Created
July 5, 2020 10:09
-
-
Save neelriyer/ca2f0682ebf9c5d6f70643f62fe7cb4a to your computer and use it in GitHub Desktop.
Run inference on images using trained model
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
mport PIL | |
import glob | |
import os | |
from tqdm.notebook import tqdm | |
render_factor = 40 | |
if os.path.exists('imagepaths.txt'): | |
os.remove('imagepaths.txt') | |
!rm -Rf seinfeld_inference/high_res/ | |
!mkdir seinfeld_inference/high_res/ | |
def write_to_txt(dest): | |
file = open("imagepaths.txt", "a") | |
write = "file '" + dest.strip() + "'" + "\n" | |
file.write(write) | |
file.close() | |
files = sorted(glob.glob('seinfeld_inference/images/*.*g'), key = lambda x: int(os.path.basename(x).split('.')[0])) | |
files = files[300:] | |
for i in tqdm(range(1000)): | |
# file = random.choice(files) | |
file = files[i] | |
dest = 'seinfeld_inference/high_res/'+os.path.basename(file) | |
# scale to square | |
new_path = scale_to_square(PIL.Image.open(file), render_factor*16, dest = dest.split('.')[0]+'_square.jpg') | |
# run inference | |
run_inference_images(new_path, dest) | |
# unsquare | |
dest = unsquare(PIL.Image.open(dest), PIL.Image.open(file), dest = dest.split('.')[0]+'_unsquared.jpg') | |
# write to txt | |
write_to_txt(dest) | |
# increase brightness | |
adjust_brightness(PIL.Image.open(dest),factor = 1.75, dest=dest) |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment