a)a = np.all([[1, 0],[1, 1]], axis=1) print(a)可用來清掉，全0的元素。
x=np.array([[0,1,0,1],[0,200,1,0],[34,40,0,3],[35,0,3,4],[0,0,0,0]]) print(np.all(x == 0, axis=1))y=x[~np.all(x == 0, axis=1)] print(x) print(y)7. np.stack,np.hstack,np.vstack
np.stack二維情況
import numpy as np a=[[1,2,3],[4,5,6]] print("列表a如下：") print(a)print("增加一維，新維度的下標為0") c=np.stack(a,axis=0) print(c)print("增加一維，新維度的下標為1") c=np.stack(a,axis=1) print(c) a=[[1,2,3],[4,5,6]] b=[[1,2,3],[4,5,6]] c=[[1,2,3],[4,5,6]] print("a=",a) print("b=",b) print("c=",c)print("增加一維，新維度的下標為0") d=np.stack((a,b,c),axis=0) print(d) print(d.shape)print("增加一維，新維度的下標為1") d=np.stack((a,b,c),axis=1) print(d) print(d.shape) print("增加一維，新維度的下標為2") d=np.stack((a,b,c),axis=2) print(d) print(d.shape)np.hstack按照水平方向連接
import numpy as np a=[[1],[2],[3]] b=[[1],[2],[3]] c=[[1],[2],[3]] d=[[1],[2],[3]] print(np.hstack((a,b,c,d)))np.vstack按垂直方向連接
import numpy as np a=[[1],[2],[3]] b=[[1],[2],[3]] c=[[1],[2],[3]] d=[[1],[2],[3]] print(np.vstack((a,b,c,d)))把sober算子變成兩個通道的sober算子，其中生成的2用作剛好是輸入的channel
import tensorflow as tf import numpy as np fx = np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]).astype(np.float32) fy = np.array([[-1, -2, -1], [0, 0, 0], [1, 2, 1]]).astype(np.float32)fx = np.stack((fx, fx), axis=2) print(fx) # fy = np.stack((fy, fy), axis=2) # print(fy) fx = np.reshape(fx, (3, 3, 2, 1)) print(fx) # fy = np.reshape(fy, (3, 3, 2, 1))tf_fx = tf.Variable(tf.constant(fx))8. np.array
>>> a
array([0, 2, 1])
>>> scores
array([[1, 2, 3],
?????? [2, 3, 4],
?????? [4, 5, 6]])
>>> scores[a]
array([[1, 2, 3],
?????? [4, 5, 6],
?????? [2, 3, 4]])
一個樣本對應一列，下面的代碼可以用來尋找每個樣本標簽量對應的數(shù)值。
9. np.concatenate
import numpy as np a = np.array([[1, 2], [3, 4]]) b=np.array([[5,6]]) c=np.concatenate((a, b), axis=0) print(c) c=np.concatenate((a, b.T), axis=1) print(c) c=np.concatenate((a, b.T), axis=None) print(c)對于列表也可以?
