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import numpy as np
import cv2
import math
import json
import re

path_to_files = '/Users/shtern/runner-new-motion'
movement_json_path = '/Users/shtern/Downloads/movement.json'
res_prod_map = {2773: 'd2mj5veol0', 3139: '493lkx9o7e', 3113: 'y9m6nnl3e2', 2938: 'vjmkdv93qy'}
extra_json_name = 'scan_0_extra.json'

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OpenCV Video Writer on Mac OS X

Fourcc tests:

• x : not worked
• o: worked (able to open in quicktime but need conversion)
• oo: worked well (preview worked but wrong aspect ratio)
• ooo: worked very well (preview worked, and aspect ratio’s right)

(Codecs are extracted from https://web.archive.org/web/20120722124832/http://opencv.willowgarage.com/wiki/QuickTimeCodecs )

shtern

OpenCV Video Writer on Mac OS X

Fourcc tests:

• x : not worked
• o: worked (able to open in quicktime but need conversion)
• oo: worked well (preview worked but wrong aspect ratio)
• ooo: worked very well (preview worked, and aspect ratio’s right)

(Codecs are extracted from https://web.archive.org/web/20120722124832/http://opencv.willowgarage.com/wiki/QuickTimeCodecs )

shtern

import matplotlib.pyplot as plt
from keras.datasets import mnist
import numpy as np
from keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D
from keras.models import Model
from keras import backend as K

(x_train, _), (x_test, _) = mnist.load_data()

x_train = x_train.astype('float32') / 255.

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#
# packages 2 install
#

# !pip install tqdm
# !conda install -y Pillow


# ---------------------------------------------------------------------
# Load util

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import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import ImageGrid
import numpy as np
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
print('Training.images shape: ', mnist.train.images.shape)
print('Training.labels shape: ', mnist.train.labels.shape)
print('Shape of an image: ', mnist.train.images[0].shape)

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import tensorflow as tf
import numpy as np


class AlexNet(object):

    def __init__(self, x, keep_prob, num_classes, skip_layer,
                 weights_path='DEFAULT'):

        # Parse input arguments into class variables

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def adaboost_fit(X,y, M=10, learning_rate = 1):
    #Initialization of utility variables
    N = len(y)
    estimator_list, y_predict_list, estimator_error_list, estimator_weight_list, sample_weight_list = [], [],[],[],[]

    #Initialize the sample weights
    sample_weight = np.ones(N) / N
    sample_weight_list.append(sample_weight.copy())

    for m in range(M):