Merwanski

import pymeshlab as ml

ms = ml.MeshSet()
ms.load_new_mesh('input.ply')
m = ms.current_mesh()
print('input mesh has', m.vertex_number(), 'vertex and', m.face_number(), 'faces')

# Target number of vertex
TARGET=10000

Merwanski

# Importing necessary functions
from keras.preprocessing.image import ImageDataGenerator, 
array_to_img, img_to_array, load_img
   
# Initialising the ImageDataGenerator class.
# We will pass in the augmentation parameters in the constructor.
datagen = ImageDataGenerator(
        rotation_range = 40,
        shear_range = 0.2,
        zoom_range = 0.2,

Merwanski

# Create The Sandbox You Play In
conda create --name your_env_name python=3.7 -y

conda create --name your_env_name python=3.7 scipy=0.15.0 astroid babel

conda env export > my_environment.yml

conda info --envs

conda activate your_env_name

Merwanski

import cv2
import numpy as np
from matplotlib import pyplot as plt
import os

"""
source: https://medium.com/@nhancv/opencv-python-connect-to-android-camera-via-rstp-9eb78e2903d5
NB: Did not work on windows 10
"""
os.environ["OPENCV_FFMPEG_CAPTURE_OPTIONS"] = "rtsp_transport;0"

Merwanski

# import necessary packages
import numpy as np
import cv2

# path to image
path_to_image = "data/cv_image_000.jpg"
# read the image with cv2
image = cv2.imread(path_to_image)

print('Original Dimensions : ', image.shape)

Merwanski

import cv2
import os
import numpy as np
import glob

video_file_name = 'FILE_NAME.mp4'
path_to_images = 'PATH'

extension = "png" # png, jpg, ...

Merwanski

kinect_v2_ubuntu18

Kinect2 Setup Guide

https://scazlab.github.io/kinect2_setup_guide.html


Welcome to the Setup Guide for Kinect2.
System

Merwanski

# import necessary packages 
import tensorflow as tf
import tensorflow_datasets as tfds

# Construct a tf.data.Dataset
# Data loaded into ~/tensorflow_datasets/mnist
ds = tfds.load('mnist', split='train', shuffle_files=True)

# Build your input pipeline
ds=ds.shuffle(1024).batch(32).prefetch(tf.data.experimental.AUTOTUNE)

Merwanski

from keras.models import load_model
from PIL import Image, ImageOps
import numpy as np

# Load the model
model = load_model('keras_model.h5')

# Create the array of the right shape to feed into the keras model
# The 'length' or number of images you can put into the array is
# determined by the first position in the shape tuple, in this case 1.

Merwanski

# import necessary packages 
import numpy as np
import cv2

# path to image
path_to_image = "data/cv_image_000.jpg"
# read the image with cv2
image = cv2.imread(path_to_image)

print('Original Dimensions : ', image.shape)