def split(matrix, uploaded_file_path, timestamp):
split_image(uploaded_file_path, matrix)
os.remove(uploaded_file_path)
json_res = jsonify({
'dirname':
timestamp,
'files':
os.listdir(app.config['UPLOAD_FOLDER'] + timestamp)
})
json_res.status_code = HTTPStatus.CREATED
return json_res
import os
import sys
import json
import subprocess
from utils import STATUS, PREFIX
from utils import load_yaml, save_yaml, load_images, split_image, gen_tag
folder = sys.argv[1]
image_dict = {}
image_set = set()
for image in load_images():
if image.get(STATUS, None) is not None:
image_set.add(image['name'])
image_repo, image_tag = split_image(image['name'])
image_dict[image_repo] = {
"full": image['name'],
"repo": image_repo,
"tag": image_tag,
}
replaces = {}
current = ""
def reg(s):
return s.replace('/', '\/')
def replace_container(c):
from utils import AUTH_CONFIG, STATUS, PREFIX
from utils import gen_tag, split_image, load_images
client = docker.from_env()
images = load_images()
for entry in images:
image = entry['name']
status = entry.get(STATUS, None)
if status is not None:
continue
relabel = gen_tag(image)
relabel_image = PREFIX + ":" + relabel
image_repo, image_tag = split_image(image)
print("Remapping {} to {}".format(image, relabel_image))
cmd = ['sudo', 'docker', 'pull', image]
subprocess.run(cmd)
cmd = ['sudo', 'docker', 'tag', image, relabel_image]
subprocess.run(cmd)
cmd = [
'sudo', 'docker', 'login', '--username='******'username'],
'--password='******'password'],
PREFIX.split('/')[0]
]
subprocess.run(cmd)
cmd = ['sudo', 'docker', 'push', PREFIX + ":" + relabel]
subprocess.run(cmd)
#client.images.push(PREFIX, tag=relabel, auth_config=AUTH_CONFIG)
from utils import split_image, pack_sequences
from chars import *
import matplotlib.pyplot as plt
if __name__ == '__main__':
model_f, img_f = sys.argv[1:]
model = OCRModel(num_chars=NUM_TOKENS)
device = torch.device('cpu')
model.load_state_dict(torch.load(model_f, map_location=device))
model.eval()
img = cv2.imread(img_f)
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
images = split_image(img)
logits = model.encoder(pack_sequences([images], device), device)
logits, input_lengths = nn.utils.rnn.pad_packed_sequence(logits,
batch_first=False)
probs, ids = logits.view(len(images), -1).softmax(dim=1).max(dim=1)
chars = [ids2chars[int(i)] for i in ids]
h, w = img.shape
ratio = 2
img = cv2.resize(img, (int(w * ratio), int(h * ratio)),
interpolation=cv2.INTER_NEAREST)
plt.imshow(img, cmap='gray')
x = [14 * i + 7 / 2 for i in range(len(images))]
heights = probs.detach().numpy()
from PIL import Image
from utils import key, encryptChannel, decryptChannel, split_image
keySpace=256
imgPath='input.jpeg'
# Open the image using PIL
im = Image.open(imgPath, 'r')
# Resize the image to 256*256 pixels
# im = im.resize((256, 256), Image.ANTIALIAS)
# Extract pizel values
img0, img1 = split_image(im.getdata())
rVal, gVal, bVal = zip(*im.getdata())
rVal0, gVal0, bVal0 = zip(*img0)
rVal1, gVal1, bVal1 = zip(*img1)
partImg = Image.new(im.mode, im.size)
partImg.putdata(list(zip(rVal0, gVal0, bVal0)))
partImg.save("part1.jpg")
partImg = Image.new(im.mode, im.size)
partImg.putdata(list(zip(rVal1, gVal1, bVal1)))
partImg.save("part2.jpg")
newImg = Image.new(im.mode, im.size)
newImg.putdata(list(zip(rVal, gVal, bVal)))