a=np.concatenate([[[1,2]],[[3,4]]],axis=0) print(a)
10.? np.transpose()
import numpy as np a=np.array([[1],[2],[3],[4],[5],[6]]) print(a) b=a[:2,:].transpose() c=a[2:4,:].transpose() d=a[4:,:].transpose() print(b,c,d) e=np.append(b,c,axis=0) a=np.append(e,d,axis=0) print(a)11. one_hot
import numpy as np #轉(zhuǎn)換one-hot def convert_to_one_hot(label):n_classes=max(label)+1label = np.eye(n_classes)[label.reshape(-1)]return label label=[1,0,2,3,0] Y=convert_to_one_hot(np.array(label)) print(Y)
12. np廣播機制
13. np.split
方便對生成的box坐標進切分
import numpy as np A = np.arange(16).reshape(4,4) print('A:',A) print(np.split(A,2,axis = 1)) c,d=np.split(A,2,axis = 1) print(c) print(d) import numpy as np A = np.arange(16).reshape(4,4) print(A) print(np.split(A,2,axis = 0)) c,d=np.split(A,2,axis = 0) print(c) print(d)
14. np.rot90,圖片旋轉(zhuǎn)90度
path='./data/2.png'
img=cv2.imread(path)# 逆時針90度
img=np.rot90(img)
cv2.imwrite('./data/img_size_out_1.jpg',img)
15. np.unravel_index,找最值的索引值
a = np.array([[1, 2, 3],[4, 5, 6]]) index = np.unravel_index(np.argmax(a), a.shape) print(index)16. np.where
np.where兩種用法
where(condition,?x=None,?y=None)
如果x,y為空，返回condition中值為True的位置的ndarray
對于三維
#三維情況 import numpy as npb = np.where([[0, 1],[1, 0]]) print('==b:', b) x = np.arange(12).reshape(2, 3, 2) print('==x:', x)index = np.where(x > 3) print('==index:', index) print('==x[index]:', x[index])對于二維
import numpy as npb = np.where([[0, 1],[1, 0]]) print('==b:', b) x = np.arange(9.).reshape(3, 3) print('==x:', x)index = np.where(x > 3) print('==index:', index) print('==x[index]:', x[index]) index_y, index_x = index print('==index_y, index_x:', index_y, index_x)need_value = [] for i in range(len(index_y)):need_value.append(x[index_y[i]][index_x[i]]) print('==need_value:', need_value)# 同時滿足兩個條件的 index = np.where((x[:, 0] > 3) & (x[:, 1] < 8)) print('==index', index) print('x[index]:', x[index])對于一維
class_ids=np.array([1,2,3]) index = np.where(class_ids == 1) print('==index:', index) print('==class_ids[index]:', class_ids[index])如果x,y不為空，返回值和x、y有相同的形狀，如果condition對應位置值為True那么返回ndarrayr對應位置為x的值，否則為y的值
a=np.where([[True, False],[True, True]],[[1, 2],[3, 4]],[[9, 8],[7, 6]]) print(a)可以用來找出某個點的索引
a = np.array([[255, 0, 0],[0,0,255],[0,255,0]]) # 找出255的索引號 b=np.where(a[...]==255) print(b) y,x=b#是索引號，故行對應y，列對應x print(y) print(x) point=np.stack((x,y),axis=-1) plt.plot(point[:,0],point[:,1],'o') plt.show()?同時要滿足兩個np.where
index_x = np.where(abs(points[:, 0] - points[0][-2]) < x_dis)[0]print('index_x:',index_x)# if len(index_x):# #y1-y1index_y = np.where(abs(points[:, 1] - points[0][1]) < y_dis)[0]print('index_y: ',index_y)common_index=list(set(index_x)&set(index_y))print('common_index:',common_index)更簡單寫法：
kpts_2d = np.array([[680, 0],[10, 10]], dtype=np.float32) print((kpts_2d[:, 0] < 640)*(kpts_2d[:, 0] >= 0)) kpts_2d = kpts_2d[(kpts_2d[:, 0] < 640)*(kpts_2d[:, 0] >= 0)]print(kpts_2d)?例如centernet中用于后處理找到每一類對應的類別，長寬與偏移量
import numpy as np# (c, h, w) cls = np.array([[[0.5, 0.2],[0.9, 0.8],[0.8, 0.1]],[[0.6, 0.7],[0.8, 0.6],[0.9, 0.8]]]) # (2, h, w) wh = np.array([[[5, 2],[9, 8],[8, 1]],[[6, 7],[8, 9],[9, 1]]])# (2, h, w) reg = np.random.rand(2, 3, 3) print('==reg:', reg)print('==cls.shape:', cls.shape) index = np.where(cls >= 0.8) print('==index:', index) score = np.array(cls[index]) print('==score:', score) cat = np.array(index[0]) print('===cat:', cat) ctx, cty = index[-1], index[-2] print('==before ctx, cty', ctx, cty) w, h = wh[0, cty, ctx], wh[1, cty, ctx] # 預測的長寬 print('==w, h:', w, h) off_x, off_y = reg[0, cty, ctx], reg[1, cty, ctx] # 預測的中心點偏移量 print('==off_x, off_y:', off_x, off_y) ctx = np.array(ctx) + np.array(off_x) # 中心點x cty = np.array(cty) + np.array(off_y) # 中心點y print('==after ctx, cty', ctx, cty)x1, x2 = ctx - np.array(w) / 2, ctx + np.array(w) / 2 y1, y2 = cty - np.array(h) / 2, cty + np.array(h) / 2 bbox = np.stack((cat, score, x1, y1, x2, y2), axis=1).tolist() bbox = sorted(bbox, key=lambda x: x[1], reverse=True) print('==bbox:', bbox)17. argsort,可用來尋找score的最大值
scores=np.array([0.5,0.7,0.3,0.2]) #得到從小到大的索引值 print(scores.argsort()) # 得到從大到小的索引值 print(scores.argsort()[::-1]) for i in scores.argsort()[::-1]:print(scores[i]) a=np.array([1,3,0,2]) b=np.argsort(a) print(b)a = np.array([[1, 5, 0, 8],[ 3, 4, 7, 3]]) ind = np.unravel_index(np.argsort(a, axis=None), a.shape) print(ind) print(ind[0][-1],ind[1][-1])print(a[ind[0][-1],ind[1][-1]]) print(a[ind[0][-2], ind[1][-2]])18. np.delete
import numpy as np a = np.array(np.arange(12).reshape(3, 4)) print(a) #沿著行刪除，刪除第一行 print(np.delete(a,obj=1,axis=0)) # 沿著行刪除，刪除第0行和第一行 print(np.delete(a, obj=[0,1], axis=0)) # 沿著列刪除，刪除第三列 print(np.delete(a, obj=3, axis=1))#刪除多個行 print(np.delete(a, obj=[0,1], axis=0))19. np.tile，沿著行和列復制
沿著列復制
mat = np.array([[1,2],[3, 4]]) mat=np.tile(mat,(1,3)) print(mat)沿著行復制
mat = np.array([[1,2],[3, 4]]) mat=np.tile(mat,(3,1)) print(mat)20. np.prod連乘
a=np.array([[1,2],[3,4]]) print(np.prod(a)) print(np.prod(a,axis=0)) print(np.prod(a, axis=1))21.np.reshape,flatten()
a=np.array([[1,2]],dtype=np.float32) print(a.reshape(-1)) print(a.flatten().astype(np.int32))得到的每一列代表每一個channel
a=np.array([[[1,0,1],[1,1,0]],[[0,1,1],[0,1,0]]]) print(a) print(a.shape) b=a.reshape(-1, a.shape[-1]) print(b) print('============') """轉(zhuǎn)置方便求交集""" print(b.T)制作閾值大于0.5預測的mask.
