model.py

from strhub.data.module import SceneTextDataModule
from strhub.models.utils import load_from_checkpoint
from post import filter_mask
import segmentation_models_pytorch as smp
import albumentations as albu
from torchvision import transforms
from PIL import Image
import torch
import cv2
from time import process_time
import imutils
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

model_recog = load_from_checkpoint("weights/parseq/last.ckpt").eval().to(device).float()
img_transform = SceneTextDataModule.get_transform(model_recog.hparams.img_size)

model = torch.load('weights/best_model.pth', map_location=torch.device(device))
model.eval()


SHAPE_X = 384
SHAPE_Y = 384
preprocessing_fn = smp.encoders.get_preprocessing_fn('resnet50')



def prediction(image_path):
    t_start = process_time()
    image = cv2.imread(image_path)
    image_original = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    image_original = imutils.resize(image_original, width=image_original.shape[1]//5) 
    transform_compose = albu.Compose([
            albu.Lambda(image=preprocessing_fn), albu.Resize(SHAPE_X, SHAPE_Y)
          ])
    image_result = transform_compose(image=image_original)["image"]
    transform_tensor = transforms.ToTensor()
    tensor = transform_tensor(image_result)
    tensor = torch.unsqueeze(tensor, 0)
    output = model.predict(tensor.float().to(device))

    result, img_vis = filter_mask(output, image_original)
    
    image = cv2.cvtColor(result, cv2.COLOR_BGR2RGB)
    im_pil = Image.fromarray(image)
    image = img_transform(im_pil).float().unsqueeze(0).to(device)
    
    p = model_recog(image).softmax(-1)
    pred, p = model_recog.tokenizer.decode(p)
    t_stop = process_time()
    t_process = (t_stop - t_start)/3
    print(f'{image_path}: {pred[0]}, {t_process} seconds')
    
    return img_vis, pred[0], t_process