Syntax np.asarray(a, dtype=None, order=None) 将结构数据转化为ndarray。...Code # 将list转换为ndarray a = [1, 2] print(np.asarray(a)) # array([1, 2]) # 如果对象本身即为ndarray,且不改变dtype...,则不会copy之 a = np.array([1, 2]) print(np.asarray(a) is a) # True # 如果对象本身即为ndarray,且改变dtype,则还是会copy...之 a = np.array([1, 2], dtype=np.float32) print(np.asarray(a, dtype=np.float32) is a) # True print(...np.asarray(a, dtype=np.float64) is a) # False
1、输入为列表时a=[[1,2,3],[4,5,6],[7,8,9]]b=np.array(a)c=np.asarray(a)a[2]=1print(a)print(b)print(c)?...从中我们可以看出np.array与np.asarray功能是一样的,都是将输入转为矩阵格式。当输入是列表的时候,更改列表的值并不会影响转化为矩阵的值。...2、输入为数组时a=np.random.random((3,3))print(a.dtype)b=np.array(a,dtype='float64')c=np.asarray(a,dtype='float64...从上述结果我们可以看出np.array与np.asarray的区别,其在于输入为数组时,np.array是将输入copy过去而np.asarray是将输入cut过去,所以随着输入的改变np.array的输出不变...,而np.asarray的输出在变化,并且当我们使用np.asarray改变其类型的时候(输入是float64,改为float32),这样当输入改变的时候,np.asarray的输出也不会改变。
1.输入为列表时 a=[[1,2,3],[4,5,6],[7,8,9]] b=np.array(a) c=np.asarray(a) a[2]=1 print(a) print(b) print(c)...从中我们可以看出np.array与np.asarray功能是一样的,都是将输入转为矩阵格式。当输入是列表的时候,更改列表的值并不会影响转化为矩阵的值。...2.输入为数组时 a=np.random.random((3,3)) print(a.dtype) b=np.array(a,dtype='float64') c=np.asarray(a,dtype=...从上述结果我们可以看出np.array与np.asarray的区别,其在于输入为数组时,np.array是将输入copy过去而np.asarray是将输入cut过去,所以随着输入的改变np.array的输出不变...,而np.asarray的输出在变化,并且当我们使用np.asarray改变其类型的时候(输入是float64,改为float32),这样当输入改变的时候,np.asarray的输出也不会改变。
if x.size == 0: return np.nan else: return np.sum(np.abs(x - np.mean(x)) > r * np.asarray...else: j = np.max(series[:i]) return j-k def drawdown_duration(series): series = np.asarray...1] - x[0]) / (len(x) - 1) if len(x) > 1 else np.NaN def mean_second_derivative_central(x): x = np.asarray...) def longest_strike_below_mean(x): if not isinstance(x, (np.ndarray, pd.Series)): x = np.asarray...def number_crossing_m(x, m): if not isinstance(x, (np.ndarray, pd.Series)): x = np.asarray
fpath.append(path+im) #图像路径名 #print(path+im, idx) return np.asarray...(fpath, np.string_), np.asarray(imgs, np.float32), np.asarray(labels, np.int32) # 读取图像 fpaths, data,
例子: import numpy as np import cv2 def fun1(im): im=np.asarray(im,np.float32) return im def fun2(im...): im=np.asarray(im,np.uint16) return im if __name__ == '__main__': #set a depth map using np.random...depth_frame = frames.get_depth_frame() color_frame = frames.get_color_frame() depth_image = np.asarray...(depth_frame.get_data(), dtype=np.float32) color_image = np.asarray(color_frame.get_data(), dtype.../examples/savefig/depth/Tbimage_d_{}.png'.format(str(0).zfill(5)), np.asarray(depth_map,np.uint16))
三个通道数值都置为r*0.299+g*0.587+b*0.114 def blackWithe(imagename): # r,g,b = r*0.299+g*0.587+b*0.114 im = np.asarray...流年 把R通道的数值开平方,然后乘以一个参数 def fleeting(imagename,params=12): im = np.asarray(Image.open(imagename).convert...旧电影 把像素的R,G,B三个通道数值,3个通道的分别乘以3个参数后求和,最后把超过255的值置为255 def oldFilm(imagename): im = np.asarray(Image.open...反色 这个最简单了,用255减去每个通道的原来的数值 def reverse(imagename): im = 255 - np.asarray(Image.open(imagename).convert
cumsum(), columns=['weight']) df['pct_change'] = df.weight.pct_change() df['w_log'] = np.log(np.asarray...(df['weight']+2 , dtype=object)) print df['w_log'] 会出现这个问题: df['w_log'] = np.log(np.asarray(df...cumsum(), columns=['weight']) df['pct_change'] = df.weight.pct_change() df['w_log'] = np.log(np.asarray
reverse_hue(image): # 反转色相 image_hls = cv2.cvtColor(image, cv2.COLOR_BGR2HLS) image_hls = np.asarray..., :, 0] hue[hue < 90] = 180 - hue[hue < 90] - 10 image_hls[:, :, 0] = hue image_hls = np.asarray...image def cyberpunk(image): image_lab = cv2.cvtColor(image, cv2.COLOR_BGR2Lab) image_lab = np.asarray...# 提高像素亮度,让亮的地方更亮 light_gamma_high = np.power(image_lab[:, :, 0], 0.9) light_gamma_high = np.asarray...# 降低像素亮度,让暗的地方更暗 light_gamma_low = np.power(image_lab[:, :, 0], 1.1) light_gamma_low = np.asarray