masks1=np.array([[0.6,1,0],[0.2,0,1],[0.3,1,0],[0.6,0,1]]) print(masks1.shape[-1]) masks1 = np.reshape(masks1 > .5, (-1, masks1.shape[-1])) print(masks1)總的計算多個channel的IOU
def compute_overlaps_masks(masks1, masks2):"""Computes IoU overlaps between two sets of masks.masks1, masks2: [Height, Width, instances]"""# If either set of masks is empty return empty resultif masks1.shape[-1] == 0 or masks2.shape[-1] == 0:return np.zeros((masks1.shape[-1], masks2.shape[-1]))# flatten masks and compute their areasmasks1 = np.reshape(masks1 > .5, (-1, masks1.shape[-1])).astype(np.float32)masks2 = np.reshape(masks2 > .5, (-1, masks2.shape[-1])).astype(np.float32)area1 = np.sum(masks1, axis=0)area2 = np.sum(masks2, axis=0)# intersections and unionintersections = np.dot(masks1.T, masks2)union = area1[:, None] + area2[None, :] - intersectionsoverlaps = intersections / unionreturn overlaps22. np 切片
a= np.array([[1, 2, 3],[4, 5, 6],[7, 8, 9],[10, 11, 12]]) b = np.array([0, 2, 0, 1]) print(a[np.arange(4),b]) a[np.arange(4), b]+=10 print('a=',a) #所有大于5的替換為5 MAX = 5 nums = np.array([1, 4, 10, -1, 15, 0, 5]) print(nums > MAX) nums[nums > MAX] = MAX print(nums) #直接根據(jù)索引找到一行向量的值 nums = np.array([1, 4, 10, -1, 15, 0, 5]) print(nums[[1, 2, 3, 1, 0]])23.?np.flatnonzero與np.nonzero(x)
(1)np.flatnonzero
該函數(shù)輸入一個矩陣，返回拉伸后矩陣中非零元素的索引
x = np.arange(-3, 5) print('x=',x) print(np.flatnonzero(x)) print(np.flatnonzero(x==-3))(2)np.nonzero(x), 返回非零元素的索引
x = np.array([[3, 0, 0], [0, 4, 0], [5, 6, 0]])print(x)res = np.nonzero(x)print('res:', res)res_ = x[np.nonzero(x)]print('res_:', res_)24. np.bincount統(tǒng)計元素出現(xiàn)的次數(shù)
x = np.array([0, 1, 2, 2, 1, 1]) print(np.bincount(x))25. 求交集np.intersect1d
print(np.intersect1d([1, 3, 4, 2], [3, 1, 2, 1]))26. np.arctan2與np.arctan
x = np.array([-1, +1, +1, -1]) y = np.array([-1, -1, +1, +1]) #-180~180 print(np.arctan2(y, x) * 180 / np.pi) #-90~90 print(np.arctan(y/x) * 180 / np.pi)27. np.transpose
path='./data/raw.jpg' img=cv2.imread(path) img=np.transpose(img,(1,0,2)) cv2.imwrite('./data/raw_1.jpg',img)實現(xiàn)圖片轉(zhuǎn)90度
變?yōu)?/p>
上下翻轉(zhuǎn)? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? 左右翻轉(zhuǎn)? ? ? ? ? ? ? ? ? ? ? ? ? ? ? BGR變?yōu)镽GB
img=img[::-1,...] img=img[:,::-1,:] img=img[:,:,::-1]?? ?
28. np.zeros
a=np.zeros((1,2,3)) print(a) a[0,1,:]=255 print(a)29. 返回上三角的索引，可用來制作顯示下三角的相關系數(shù)矩陣。np.triu_indices_from()
import numpy as np mask=np.array([[1,2,3],[4,5,6],[5,6,7]]) print(mask) print(np.triu_indices_from(mask)) mask[np.triu_indices_from(mask)] = True print(mask)30. random.getrandbits(1)用來產(chǎn)生0,1其中之一的隨機數(shù)，用來做數(shù)據(jù)增強
import random for i in range(10):a = random.getrandbits(1)print(a)if a:print(True)31. np.fliplr，np.flipud實現(xiàn)左右 上下翻轉(zhuǎn)
32. np.random.shuffle
import numpy as np X=np.array([[1,2,3],[3,4,5],[5,6,7],[7,8,9]]) print(X) idx_random = np.arange(X.shape[0]) print(idx_random) np.random.shuffle(idx_random) print(idx_random) X= X[idx_random] print('==========after shuffle=============') print(X)33.np.clip
