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import os
import sys
import time
import torch
import argparse
import torchvision
import numpy as np
import torch.nn as nn
from PIL import Image
from skimage.io import imread
import torch.nn.functional as F
from torch.utils.data import DataLoader
import torchvision.transforms as transforms
from dataset import HWRecogIAMDataset
from model_main import CRNN, STN_CRNN
from utils import ctc_decode, compute_wer_and_cer_for_sample
class DatasetFinalEval(HWRecogIAMDataset):
"""
Dataset class for final evaluation - inherits main dataset class
"""
def __init__(self, dir_images, image_height=32, image_width=768):
"""
---------
Arguments
---------
dir_images : str
full path to directory containing images
image_height : int
image height (default: 32)
image_width : int
image width (default: 768)
"""
self.dir_images = dir_images
self.image_files = [
f for f in os.listdir(self.dir_images) if f.endswith(".png")
]
self.image_width = image_width
self.image_height = image_height
self.transform = transforms.Compose(
[
transforms.ToPILImage(),
transforms.Resize(
(self.image_height, self.image_width), Image.BILINEAR
),
transforms.ToTensor(),
transforms.Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
),
]
)
def __len__(self):
return len(self.image_files)
def __getitem__(self, idx):
image_file_name = self.image_files[idx]
image_gray = imread(os.path.join(self.dir_images, image_file_name))
image_3_channel = np.repeat(np.expand_dims(image_gray, -1), 3, -1)
image_3_channel = self.transform(image_3_channel)
return image_3_channel
def get_dataloader_for_evaluation(
dir_images, image_height=32, image_width=768, batch_size=1
):
"""
---------
Arguments
---------
dir_images : str
full path to directory containing images
image_height : int
image height (default: 32)
image_width : int
image width (default: 768)
batch_size : int
batch size to use for final evaluation (default: 1)
-------
Returns
-------
test_loader : object
dataset loader object for final evaluation
"""
test_dataset = DatasetFinalEval(
dir_images=dir_images, image_height=image_height, image_width=image_width
)
test_loader = DataLoader(
test_dataset,
batch_size=batch_size,
shuffle=False,
num_workers=4,
)
return test_loader
def final_eval(hw_model, device, test_loader, dir_images, dir_results):
"""
---------
Arguments
---------
hw_model : object
handwriting recognition model object
device : str
device to be used for running the evaluation
test_loader : object
dataset loader object
dir_images : str
full path to directory containing test images
dir_results : str
relative path to directory to save the predictions as txt files
"""
hw_model.eval()
count = 0
num_test_samples = len(test_loader.dataset)
list_test_files = os.listdir(dir_images)
if not os.path.isdir(dir_results):
print(f"creating directory: {dir_results}")
os.makedirs(dir_results)
with torch.no_grad():
for image_test in test_loader:
file_test = list_test_files[count]
count += 1
"""
if count == 11:
break
"""
image_test = image_test.to(device, dtype=torch.float)
log_probs = hw_model(image_test)
pred_labels = ctc_decode(log_probs)
str_pred = [DatasetFinalEval.LABEL_2_CHAR[i] for i in pred_labels[0]]
str_pred = "".join(str_pred)
with open(
os.path.join(dir_results, file_test + ".txt"),
"w",
encoding="utf-8",
newline="\n",
) as fh_pred:
fh_pred.write(str_pred)
print(
f"progress: {count}/{num_test_samples}, test file: {list_test_files[count-1]}"
)
print(f"{str_pred}\n")
print(f"predictions saved in directory: ./{dir_results}\n")
return
def test_hw_recognizer(FLAGS):
os.environ["CUDA_VISIBLE_DEVICES"] = "0,1"
num_classes = len(DatasetFinalEval.LABEL_2_CHAR) + 1
print(f"task - handwriting recognition")
print(f"model: {FLAGS.which_hw_model}")
print(f"image height: {FLAGS.image_height}, image width: {FLAGS.image_width}")
# load the right model
if FLAGS.which_hw_model == "crnn":
hw_model = CRNN(num_classes, FLAGS.image_height)
elif FLAGS.which_hw_model == "stn_crnn":
hw_model = STN_CRNN(num_classes, FLAGS.image_height, FLAGS.image_width)
else:
print(f"unidentified option : {FLAGS.which_hw_model}")
sys.exit(0)
dir_results = f"results_{FLAGS.which_hw_model}"
# choose a device for evaluation
if torch.cuda.is_available():
device = torch.device("cuda")
else:
device = torch.device("cpu")
hw_model.to(device)
hw_model.load_state_dict(torch.load(FLAGS.file_model))
# get test set dataloader
test_loader = get_dataloader_for_evaluation(
dir_images=FLAGS.dir_images,
image_height=FLAGS.image_height,
image_width=FLAGS.image_width,
)
# start the evaluation on the final test set
print(
f"final evaluation of handwriting recognition model {FLAGS.which_hw_model} started\n"
)
final_eval(hw_model, device, test_loader, FLAGS.dir_images, dir_results)
print(f"final evaluation of handwriting recognition model completed!!!!")
return
def main():
image_height = 32
image_width = 768
which_hw_model = "crnn"
dir_images = "/home/abhishek/Desktop/RUG/hw_recognition/IAM-data/img/"
file_model = "model_crnn/crnn_H_32_W_768_E_177.pth"
save_predictions = 1
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter
)
parser.add_argument(
"--image_height",
default=image_height,
type=int,
help="image height to be used to predict with the model",
)
parser.add_argument(
"--image_width",
default=image_width,
type=int,
help="image width to be used to predict with the model",
)
parser.add_argument(
"--dir_images",
default=dir_images,
type=str,
help="full directory path to directory containing images",
)
parser.add_argument(
"--which_hw_model",
default=which_hw_model,
type=str,
choices=["crnn", "stn_crnn"],
help="which model to be used for prediction",
)
parser.add_argument(
"--file_model",
default=file_model,
type=str,
help="full path to trained model file (.pth)",
)
parser.add_argument(
"--save_predictions",
default=save_predictions,
type=int,
choices=[0, 1],
help="save or do not save the predictions (1 - save, 0 - do not save)",
)
FLAGS, unparsed = parser.parse_known_args()
test_hw_recognizer(FLAGS)
return
if __name__ == "__main__":
main()
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