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

# 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

# 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

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

# initial code from https://github.com/ros-perception/vision_opencv/blob/noetic/cv_bridge/python/cv_bridge/core.py
import cv2
import numpy as np
import sensor_msgs

def cv2_to_imgmsg(cvim, encoding="passthrough", header=None):
    if not isinstance(cvim, (np.ndarray, np.generic)):
        raise TypeError('Your input type is not a numpy array')
    # prepare msg
    img_msg = sensor_msgs.msg.Image()

Merwanski

#!/bin/bash
# Installation instructions from https://docs.docker.com/engine/install/ubuntu/
# Update the apt package index and install packages to allow apt to use a repository over HTTPS:
sudo apt update
sudo apt install -y apt-transport-https ca-certificates curl gnupg-agent software-properties-common
# Add Docker’s official GPG key
curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -
# Use the following command to set up the stable repository
sudo add-apt-repository "deb [arch=amd64] https://download.docker.com/linux/ubuntu $(lsb_release -cs) stable"
# INSTALL DOCKER ENGINE

Merwanski

# Docker-CE on Ubuntu can be installed using Docker's official convenience script:
curl https://get.docker.com | sh \
  && sudo systemctl --now enable docker

# Setting up NVIDIA Container Toolkit
# Setup the stable repository and the GPG key:
distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \
   && curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add - \
   && curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list

Merwanski

import argparse

import cv2
import numpy as np

parser = argparse.ArgumentParser(add_help=False)
parser.add_argument("--image", default='samples/image.jpg', help="image for prediction")
parser.add_argument("--config", default='cfg/yolov3.cfg', help="YOLO config path")
parser.add_argument("--weights", default='yolov3.weights', help="YOLO weights path")
parser.add_argument("--names", default='data/coco.names', help="class names path")

Merwanski

# import necessary packages
from numpy import expand_dims
from keras.models import load_model
from keras.preprocessing.image import load_img
from keras.preprocessing.image import img_to_array
import time
import pdb

# load and prepare an image
def load_image_pixels(filename, shape):

Merwanski

import cv2
import numpy as np
import glob

video_file_name = 'project.wmv'
path_to_images  = 'path_2_video/*.bmp' 
 
cmpt = 0

for filename in glob.glob(path_to_images):