將小于3的和大于8的強制為3和8
x = np.array([[1, 2, 3, 5],[6, 7, 8, 9]]) print('x=', x) y = np.clip(x, 3, 8) print('y=', y) #等價于 x[x>=8]=8 x[x<=3]=3 print('x=', x)34.np.percentile求百分位數(shù)，可用來確定衛(wèi)星圖像的RGB三個波段的閾值，方便進行0～255歸一化處理，其中取值為50時相當于求中位數(shù)。
a=np.array([[1,2,3,4],[0,1,2,0]]) print(a[a>0]) print(np.percentile(a[a>0],50)) print(np.percentile(a, 50))35. np.ploy1d 多項式子
print(np.poly1d(3)) print('=============') print(np.poly1d([1,2,3,4]))36. np.exp,注意數(shù)值不穩(wěn)定問題
f = np.array([123, 456, 789]) # 例子中有3個分類，每個評分的數(shù)值都很大 p = np.exp(f) / np.sum(np.exp(f)) # 不妙：數(shù)值問題，可能導致數(shù)值爆炸 print('p=',p) # 那么將f中的值平移到最大值為0： f -= np.max(f) # f becomes [-666, -333, 0] p = np.exp(f) / np.sum(np.exp(f)) # 現(xiàn)在OK了，將給出正確結(jié)果 print('p=', p)37. np.r_,np.c_，類似np.vstack.np.hstack
import numpy as np a = np.array([[1, 2],[3,4]]) b = np.array([[4, 5],[6,7]]) c = np.c_[a,b]print(np.r_[a,b]) print(np.c_[a,b]) print(np.c_[c,a])38. 實現(xiàn)max(0, Sjc - Sjyj + 1)
第一種解法?：
x1 = np.array(np.arange(6)).reshape(3, 2) x2 = np.array([2, 3, 3]).reshape(-1, 1) print('x1=') print(x1) print('x2=') print(x2) print('x1-x2+1=') print(x1-x2+1) mask=x1-x2+1>0 print('mask=') print(mask) scores=(x1-x2+1)*mask print('scores=') print(scores)第二種解法：
x1 = np.array(np.arange(6)).reshape(3, 2) x2 = np.array([2, 3, 3]).reshape(-1, 1) for i in range(x1.shape[0]):for j in range(x1.shape[1]):x1[i][j] = max(0, x1[i][j] - x2[i][0] + 1) print(x1)39. label smoothing，制作平滑的標簽
new_labels = (1.0 - label_smoothing) * one_hot_labels + label_smoothing / （num_classes-1）label_smoothing是一個很小的常數(shù)
40. np.unique 去重（可以對坐標）
a=np.unique([1, 1, 2, 2, 3, 3]) print(a) a = np.array([[1, 1], [2, 3]]) print(np.unique(a))a = np.array([[1, 0, 0], [1, 0, 0], [2, 3, 4]]) print(np.unique(a, axis=0))41. np.pad
用來補黑邊，變成1024×1024
_, ax = plt.subplots(1, figsize=(16, 16)) ax.axis('off') image=cv2.imread('1.jpg') print(image.shape) h, w = image.shape[:2] max_dim=1024 top_pad = (max_dim - h) // 2 bottom_pad = max_dim - h - top_pad left_pad = (max_dim - w) // 2 right_pad = max_dim - w - left_pad padding = [(top_pad, bottom_pad), (left_pad, right_pad), (0, 0)] image = np.pad(image, padding, mode='constant', constant_values=0) cv2.imwrite('2.jpg',image) # ax.imshow(image) # plt.show()42. np.cumsum累加
stage_epochs=[50,50,50] a=np.cumsum(stage_epochs)[:-1] print(a)43. 交換二維數(shù)據(jù)的列向量
print(coord_Y) coord_Y[:, [0, 1]] = coord_Y[:, [1, 0]] print(coord_Y)44. 將一個二維數(shù)組按照X先排序,Y在排序,np.lexsort
def coord_sort(x,y): #按照x,y來排序ind = np.lexsort((y.tolist(), x.tolist())) # Sort by x, then by ycoord = [(x.tolist()[i], y.tolist()[i]) for i in ind]coord = np.array(coord).reshape(-1, 2)return coordcoord = np.array([[10, 60],[10, 20],[20, 20],[40, 40],[40, 60],[20, 40]])#按照先X后Y排的序coord_X=coord_sort(coord[:,0],coord[:,1])print(coord_X)四個點的
import numpy as np def coord_sort(points):# 按照x,y來排序ind = np.lexsort((points[:,1].tolist(), points[:,0].tolist())) # Sort by x, then by yreturn points[ind]coord = np.array([[10, 60, 10,20],[11, 70, 20, 20],[11, 40,40,60],[30, 20,200,100],[30, 30,40,50]]) # 按照先X后Y排的序 coord_X = coord_sort(coord) print(coord_X)45. 刪除副黑色圖片中過短的白線
