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#!python3
import argparse
import os
import torch
import cv2
import numpy as np
from experiment import Structure, Experiment
from concern.config import Configurable, Config
import math
def main():
parser = argparse.ArgumentParser(description='Text Recognition Training')
parser.add_argument('exp', type=str)
parser.add_argument('--resume', type=str, help='Resume from checkpoint')
parser.add_argument('--image_path', type=str, help='image path')
parser.add_argument('--result_dir', type=str, default='./demo_results/', help='path to save results')
parser.add_argument('--data', type=str,
help='The name of dataloader which will be evaluated on.')
parser.add_argument('--image_short_side', type=int, default=736,
help='The threshold to replace it in the representers')
parser.add_argument('--thresh', type=float,
help='The threshold to replace it in the representers')
parser.add_argument('--box_thresh', type=float, default=0.6,
help='The threshold to replace it in the representers')
parser.add_argument('--visualize', action='store_true',
help='visualize maps in tensorboard')
parser.add_argument('--resize', action='store_true',
help='resize')
parser.add_argument('--polygon', action='store_true',
help='output polygons if true')
parser.add_argument('--eager', '--eager_show', action='store_true', dest='eager_show',
help='Show iamges eagerly')
args = parser.parse_args()
args = vars(args)
args = {k: v for k, v in args.items() if v is not None}
conf = Config()
experiment_args = conf.compile(conf.load(args['exp']))['Experiment']
experiment_args.update(cmd=args)
experiment = Configurable.construct_class_from_config(experiment_args)
Demo(experiment, experiment_args, cmd=args).inference(args['image_path'], args['visualize'])
class Demo:
def __init__(self, experiment, args, cmd=dict()):
self.RGB_MEAN = np.array([122.67891434, 116.66876762, 104.00698793])
self.experiment = experiment
experiment.load('evaluation', **args)
self.args = cmd
model_saver = experiment.train.model_saver
self.structure = experiment.structure
self.model_path = self.args['resume']
def init_torch_tensor(self):
# Use gpu or not
torch.set_default_tensor_type('torch.FloatTensor')
if torch.cuda.is_available():
self.device = torch.device('cuda')
torch.set_default_tensor_type('torch.cuda.FloatTensor')
else:
self.device = torch.device('cpu')
def init_model(self):
model = self.structure.builder.build(self.device)
return model
def resume(self, model, path):
if not os.path.exists(path):
print("Checkpoint not found: " + path)
return
print("Resuming from " + path)
states = torch.load(
path, map_location=self.device)
model.load_state_dict(states, strict=False)
print("Resumed from " + path)
def resize_image(self, img):
height, width, _ = img.shape
if height < width:
new_height = self.args['image_short_side']
new_width = int(math.ceil(new_height / height * width / 32) * 32)
else:
new_width = self.args['image_short_side']
new_height = int(math.ceil(new_width / width * height / 32) * 32)
resized_img = cv2.resize(img, (new_width, new_height))
return resized_img
def load_image(self, image_path):
img = cv2.imread(image_path, cv2.IMREAD_COLOR).astype('float32')
original_shape = img.shape[:2]
img = self.resize_image(img)
img -= self.RGB_MEAN
img /= 255.
img = torch.from_numpy(img).permute(2, 0, 1).float().unsqueeze(0)
return img, original_shape
def format_output(self, batch, output):
batch_boxes, batch_scores = output
for index in range(batch['image'].size(0)):
original_shape = batch['shape'][index]
filename = batch['filename'][index]
result_file_name = 'res_' + filename.split('/')[-1].split('.')[0] + '.txt'
result_file_path = os.path.join(self.args['result_dir'], result_file_name)
boxes = batch_boxes[index]
scores = batch_scores[index]
if self.args['polygon']:
with open(result_file_path, 'wt') as res:
for i, box in enumerate(boxes):
box = np.array(box).reshape(-1).tolist()
result = ",".join([str(int(x)) for x in box])
score = scores[i]
res.write(result + ',' + str(score) + "\n")
else:
with open(result_file_path, 'wt') as res:
for i in range(boxes.shape[0]):
score = scores[i]
if score < self.args['box_thresh']:
continue
box = boxes[i,:,:].reshape(-1).tolist()
result = ",".join([str(int(x)) for x in box])
res.write(result + ',' + str(score) + "\n")
def inference(self, image_path, visualize=False):
self.init_torch_tensor()
model = self.init_model()
self.resume(model, self.model_path)
all_matircs = {}
model.eval()
batch = dict()
batch['filename'] = [image_path]
img, original_shape = self.load_image(image_path)
batch['shape'] = [original_shape]
with torch.no_grad():
batch['image'] = img
pred = model.forward(batch, training=False)
output = self.structure.representer.represent(batch, pred, is_output_polygon=self.args['polygon'])
if not os.path.isdir(self.args['result_dir']):
os.mkdir(self.args['result_dir'])
self.format_output(batch, output)
if visualize and self.structure.visualizer:
vis_image = self.structure.visualizer.demo_visualize(image_path, output)
cv2.imwrite(os.path.join(self.args['result_dir'], image_path.split('/')[-1].split('.')[0]+'.jpg'), vis_image)
if __name__ == '__main__':
main()
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