magesh-technovator

"""  
Copyright (c) 2019-present NAVER Corp.
MIT License
"""

# -*- coding: utf-8 -*-
import numpy as np
import cv2
import math

magesh-technovator

# Load image as torch tensor
x = torch.from_numpy(x).permute(2, 0, 1)    # [h, w, c] to [c, h, w]
x = Variable(x.unsqueeze(0))                # [c, h, w] to [b, c, h, w]

if cuda:
    x = x.cuda()

# forward pass
with torch.no_grad():
    y, feature = net(x)

magesh-technovator

import numpy as np
from skimage import io
import cv2

def loadImage(img_file):
    img = io.imread(img_file)           # RGB order
    if img.shape[0] == 2: img = img[0]
    if len(img.shape) == 2 : img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
    if img.shape[2] == 4:   img = img[:,:,:3]
    img = np.array(img)

magesh-technovator

cuda = False # set to True for GPU

# load net
net = CRAFT()

# Load the weights from pre-trained model
if cuda:
    net.load_state_dict(copyStateDict(torch.load(args.trained_model)))
    
else:

magesh-technovator

"""  
Copyright (c) 2019-present NAVER Corp.
MIT License
"""

# -*- coding: utf-8 -*-
import torch
import torch.nn as nn
import torch.nn.functional as F

magesh-technovator

import os, sys
import numpy as np
import cv2
import time
from imutils.object_detection import non_max_suppression

def east_detect(image):
    layerNames = [
    	"feature_fusion/Conv_7/Sigmoid",
    	"feature_fusion/concat_3"]

magesh-technovator

import torch
import matplotlib.pyplot as plt
import cv2
import pandas as pd
import time

model_path = "<model path>"
image_path = "<image_path>"

# Load the trained model

magesh-technovator

import csv
import copy
import time
from tqdm import tqdm
import torch
import numpy as np
import os


def train_model(model, criterion, dataloaders, optimizer, metrics, bpath, num_epochs=3):

magesh-technovator

from torchvision import models
from torchvision.models.segmentation.deeplabv3 import DeepLabHead

def createDeepLabv3(outputchannels=1):
    model = models.segmentation.deeplabv3_resnet101(
        pretrained=True, progress=True)
    # Added a Tanh activation after the last convolution layer
    model.classifier = DeepLabHead(2048, outputchannels)
    # Set the model in training mode
    model.train()

magesh-technovator

from torch.utils.data import Dataset, DataLoader
import glob
import os
import numpy as np
import cv2
import torch
from torchvision import transforms, utils
from PIL import Image