On Thursday, 17 June 2021 at 08:52:55 UTC+2, Peter J. Holzer wrote:
> On 2021-06-16 15:51:49 -0700, Arak Rachael wrote:
> > On Wednesday, 16 June 2021 at 23:44:02 UTC+2, Chris Angelico wrote:
> > > On Thu, Jun 17, 2021 at 7:35 AM Dan Stromberg <drsa...@gmail.com> wrote:
> > > >
> > > > On Wed, Jun 16, 2021 at 2:04 PM Barry <ba...@barrys-emacs.org> wrote:
> > > >
> > > > > >>> On Thu, Jun 17, 2021 at 6:06 AM Arak Rachael
> > > > > >>> <arakelt...@gmail.com>
> > > > > wrote:
> > > > > >>> I have an image from google maps to say and I need to check if it
> > > > > >>> has
> > > > > road markings, in order to do that, I believe I need to change the
> > > > > effects
> > > > > on the image so the markings and road can be white or something and
> > > > > the
> > > > > things I don't need like cars, trees and so on to be black.
> [...]
> > I understand your concerns. Actually I am doing image processing of
> > satellite pictures for smart cars.
> Using satellite images probably makes that a lot easier (yes, you wrote
> "Google maps", but for some reason I thought of Google Street View). At
> least you have a consistent angle (almost from straight above) and know
> the scale (given the zoom factor and the latitude).
>
> hp
>
> --
> _ | Peter J. Holzer | Story must make more sense than reality.
> |_|_) | |
> | | | h...@hjp.at | -- Charles Stross, "Creative writing
> __/ | http://www.hjp.at/ | challenge!"
I made the crop code, before I posted the question, I just need the
identification part:
[code]
# Library includes
import os
import cv2 # image and other
special formats processing library
# for computer vision
import numpy as np
from numpy import asarray
import PIL
from PIL import Image
# Global variables
# Recommended: move to
a separate file
# Recommended approach
instead of using the "target"
# name directly
source_directory = "test" # Do not put a slash at
the beginning
output_directory = "test2" # Do not put a slash at
the beginning
# Function definitions
def image_contains_required_elements(image): # Description:
# The function will
check if the cropped image contains the
# required elements of
the road(markings, road and others)
# Local variables and
initialization
raise Exception("Not implemented yet")
def split_image(path, dstpath): # Description:
# Convert the generated
frames(images) from
#
extract_video(video_path, target_dir_path) to grayscale
# and reduce their size
to half
# Local variables and
initialization
# Requires the
libraries:
# import cv2
# import os
# import pytest
# import numpy as np
# Processing
# Reading an image in
default mode
#path = r'/home/yordan/devel/python.assignments/topgis-viz.2/data'
# Source Folder
#dstpath =
r'/home/yordan/devel/python.assignments/topgis-viz.2/output_directory'
# Destination Folder
#path = source_directory # source_directory containing the images before
the processing
#dstpath = output_directory # output_directory containing the images after
the processing
"""
# Test if
target_directory exists
try: # Try
makedirs(dstpath) # to create target_directory
except: # if there is an error
print("Directory already exist, images will be written in same folder")
# print the error message
"""
files = os.listdir(path) # Read the files from
source_directory and record them in a list
for image in files: # For index in list
img = cv2.imread(os.path.join(path, image)) # Read the image from
path + image name as an array into img
# Split image into b, g
,r function
gray = img
#b, g, r = cv2.split(gray) # Split the image into
BGR channels
#print(b, g, r) # print the b, g, r
channels(codes)
crop_img = gray[100:100 + 100, 100:100 + 100] # Crop the image with
numpy, x and y are flipped,
# example crop_img =
img[margin:-margin, margin:-margin]
if image_contains_required_elements(crop_img) == True:
cv2.imwrite(os.path.join(dstpath, image), crop_img) # Write the
image crop_img to a file with name
# target_directory +
image name
# Displaying the image
#cv2.imshow("Test", crop_img) # Show the image
split_image(source_directory, output_directory)
[code]
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