@param src_clr: 需要替换的颜色(r,g,b) @param dst_clr: 目标颜色 (r,g,b) @return 替换后的图像矩阵 ''' img_arr = np.asarray...img_arr.shape[0]): if (img_arr[j][i] == src_clr)[0] == True: dst_arr[j][i] = dst_clr return np.asarray...@param src_clr: 需要替换的颜色(r,g,b) @param dst_clr: 目标颜色 (r,g,b) @return 替换后的图像矩阵 ''' img_arr = np.asarray...@param src_clr: 需要替换的颜色(r,g,b) @param dst_clr: 目标颜色 (r,g,b) @return 替换后的图像矩阵 ''' img_arr = np.asarray...@param src_clr: 需要替换的颜色(r,g,b) @param dst_clr: 目标颜色 (r,g,b) @return 替换后的图像矩阵 ''' img_arr = np.asarray
self.optimize = optimize def fit(self, X, y): # store train data self.train_X = np.asarray...(X) self.train_y = np.asarray(y) self.is_fit = True def predict(self, X):...return X = np.asarray(X) Kff = self.kernel(self.train_X, self.train_X) # (N, N)...def y_2d(x, noise_sigma=0.0): x = np.asarray(x) y = np.sin(0.5 * np.linalg.norm(x, axis=1))...(train_X)[:,0], np.asarray(train_X)[:,1], train_y, c=train_y, cmap=cm.coolwarm) ax.contourf(test_d1,
ser_range * ema_weight).sum() ema.append(avg) std = ser_range.std() mstd.append(std) sma = np.asarray...(sma) mstd = np.asarray(mstd) # 上布林带是简单滑动均值加上两个滑动标准差 # 下布林带是简单滑动均值减去两个滑动标准差 upper = sma + 2 * mstd lower
Shapely模块可轻松地进行Skew IOU Computation:from shapely.geometry import Polygondef intersection(g, p): g=np.asarray...(g) p=np.asarray(p) g = Polygon(g[:8].reshape((4, 2))) p = Polygon(p[:8].reshape((4, 2)))
print("Testing mesh in open3d ...") mesh = o3dtut.get_knot_mesh() print(mesh) print('Vertices:') print(np.asarray...(mesh.vertices)) print('Triangles:') print(np.asarray(mesh.triangles)) >>>>Testing mesh in open3d ....half triangles.") mesh1 = copy.deepcopy(mesh) mesh1.triangles = o3d.utility.Vector3iVector( np.asarray...triangles)[:len(mesh1.triangles) // 2, :]) mesh1.triangle_normals = o3d.utility.Vector3dVector( np.asarray...print('create noisy mesh') mesh_in = o3dtut.get_knot_mesh() vertices = np.asarray(mesh_in.vertices) noise
as pd import matplotlib.pyplot as plt # df_samp, df_clu are two dataframes with input data set ref = np.asarray...(df_clu) samp = np.asarray(df_samp) ref_id = df_clu.columns samp_id = df_samp.columns # theoretical quantiles...samp_pct_x = np.asarray([percentileofscore(ref, x) for x in samp]) # sample quantiles samp_pct_y = np.asarray
([float(line[header.index('Nodule')]) for line in lines]) Nn = 1-Nd V0 = np.asarray([...in lines]) T0 = np.asarray([float(line[header.index('Text0')]) for line in lines]) T1...= np.asarray([float(line[header.index('Text1')]) for line in lines]) T2 = np.asarray([float(...('Volume')]) for line in lines]) T = np.asarray([float(line[header.index('Text')]) for line in...('VolumeClass')]) for line in lines]) Tclass = np.asarray([float(line[header.index('TextClass
indices.extend(zip([n] * len(seq), xrange(len(seq)))) values.extend(seq) indices = np.asarray...(indices, dtype=np.int64) values = np.asarray(values, dtype=dtype) shape = np.asarray([len(sequences...), np.asarray(indices).max(0)[1] + 1], dtype=np.int64) return indices, values, shape...(indices, dtype=np.int64) values = np.asarray(values, dtype=dtype) shape = np.asarray([len(sequences...), np.asarray(indices).max(0)[1] + 1], dtype=np.int64) return indices, values, shape
X = np.asarray(X) minX, maxX = np.min(X), np.max(X) # normalize to [0...1]....X = X * (high-low) X = X + low if dtype is None: return np.asarray(X) return np.asarray...not None): img = cv2.resize(img, (200, 200)) X.append(np.asarray...facerec.py []") sys.exit() [X,y] = load_images(sys.argv[1]) y = np.asarray...(y, dtype=np.int32) if len(sys.argv) == 3: out_dir = sys.argv[2] model.train(np.asarray
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