import numpy as np def delete_short_line(img,line_length):sum_axis = np.sum(img == 255,axis=0,keepdims=True)print('sum_axis:',sum_axis)print('sum_axis>line_length:',sum_axis>line_length)img = img*(sum_axis>line_length)return img if __name__ == '__main__':img=np.array([[255,0,0],[255,0,255],[255,255,0]])img=delete_short_line(img,line_length=1)print('==img==')print(img)46.對豎值方向的一系列點做聚類
import numpy as np def get_cluster(mode,x_dis,y_dis):points=np.array([[3,2],[10,2],[1,4],[2,5],[1,3],[12,3],[15,4],[10,5],[20,1]])if mode == 'veri':points = points[points[:,0].argsort()]else:points = points[points[:,1].argsort()]# print(points)# print(points.shape[0])cluster = []while points.shape[0] > 2:# print('points:',points)if mode=='veri':index = np.where(abs(points[:, 0] - points[0][0]) < x_dis)[0]else:index = np.where(abs(points[:, 1] - points[0][1]) < y_dis)[0]index = sorted(index)cluster.append(points[index])points=points[index[-1]+1:]print('cluster:',cluster)return cluster if __name__ == '__main__':get_cluster(mode='veri',x_dis=5,y_dis=5)47. np.max,np.maximum
np.max是計算最大值，而np.maximum是計算相比較的值
a=np.array([1,2,3]) print(np.max(a)) print(np.maximum(a,2))48. np.setdiff1d,可以得到一個ndarray中有，一個無
a = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9]) b = np.array([3, 4, 7, 6, 7, 8, 11, 12, 14]) c = np.setdiff1d(a, b) print('a中有,b中無') print('c:', c)49. np.count_nonzero得到非零元素個數(shù)
a=np.count_nonzero([[0, 1, 7, 0, 0],[3, 0, 0, 2, 19]], axis=0)print('a:', a)50.np.nonzero得到非零元素索引
x = np.array([[3, 0, 0],[0, 4, 0],[5, 6, 0]])a = np.nonzero(x)print('a:', a)51.np.diff
矩陣中后一個元素減去前一個元素的差值,此處用于找到polygon的四個點(局限于水平框)
import numpy as nprect = np.zeros((4,2))# pts = np.array([[0, 0],# [0, 2],# [2, 2],# [2, 0]])pts = np.array([933,255,954,255,956,277,936,277])pts = pts.reshape(-1,2)print('===pts', pts)d = np.diff(pts, axis=1)print('===d:', d)s = np.sum(pts, axis=1)rect[0] = pts[np.argmin(s)]rect[2] = pts[np.argmax(s)]# the top-right point will have the smallest difference,# whereas the bottom-left will have the largest differenced = np.diff(pts, axis=1)rect[1] = pts[np.argmin(d)]rect[3] = pts[np.argmax(d)]print('===rect:', rect)利用np.argmin找到框的最小點,再用np.roll滾動獲取最小點開始的點(局限于框是有順序的,不是亂序)
#得到左上右下 def cal_stand_points(points):s = np.sum(points, axis=1)left_top_index = np.argmin(s)right_bottom_index = np.argmax(s)rect = np.roll(points, 4-left_top_index, axis=0)return rectpts = np.array([933, 255, 954, 255, 956, 277, 936, 277]) pts = pts.reshape(-1, 2) new_pts = cal_stand_points(pts) print('==new_pts:', new_pts)52.np.meshgrid生成網(wǎng)格點
import numpy as np import matplotlib.pyplot as pltx = np.array([0, 1, 2]) y = np.array([0, 1])X, Y = np.meshgrid(x, y) print('==X:', X) print('==Y:', Y)plt.plot(X, Y,color='red', # 全部點設置為紅色marker='.', # 點的形狀為圓點linestyle='') # 線型為空，也即點與點之間不用線連接 plt.xlim(-1, 3) plt.ylim(-1, 2) plt.grid(True) plt.show()其中X代表橫坐標 Y代表縱坐標?
