# Sepia transformation matrix sepia_matrix = np.array([[0.393, 0.769, 0.189], [0.349, 0.686, 0.168], [0.272, 0.534, 0.131]]) # Apply the sepia transformation sepia_img = image.dot(sepia_matrix.T) # Using matrix
def apply_sepia(image): # Sepia transformation matrix sepia_matrix = np.array([[0.393, 0.769, 0.189], [0.349, 0.686, 0.168], [0.272, 0.534, 0.131]]) # Apply the sepia transformation sepia_img = image.dot(sepia_matrix.T) # Using matrix multiplication # Ensure values are within valid ra...
def apply_sepia(image): # Sepia transformation matrix sepia_matrix = np.array([[0.393, 0.769, 0.189], [0.349, 0.686, 0.168], [0.272, 0.534, 0.131]]) # Apply the sepia transformation sepia_img = image.dot(sepia_matrix.T) # Using matrix multiplication # Ensure values are within valid ra...
def apply_sepia(image): # Sepia transformation matrix sepia_matrix = np.array([[0.393, 0.769, 0.189], [0.349, 0.686, 0.168], [0.272, 0.534, 0.131]]) # Apply the sepia transformation sepia_img = image.dot(sepia_matrix.T) # Using matrix multiplication # Ensure values are within valid ra...
# Apply the sepia transformation sepia_img =image.dot(sepia_matrix.T) # Usingmatrixmultiplication # Ensurevaluesare within validrange[0,255] sepia_img =np.clip(sepia_img,0,255)returnsepia_img.astype(np.uint8) # Apply sepia effect
T)), np.matrix([0., 0., 1.])]) def warp_im(self, im, M, dshape): ''' 人脸位置仿射变换 ''' output_im = np.zeros(dshape, dtype=im.dtype) cv2.warpAffine(im, M[:2], (dshape[1], dshape[0]), dst=output_im, borderMode=cv2.BORDER_TRANSPARENT, flags=cv2.WARP_INVERSE_...
a是一个矩阵Matrix的数组。每个矩阵M都会被计算其特征值与特征向量。 Returns w : (…, M) array The eigenvalues, each repeated according to its multiplicity. The eigenvalues are not necessarily ordered. The resulting array will be of complex type, unless the imaginary part is zero in which case ...
(refArr) # Solve the least squares problem X * A = Y # to find our transformation matrix A A, res, rank, s = np.linalg.lstsq(X, Y) transform = lambda x: unpad(np.dot(pad(x), A)) print "Target:" print refArr print "Result:" print transform(inArr) print "Max erro...
Write a Numpy program to compute a matrix product followed by an element-wise transformation using loops, then optimize with vectorized operations. Go to: NumPy Memory Layout Exercises Home ↩ NumPy Exercises Home ↩ Previous:Numpy - Compute Variance of large array using For loop and Optimizatio...
科学计算是一个多学科领域,其应用跨越数值分析,计算金融和生物信息学等学科。 让我们考虑一下金融市场的情况。 当您考虑金融市场时,会有巨大的相互联系的互动网络。 政府,银行,投资基金,保险公司,养老金,个人投资者和其他人都参与了这种金融工具的交换。 您不能简单地模拟市场参与者之间的所有互动,因为参与金融交易...