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...@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] 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 -- https://mail.python.org/mailman/listinfo/python-list
