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test.py
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test.py
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import argparse
import cv2
import os
import time
import numpy as np
import torch
from dataset.coco import build_coco, coco_class_labels
from dataset.transforms import build_transform
from utils.misc import load_weight
from config import build_config
from models import build_model
def parse_args():
parser = argparse.ArgumentParser(description='DETR Library')
# basic
parser.add_argument('--show', action='store_true', default=False,
help='show the visulization results.')
parser.add_argument('--cuda', action='store_true', default=False,
help='use cuda.')
parser.add_argument('--save_folder', default='det_results/', type=str,
help='Dir to save results')
parser.add_argument('--vis_thresh', default=0.5, type=float,
help='visualize threshold')
parser.add_argument('--aux_loss', action='store_true', default=False,
help='use intermediate output.')
parser.add_argument('--use_nms', action='store_true', default=False,
help='use NMS.')
# model
parser.add_argument('-v', '--version', default='detr_r50', type=str,
help='build DETR')
parser.add_argument('--weight', default='weight/',
type=str, help='Trained state_dict file path to open')
# dataset
parser.add_argument('--root', default='/mnt/share/ssd2/dataset',
help='data root')
parser.add_argument('-d', '--dataset', default='coco',
help='coco, voc.')
return parser.parse_args()
def plot_bbox_labels(img, bbox, label=None, cls_color=None, text_scale=0.4):
x1, y1, x2, y2 = bbox
x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
t_size = cv2.getTextSize(label, 0, fontScale=1, thickness=2)[0]
# plot bbox
cv2.rectangle(img, (x1, y1), (x2, y2), cls_color, 2)
if label is not None:
# plot title bbox
cv2.rectangle(img, (x1, y1-t_size[1]), (int(x1 + t_size[0] * text_scale), y1), cls_color, -1)
# put the test on the title bbox
cv2.putText(img, label, (int(x1), int(y1 - 5)), 0, text_scale, (0, 0, 0), 1, lineType=cv2.LINE_AA)
return img
def visualize(img,
bboxes,
scores,
cls_inds,
vis_thresh,
class_colors,
class_names):
ts = 0.4
for i, bbox in enumerate(bboxes):
if scores[i] > vis_thresh:
cls_id = int(cls_inds[i])
cls_color = class_colors[cls_id]
if len(class_names) > 1:
mess = '%s: %.2f' % (class_names[cls_id], scores[i])
else:
cls_color = [255, 0, 0]
mess = None
img = plot_bbox_labels(img, bbox, mess, cls_color, text_scale=ts)
return img
def test(args,
model,
device,
dataset,
transform,
class_colors=None,
class_names=None,
show=False):
num_images = len(dataset)
save_path = os.path.join('det_results/', args.dataset, args.version)
os.makedirs(save_path, exist_ok=True)
for index in range(num_images):
print('Testing image {:d}/{:d}....'.format(index+1, num_images))
image, _ = dataset.pull_image(index)
orig_h = image.height
orig_w = image.width
# prepare
x = transform(image)[0]
x = x.unsqueeze(0).to(device)
t0 = time.time()
# inference
bboxes, scores, cls_inds = model(x)
print("detection time used ", time.time() - t0, "s")
# rescale
bboxes[..., [0, 2]] = np.clip(bboxes[..., [0, 2]] * orig_w, a_min=0., a_max=orig_w)
bboxes[..., [1, 3]] = np.clip(bboxes[..., [1, 3]] * orig_h, a_min=0., a_max=orig_h)
# visulize results
image = np.array(image)[..., (2, 1, 0)].astype(np.uint8)
image = image.copy()
img_processed = visualize(
img=image,
bboxes=bboxes,
scores=scores,
cls_inds=cls_inds,
vis_thresh=args.vis_thresh,
class_colors=class_colors,
class_names=class_names
)
if show:
cv2.imshow('detection', img_processed)
cv2.waitKey(0)
# save result
cv2.imwrite(os.path.join(save_path, str(index).zfill(6) +'.jpg'), img_processed)
if __name__ == '__main__':
args = parse_args()
# cuda
if args.cuda:
print('use cuda')
device = torch.device("cuda")
else:
device = torch.device("cpu")
# config
cfg = build_config(args)
# transform
transform = build_transform(
is_train=False,
pixel_mean=cfg['pixel_mean'],
pixel_std=cfg['pixel_std'],
min_size=cfg['test_min_size'],
max_size=cfg['test_max_size'],
random_size=None
)
# dataset
if args.dataset == 'coco':
data_dir = os.path.join(args.root, 'COCO')
num_classes = 91
class_names = coco_class_labels
# dataset
dataset = build_coco(
root=data_dir,
transform=None,
is_train=False,
return_masks=False
)
else:
print('unknow dataset !! Only support voc and coco !!')
exit(0)
np.random.seed(0)
class_colors = [(np.random.randint(255),
np.random.randint(255),
np.random.randint(255)) for _ in range(num_classes)]
# build model
model, _ = build_model(
args=args,
cfg=cfg,
device=device,
num_classes=num_classes,
trainable=False
)
# load trained weight
model = load_weight(model, args.weight)
model.to(device).eval()
# run
test(args=args,
model=model,
device=device,
dataset=dataset,
transform=transform,
class_colors=class_colors,
class_names=class_names,
show=args.show)