53.np.linalg.norm計算范數(shù)(默認計算2范數(shù))
1.得到兩個向量角度
import mathfeature0 = np.array([1, 1]) feature1 = np.array([2, 2]) x0 = feature0 / np.linalg.norm(feature0) print('==x0:', x0) x1 = feature1 / np.linalg.norm(feature1) print('==x1:', x1) cosine = np.dot(x0, x1) print('==cosine:', cosine) cosine = np.clip(cosine, -1.0, 1.0) theta = math.acos(cosine) theta = theta * 180 / math.pi print('===theta:', theta)2.得到box長寬
box = np.array([[0, 0],[2, 0],[2, 1],[0, 1]])w, h = np.linalg.norm(box[0] - box[1]), np.linalg.norm(box[1] - box[2]) print('寬度:{},長度:{}'.format(w, h))54.np.roll滾動(默認axis是1)
x = np.arange(10) print('x:',x) y = np.roll(x, 2) print('==y:', y)x2 = np.reshape(x, (2,5)) print('==x2:', x2)y2 = np.roll(x2, 1) print('==y2:', y2)y3 = np.roll(x2, 1, axis=0) print('===y3:', y3) y4 = np.roll(x2, 2, axis=1) print('====y4:', y4)55:計算矩形的相鄰邊長度與周長面積
import Polygon as plg import cv2 def dist(a, b):return np.sqrt(np.sum((a - b) ** 2))bbox = np.array([[0, 0],[2, 0],[2, 1],[0, 1]])compute_1_area = plg.Polygon(bbox).area() compute_2_area = cv2.contourArea(bbox)#方式3 compute_3_peri = cv2.arcLength(bbox, True)compute_1_peri, compute_2_peri = 0, 0 for i in range(bbox.shape[0]):print('==bbox[i], bbox[i + 1]:', bbox[i], bbox[(i + 1) % bbox.shape[0]])# 方式1compute_1_dis = dist(bbox[i], bbox[(i + 1) % bbox.shape[0]])print('===compute_1_dis:', compute_1_dis)compute_1_peri += compute_1_dis# 方式2compute_2_dis = np.linalg.norm(bbox[i] - bbox[(i + 1) % bbox.shape[0]])compute_2_peri += compute_2_disprint('===compute_2_dis:', compute_2_dis)print('方式1計算面積為:{},方式2計算面積為:{}'.format(compute_1_area, compute_2_area)) print('方式1計算周長:{},方式2計算周長:{},方式3計算周長:{}'.format(compute_1_peri, compute_2_peri, compute_3_peri))56.np.linalg.eig計算特征值和特征向量
x = np.diag((1, 2, 3)) print('x:\n', x) w, v = np.linalg.eig(x) print('w:\n', w) print('v:\n', v)57.奇異值分解np.linalg.svd?
