How to check if an image contains an element I am searchig for
Arak Rachael
arakelthedragon at gmail.com
Thu Jun 17 09:35:24 EDT 2021
On Thursday, 17 June 2021 at 09:59:49 UTC+2, Arak Rachael wrote:
> 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... at gmail.com> wrote:
> > > > >
> > > > > On Wed, Jun 16, 2021 at 2:04 PM Barry <ba... at barrys-emacs.org> wrote:
> > > > >
> > > > > > >>> On Thu, Jun 17, 2021 at 6:06 AM Arak Rachael <arakelt... at 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... at 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]
Thanks for the help guys, I did it with OpenCV and Numpy, here is the code, but please download it, because its in my dropbox unsorted folder and I might delete it in time:
https://www.dropbox.com/s/cr46m538to0j9ja/file_split.py?dl=0
here is a video processing file for who ever needs it:
https://www.dropbox.com/s/39lrxkrfxuws1yd/videotest.py?dl=0
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