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import numpy as np
import cv2
img = np.zeros((480,640))
img[(300,100)] = 1
# img[(400,490)] = 1
img_final = img.copy()
indices = np.column_stack(np.where(img > 0))
cv2.imshow("img", img)

alias squeue_job="squeue -o \"%.7i %.9P %.100j %.8u %.2t %.10M %.6D %R\""

[image]

width
640

height
480

[narrow_stereo]
/tmp/ccEiMrO7.s: Assembler messages:
/tmp/ccEiMrO7.s:1533: Error: unknown mnemonic pause' --pause'
/tmp/ccEiMrO7.s:1857: Error: unknown mnemonic pause' --pause'
/tmp/ccEiMrO7.s:2204: Error: unknown mnemonic pause' --pause'
/tmp/ccEiMrO7.s:2679: Error: unknown mnemonic pause' --pause'
/tmp/ccEiMrO7.s:4226: Error: unknown mnemonic pause' --pause'
/tmp/ccEiMrO7.s:5069: Error: unknown mnemonic pause' --pause'
/tmp/ccEiMrO7.s:5320: Error: unknown mnemonic pause' --pause'
/tmp/ccEiMrO7.s:5441: Error: unknown mnemonic pause' --pause'
from keras.models import Sequential
from keras.layers.core import Flatten, Dense, Dropout
from keras.layers.convolutional import Convolution2D, MaxPooling2D, ZeroPadding2D
from keras.optimizers import SGD
from keras.layers import Activation
from keras.layers.normalization import BatchNormalization
import cv2, numpy as np
def build_model():
model = Sequential()
mean = [5;5;10];
sigma = [6 -3 0; -3 6 0; 0 0 10];
no_of_samples = 100000;
samples = mvnrnd(mean, sigma, no_of_samples);
covariances = cov(samples);
[v, D, W] = eig(covariances);
v(:,1)
D(1,1)