Merwanski

# REMOVE the containers no longer running 
docker rm $(docker ps -a -q) 

# REMOVE the images without any attached container to them
docker rmi $(docker images -q -f dangling=true)

Merwanski

!bash

# install docker
# Step 1 — Installing Docker
# First, update your existing list of packages:
sudo apt update

# Next, install a few prerequisite packages which let apt use packages over HTTPS:
sudo apt install -y apt-transport-https ca-certificates curl software-properties-common

Merwanski

# You could use numpy directly, instead of cv_bridge

import numpy as np
im = np.frombuffer(image_data.data, dtype=np.uint8).reshape(image_data.height, image_data.width, -1)

pi

# Another solution  // Still not yet tested 
# Beautiful solution is to use ros_numpy (https://github.com/eric-wieser/ros_numpy). 
# It is similar to the function of cv_bridge, but without the dependency on cv2:

Merwanski

# YAMLLoadWarning: calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. 
# Please read https://msg.pyyaml.org/load for full details.

import yaml

f = open(filaname_path, 'r')   
yaml.load(f, Loader=yaml.FullLoader)
f.close()

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):

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 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

# 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

#!/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

# 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()