# 創(chuàng)建一個矩陣A: A = u s vt # A = np.array([[0, 1], # [1, 1], # [1, 0]]) A = np.array([[4, 1, 3],[2, 2, 4]]) print('A:\n', A)# 對其進行SVD分解 s是對角矩陣的值 u, s, vt = np.linalg.svd(A, full_matrices=True) print('u.shape, s.shape, vt.shape:', u.shape, s.shape, vt.shape) res = np.dot(u*s, vt[:2, :]) print('res:', res) #U UT=I print('np.dot(u, u.T):\n', np.dot(u, u.T)) #v vT=I print('np.dot(vt, vt.T):\n', np.dot(vt, vt.T))58. np實現(xiàn)avg_pool和max_pool
import numpy as np def pooling(inputMap, k = 3, s=1, pad=False, mode='max'):""":param inputMap::param k::param s::param pad::return:"""# inputMap sizesh, w = np.shape(inputMap)if pad:new_h, new_w = h, welse:new_h, new_w = (h - k)//s + 1, (w - k)//s + 1outputMap = np.zeros((new_h, new_w))# paddingif pad:padding = [(0, (h*s - s + k - h)//2 + 1), (0, (h*s - s + k - h)//2+1)]temp_map = np.pad(inputMap, padding, mode='constant', constant_values=0)else:temp_map = inputMapprint('===temp_map:', temp_map)# # max poolingfor i in range(0, new_h):for j in range(0, new_w):start_y = i * sstart_x = j * spool_region = temp_map[start_y:start_y+k, start_x:start_x+k]# print('===pool_region:', pool_region)if mode == 'max':value = np.max(pool_region)else:value = np.mean(pool_region)outputMap[i, j] = valueprint('==outputMap:', outputMap)return outputMap# 測試實例 # test = np.array([[1, 2, 3, 4], # [5, 6, 7, 8], # [9, 10, 11, 12], # [10, 11, 13, 14]]) test = np.array([[1, 2, 3, 4, 5],[5, 6, 7, 8, 9],[9, 10, 11, 12, 13],[10, 11, 13, 14, 15],[10, 11, 13, 14, 15]]) test_result = pooling(test, k=3, s=1, pad=False, mode='max') print('==test_result:', test_result)59.實現(xiàn)高斯分布
import numpy as np def get_gaussian_kernel(k=3, mu=0, sigma=1, normalize=True):# compute 1 dimension gaussiangaussian_1D = np.linspace(-1, 1, k)print('===gaussian_1D:', gaussian_1D)# compute a grid distance from centerx, y = np.meshgrid(gaussian_1D, gaussian_1D)print('===x:', x)print('===y:', y)distance = (x ** 2 + y ** 2) ** 0.5print('==distance:', distance)# compute the 2 dimension gaussiangaussian_2D = np.exp(-(distance - mu) ** 2 / (2 * sigma ** 2))gaussian_2D = gaussian_2D / (2 * np.pi *sigma **2)print('==gaussian_2D:\n', gaussian_2D)# normalize part (mathematically)if normalize:gaussian_2D = gaussian_2D / np.sum(gaussian_2D)return gaussian_2Dget_gaussian_kernel()60.二維圖片(圖像索引)變?nèi)S(顏色)
常用于分割
seg = np.array([[0, 2, 3],[0, 1, 4]]) color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8)palette = [[0, 0, 0], [0, 0, 255], [0, 255, 0], [255, 0, 0], [255, 255, 0]] for label, color in enumerate(palette):color_seg[seg == label, :] = color print('==color_seg:', color_seg)61.求解多元一次方程組
def test_np_slove():a = np.array([[1, 2, 3],[5, 23, 5],[4, 7, 9]])b = np.array([10,20,30])x = np.linalg.solve(a, b)print(x)print(np.sum(np.dot(a, x.reshape(3, 1)), axis=1))62.利用數(shù)組索引獲取每個值
target = np.array([[1, 2, 3],[4, 5, 6]]) idx = np.array([[0, 1],[1, 0],[0, 2],[0, 0],[1, 1],[1, 2]]) print('target[idx[:, 0], idx[:, 1]]', target[idx[:, 0], idx[:, 1]])總結(jié)
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